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Liu L, Jia D, He Z, Wen B, Zhang X, Han S. Individualized functional connectome abnormalities obtained using two normative model unveil neurophysiological subtypes of obsessive compulsive disorder. Prog Neuropsychopharmacol Biol Psychiatry 2024; 135:111122. [PMID: 39154932 DOI: 10.1016/j.pnpbp.2024.111122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 07/26/2024] [Accepted: 08/15/2024] [Indexed: 08/20/2024]
Abstract
The high heterogeneity observed among patients with obsessive-compulsive disorder (OCD) underscores the need to identify neurophysiological OCD subtypes to facilitate personalized diagnosis and treatment. In this study, our aim was to identify potential OCD subtypes based on individualized functional connectome abnormalities. We recruited a total of 99 patients with OCD and 104 healthy controls (HCs) matched for demographic characteristics. Individualized functional connectome abnormalities were obtained using normative models of functional connectivity strength (FCS) and used as features to unveil OCD subtypes. Sensitivity analyses were conducted to assess the reproducibility and robustness of the clustering outcomes. Patients exhibited significant intersubject heterogeneity in individualized functional connectome abnormalities. Two subtypes with distinct patterns of FCS abnormalities relative to HCs were identified. Subtype 1 patients primarily exhibited significantly decreased FCS in regions including the frontal gyrus, insula, hippocampus, and precentral/postcentral gyrus, whereas subtype 2 patients demonstrated increased FCS in widespread brain regions. When all patients were combined, no significant differences were observed. Additionally, the identified subtypes showed significant differences in age of onset. Furthermore, sensitivity analyses confirmed the reproducibility of our subtyping results. In conclusion, the identified OCD subtypes shed new light on the taxonomy and neurophysiological heterogeneity of OCD.
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Affiliation(s)
- Liang Liu
- School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China
| | - Dongyao Jia
- School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China.
| | - Zihao He
- School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China
| | - Baohong Wen
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiaopan Zhang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Shaoqiang Han
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
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Nőger K, Rádosi A, Pászthy B, Réthelyi J, Ulbert I, Bunford N. Maternal psychopathology is differentially associated with adolescent offspring neural response to reward given offspring ADHD risk. J Psychiatr Res 2024; 178:188-200. [PMID: 39151212 DOI: 10.1016/j.jpsychires.2024.06.054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 05/29/2024] [Accepted: 06/13/2024] [Indexed: 08/18/2024]
Abstract
Reinforcement sensitivity is a hypothesized attention-deficit/hyperactivity disorder (ADHD) intermediate phenotype but its role in transgenerational transmission of ADHD-linked psychopathology risk is largely unknown. We examined, in a carefully phenotyped, N = 123 sample of adolescents (Mage = 15.27 years, SD = 0.984; 61.78% boys), whether (1) parental psychopathology is differentially associated with fMRI-indexed neural response to reward receipt and (2) both maternal and paternal psychopathology are associated with neural response to reward; across adolescents at-risk for and not at-risk for ADHD. Indices of parental psychopathology were differentially associated with adolescent offspring neural response to reward such that across measures, parental psychopathology was negatively or not associated with offspring superior frontal gyrus (SFG) response to reward receipt in adolescents at-risk for ADHD, but parental psychopathology was positively associated with offspring SFG response in adolescents not at-risk. Further, across measures, greater maternal psychopathology was associated with blunted adolescent SFG response to reward in adolescents at-risk for ADHD whereas greater maternal externalizing problems were linked to enhanced adolescent SFG response in adolescents not at-risk. Across measures, paternal psychopathology was not associated with adolescent response to reward, in either group. ADHD risk confers differential reward-related susceptibility to the effects of parental psychopathology. Results also show this association is nonspecific in terms of parental psychopathology type but is specific to maternal psychopathology.
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Affiliation(s)
- Kinga Nőger
- Institute of Psychology, Faculty of Humanities and Social Sciences, Károli Gáspár University of the Reformed Church, Budapest, Hungary; MCC-Mindset Psychology School, Budapest, Hungary
| | - Alexandra Rádosi
- Clinical and Developmental Neuropsychology Research Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary; Semmelweis University, Doctoral School, Budapest, Hungary
| | - Bea Pászthy
- Semmelweis University, 1st Department of Pediatrics, Budapest, Hungary
| | - János Réthelyi
- Semmelweis University, Department of Psychiatry and Psychotherapy, Budapest, Hungary
| | - István Ulbert
- Integrative Neuroscience Research Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary; Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Nóra Bunford
- Clinical and Developmental Neuropsychology Research Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary.
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Corrigan NM, Rokem A, Kuhl PK. COVID-19 lockdown effects on adolescent brain structure suggest accelerated maturation that is more pronounced in females than in males. Proc Natl Acad Sci U S A 2024; 121:e2403200121. [PMID: 39250666 DOI: 10.1073/pnas.2403200121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 07/26/2024] [Indexed: 09/11/2024] Open
Abstract
Adolescence is a period of substantial social-emotional development, accompanied by dramatic changes to brain structure and function. Social isolation due to lockdowns that were imposed because of the COVID-19 pandemic had a detrimental impact on adolescent mental health, with the mental health of females more affected than males. We assessed the impact of the COVID-19 pandemic lockdowns on adolescent brain structure with a focus on sex differences. We collected MRI structural data longitudinally from adolescents prior to and after the pandemic lockdowns. The pre-COVID data were used to create a normative model of cortical thickness change with age during typical adolescent development. Cortical thickness values in the post-COVID data were compared to this normative model. The analysis revealed accelerated cortical thinning in the post-COVID brain, which was more widespread throughout the brain and greater in magnitude in females than in males. When measured in terms of equivalent years of development, the mean acceleration was found to be 4.2 y in females and 1.4 y in males. Accelerated brain maturation as a result of chronic stress or adversity during development has been well documented. These findings suggest that the lifestyle disruptions associated with the COVID-19 pandemic lockdowns caused changes in brain biology and had a more severe impact on the female than the male brain.
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Affiliation(s)
- Neva M Corrigan
- Institute for Learning & Brain Sciences, University of Washington, Seattle, WA 98195
- Institute on Human Development and Disability, University of Washington, Seattle, WA 98195
| | - Ariel Rokem
- Institute on Human Development and Disability, University of Washington, Seattle, WA 98195
- Department of Psychology, University of Washington, Seattle, WA 98195
- eScience Institute, University of Washington, Seattle, WA 98195
| | - Patricia K Kuhl
- Institute for Learning & Brain Sciences, University of Washington, Seattle, WA 98195
- Department of Speech and Hearing Sciences, University of Washington, Seattle, WA 98195
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Liloia D, Zamfira DA, Tanaka M, Manuello J, Crocetta A, Keller R, Cozzolino M, Duca S, Cauda F, Costa T. Disentangling the role of gray matter volume and concentration in autism spectrum disorder: A meta-analytic investigation of 25 years of voxel-based morphometry research. Neurosci Biobehav Rev 2024; 164:105791. [PMID: 38960075 DOI: 10.1016/j.neubiorev.2024.105791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 05/22/2024] [Accepted: 06/27/2024] [Indexed: 07/05/2024]
Abstract
Despite over two decades of neuroimaging research, a unanimous definition of the pattern of structural variation associated with autism spectrum disorder (ASD) has yet to be found. One potential impeding issue could be the sometimes ambiguous use of measurements of variations in gray matter volume (GMV) or gray matter concentration (GMC). In fact, while both can be calculated using voxel-based morphometry analysis, these may reflect different underlying pathological mechanisms. We conducted a coordinate-based meta-analysis, keeping apart GMV and GMC studies of subjects with ASD. Results showed distinct and non-overlapping patterns for the two measures. GMV decreases were evident in the cerebellum, while GMC decreases were mainly found in the temporal and frontal regions. GMV increases were found in the parietal, temporal, and frontal brain regions, while GMC increases were observed in the anterior cingulate cortex and middle frontal gyrus. Age-stratified analyses suggested that such variations are dynamic across the ASD lifespan. The present findings emphasize the importance of considering GMV and GMC as distinct yet synergistic indices in autism research.
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Affiliation(s)
- Donato Liloia
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Denisa Adina Zamfira
- School of Psychology, Vita-Salute San Raffaele University, Milan, Italy; Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Masaru Tanaka
- HUN-REN-SZTE Neuroscience Research Group, Hungarian Research Network, University of Szeged (HUN-REN-SZTE), Danube Neuroscience Research Laboratory, Szeged, Hungary
| | - Jordi Manuello
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Annachiara Crocetta
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Roberto Keller
- Adult Autism Center, DSM Local Health Unit, ASL TO, Turin, Italy
| | - Mauro Cozzolino
- Department of Humanities, Philosophical and Educational Sciences, University of Salerno, Fisciano, Italy
| | - Sergio Duca
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Franco Cauda
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin (NIT), Turin, Italy
| | - Tommaso Costa
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin (NIT), Turin, Italy
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Yu H, Chen Y, Bao Z, Luo J, Liu Q, Qin P, Wang C, Qu J, Wang W, Cai L, Gong G. Behavioral and brain morphological changes before and after hemispherotomy. Hum Brain Mapp 2024; 45:e70020. [PMID: 39225128 PMCID: PMC11369683 DOI: 10.1002/hbm.70020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 08/12/2024] [Accepted: 08/21/2024] [Indexed: 09/04/2024] Open
Abstract
Hemispherotomy is an effective surgery for treating refractory epilepsy from diffuse unihemispheric lesions. To date, postsurgery neuroplastic changes supporting behavioral recovery after left or right hemispherotomy remain unclear. In the present study, we systematically investigated changes in gray matter volume (GMV) before and after surgery and further analyzed their relationships with behavioral scores in two large groups of pediatric patients with left and right hemispherotomy (29 left and 28 right). To control for the dramatic developmental effect during this stage, age-adjusted GMV within unaffected brain regions was derived voxel by voxel using a normative modeling approach with an age-matched reference cohort of 2115 healthy children. Widespread GMV increases in the contralateral cerebrum and ipsilateral cerebellum and GMV decreases in the contralateral cerebellum were consistently observed in both patient groups, but only the left hemispherotomy patients showed GMV decreases in the contralateral cingulate gyrus. Intriguingly, the GMV decrease in the contralateral cerebellum was significantly correlated with improvement in behavioral scores in the right but not the left hemispherotomy patients. Importantly, the preoperative voxelwise GMV features can be used to significantly predict postoperative behavioral scores in both patient groups. These findings indicate an important role of the contralateral cerebellum in the behavioral recovery following right hemispherotomy and highlight the predictive potential of preoperative imaging features in postoperative behavioral performance.
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Affiliation(s)
- Hao Yu
- Pediatric Epilepsy CenterPeking University First HospitalBeijingChina
| | - Yijun Chen
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
| | - Ziyu Bao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
| | - Junhao Luo
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
| | - Qingzhu Liu
- Pediatric Epilepsy CenterPeking University First HospitalBeijingChina
| | - Peipei Qin
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
| | - Changtong Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
| | - Jingli Qu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
| | - Wei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
| | - Lixin Cai
- Pediatric Epilepsy CenterPeking University First HospitalBeijingChina
| | - Gaolang Gong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
- Beijing Key Laboratory of Brain Imaging and ConnectomicsBeijing Normal UniversityBeijingChina
- Chinese Institute for Brain ResearchBeijingChina
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Vandewouw MM, Ye YJ, Crosbie J, Schachar RJ, Iaboni A, Georgiades S, Nicolson R, Kelley E, Ayub M, Jones J, Arnold PD, Taylor MJ, Lerch JP, Anagnostou E, Kushki A. Dataset factors associated with age-related changes in brain structure and function in neurodevelopmental conditions. Hum Brain Mapp 2024; 45:e26815. [PMID: 39254138 PMCID: PMC11386318 DOI: 10.1002/hbm.26815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 07/08/2024] [Accepted: 07/29/2024] [Indexed: 09/11/2024] Open
Abstract
With brain structure and function undergoing complex changes throughout childhood and adolescence, age is a critical consideration in neuroimaging studies, particularly for those of individuals with neurodevelopmental conditions. However, despite the increasing use of large, consortium-based datasets to examine brain structure and function in neurotypical and neurodivergent populations, it is unclear whether age-related changes are consistent between datasets and whether inconsistencies related to differences in sample characteristics, such as demographics and phenotypic features, exist. To address this, we built models of age-related changes of brain structure (regional cortical thickness and regional surface area; N = 1218) and function (resting-state functional connectivity strength; N = 1254) in two neurodiverse datasets: the Province of Ontario Neurodevelopmental Network and the Healthy Brain Network. We examined whether deviations from these models differed between the datasets, and explored whether these deviations were associated with demographic and clinical variables. We found significant differences between the two datasets for measures of cortical surface area and functional connectivity strength throughout the brain. For regional measures of cortical surface area, the patterns of differences were associated with race/ethnicity, while for functional connectivity strength, positive associations were observed with head motion. Our findings highlight that patterns of age-related changes in the brain may be influenced by demographic and phenotypic characteristics, and thus future studies should consider these when examining or controlling for age effects in analyses.
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Affiliation(s)
- Marlee M Vandewouw
- Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
| | - Yifan Julia Ye
- Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
- Division of Engineering Science, University of Toronto, Toronto, Canada
| | - Jennifer Crosbie
- Department of Psychiatry, University of Toronto, Toronto, Canada
- Department of Psychiatry, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Russell J Schachar
- Department of Psychiatry, University of Toronto, Toronto, Canada
- Department of Psychiatry, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Alana Iaboni
- Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
| | - Stelios Georgiades
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Canada
| | - Robert Nicolson
- Department of Psychiatry, Western University, London, Canada
| | - Elizabeth Kelley
- Department of Psychology, Queen's University, Kingston, Canada
- Centre for Neuroscience Studies, Queen's University, Kingston, Canada
- Department of Psychiatry, Queen's University, Kingston, Canada
| | - Muhammad Ayub
- Department of Psychiatry, Queen's University, Kingston, Canada
- Division of Psychiatry, University of College London, London, UK
| | - Jessica Jones
- Department of Psychology, Queen's University, Kingston, Canada
- Centre for Neuroscience Studies, Queen's University, Kingston, Canada
- Department of Psychiatry, Queen's University, Kingston, Canada
| | - Paul D Arnold
- The Mathison Centre for Mental Health Research & Education, Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - Margot J Taylor
- Department of Diagnostic and Interventional Radiology, The Hospital for Sick Children, Toronto, Canada
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Canada
- Department of Psychology, University of Toronto, Toronto, Canada
- Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Jason P Lerch
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Canada
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Evdokia Anagnostou
- Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Canada
- Institute of Medical Science, University of Toronto, Toronto, Canada
| | - Azadeh Kushki
- Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
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Sun X, Xia M. Schizophrenia and Neurodevelopment: Insights From Connectome Perspective. Schizophr Bull 2024:sbae148. [PMID: 39209793 DOI: 10.1093/schbul/sbae148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
BACKGROUND Schizophrenia is conceptualized as a brain connectome disorder that can emerge as early as late childhood and adolescence. However, the underlying neurodevelopmental basis remains unclear. Recent interest has grown in children and adolescent patients who experience symptom onset during critical brain development periods. Inspired by advanced methodological theories and large patient cohorts, Chinese researchers have made significant original contributions to understanding altered brain connectome development in early-onset schizophrenia (EOS). STUDY DESIGN We conducted a search of PubMed and Web of Science for studies on brain connectomes in schizophrenia and neurodevelopment. In this selective review, we first address the latest theories of brain structural and functional development. Subsequently, we synthesize Chinese findings regarding mechanisms of brain structural and functional abnormalities in EOS. Finally, we highlight several pivotal challenges and issues in this field. STUDY RESULTS Typical neurodevelopment follows a trajectory characterized by gray matter volume pruning, enhanced structural and functional connectivity, improved structural connectome efficiency, and differentiated modules in the functional connectome during late childhood and adolescence. Conversely, EOS deviates with excessive gray matter volume decline, cortical thinning, reduced information processing efficiency in the structural brain network, and dysregulated maturation of the functional brain network. Additionally, common functional connectome disruptions of default mode regions were found in early- and adult-onset patients. CONCLUSIONS Chinese research on brain connectomes of EOS provides crucial evidence for understanding pathological mechanisms. Further studies, utilizing standardized analyses based on large-sample multicenter datasets, have the potential to offer objective markers for early intervention and disease treatment.
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Affiliation(s)
- Xiaoyi Sun
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- School of Systems Science, Beijing Normal University, Beijing, China
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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Thukral RA, Maximo JO, Lahti AC, Rutherford SE, Larson JS, Zhang H, Marquand AF, Kraguljac NV. Dissecting Heterogeneity in Functional Network Connectivity Aberrations in Antipsychotic Medication-Naïve First Episode Psychosis Patients - A Normative Modeling Study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.08.23.24312480. [PMID: 39228736 PMCID: PMC11370546 DOI: 10.1101/2024.08.23.24312480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
Importance While there is a general consensus that functional connectome pathology is a key mechanism underlying psychosis spectrum disorders, the literature is plagued with inconsistencies and translation into clinical practice is non-existent. This is perhaps because group-level findings may not be accurate reflections of pathology at the individual patient level. Objective To characterize inter-individual heterogeneity in functional networks and investigate if normative values can be leveraged to identify biologically less heterogeneous subgroups of patients. Design Setting and Participants We used data collected in a case-control study conducted at the University of Alabama at Birmingham (UAB). We recruited antipsychotic medication-naïve first-episode psychosis patients from UAB outpatient, inpatient, and emergency room settings. Main Outcomes and Measures Individual-level patterns of deviations from a normative reference range in resting-state functional networks using the Yeo-17 atlas for parcellations. Results Statistical analyses included 108 medication-naïve first-episode psychosis patients. We found that there is a high level of inter-individual heterogeneity in resting-state network connectivity deviations from the normative reference range. Interestingly 48% of patients did not have any functional connectivity deviations, and no more than 11.1% of patients shared functional deviations between the same regions of interest. In a post hoc analysis, we grouped patients based on deviations into four theoretically possible groups. We discovered that all four groups do exist in our experimental data and showed that subgroups based on deviation profiles were significantly less heterogeneous compared to the overall group (positive deviation group: z= -2.88, p = 0.002; negative deviation group: z= -3.36, p<0.001). Conclusions and Relevance Our findings experimentally demonstrate that there is a high level of inter-individual heterogeneity in resting-state network pathology in first-episode psychosis patients which support the idea that group-level findings are not accurate reflections of pathology at the individual level. We also demonstrated that normative functional connectivity deviations may have utility for identifying biologically less heterogeneous subgroups of patients, even though they are not distinguishable clinically. Our findings constitute a significant step towards making precision psychiatry a reality, where patients are selected for treatments based on their individual biological characteristics. KEY POINTS Question: How heterogeneous is individual-level resting-state functional network pathology in patients suffering from a first psychotic episode? Can normative reference values in functional network connectivity be leveraged to identify biologically more homogenous subgroups of patients?Findings: We report that functional network pathology is highly heterogeneous, with no more than 11% of patients sharing functional deviations between the same regions of interest.Meaning: Normative modeling is a tool that can map individual neurobiological differences and enables the classification of a clinically heterogenous patient group into subgroups that are neurobiologically less heterogenous.
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Duan X, Shan X, Uddin LQ, Chen H. The future of disentangling the heterogeneity of autism with neuroimaging studies. Biol Psychiatry 2024:S0006-3223(24)01536-1. [PMID: 39181387 DOI: 10.1016/j.biopsych.2024.08.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 08/01/2024] [Accepted: 08/07/2024] [Indexed: 08/27/2024]
Abstract
Autism spectrum disorder (ASD) is a lifelong neurodevelopmental condition. Over the past decade, a considerable number of approaches have been developed to identify potential neuroimaging-based biomarkers of ASD and have uncovered specific neural mechanisms underlying behaviors associated with ASD. However, the substantial heterogeneity among those diagnosed with ASD hinders the development of biomarkers. Disentangling the heterogeneity of ASD is pivotal to improve quality of life for individuals with ASD by facilitating early diagnosis and individualized interventions for those who need support. In this Review, we discuss recent advances in neuroimaging that have facilitated the characterization of the heterogeneity of this condition from three frameworks: neurosubtyping, dimensional models, and normative models. In addition, we discuss the challenges, possible solutions, and clinical utility of these three frameworks. We argue that several factors need to be considered when parsing heterogeneity using neuroimaging, including co-occurring conditions, neurodevelopment, heredity and environment, and multi-site and multi-modality data. We close with a discussion of future directions for achieving a better understanding of the neural mechanisms underlying neurodevelopmental heterogeneity and the future of precision medicine in ASD.
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Affiliation(s)
- Xujun Duan
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, PR China.
| | - Xiaolong Shan
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
| | - Lucina Q Uddin
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California, United States; Department of Psychology, University of California Los Angeles, Los Angeles, California, United States
| | - Huafu Chen
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, PR China.
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Segal A, Smith RE, Chopra S, Oldham S, Parkes L, Aquino K, Kia SM, Wolfers T, Franke B, Hoogman M, Beckmann CF, Westlye LT, Andreassen OA, Zalesky A, Harrison BJ, Davey CG, Soriano-Mas C, Cardoner N, Tiego J, Yücel M, Braganza L, Suo C, Berk M, Cotton S, Bellgrove MA, Marquand AF, Fornito A. Multiscale heterogeneity of white matter morphometry in psychiatric disorders. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.04.606523. [PMID: 39149253 PMCID: PMC11326206 DOI: 10.1101/2024.08.04.606523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Background Inter-individual variability in neurobiological and clinical characteristics in mental illness is often overlooked by classical group-mean case-control studies. Studies using normative modelling to infer person-specific deviations of grey matter volume have indicated that group means are not representative of most individuals. The extent to which this variability is present in white matter morphometry, which is integral to brain function, remains unclear. Methods We applied Warped Bayesian Linear Regression normative models to T1-weighted magnetic resonance imaging data and mapped inter-individual variability in person-specific white matter volume deviations in 1,294 cases (58% male) diagnosed with one of six disorders (attention-deficit/hyperactivity, autism, bipolar, major depressive, obsessive-compulsive and schizophrenia) and 1,465 matched controls (54% male) recruited across 25 scan sites. We developed a framework to characterize deviation heterogeneity at multiple spatial scales, from individual voxels, through inter-regional connections, specific brain regions, and spatially extended brain networks. Results The specific locations of white matter volume deviations were highly heterogeneous across participants, affecting the same voxel in fewer than 8% of individuals with the same diagnosis. For autism and schizophrenia, negative deviations (i.e., areas where volume is lower than normative expectations) aggregated into common tracts, regions and large-scale networks in up to 35% of individuals. Conclusions The prevalence of white matter volume deviations was lower than previously observed in grey matter, and the specific location of these deviations was highly heterogeneous when considering voxel-wise spatial resolution. Evidence of aggregation within common pathways and networks was apparent in schizophrenia and autism but not other disorders.
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Affiliation(s)
- Ashlea Segal
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Australia
- Wu Tsai Institute, Department of Neuroscience, Yale University, New Haven, United States
| | - Robert E Smith
- The Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia
- Florey Department of Neuroscience and Mental Health, Parkville, Victoria, Australia
| | - Sidhant Chopra
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Stuart Oldham
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Australia
- Developmental Imaging, Murdoch Children’s Research Institute, Melbourne, Australia
| | - Linden Parkes
- Department of Psychiatry, Rutgers University, Piscataway, NJ 08854, USA
| | | | - Seyed Mostafa Kia
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Cognitive Science and Artificial Intelligence, Tilburg School of Humanities and Digital Sciences, Tilburg University, Tilburg, the Netherlands
| | - Thomas Wolfers
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo & Oslo University Hospital, Oslo, Norway
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health (TÜCMH), University of Tübingen, Tübingen, Germany
| | - Barbara Franke
- Department of Cognitive Neuroscience, Donders Institute of Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Human Genetics, Donders Institute of Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Martine Hoogman
- Department of Human Genetics, Donders Institute of Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Psychiatry, Donders Institute of Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Christian F. Beckmann
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands
- Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Lars T. Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo & Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Ole A. Andreassen
- Department of Psychology, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Victoria, Australia
- Department of Biomedical Engineering, The University of Melbourne, Victoria, Australia
| | - Ben J. Harrison
- Department of Psychiatry, The University of Melbourne, Victoria, Australia
| | | | - Carles Soriano-Mas
- Department of Psychiatry, Bellvitge University Hospital. Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Carlos III Health Institute, Madrid, Spain
- Department of Social Psychology and Quantitative Psychology, Universitat de Barcelona-UB, Barcelona, Spain
| | - Narcís Cardoner
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Carlos III Health Institute, Madrid, Spain
- Sant Pau Mental Health Research Group, Institut d’Investigació Biomèdica Sant Pau (IIB-Sant Pau), Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Jeggan Tiego
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Australia
| | - Murat Yücel
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Leah Braganza
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Chao Suo
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Australia
- Australian Characterisation Commons at Scale (ACCS) Project, Monash eResearch Centre, Melbourne, Australia
| | - Michael Berk
- Deakin University, IMPACT – the Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon Health, Geelong, Australia
- Orygen, Melbourne, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia
- Florey Institute for Neuroscience and Mental Health, Parkville, Australia
| | - Sue Cotton
- Orygen, Melbourne, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia
| | - Mark A. Bellgrove
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Andre F. Marquand
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands
- Department of Neuroimaging, Centre of Neuroimaging Sciences, Institute of Psychiatry, King’s College London, London, The United Kingdom
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Australia
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11
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Hernandez H, Baez S, Medel V, Moguilner S, Cuadros J, Santamaria-Garcia H, Tagliazucchi E, Valdes-Sosa PA, Lopera F, OchoaGómez JF, González-Hernández A, Bonilla-Santos J, Gonzalez-Montealegre RA, Aktürk T, Yıldırım E, Anghinah R, Legaz A, Fittipaldi S, Yener GG, Escudero J, Babiloni C, Lopez S, Whelan R, Lucas AAF, García AM, Huepe D, Caterina GD, Soto-Añari M, Birba A, Sainz-Ballesteros A, Coronel C, Herrera E, Abasolo D, Kilborn K, Rubido N, Clark R, Herzog R, Yerlikaya D, Güntekin B, Parra MA, Prado P, Ibanez A. Brain health in diverse settings: How age, demographics and cognition shape brain function. Neuroimage 2024; 295:120636. [PMID: 38777219 DOI: 10.1016/j.neuroimage.2024.120636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 04/17/2024] [Accepted: 05/04/2024] [Indexed: 05/25/2024] Open
Abstract
Diversity in brain health is influenced by individual differences in demographics and cognition. However, most studies on brain health and diseases have typically controlled for these factors rather than explored their potential to predict brain signals. Here, we assessed the role of individual differences in demographics (age, sex, and education; n = 1298) and cognition (n = 725) as predictors of different metrics usually used in case-control studies. These included power spectrum and aperiodic (1/f slope, knee, offset) metrics, as well as complexity (fractal dimension estimation, permutation entropy, Wiener entropy, spectral structure variability) and connectivity (graph-theoretic mutual information, conditional mutual information, organizational information) from the source space resting-state EEG activity in a diverse sample from the global south and north populations. Brain-phenotype models were computed using EEG metrics reflecting local activity (power spectrum and aperiodic components) and brain dynamics and interactions (complexity and graph-theoretic measures). Electrophysiological brain dynamics were modulated by individual differences despite the varied methods of data acquisition and assessments across multiple centers, indicating that results were unlikely to be accounted for by methodological discrepancies. Variations in brain signals were mainly influenced by age and cognition, while education and sex exhibited less importance. Power spectrum activity and graph-theoretic measures were the most sensitive in capturing individual differences. Older age, poorer cognition, and being male were associated with reduced alpha power, whereas older age and less education were associated with reduced network integration and segregation. Findings suggest that basic individual differences impact core metrics of brain function that are used in standard case-control studies. Considering individual variability and diversity in global settings would contribute to a more tailored understanding of brain function.
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Affiliation(s)
- Hernan Hernandez
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile
| | - Sandra Baez
- Universidad de los Andes, Bogota, Colombia; Global Brain Health Institute (GBHI), University of California, San Francisco, US Trinity College Dublin, Dublin, Ireland
| | - Vicente Medel
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile
| | - Sebastian Moguilner
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Harvard Medical School, Boston, MA, USA
| | - Jhosmary Cuadros
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile; Grupo de Bioingeniería, Decanato de Investigación, Universidad Nacional Experimental del Táchira, San Cristóbal 5001, Venezuela
| | - Hernando Santamaria-Garcia
- Pontificia Universidad Javeriana (PhD Program in Neuroscience) Bogotá, San Ignacio, Colombia; Center of Memory and Cognition Intellectus, Hospital Universitario San Ignacio Bogotá, San Ignacio, Colombia
| | - Enzo Tagliazucchi
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; University of Buenos Aires, Argentina
| | - Pedro A Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Sciences Technology of China, Chengdu, China; Cuban Neuroscience Center, La Habana, Cuba
| | - Francisco Lopera
- Grupo de Neurociencias de Antioquia, University of Antioquia, Medellín, Colombia
| | | | | | | | | | - Tuba Aktürk
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylül University, Izmir, Turkey
| | - Ebru Yıldırım
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylül University, Izmir, Turkey
| | - Renato Anghinah
- Reference Center of Behavioural Disturbances and Dementia, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil; Traumatic Brain Injury Cognitive Rehabilitation Out-Patient Center, University of Sao Paulo, Sao Paulo, Brazil
| | - Agustina Legaz
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina; National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina; Facultad de Psicología, Universidad Nacional de Córdoba, Córdoba, Argentina
| | - Sol Fittipaldi
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Global Brain Health Institute (GBHI), University of California, San Francisco, US Trinity College Dublin, Dublin, Ireland; School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - Görsev G Yener
- Faculty of Medicine, Izmir University of Economics, 35330, Izmir, Turkey; Brain Dynamics Multidisciplinary Research Center, Dokuz Eylul University, Izmir, Turkey; Izmir Biomedicine and Genome Center, Izmir, Turkey
| | - Javier Escudero
- School of Engineering, Institute for Imaging, Data and Communications, University of Edinburgh, Scotland, UK
| | - Claudio Babiloni
- Department of Physiology and Pharmacology "V. Erspamer", Sapienza University of Rome, Rome, Italy; Hospital San Raffaele Cassino, Cassino, (FR), Italy
| | - Susanna Lopez
- Department of Physiology and Pharmacology "V. Erspamer", Sapienza University of Rome, Rome, Italy
| | - Robert Whelan
- Global Brain Health Institute (GBHI), University of California, San Francisco, US Trinity College Dublin, Dublin, Ireland; Department of Legal Medicine, Psychiatry and Pathology at the Complutense University of Madrid, Madrid, Spain
| | - Alberto A Fernández Lucas
- Department of Legal Medicine, Psychiatry and Pathology at the Complutense University of Madrid, Madrid, Spain
| | - Adolfo M García
- Global Brain Health Institute (GBHI), University of California, San Francisco, US Trinity College Dublin, Dublin, Ireland; Cognitive Neuroscience Center, Universidad de San Andréss, Buenos Aires, Argentina; Departamento de Lingüística y Literatura, Facultad de Humanidades, Universidad de Santiago de Chile, Santiago, Chile
| | - David Huepe
- Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibáñez
| | - Gaetano Di Caterina
- Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, UK
| | | | - Agustina Birba
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile
| | | | - Carlos Coronel
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Global Brain Health Institute (GBHI), University of California, San Francisco, US Trinity College Dublin, Dublin, Ireland; Centro Interdisciplinario de Neurociencia de Valparaíso (CINV), Universidad de Valparaíso, Valparaíso, Chile
| | - Eduar Herrera
- Departamento de Estudios Psicológicos, Universidad ICESI, Cali, Colombia
| | - Daniel Abasolo
- Centre for Biomedical Engineering, School of Mechanical Engineering Sciences, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, UK
| | - Kerry Kilborn
- School of Psychology, University of Glasgow, Glasgow, Scotland, UK
| | - Nicolás Rubido
- Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, AB24 3UE, UK
| | - Ruaridh Clark
- Centre for Signal and Image Processing, Department of Electronic and Electrical Engineering, University of Strathclyde, UK
| | - Ruben Herzog
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, Paris 75013, France
| | - Deniz Yerlikaya
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylül University, Izmir, Turkey
| | - Bahar Güntekin
- Health Sciences and Technology Research Institute (SABITA), Istanbul Medipol University, Istanbul, Turkey; Department of Biophysics, School of Medicine, Istanbul Medipol University, Turkey
| | - Mario A Parra
- Department of Psychological Sciences and Health, University of Strathclyde, United Kingdom and Associate Researcher of the Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Pavel Prado
- Escuela de Fonoaudiología, Facultad de Odontología y Ciencias de la Rehabilitación, Universidad San Sebastián, Santiago, Chile
| | - Agustin Ibanez
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Global Brain Health Institute, University of California San Francisco, San Francisco, CA, USA; Cognitive Neuroscience Center, Universidad de San Andrés and Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina; Trinity College Dublin, The University of Dublin, Dublin, Ireland.
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12
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Berthet P, Haatveit BC, Kjelkenes R, Worker A, Kia SM, Wolfers T, Rutherford S, Alnaes D, Dinga R, Pedersen ML, Dahl A, Fernandez-Cabello S, Dazzan P, Agartz I, Nesvåg R, Ueland T, Andreassen OA, Simonsen C, Westlye LT, Melle I, Marquand A. A 10-Year Longitudinal Study of Brain Cortical Thickness in People with First-Episode Psychosis Using Normative Models. Schizophr Bull 2024:sbae107. [PMID: 38970378 DOI: 10.1093/schbul/sbae107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/08/2024]
Abstract
BACKGROUND Clinical forecasting models have potential to optimize treatment and improve outcomes in psychosis, but predicting long-term outcomes is challenging and long-term follow-up data are scarce. In this 10-year longitudinal study, we aimed to characterize the temporal evolution of cortical correlates of psychosis and their associations with symptoms. DESIGN Structural magnetic resonance imaging (MRI) from people with first-episode psychosis and controls (n = 79 and 218) were obtained at enrollment, after 12 months (n = 67 and 197), and 10 years (n = 23 and 77), within the Thematically Organized Psychosis (TOP) study. Normative models for cortical thickness estimated on public MRI datasets (n = 42 983) were applied to TOP data to obtain deviation scores for each region and timepoint. Positive and Negative Syndrome Scale (PANSS) scores were acquired at each timepoint along with registry data. Linear mixed effects models assessed effects of diagnosis, time, and their interactions on cortical deviations plus associations with symptoms. RESULTS LMEs revealed conditional main effects of diagnosis and time × diagnosis interactions in a distributed cortical network, where negative deviations in patients attenuate over time. In patients, symptoms also attenuate over time. LMEs revealed effects of anterior cingulate on PANSS total, and insular and orbitofrontal regions on PANSS negative scores. CONCLUSIONS This long-term longitudinal study revealed a distributed pattern of cortical differences which attenuated over time together with a reduction in symptoms. These findings are not in line with a simple neurodegenerative account of schizophrenia, and deviations from normative models offer a promising avenue to develop biomarkers to track clinical trajectories over time.
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Affiliation(s)
- Pierre Berthet
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
| | - Beathe C Haatveit
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
| | - Rikka Kjelkenes
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
| | - Amanda Worker
- Department of Psychosis Studies, Institute of Psychiatry, King's College, London, UK
| | - Seyed Mostafa Kia
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands
- Department of Psychiatry, Utrecht University Medical Center, Utrecht, the Netherlands
- Department Cognitive Science and Artificial Intelligence, Tilburg University, the Netherlands
| | - Thomas Wolfers
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany
| | - Saige Rutherford
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Dag Alnaes
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
| | - Richard Dinga
- Department Cognitive Science and Artificial Intelligence, Tilburg University, the Netherlands
| | - Mads L Pedersen
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
| | - Andreas Dahl
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
| | - Sara Fernandez-Cabello
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
| | - Paola Dazzan
- Department of Psychosis Studies, Institute of Psychiatry, King's College, London, UK
| | - Ingrid Agartz
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Ragnar Nesvåg
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
| | - Torill Ueland
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
| | - Ole A Andreassen
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
| | - Carmen Simonsen
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
| | - Ingrid Melle
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
| | - Andre Marquand
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands
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13
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Huo Y, Jing R, Li P, Chen P, Si J, Liu G, Liu Y. Delineating the Heterogeneity of Alzheimer's Disease and Mild Cognitive Impairment Using Normative Models of Dynamic Brain Functional Networks. Biol Psychiatry 2024:S0006-3223(24)01365-9. [PMID: 38857821 DOI: 10.1016/j.biopsych.2024.05.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 05/15/2024] [Accepted: 05/30/2024] [Indexed: 06/12/2024]
Abstract
BACKGROUND Alzheimer's disease (AD), which has been identified as the most common type of dementia, presents considerable heterogeneity in its clinical manifestations. Early intervention at the stage of mild cognitive impairment (MCI) holds potential in AD prevention. However, characterizing the heterogeneity of neurobiological abnormalities and identifying MCI subtypes pose significant challenges. METHODS We constructed sex-specific normative age models of dynamic brain functional networks and mapped the deviations of the brain characteristics for individuals from multiple datasets, including 295 patients with AD, 441 patients with MCI, and 1160 normal control participants. Then, based on these individual deviation patterns, subtypes for both AD and MCI were identified using the clustering method, and their similarities and differences were comprehensively assessed. RESULTS Individuals with AD and MCI were clustered into 2 subtypes, and these subtypes exhibited significant differences in their intrinsic brain functional phenotypes and spatial atrophy patterns, as well as in disease progression and cognitive decline trajectories. The subtypes with positive deviations in AD and MCI shared similar deviation patterns, as did those with negative deviations. There was a potential transformation of MCI with negative deviation patterns into AD, and participants with MCI had a more severe cognitive decline rate. CONCLUSIONS In this study, we quantified neurophysiological heterogeneity by analyzing deviation patterns from the dynamic functional connectome normative model and identified disease subtypes of AD and MCI using a comprehensive resting-state functional magnetic resonance imaging multicenter dataset. The findings provide new insights for developing early prevention and personalized treatment strategies for AD.
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Affiliation(s)
- Yanxi Huo
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing, China
| | - Rixing Jing
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing, China.
| | - Peng Li
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Chinese Academy of Medical Sciences Research Unit, Peking University, Beijing, China
| | - Pindong Chen
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Juanning Si
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing, China
| | - Guozhong Liu
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing, China
| | - Yong Liu
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
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14
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Lawn T, Giacomel A, Martins D, Veronese M, Howard M, Turkheimer FE, Dipasquale O. Normative modelling of molecular-based functional circuits captures clinical heterogeneity transdiagnostically in psychiatric patients. Commun Biol 2024; 7:689. [PMID: 38839931 PMCID: PMC11153627 DOI: 10.1038/s42003-024-06391-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 05/27/2024] [Indexed: 06/07/2024] Open
Abstract
Advanced methods such as REACT have allowed the integration of fMRI with the brain's receptor landscape, providing novel insights transcending the multiscale organisation of the brain. Similarly, normative modelling has allowed translational neuroscience to move beyond group-average differences and characterise deviations from health at an individual level. Here, we bring these methods together for the first time. We used REACT to create functional networks enriched with the main modulatory, inhibitory, and excitatory neurotransmitter systems and generated normative models of these networks to capture functional connectivity deviations in patients with schizophrenia, bipolar disorder (BPD), and ADHD. Substantial overlap was seen in symptomatology and deviations from normality across groups, but these could be mapped into a common space linking constellations of symptoms through to underlying neurobiology transdiagnostically. This work provides impetus for developing novel biomarkers that characterise molecular- and systems-level dysfunction at the individual level, facilitating the transition towards mechanistically targeted treatments.
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Affiliation(s)
- Timothy Lawn
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Alessio Giacomel
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Daniel Martins
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Division of Adult Psychiatry, Department of Psychiatry, Geneva University Hospitals, Geneva, Switzerland
| | - Mattia Veronese
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Department of Information Engineering, University of Padua, Padua, Italy
| | - Matthew Howard
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Federico E Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Ottavia Dipasquale
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
- Department of Research & Development Advanced Applications, Olea Medical, La Ciotat, France.
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Lotter LD, Saberi A, Hansen JY, Misic B, Paquola C, Barker GJ, Bokde ALW, Desrivieres S, Flor H, Grigis A, Garavan H, Gowland P, Heinz A, Bruehl R, Martinot JL, Paillere ML, Artiges E, Papadopoulos Orfanos D, Paus T, Poustka L, Hohmann S, Froehner JH, Smolka MN, Vaidya N, Walter H, Whelan R, Schumann G, Nees F, Banaschewski T, Eickhoff SB, Dukart J. Regional patterns of human cortex development colocalize with underlying neurobiology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.05.539537. [PMID: 37205539 PMCID: PMC10187287 DOI: 10.1101/2023.05.05.539537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Human brain morphology undergoes complex changes over the lifespan. Despite recent progress in tracking brain development via normative models, current knowledge of underlying biological mechanisms is highly limited. We demonstrate that human cerebral cortex development and aging trajectories unfold along patterns of molecular and cellular brain organization, traceable from population-level to individual developmental trajectories. During childhood and adolescence, cortex-wide spatial distributions of dopaminergic receptors, inhibitory neurons, glial cell populations, and brain-metabolic features explain up to 50% of variance associated with a lifespan model of regional cortical thickness trajectories. In contrast, modeled cortical change patterns during adulthood are best explained by cholinergic and glutamatergic neurotransmitter receptor and transporter distributions. These relationships are supported by developmental gene expression trajectories and translate to individual longitudinal data from over 8,000 adolescents, explaining up to 59% of developmental change at cohort- and 18% at single-subject level. Integrating neurobiological brain atlases with normative modeling and population neuroimaging provides a biologically meaningful path to understand brain development and aging in living humans.
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16
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Shan X, Uddin LQ, Ma R, Xu P, Xiao J, Li L, Huang X, Feng Y, He C, Chen H, Duan X. Disentangling the Individual-Shared and Individual-Specific Subspace of Altered Brain Functional Connectivity in Autism Spectrum Disorder. Biol Psychiatry 2024; 95:870-880. [PMID: 37741308 DOI: 10.1016/j.biopsych.2023.09.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 08/25/2023] [Accepted: 09/15/2023] [Indexed: 09/25/2023]
Abstract
BACKGROUND Despite considerable effort toward understanding the neural basis of autism spectrum disorder (ASD) using case-control analyses of resting-state functional magnetic resonance imaging data, findings are often not reproducible, largely due to biological and clinical heterogeneity among individuals with ASD. Thus, exploring the individual-shared and individual-specific altered functional connectivity (AFC) in ASD is important to understand this complex, heterogeneous disorder. METHODS We considered 254 individuals with ASD and 295 typically developing individuals from the Autism Brain Imaging Data Exchange to explore the individual-shared and individual-specific subspaces of AFC. First, we computed AFC matrices of individuals with ASD compared with typically developing individuals. Then, common orthogonal basis extraction was used to project AFC of ASD onto 2 subspaces: an individual-shared subspace, which represents altered connectivity patterns shared across ASD, and an individual-specific subspace, which represents the remaining individual characteristics after eliminating the individual-shared altered connectivity patterns. RESULTS Analysis yielded 3 common components spanning the individual-shared subspace. Common components were associated with differences of functional connectivity at the group level. AFC in the individual-specific subspace improved the prediction of clinical symptoms. The default mode network-related and cingulo-opercular network-related magnitudes of AFC in the individual-specific subspace were significantly correlated with symptom severity in social communication deficits and restricted, repetitive behaviors in ASD. CONCLUSIONS Our study decomposed AFC of ASD into individual-shared and individual-specific subspaces, highlighting the importance of capturing and capitalizing on individual-specific brain connectivity features for dissecting heterogeneity. Our analysis framework provides a blueprint for parsing heterogeneity in other prevalent neurodevelopmental conditions.
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Affiliation(s)
- Xiaolong Shan
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Lucina Q Uddin
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, California
| | - Rui Ma
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Pengfei Xu
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Jinming Xiao
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Lei Li
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Xinyue Huang
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Yu Feng
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Changchun He
- College of Blockchain Industry, Chengdu University of Information Technology, Chengdu, China
| | - Huafu Chen
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China.
| | - Xujun Duan
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China.
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17
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Wellen BCM, Ramanujam K, Lavelle M, Capriotti MR, Butner J, Euler MJ, Himle MB. A Test of the Behavioral Model of Tic Disorders Using a Dynamical Systems Framework. Behav Ther 2024; 55:513-527. [PMID: 38670665 DOI: 10.1016/j.beth.2023.08.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 07/01/2023] [Accepted: 08/15/2023] [Indexed: 04/28/2024]
Abstract
Tic disorders are a class of neurodevelopmental disorders characterized by involuntary motor and/or vocal tics. It has been hypothesized that tics function to reduce aversive premonitory urges (i.e., negative reinforcement) and that suppression-based behavioral interventions such as habit reversal training (HRT) and exposure and response prevention (ERP) disrupt this process and facilitate urge reduction through habituation. However, previous findings regarding the negative reinforcement hypothesis and the effect of suppression on the urge-tic relationship have been inconsistent. The present study applied a dynamical systems framework and within-subject time-series autoregressive models to examine the temporal dynamics of urges and tics and assess whether their relationship changes over time. Eleven adults with tic disorders provided continuous urge ratings during separate conditions in which they were instructed to tic freely or to suppress tics. During the free-to-tic conditions, there was considerable heterogeneity across participants in whether and how the urge-tic relationship followed a pattern consistent with the automatic negative reinforcement hypothesis. Further, little evidence for within-session habituation was seen; tic suppression did not result in a reduction in premonitory urges for most participants. Analysis of broader urge change metrics did show significant disruption to the urge pattern during suppression, which has implications for the current biobehavioral model of tics.
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18
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Kasper J, Caspers S, Lotter LD, Hoffstaedter F, Eickhoff SB, Dukart J. Resting-State Changes in Aging and Parkinson's Disease Are Shaped by Underlying Neurotransmission: A Normative Modeling Study. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00112-5. [PMID: 38679325 DOI: 10.1016/j.bpsc.2024.04.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Revised: 03/15/2024] [Accepted: 04/16/2024] [Indexed: 05/01/2024]
Abstract
BACKGROUND Human healthy and pathological aging is linked to a steady decline in brain resting-state activity and connectivity measures. The neurophysiological mechanisms that underlie these changes remain poorly understood. METHODS Making use of recent developments in normative modeling and availability of in vivo maps for various neurochemical systems, we tested in the UK Biobank cohort (n = 25,917) whether and how age- and Parkinson's disease-related resting-state changes in commonly applied local and global activity and connectivity measures colocalize with underlying neurotransmitter systems. RESULTS We found that the distributions of several major neurotransmitter systems including serotonergic, dopaminergic, noradrenergic, and glutamatergic neurotransmission correlated with age-related changes across functional activity and connectivity measures. Colocalization patterns in Parkinson's disease deviated from normative aging trajectories for these, as well as for cholinergic and GABAergic (gamma-aminobutyric acidergic) neurotransmission. The deviation from normal colocalization of brain function and GABAA correlated with disease duration. CONCLUSIONS These findings provide new insights into molecular mechanisms underlying age- and Parkinson's-related brain functional changes by extending the existing evidence elucidating the vulnerability of specific neurochemical attributes to normal aging and Parkinson's disease. The results particularly indicate that alongside dopamine and serotonin, increased vulnerability of glutamatergic, cholinergic, and GABAergic systems may also contribute to Parkinson's disease-related functional alterations. Combining normative modeling and neurotransmitter mapping may aid future research and drug development through deeper understanding of neurophysiological mechanisms that underlie specific clinical conditions.
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Affiliation(s)
- Jan Kasper
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany
| | - Svenja Caspers
- Institute for Anatomy I, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Leon D Lotter
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany; Max Planck School of Cognition, Leipzig, Germany
| | - Felix Hoffstaedter
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany
| | - Juergen Dukart
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany.
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19
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Pérez-Millan A, Borrego-Écija S, Falgàs N, Juncà-Parella J, Bosch B, Tort-Merino A, Antonell A, Bargalló N, Rami L, Balasa M, Lladó A, Sala-Llonch R, Sánchez-Valle R. Cortical thickness modeling and variability in Alzheimer's disease and frontotemporal dementia. J Neurol 2024; 271:1428-1438. [PMID: 38012398 PMCID: PMC10896866 DOI: 10.1007/s00415-023-12087-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 09/29/2023] [Accepted: 10/31/2023] [Indexed: 11/29/2023]
Abstract
BACKGROUND AND OBJECTIVE Alzheimer's disease (AD) and frontotemporal dementia (FTD) show different patterns of cortical thickness (CTh) loss compared with healthy controls (HC), even though there is relevant heterogeneity between individuals suffering from each of these diseases. Thus, we developed CTh models to study individual variability in AD, FTD, and HC. METHODS We used the baseline CTh measures of 379 participants obtained from the structural MRI processed with FreeSurfer. A total of 169 AD patients (63 ± 9 years, 65 men), 88 FTD patients (64 ± 9 years, 43 men), and 122 HC (62 ± 10 years, 47 men) were studied. We fitted region-wise temporal models of CTh using Support Vector Regression. Then, we studied associations of individual deviations from the model with cerebrospinal fluid levels of neurofilament light chain (NfL) and 14-3-3 protein and Mini-Mental State Examination (MMSE). Furthermore, we used real longitudinal data from 144 participants to test model predictivity. RESULTS We defined CTh spatiotemporal models for each group with a reliable fit. Individual deviation correlated with MMSE for AD and with NfL for FTD. AD patients with higher deviations from the trend presented higher MMSE values. In FTD, lower NfL levels were associated with higher deviations from the CTh prediction. For AD and HC, we could predict longitudinal visits with the presented model trained with baseline data. For FTD, the longitudinal visits had more variability. CONCLUSION We highlight the value of CTh models for studying AD and FTD longitudinal changes and variability and their relationships with cognitive features and biomarkers.
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Affiliation(s)
- Agnès Pérez-Millan
- Alzheimer's Disease and Other Cognitive Disorders Unit. Service of Neurology, Hospital Clínic de Barcelona. Fundació Recerca Clínic Barcelona-IDIBAPS, Villarroel, 170, 08036, Barcelona, Spain
- Institut de Neurociències, University of Barcelona, 08036, Barcelona, Spain
- Department of Biomedicine, Faculty of Medicine, University of Barcelona, 08036, Barcelona, Spain
| | - Sergi Borrego-Écija
- Alzheimer's Disease and Other Cognitive Disorders Unit. Service of Neurology, Hospital Clínic de Barcelona. Fundació Recerca Clínic Barcelona-IDIBAPS, Villarroel, 170, 08036, Barcelona, Spain
| | - Neus Falgàs
- Alzheimer's Disease and Other Cognitive Disorders Unit. Service of Neurology, Hospital Clínic de Barcelona. Fundació Recerca Clínic Barcelona-IDIBAPS, Villarroel, 170, 08036, Barcelona, Spain
- Atlantic Fellow for Equity in Brain Health, Global Brain Health Institute, University of California San Francisco, San Francisco, 94143, USA
| | - Jordi Juncà-Parella
- Alzheimer's Disease and Other Cognitive Disorders Unit. Service of Neurology, Hospital Clínic de Barcelona. Fundació Recerca Clínic Barcelona-IDIBAPS, Villarroel, 170, 08036, Barcelona, Spain
| | - Beatriz Bosch
- Alzheimer's Disease and Other Cognitive Disorders Unit. Service of Neurology, Hospital Clínic de Barcelona. Fundació Recerca Clínic Barcelona-IDIBAPS, Villarroel, 170, 08036, Barcelona, Spain
| | - Adrià Tort-Merino
- Alzheimer's Disease and Other Cognitive Disorders Unit. Service of Neurology, Hospital Clínic de Barcelona. Fundació Recerca Clínic Barcelona-IDIBAPS, Villarroel, 170, 08036, Barcelona, Spain
| | - Anna Antonell
- Alzheimer's Disease and Other Cognitive Disorders Unit. Service of Neurology, Hospital Clínic de Barcelona. Fundació Recerca Clínic Barcelona-IDIBAPS, Villarroel, 170, 08036, Barcelona, Spain
| | - Nuria Bargalló
- Image Diagnostic Centre, CIBER de Salud Mental, Instituto de Salud Carlos III, Magnetic Resonance Image Core Facility, IDIBAPS, Hospital Clínic de Barcelona, Barcelona, Spain
| | - Lorena Rami
- Alzheimer's Disease and Other Cognitive Disorders Unit. Service of Neurology, Hospital Clínic de Barcelona. Fundació Recerca Clínic Barcelona-IDIBAPS, Villarroel, 170, 08036, Barcelona, Spain
| | - Mircea Balasa
- Alzheimer's Disease and Other Cognitive Disorders Unit. Service of Neurology, Hospital Clínic de Barcelona. Fundació Recerca Clínic Barcelona-IDIBAPS, Villarroel, 170, 08036, Barcelona, Spain
- Atlantic Fellow for Equity in Brain Health, Global Brain Health Institute, University of California San Francisco, San Francisco, 94143, USA
| | - Albert Lladó
- Alzheimer's Disease and Other Cognitive Disorders Unit. Service of Neurology, Hospital Clínic de Barcelona. Fundació Recerca Clínic Barcelona-IDIBAPS, Villarroel, 170, 08036, Barcelona, Spain
- Institut de Neurociències, University of Barcelona, 08036, Barcelona, Spain
| | - Roser Sala-Llonch
- Institut de Neurociències, University of Barcelona, 08036, Barcelona, Spain
- Department of Biomedicine, Faculty of Medicine, University of Barcelona, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 08036, Barcelona, Spain
| | - Raquel Sánchez-Valle
- Alzheimer's Disease and Other Cognitive Disorders Unit. Service of Neurology, Hospital Clínic de Barcelona. Fundació Recerca Clínic Barcelona-IDIBAPS, Villarroel, 170, 08036, Barcelona, Spain.
- Institut de Neurociències, University of Barcelona, 08036, Barcelona, Spain.
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona, 08036, Barcelona, Spain.
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20
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Ge R, Yu Y, Qi YX, Fan YN, Chen S, Gao C, Haas SS, New F, Boomsma DI, Brodaty H, Brouwer RM, Buckner R, Caseras X, Crivello F, Crone EA, Erk S, Fisher SE, Franke B, Glahn DC, Dannlowski U, Grotegerd D, Gruber O, Hulshoff Pol HE, Schumann G, Tamnes CK, Walter H, Wierenga LM, Jahanshad N, Thompson PM, Frangou S. Normative modelling of brain morphometry across the lifespan with CentileBrain: algorithm benchmarking and model optimisation. Lancet Digit Health 2024; 6:e211-e221. [PMID: 38395541 PMCID: PMC10929064 DOI: 10.1016/s2589-7500(23)00250-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 10/04/2023] [Accepted: 12/01/2023] [Indexed: 02/25/2024]
Abstract
The value of normative models in research and clinical practice relies on their robustness and a systematic comparison of different modelling algorithms and parameters; however, this has not been done to date. We aimed to identify the optimal approach for normative modelling of brain morphometric data through systematic empirical benchmarking, by quantifying the accuracy of different algorithms and identifying parameters that optimised model performance. We developed this framework with regional morphometric data from 37 407 healthy individuals (53% female and 47% male; aged 3-90 years) from 87 datasets from Europe, Australia, the USA, South Africa, and east Asia following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The multivariate fractional polynomial regression (MFPR) emerged as the preferred algorithm, optimised with non-linear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3000 study participants. This model can inform about the biological and behavioural implications of deviations from typical age-related neuroanatomical changes and support future study designs. The model and scripts described here are freely available through CentileBrain.
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Affiliation(s)
- Ruiyang Ge
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Yuetong Yu
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Yi Xuan Qi
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Yu-Nan Fan
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Shiyu Chen
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Chuntong Gao
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Shalaila S Haas
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Faye New
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Dorret I Boomsma
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit, Amsterdam, Netherlands
| | - Henry Brodaty
- Centre for Healthy Brain Ageing, University of New South Wales, Sydney, NSW, Australia
| | - Rachel M Brouwer
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit, Amsterdam, Netherlands
| | - Randy Buckner
- Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Xavier Caseras
- Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, Wales, UK
| | - Fabrice Crivello
- Groupe d'Imagerie Neurofonctionnelle-Institut des Maladies Neurodégénératives, Université de Bordeaux, CNRS UMR 5293, Bordeaux, France
| | - Eveline A Crone
- Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, Netherlands
| | - Susanne Erk
- Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Simon E Fisher
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands
| | - Barbara Franke
- Departments of Human Genetics, Psychiatry and Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
| | - David C Glahn
- Department of Psychiatry and Behavioral Sciences, Boston Children's Hospital, Boston, MA, USA
| | - Udo Dannlowski
- Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany
| | - Dominik Grotegerd
- Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany
| | - Oliver Gruber
- Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, Heidelberg, Germany
| | - Hilleke E Hulshoff Pol
- Department of Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, Netherlands
| | - Gunter Schumann
- Centre for Population Neuroscience and Stratified Medicine, Institute for Science and Technology of Brain-inspired Intelligence, Fudan University, Shanghai, China; PONS Centre, Department of Psychiatry and Clinical Neuroscience, CCM, Charite Universitätsmedizin Berlin, Berlin, Germany
| | | | - Henrik Walter
- Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Lara M Wierenga
- Brain and Development Research Center, Leiden University, Leiden, Netherlands
| | - Neda Jahanshad
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, CA, USA
| | - Paul M Thompson
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, CA, USA
| | - Sophia Frangou
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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Astle DE, Bassett DS, Viding E. Understanding divergence: Placing developmental neuroscience in its dynamic context. Neurosci Biobehav Rev 2024; 157:105539. [PMID: 38211738 DOI: 10.1016/j.neubiorev.2024.105539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 01/04/2024] [Accepted: 01/05/2024] [Indexed: 01/13/2024]
Abstract
Neurodevelopment is not merely a process of brain maturation, but an adaptation to constraints unique to each individual and to the environments we co-create. However, our theoretical and methodological toolkits often ignore this reality. There is growing awareness that a shift is needed that allows us to study divergence of brain and behaviour across conventional categorical boundaries. However, we argue that in future our study of divergence must also incorporate the developmental dynamics that capture the emergence of those neurodevelopmental differences. This crucial step will require adjustments in study design and methodology. If our ultimate aim is to incorporate the developmental dynamics that capture how, and ultimately when, divergence takes place then we will need an analytic toolkit equal to these ambitions. We argue that the over reliance on group averages has been a conceptual dead-end with regard to the neurodevelopmental differences. This is in part because any individual differences and developmental dynamics are inevitably lost within the group average. Instead, analytic approaches which are themselves new, or simply newly applied within this context, may allow us to shift our theoretical and methodological frameworks from groups to individuals. Likewise, methods capable of modelling complex dynamic systems may allow us to understand the emergent dynamics only possible at the level of an interacting neural system.
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Affiliation(s)
- Duncan E Astle
- Department of Psychiatry, University of Cambridge, United Kingdom; MRC Cognition and Brain Sciences Unit, University of Cambridge, United Kingdom.
| | - Dani S Bassett
- Departments of Bioengineering, Electrical & Systems Engineering, Physics & Astronomy, Neurology, and Psychiatry, University of Pennsylvania, United States; The Santa Fe Institute, United States
| | - Essi Viding
- Psychology and Language Sciences, University College London, United Kingdom
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22
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Lin X, Huo Y, Wang Q, Liu G, Shi J, Fan Y, Lu L, Jing R, Li P. Using normative modeling to assess pharmacological treatment effect on brain state in patients with schizophrenia. Cereb Cortex 2024; 34:bhae003. [PMID: 38252996 DOI: 10.1093/cercor/bhae003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 12/28/2023] [Accepted: 12/30/2023] [Indexed: 01/24/2024] Open
Abstract
Quantifying individual differences in neuroimaging metrics is attracting interest in clinical studies with mental disorders. Schizophrenia is diagnosed exclusively based on symptoms, and the biological heterogeneity makes it difficult to accurately assess pharmacological treatment effects on the brain state. Using the Cambridge Centre for Ageing and Neuroscience data set, we built normative models of brain states and mapped the deviations of the brain characteristics of each patient, to test whether deviations were related to symptoms, and further investigated the pharmacological treatment effect on deviation distributions. Specifically, we found that the patients can be divided into 2 groups: the normalized group had a normalization trend and milder symptoms at baseline, and the other group showed a more severe deviation trend. The baseline severity of the depression as well as the overall symptoms could predict the deviation of the static characteristics for the dorsal and ventral attention networks after treatment. In contrast, the positive symptoms could predict the deviations of the dynamic fluctuations for the default mode and dorsal attention networks after treatment. This work evaluates the effect of pharmacological treatment on static and dynamic brain states using an individualized approach, which may assist in understanding the heterogeneity of the illness pathology as well as the treatment response.
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Affiliation(s)
- Xiao Lin
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Chinese Academy of Medical Sciences Research Unit (No. 2018RU006), Peking University, Beijing 100191, China
| | - Yanxi Huo
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing 100192, China
| | - Qiandong Wang
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing 100875, China
| | - Guozhong Liu
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing 100192, China
| | - Jie Shi
- National Institute on Drug Dependence and Beijing Key Laboratory on Drug Dependence Research, Peking University, Beijing 100191, China
| | - Yong Fan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia 19104, United States
| | - Lin Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Chinese Academy of Medical Sciences Research Unit (No. 2018RU006), Peking University, Beijing 100191, China
| | - Rixing Jing
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing 100192, China
| | - Peng Li
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Chinese Academy of Medical Sciences Research Unit (No. 2018RU006), Peking University, Beijing 100191, China
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23
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Loreto F, Verdi S, Kia SM, Duvnjak A, Hakeem H, Fitzgerald A, Patel N, Lilja J, Win Z, Perry R, Marquand AF, Cole JH, Malhotra P. Alzheimer's disease heterogeneity revealed by neuroanatomical normative modeling. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2024; 16:e12559. [PMID: 38487076 PMCID: PMC10937817 DOI: 10.1002/dad2.12559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 10/11/2023] [Accepted: 01/30/2024] [Indexed: 03/17/2024]
Abstract
INTRODUCTION Overlooking the heterogeneity in Alzheimer's disease (AD) may lead to diagnostic delays and failures. Neuroanatomical normative modeling captures individual brain variation and may inform our understanding of individual differences in AD-related atrophy. METHODS We applied neuroanatomical normative modeling to magnetic resonance imaging from a real-world clinical cohort with confirmed AD (n = 86). Regional cortical thickness was compared to a healthy reference cohort (n = 33,072) and the number of outlying regions was summed (total outlier count) and mapped at individual- and group-levels. RESULTS The superior temporal sulcus contained the highest proportion of outliers (60%). Elsewhere, overlap between patient atrophy patterns was low. Mean total outlier count was higher in patients who were non-amnestic, at more advanced disease stages, and without depressive symptoms. Amyloid burden was negatively associated with outlier count. DISCUSSION Brain atrophy in AD is highly heterogeneous and neuroanatomical normative modeling can be used to explore anatomo-clinical correlations in individual patients.
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Affiliation(s)
- Flavia Loreto
- Department of Brain SciencesFaculty of MedicineImperial College LondonLondonUK
| | - Serena Verdi
- Centre for Medical Image ComputingMedical Physics and Biomedical EngineeringUniversity College LondonLondonUK
- Dementia Research CentreUCL Queen Square Institute of NeurologyLondonUK
| | - Seyed Mostafa Kia
- Donders Centre for Cognitive NeuroimagingDonders Institute for BrainCognition and BehaviourRadboud UniversityNijmegenThe Netherlands
- Department of Cognitive NeuroscienceRadboud University Medical CentreNijmegenThe Netherlands
- Department of PsychiatryUtrecht University Medical CenterUtrechtThe Netherlands
| | - Aleksandar Duvnjak
- Department of Brain SciencesFaculty of MedicineImperial College LondonLondonUK
| | - Haneen Hakeem
- Department of Brain SciencesFaculty of MedicineImperial College LondonLondonUK
| | - Anna Fitzgerald
- Department of Brain SciencesFaculty of MedicineImperial College LondonLondonUK
| | - Neva Patel
- Department of Nuclear MedicineImperial College Healthcare NHS TrustLondonUK
| | | | - Zarni Win
- Department of Nuclear MedicineImperial College Healthcare NHS TrustLondonUK
| | - Richard Perry
- Department of Brain SciencesFaculty of MedicineImperial College LondonLondonUK
- Department of NeurologyImperial College Healthcare NHS TrustLondonUK
| | - Andre F. Marquand
- Donders Centre for Cognitive NeuroimagingDonders Institute for BrainCognition and BehaviourRadboud UniversityNijmegenThe Netherlands
- Department of Cognitive NeuroscienceRadboud University Medical CentreNijmegenThe Netherlands
| | - James H. Cole
- Centre for Medical Image ComputingMedical Physics and Biomedical EngineeringUniversity College LondonLondonUK
- Dementia Research CentreUCL Queen Square Institute of NeurologyLondonUK
| | - Paresh Malhotra
- Department of Brain SciencesFaculty of MedicineImperial College LondonLondonUK
- Department of NeurologyImperial College Healthcare NHS TrustLondonUK
- UK Dementia Research Institute Care Research and Technology CentreImperial College London and the University of SurreyLondonUK
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24
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Sun X, Sun J, Lu X, Dong Q, Zhang L, Wang W, Liu J, Ma Q, Wang X, Wei D, Chen Y, Liu B, Huang CC, Zheng Y, Wu Y, Chen T, Cheng Y, Xu X, Gong Q, Si T, Qiu S, Lin CP, Cheng J, Tang Y, Wang F, Qiu J, Xie P, Li L, He Y, Xia M. Mapping Neurophysiological Subtypes of Major Depressive Disorder Using Normative Models of the Functional Connectome. Biol Psychiatry 2023; 94:936-947. [PMID: 37295543 DOI: 10.1016/j.biopsych.2023.05.021] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 05/15/2023] [Accepted: 05/29/2023] [Indexed: 06/12/2023]
Abstract
BACKGROUND Major depressive disorder (MDD) is a highly heterogeneous disorder that typically emerges in adolescence and can occur throughout adulthood. Studies aimed at quantitatively uncovering the heterogeneity of individual functional connectome abnormalities in MDD and identifying reproducibly distinct neurophysiological MDD subtypes across the lifespan, which could provide promising insights for precise diagnosis and treatment prediction, are still lacking. METHODS Leveraging resting-state functional magnetic resonance imaging data from 1148 patients with MDD and 1079 healthy control participants (ages 11-93), we conducted the largest multisite analysis to date for neurophysiological MDD subtyping. First, we characterized typical lifespan trajectories of functional connectivity strength based on the normative model and quantitatively mapped the heterogeneous individual deviations among patients with MDD. Then, we identified neurobiological MDD subtypes using an unsupervised clustering algorithm and evaluated intersite reproducibility. Finally, we validated the subtype differences in baseline clinical variables and longitudinal treatment predictive capacity. RESULTS Our findings indicated great intersubject heterogeneity in the spatial distribution and severity of functional connectome deviations among patients with MDD, which inspired the identification of 2 reproducible neurophysiological subtypes. Subtype 1 showed severe deviations, with positive deviations in the default mode, limbic, and subcortical areas and negative deviations in the sensorimotor and attention areas. Subtype 2 showed a moderate but converse deviation pattern. More importantly, subtype differences were observed in depressive item scores and the predictive ability of baseline deviations for antidepressant treatment outcomes. CONCLUSIONS These findings shed light on our understanding of different neurobiological mechanisms underlying the clinical heterogeneity of MDD and are essential for developing personalized treatments for this disorder.
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Affiliation(s)
- Xiaoyi Sun
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; School of Systems Science, Beijing Normal University, Beijing, China
| | - Jinrong Sun
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China; Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, Hunan, China; Affiliated WuTaiShan Hospital of Medical College of Yangzhou University, Yangzhou Mental Health Centre, Yangzhou, China
| | - Xiaowen Lu
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China; Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, Hunan, China; Affiliated Wuhan Mental Health Center, Huazhong University of Science and Technology, Wuhan, China
| | - Qiangli Dong
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China; Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, Hunan, China; Department of Psychiatry, Lanzhou University Second Hospital, Lanzhou, China
| | - Liang Zhang
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China; Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, Hunan, China; Mental Health Education and Counseling Center, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Wenxu Wang
- School of Systems Science, Beijing Normal University, Beijing, China
| | - Jin Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Qing Ma
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Xiaoqin Wang
- Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, China; Department of Psychology, Southwest University, Chongqing, China
| | - Dongtao Wei
- Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, China; Department of Psychology, Southwest University, Chongqing, China
| | - Yuan Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Bangshan Liu
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China; Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, Hunan, China
| | - Chu-Chung Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Yanting Zheng
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yankun Wu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, National Health Commission Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Taolin Chen
- Huaxi Magnetic Resonance Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yuqi Cheng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Xiufeng Xu
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Qiyong Gong
- Huaxi Magnetic Resonance Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Tianmei Si
- Peking University Sixth Hospital, Peking University Institute of Mental Health, National Health Commission Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Shijun Qiu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Education and Research, Taipei City Hospital, Taipei, Taiwan
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yanqing Tang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Fei Wang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, China; Department of Psychology, Southwest University, Chongqing, China
| | - Peng Xie
- Chongqing Key Laboratory of Neurobiology, Chongqing, China; Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Lingjiang Li
- Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China; Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, Hunan, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Chinese Institute for Brain Research, Beijing, China
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
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25
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Bedford SA, Lai MC, Lombardo MV, Chakrabarti B, Ruigrok A, Suckling J, Anagnostou E, Lerch JP, Taylor M, Nicolson R, Stelios G, Crosbie J, Schachar R, Kelley E, Jones J, Arnold PD, Courchesne E, Pierce K, Eyler LT, Campbell K, Barnes CC, Seidlitz J, Alexander-Bloch AF, Bullmore ET, Baron-Cohen S, Bethlehem RA. Brain-charting autism and attention deficit hyperactivity disorder reveals distinct and overlapping neurobiology. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.06.23299587. [PMID: 38106166 PMCID: PMC10723556 DOI: 10.1101/2023.12.06.23299587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Background Autism and attention deficit hyperactivity disorder (ADHD) are heterogeneous neurodevelopmental conditions with complex underlying neurobiology. Despite overlapping presentation and sex-biased prevalence, autism and ADHD are rarely studied together, and sex differences are often overlooked. Normative modelling provides a unified framework for studying age-specific and sex-specific divergences in neurodivergent brain development. Methods Here we use normative modelling and a large, multi-site neuroimaging dataset to characterise cortical anatomy associated with autism and ADHD, benchmarked against models of typical brain development based on a sample of over 75,000 individuals. We also examined sex and age differences, relationship with autistic traits, and explored the co-occurrence of autism and ADHD (autism+ADHD). Results We observed robust neuroanatomical signatures of both autism and ADHD. Overall, autistic individuals showed greater cortical thickness and volume localised to the superior temporal cortex, whereas individuals with ADHD showed more global effects of cortical thickness increases but lower cortical volume and surface area across much of the cortex. The autism+ADHD group displayed a unique pattern of widespread increases in cortical thickness, and certain decreases in surface area. We also found evidence that sex modulates the neuroanatomy of autism but not ADHD, and an age-by-diagnosis interaction for ADHD only. Conclusions These results indicate distinct cortical differences in autism and ADHD that are differentially impacted by age, sex, and potentially unique patterns related to their co-occurrence.
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Affiliation(s)
- Saashi A. Bedford
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
| | - Meng-Chuan Lai
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK
- The Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health and Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada
- Department of Psychiatry, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5T 1R8, Canada
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei 100229, Taiwan
| | - Michael V. Lombardo
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Bhismadev Chakrabarti
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK
- Centre for Autism, School of Psychology and Clinical Language Sciences, University of Reading, Reading RG6 6ES, UK
| | - Amber Ruigrok
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester
| | - John Suckling
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
| | - Evdokia Anagnostou
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
- Department of Pediatrics, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Jason P. Lerch
- Program in Neurosciences and Mental Health, Research Institute, Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
- Mouse Imaging Centre, Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - Margot Taylor
- Program in Neurosciences and Mental Health, Research Institute, Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
- Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
| | - Rob Nicolson
- Department of Psychiatry, University of Western Ontario, London, Ontario, Canada
| | | | - Jennifer Crosbie
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5T 1R8, Canada
- Program in Neurosciences and Mental Health, Research Institute, Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
- Genetics & Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Russell Schachar
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5T 1R8, Canada
- Program in Neurosciences and Mental Health, Research Institute, Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
- Genetics & Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Elizabeth Kelley
- Department of Psychology, Queen’s University, Kingston, ON K7L 3N6 Canada
- Centre for Neuroscience Studies, Queen’s University, Kingston, ON K7L 3N6 Canada
- Department of Psychiatry, Queen’s University, Kingston, ON K7L 3N6 Canada
| | - Jessica Jones
- Department of Psychology, Queen’s University, Kingston, ON K7L 3N6 Canada
- Centre for Neuroscience Studies, Queen’s University, Kingston, ON K7L 3N6 Canada
- Department of Psychiatry, Queen’s University, Kingston, ON K7L 3N6 Canada
| | - Paul D. Arnold
- The Mathison Centre for Mental Health Research & Education, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Departments of Psychiatry and Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Eric Courchesne
- Department of Neurosciences, University of California San Diego, La Jolla, California, USA
| | - Karen Pierce
- Department of Neurosciences, University of California San Diego, La Jolla, California, USA
| | - Lisa T. Eyler
- Department of Psychiatry, University of California San Diego, La Jolla, California, USA
| | - Kathleen Campbell
- Department of Neurosciences, University of California San Diego, La Jolla, California, USA
| | - Cynthia Carter Barnes
- Department of Neurosciences, University of California San Diego, La Jolla, California, USA
| | - Jakob Seidlitz
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA
| | - Aaron F. Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA
| | - Edward T. Bullmore
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
| | - Simon Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK
- Cambridge Lifetime Autism Spectrum Service (CLASS), Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Richard A.I. Bethlehem
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
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26
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Ge R, Yu Y, Qi YX, Fan YV, Chen S, Gao C, Haas SS, Modabbernia A, New F, Agartz I, Asherson P, Ayesa-Arriola R, Banaj N, Banaschewski T, Baumeister S, Bertolino A, Boomsma DI, Borgwardt S, Bourque J, Brandeis D, Breier A, Brodaty H, Brouwer RM, Buckner R, Buitelaar JK, Cannon DM, Caseras X, Cervenka S, Conrod PJ, Crespo-Facorro B, Crivello F, Crone EA, de Haan L, de Zubicaray GI, Di Giorgio A, Erk S, Fisher SE, Franke B, Frodl T, Glahn DC, Grotegerd D, Gruber O, Gruner P, Gur RE, Gur RC, Harrison BJ, Hatton SN, Hickie I, Howells FM, Pol HEH, Huyser C, Jernigan TL, Jiang J, Joska JA, Kahn RS, Kalnin AJ, Kochan NA, Koops S, Kuntsi J, Lagopoulos J, Lazaro L, Lebedeva IS, Lochner C, Martin NG, Mazoyer B, McDonald BC, McDonald C, McMahon KL, Nakao T, Nyberg L, Piras F, Portella MJ, Qiu J, Roffman JL, Sachdev PS, Sanford N, Satterthwaite TD, Saykin AJ, Schumann G, Sellgren CM, Sim K, Smoller JW, Soares J, Sommer IE, Spalletta G, Stein DJ, Tamnes CK, Thomopolous SI, Tomyshev AS, Tordesillas-Gutiérrez D, Trollor JN, van ’t Ent D, van den Heuvel OA, van Erp TGM, van Haren NEM, Vecchio D, Veltman DJ, Walter H, Wang Y, Weber B, Wei D, Wen W, Westlye LT, Wierenga LM, Williams SCR, Wright MJ, Medland S, Wu MJ, Yu K, Jahanshad N, Thompson PM, Frangou S. Normative Modeling of Brain Morphometry Across the Lifespan Using CentileBrain: Algorithm Benchmarking and Model Optimization. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.30.523509. [PMID: 38076938 PMCID: PMC10705253 DOI: 10.1101/2023.01.30.523509] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/22/2023]
Abstract
We present an empirically benchmarked framework for sex-specific normative modeling of brain morphometry that can inform about the biological and behavioral significance of deviations from typical age-related neuroanatomical changes and support future study designs. This framework was developed using regional morphometric data from 37,407 healthy individuals (53% female; aged 3-90 years) following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The Multivariate Factorial Polynomial Regression (MFPR) emerged as the preferred algorithm optimized using nonlinear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins, and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3,000 study participants. The model and scripts described here are freely available through CentileBrain (https://centilebrain.org/).
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Affiliation(s)
- Ruiyang Ge
- Djavad Mowafagian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Yuetong Yu
- Djavad Mowafagian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Yi Xuan Qi
- Djavad Mowafagian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Yunan Vera Fan
- Djavad Mowafagian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Shiyu Chen
- Djavad Mowafagian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Chuntong Gao
- Djavad Mowafagian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Shalaila S Haas
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Faye New
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden
| | - Philip Asherson
- Institute of Psychiatry, Psychology and Neuroscience, Social, Genetic and Developmental Psychiatry Center, King's College London, London, UK
| | - Rosa Ayesa-Arriola
- Department of Psychiatry, Marqués de Valdecilla University Hospital, Valdecilla Biomedical Research Institute (IDIVAL), Santander, Spain
| | - Nerisa Banaj
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Germany
| | - Sarah Baumeister
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Germany
| | - Alessandro Bertolino
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Stefan Borgwardt
- Translational Psychiatry Unit, Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Josiane Bourque
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Daniel Brandeis
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Germany
- Department of Child and Adolescent Psychiatry, University of Zürich, Zurich, Switzerland
| | - Alan Breier
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Henry Brodaty
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Rachel M Brouwer
- Department of Psychiatry, UMC Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Randy Buckner
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jan K Buitelaar
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Dara M Cannon
- Clinical Neuroimaging Laboratory, National University of Ireland Galway, Galway, Ireland
| | - Xavier Caseras
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Simon Cervenka
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden
- Department of Medical Sciences, Psychiatry, Uppsala University, Uppsala, Sweden
| | - Patricia J Conrod
- Department of Psychiatry and Addiction, Université de Montréal, CHU Ste Justine, Montréal, Canada
| | - Benedicto Crespo-Facorro
- University Hospital Virgen del Rocio, Seville, Spain; Department of Psychiatry, University of Seville, Institute of Biomedicine of Seville (IBIS), Seville, Spain
- Mental Health Research Networking Center (CIBERSAM), Madrid, Spain
| | - Fabrice Crivello
- Institut des Maladies Neurodégénératives, Université de Bordeaux, Bordeaux, France
| | - Eveline A Crone
- Department of Psychology, Education and Child Studies, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Institute of Psychology, Leiden University, Leiden, The Netherlands; Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Liewe de Haan
- Department of Psychiatry, Amsterdam UMC, Amsterdam, The Netherlands
| | - Greig I de Zubicaray
- School of Psychology & Counselling, Faculty of Health, Queensland University of Technology, Brisbane, Australia
| | - Annabella Di Giorgio
- Laboratory of Biological Psychiatry, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Susanne Erk
- Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Simon E Fisher
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Barbara Franke
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Psychiatry, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Thomas Frodl
- University Clinics and Clinics for Psychiatry, Psychotherapy and Psychosomatic Medicine, RWTH Aachen University, Aachen, Germany
| | - David C Glahn
- Department of Psychiatry, Tommy Fuss Center for Neuropsychiatric Disease Research Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Dominik Grotegerd
- Department of Psychiatry and Psychotherapy, University of Muenster, Muenster, Germany
| | - Oliver Gruber
- Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, Heidelberg, Germany
| | - Patricia Gruner
- Department of Psychiatry, Yale University, New Haven, Connecticut, USA
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ruben C Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ben J Harrison
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne & Melbourne Health, Melbourne, Australia
| | - Sean N Hatton
- Center for Multimodal Imaging and Genetics, University of California San Diego, La jolla, California, USA
| | - Ian Hickie
- Brain and Mind Centre, University of Sydney, Sydney, Australia
| | - Fleur M Howells
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Hilleke E Hulshoff Pol
- Department of Psychiatry, UMC Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Department of Psychology, Utrecht University, Utrecht, The Netherlands
| | - Chaim Huyser
- Department of Child and Adolescent Psychiatry, Academic Medical Centre/De Bascule, Amsterdam, The Netherlands
| | - Terry L Jernigan
- Center for Human Development, Departments of Cognitive Science, Psychiatry, and Radiology, University of California, San Diego, USA
| | - Jiyang Jiang
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, Australia
| | - John A Joska
- Department of Neuropsychiatry, University of Cape Town, Cape Town, South Africa
| | - René S Kahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Andrew J Kalnin
- Department of Radiology, The Ohio State University College of Medicine, Columbus, Ohio, USA
| | - Nicole A Kochan
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Sanne Koops
- Department of Psychiatry and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jonna Kuntsi
- Institute of Psychiatry, Psychology and Neuroscience, Social, Genetic and Developmental Psychiatry Center, King's College London, London, UK
| | - Jim Lagopoulos
- Sunshine Coast Mind and Neuroscience - Thompson Institute, University of the Sunshine Coast, Queensland, Australia
| | - Luisa Lazaro
- Department of Child and Adolescent Psychiatry and Psychology, Hospital Clínic Barcelona, Barcelona, Spain
| | | | - Christine Lochner
- SA MRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, Stellenbosch University, Stellenbosch, South Africa
| | - Nicholas G Martin
- Queensland Institute of Medical Research, Berghofer Medical Research Institute, Brisbane, Australia
| | - Bernard Mazoyer
- Institut des Maladies Neurodégénératives, Université de Bordeaux, Bordeaux, France
| | - Brenna C McDonald
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Colm McDonald
- Centre for Neuroimaging & Cognitive Genomics (NICOG), NCBES Galway Neuroscience Centre, National University of Ireland Galway, Galway, Ireland
| | - Katie L McMahon
- School of Clinical Sciences, Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, Australia
| | - Tomohiro Nakao
- Department of Neuropsychiatry, Kyushu University, Fukuoka, Japan
| | - Lars Nyberg
- Department of Radiation Sciences, Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden; Department of Integrative Medical Biology, Umeå University, Umeå, Sweden
| | - Fabrizio Piras
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Maria J Portella
- Mental Health Research Networking Center (CIBERSAM), Madrid, Spain
- Department of Psychiatry, Hospital de la Santa Creu iSant Pau, Institutd' Investigació Biomèdica SantPau, Universitat Autònomade Barcelona (UAB), Barcelona, Spain
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality, Southwest University, Ministry of Education, Chongqing, PR China
- Faculty of Psychology, Southwest University, Chongqing, PR China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, PR China
| | - Joshua L Roffman
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Nicole Sanford
- Djavad Mowafagian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | | | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Gunter Schumann
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology, and Neuroscience, Social, Genetic & Developmental Psychiatry Centre, King's College London, London, UK; Institute for Science and Technology of Brain-inspired Intelligence, Fudan University, Shanghai, PR China; Centre for Population Neuroscience and Stratified Medicine (PONS), Charite Mental Health, Department of Psychiatry and Psychotherapy, CCM, Charite Universitätsmedizin Berlin, Berlin, Germany
| | - Carl M Sellgren
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden
- Department of Physiology and Pharmacology, Karolinska Institute, Stockholm, Sweden
| | - Kang Sim
- Institute of Mental Health, Singapore
| | - Jordan W Smoller
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jair Soares
- University of Texas Health Harris County Psychiatric Center, Houston, Texas, USA
| | - Iris E Sommer
- Department of Biomedical Sciences of Cells and Systems, Rijksuniversiteit Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | | | - Dan J Stein
- SA MRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Christian K Tamnes
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Sophia I Thomopolous
- Genetics Center, Stevens Institute for Neuroimaging and Informatics, Keck USC School of Medicine, Marina del Rey, California, USA
| | | | - Diana Tordesillas-Gutiérrez
- Department of Radiology, Marqués de Valdecilla University Hospital, Valdecilla Biomedical Research Institute (IDIVAL), Santander, Spain; Advanced Computing and e-Science, Instituto de Física de Cantabria (UC-CSIC), Santander, Spain
| | - Julian N Trollor
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, Australia
- Department of Developmental Disability Neuropsychiatry, School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Dennis van ’t Ent
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Odile A van den Heuvel
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Department of Anatomy & Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Theo GM van Erp
- Department of Psychiatry and Human Behavior, University of California, Irvine, California, USA
| | - Neeltje EM van Haren
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Daniela Vecchio
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Dick J Veltman
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Henrik Walter
- Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Yang Wang
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Bernd Weber
- Institute for Experimental Epileptology and Cognition Research, University of Bonn Germany, Bonn, Germany; University Hospital Bonn, Bonn, Germany
| | - Dongtao Wei
- Key Laboratory of Cognition and Personality, Southwest University, Ministry of Education, Chongqing, PR China
- Faculty of Psychology, Southwest University, Chongqing, PR China
| | - Wei Wen
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Lara M Wierenga
- Institute of Psychology, Leiden University, Leiden, The Netherlands; Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Steven CR Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Margaret J Wright
- Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia
| | - Sarah Medland
- Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia
| | - Mon-Ju Wu
- Department of Psychiatry and Behavioral Science, University of Texas Health Science Center, Houston, Texas, USA
| | - Kevin Yu
- Djavad Mowafagian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Neda Jahanshad
- Genetics Center, Stevens Institute for Neuroimaging and Informatics, Keck USC School of Medicine, Marina del Rey, California, USA
| | - Paul M Thompson
- Genetics Center, Stevens Institute for Neuroimaging and Informatics, Keck USC School of Medicine, Marina del Rey, California, USA
| | - Sophia Frangou
- Djavad Mowafagian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Barkema P, Rutherford S, Lee HC, Kia SM, Savage H, Beckmann C, Marquand A. Predictive Clinical Neuroscience Portal (PCNportal): instant online access to research-grade normative models for clinical neuroscientists. Wellcome Open Res 2023; 8:326. [PMID: 37663797 PMCID: PMC10474337 DOI: 10.12688/wellcomeopenres.19591.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/17/2023] [Indexed: 09/05/2023] Open
Abstract
Background The neurobiology of mental disorders remains poorly understood despite substantial scientific efforts, due to large clinical heterogeneity and to a lack of tools suitable to map individual variability. Normative modeling is one recently successful framework that can address these problems by comparing individuals to a reference population. The methodological underpinnings of normative modelling are, however, relatively complex and computationally expensive. Our research group has developed the python-based normative modelling package Predictive Clinical Neuroscience toolkit (PCNtoolkit) which provides access to many validated algorithms for normative modelling. PCNtoolkit has since proven to be a strong foundation for large scale normative modelling, but still requires significant computation power, time and technical expertise to develop. Methods To address these problems, we introduce PCNportal. PCNportal is an online platform integrated with PCNtoolkit that offers access to pre-trained research-grade normative models estimated on tens of thousands of participants, without the need for computation power or programming abilities. PCNportal is an easy-to-use web interface that is highly scalable to large user bases as necessary. Finally, we demonstrate how the resulting normalized deviation scores can be used in a clinical application through a schizophrenia classification task applied to cortical thickness and volumetric data from the longitudinal Northwestern University Schizophrenia Data and Software Tool (NUSDAST) dataset. Results At each longitudinal timepoint, the transferred normative models achieved a mean[std. dev.] explained variance of 9.4[8.8]%, 9.2[9.2]%, 5.6[7.4]% respectively in the control group and 4.7[5.5]%, 6.0[6.2]%, 4.2[6.9]% in the schizophrenia group. Diagnostic classifiers achieved AUC of 0.78, 0.76 and 0.71 respectively. Conclusions This replicates the utility of normative models for diagnostic classification of schizophrenia and showcases the use of PCNportal for clinical neuroimaging. By facilitating and speeding up research with high-quality normative models, this work contributes to research in inter-individual variability, clinical heterogeneity and precision medicine.
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Affiliation(s)
- Pieter Barkema
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Saige Rutherford
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands
- Department of Psychiatry, University of Michigan, Ann Arbor, USA
| | - Hurng-Chun Lee
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Seyed Mostafa Kia
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands
- Department of Psychiatry, Utrecht University Medical Center, Utrecht, The Netherlands
- Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, The Netherlands
| | - Hannah Savage
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Christian Beckmann
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands
- Centre for functional MRI of the Brain, University of Oxford, Oxford, England, UK
| | - Andre Marquand
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands
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Segal A, Parkes L, Aquino K, Kia SM, Wolfers T, Franke B, Hoogman M, Beckmann CF, Westlye LT, Andreassen OA, Zalesky A, Harrison BJ, Davey CG, Soriano-Mas C, Cardoner N, Tiego J, Yücel M, Braganza L, Suo C, Berk M, Cotton S, Bellgrove MA, Marquand AF, Fornito A. Regional, circuit and network heterogeneity of brain abnormalities in psychiatric disorders. Nat Neurosci 2023; 26:1613-1629. [PMID: 37580620 PMCID: PMC10471501 DOI: 10.1038/s41593-023-01404-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 07/13/2023] [Indexed: 08/16/2023]
Abstract
The substantial individual heterogeneity that characterizes people with mental illness is often ignored by classical case-control research, which relies on group mean comparisons. Here we present a comprehensive, multiscale characterization of the heterogeneity of gray matter volume (GMV) differences in 1,294 cases diagnosed with one of six conditions (attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, depression, obsessive-compulsive disorder and schizophrenia) and 1,465 matched controls. Normative models indicated that person-specific deviations from population expectations for regional GMV were highly heterogeneous, affecting the same area in <7% of people with the same diagnosis. However, these deviations were embedded within common functional circuits and networks in up to 56% of cases. The salience-ventral attention system was implicated transdiagnostically, with other systems selectively involved in depression, bipolar disorder, schizophrenia and attention-deficit/hyperactivity disorder. Phenotypic differences between cases assigned the same diagnosis may thus arise from the heterogeneous localization of specific regional deviations, whereas phenotypic similarities may be attributable to the dysfunction of common functional circuits and networks.
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Affiliation(s)
- Ashlea Segal
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia.
- Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia.
| | - Linden Parkes
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Rutgers University, Piscataway, NJ, USA
| | - Kevin Aquino
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
- School of Physics, University of Sydney, Sydney, New South Wales, Australia
- BrainKey Inc, Palo alto, CA, USA
| | - Seyed Mostafa Kia
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands
- Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, the Netherlands
| | - Thomas Wolfers
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health (TÜCMH), University of Tübingen, Tübingen, Germany
| | - Barbara Franke
- Department of Psychiatry, Donders Institute of Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Human Genetics, Donders Institute of Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Martine Hoogman
- Department of Psychiatry, Donders Institute of Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Human Genetics, Donders Institute of Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Christian F Beckmann
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, the Netherlands
- Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Lars T Westlye
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia
| | - Ben J Harrison
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia
| | - Christopher G Davey
- Department of Psychiatry, University of Melbourne, Melbourne, Victoria, Australia
| | - Carles Soriano-Mas
- Department of Psychiatry, Bellvitge University Hospital, Bellvitge Biomedical Research Institute, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Carlos III Health Institute, Madrid, Spain
- Department of Social Psychology and Quantitative Psychology, Universitat de Barcelona, Barcelona, Spain
| | - Narcís Cardoner
- Centro de Investigación Biomédica en Red de Salud Mental, Carlos III Health Institute, Madrid, Spain
- Sant Pau Mental Health Research Group, Institut d'Investigació Biomèdica Sant Pau, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Jeggan Tiego
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
| | - Murat Yücel
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Leah Braganza
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Chao Suo
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
- Australian Characterisation Commons at Scale (ACCS) Project, Monash eResearch Centre, Melbourne, Victoria, Australia
| | - Michael Berk
- Institute for Mental and Physical Health and Clinical Translation School of Medicine, Deakin University, Geelong, Victoria, Australia
- Orygen, Melbourne, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
- Florey Institute for Neuroscience and Mental Health, Parkville, Victoria, Australia
| | - Sue Cotton
- Orygen, Melbourne, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Mark A Bellgrove
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Andre F Marquand
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, the Netherlands
- Department of Neuroimaging, Centre of Neuroimaging Sciences, Institute of Psychiatry, King's College London, London, UK
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia.
- Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia.
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Klem L, Nielsen MM, Gestsdóttir SB, Frandsen SL, Prichardt S, Andreasen JT. Assessing attention and impulsivity in the variable stimulus duration and variable intertrial interval rodent continuous performance test schedules using dopamine receptor antagonists in female C57BL/6JRj mice. Psychopharmacology (Berl) 2023; 240:1651-1666. [PMID: 37378887 PMCID: PMC10349733 DOI: 10.1007/s00213-023-06387-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 05/10/2023] [Indexed: 06/29/2023]
Abstract
RATIONALE Dopaminergic dysfunction is implicated in disorders of impulsivity and inattention. The rodent continuous performance test (rCPT) has been used to quantify changes in attention and impulsivity. OBJECTIVE To examine the roles of dopamine receptors in attention and impulsivity behaviours measured in the rCPT variable stimulus duration (vSD) and the variable intertrial interval schedules (vITI) using DA receptor antagonists. METHODS Two cohorts of 35 and 36 female C57BL/6JRj mice were examined separately in the rCPT, vSD, and vITI schedules, respectively. Both cohorts received antagonists of the following receptors: D1/5 (SCH23390, SCH: 0.01, 0.02, 0.04 mg/kg) and D2/3 (raclopride, RAC 0.03, 0.10, 0.30 mg/kg) in consecutive balanced Latin square designs with flanking reference measurements. The antagonists were subsequently examined for effects on locomotor activity. RESULTS SCH showed similar effects in both schedules, and the effects were reference-dependent in the vITI schedule. SCH reduced responding, but improved response accuracy, impulsivity, discriminability, and locomotor activity. RAC showed mixed effects on responsivity, but improved accuracy and discriminability. The discriminability improvement was driven by an increase in hit rate in the vITI schedule and a reduction in false alarm rate in the vSD schedule. RAC also decreased locomotor activity. CONCLUSION Both D1/5 and D2/3 receptor antagonism reduced responding, but the outcome on discriminability differed, stemming from individual effects on hit and false alarm rate, and the weight of omissions within the calculation. The effects of SCH and RAC suggest that endogenous DA increases responding and impulsivity, but reduces accuracy and shows mixed effects on discriminability.
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Affiliation(s)
- L Klem
- Department of Drug Design and Pharmacology, University of Copenhagen, Universitetsparken 2, 2100, Copenhagen, Denmark
| | - M M Nielsen
- Department of Drug Design and Pharmacology, University of Copenhagen, Universitetsparken 2, 2100, Copenhagen, Denmark
| | - S B Gestsdóttir
- Department of Drug Design and Pharmacology, University of Copenhagen, Universitetsparken 2, 2100, Copenhagen, Denmark
| | - S L Frandsen
- Department of Drug Design and Pharmacology, University of Copenhagen, Universitetsparken 2, 2100, Copenhagen, Denmark
| | - S Prichardt
- Department of Drug Design and Pharmacology, University of Copenhagen, Universitetsparken 2, 2100, Copenhagen, Denmark
| | - J T Andreasen
- Department of Drug Design and Pharmacology, University of Copenhagen, Universitetsparken 2, 2100, Copenhagen, Denmark.
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30
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Klem L, Nielsen MM, Gestsdóttir SB, Frandsen SL, Prichardt S, Andreasen JT. Assessing attention and impulsivity in the variable stimulus duration and variable intertrial interval rodent continuous performance test schedules using noradrenaline receptor antagonists in female C57BL/6JRj mice. Psychopharmacology (Berl) 2023; 240:1629-1650. [PMID: 37329343 PMCID: PMC10349758 DOI: 10.1007/s00213-023-06385-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Accepted: 05/10/2023] [Indexed: 06/19/2023]
Abstract
RATIONALE Noradrenergic dysfunction is associated with disorders of impulsivity and inattention. The rodent continuous performance test (rCPT) quantifies changes in attention and impulsivity. OBJECTIVE To use NA receptor antagonists to examine the roles of NA on attention and impulsivity behaviours measured in the rCPT variable stimulus duration (vSD) and the variable intertrial interval (vITI) schedules. METHODS Two cohorts of 36 female C57BL/6JRj mice were examined separately in the rCPT vSD and vITI schedules. Both cohorts received antagonists of the following adrenoceptors: α1 (doxazosin, DOX: 1.0, 3.0, 10.0 mg/kg), α2 (yohimbine, YOH: 0.1, 0.3, 1.0 mg/kg), and β1/2 (propranolol, PRO: 1.0, 3.0, 10.0 mg/kg) in consecutive balanced Latin square designs with flanking reference measurements. The antagonists were subsequently examined for effects on locomotor activity. RESULTS DOX showed similar effects in both schedules, improving discriminability and accuracy, and reducing responding and impulsivity, and DOX also reduced locomotor activity. YOH showed prominent effects in the vSD schedule to increase responding and impulsivity, while impairing discriminability and accuracy. YOH did not affect locomotor activity. PRO increased responding and impulsivity, decreased accuracy, but did not affect discriminability or locomotor activity. CONCLUSION Antagonism of α2 or β1/2 adrenoceptors caused similar increases in responding and impulsivity and worsened attentional performance, while α1 adrenoceptor antagonism showed the opposite effects. Our results suggest that endogenous NA exerts bidirectional control of most behaviours in the rCPT. The parallel vSD and vITI studies showed a substantial overlap in effects, but also some differences that indicate differing sensitivity towards noradrenergic manipulations.
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Affiliation(s)
- L Klem
- Dept. of Drug Design and Pharmacology, University of Copenhagen, Universitetsparken 2, 2100, Copenhagen, Denmark
| | - M M Nielsen
- Dept. of Drug Design and Pharmacology, University of Copenhagen, Universitetsparken 2, 2100, Copenhagen, Denmark
| | - S B Gestsdóttir
- Dept. of Drug Design and Pharmacology, University of Copenhagen, Universitetsparken 2, 2100, Copenhagen, Denmark
| | - S L Frandsen
- Dept. of Drug Design and Pharmacology, University of Copenhagen, Universitetsparken 2, 2100, Copenhagen, Denmark
| | - S Prichardt
- Dept. of Drug Design and Pharmacology, University of Copenhagen, Universitetsparken 2, 2100, Copenhagen, Denmark
| | - J T Andreasen
- Dept. of Drug Design and Pharmacology, University of Copenhagen, Universitetsparken 2, 2100, Copenhagen, Denmark.
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Isakoglou C, Haak KV, Wolfers T, Floris DL, Llera A, Oldehinkel M, Forde NJ, Oakley BFM, Tillmann J, Holt RJ, Moessnang C, Loth E, Bourgeron T, Baron-Cohen S, Charman T, Banaschewski T, Murphy DGM, Buitelaar JK, Marquand AF, Beckmann CF. Fine-grained topographic organization within somatosensory cortex during resting-state and emotional face-matching task and its association with ASD traits. Transl Psychiatry 2023; 13:270. [PMID: 37500630 PMCID: PMC10374902 DOI: 10.1038/s41398-023-02559-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 03/26/2023] [Accepted: 07/04/2023] [Indexed: 07/29/2023] Open
Abstract
Sensory atypicalities are particularly common in autism spectrum disorders (ASD). Nevertheless, our knowledge about the divergent functioning of the underlying somatosensory region and its association with ASD phenotype features is limited. We applied a data-driven approach to map the fine-grained variations in functional connectivity of the primary somatosensory cortex (S1) to the rest of the brain in 240 autistic and 164 neurotypical individuals from the EU-AIMS LEAP dataset, aged between 7 and 30. We estimated the S1 connection topography ('connectopy') at rest and during the emotional face-matching (Hariri) task, an established measure of emotion reactivity, and accessed its association with a set of clinical and behavioral variables. We first demonstrated that the S1 connectopy is organized along a dorsoventral axis, mapping onto the S1 somatotopic organization. We then found that its spatial characteristics were linked to the individuals' adaptive functioning skills, as measured by the Vineland Adaptive Behavior Scales, across the whole sample. Higher functional differentiation characterized the S1 connectopies of individuals with higher daily life adaptive skills. Notably, we detected significant differences between rest and the Hariri task in the S1 connectopies, as well as their projection maps onto the rest of the brain suggesting a task-modulating effect on S1 due to emotion processing. All in all, variation of adaptive skills appears to be reflected in the brain's mesoscale neural circuitry, as shown by the S1 connectivity profile, which is also differentially modulated during rest and emotional processing.
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Affiliation(s)
- Christina Isakoglou
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, Netherlands.
- Department for Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, Netherlands.
| | - Koen V Haak
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, Netherlands
| | - Thomas Wolfers
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, Netherlands
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Dorothea L Floris
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, Netherlands
- Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, Switzerland
| | - Alberto Llera
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, Netherlands
| | - Marianne Oldehinkel
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, Netherlands
| | - Natalie J Forde
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, Netherlands
| | - Bethany F M Oakley
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Julian Tillmann
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Rosemary J Holt
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Carolin Moessnang
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
- Department of Applied Psychology, SRH University, Heidelberg, Germany
| | - Eva Loth
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Thomas Bourgeron
- Human Genetics and Cognitive Functions, Institut Pasteur, Université de Paris, Paris, France
| | - Simon Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Tony Charman
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Declan G M Murphy
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Jan K Buitelaar
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, Netherlands
- Karakter Child and Adolescent Psychiatry University Centre, Nijmegen, Netherlands
| | - Andre F Marquand
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, Netherlands
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Christian F Beckmann
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, Netherlands
- Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom
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Han S, Cui Q, Zheng R, Li S, Zhou B, Fang K, Sheng W, Wen B, Liu L, Wei Y, Chen H, Chen Y, Cheng J, Zhang Y. Parsing altered gray matter morphology of depression using a framework integrating the normative model and non-negative matrix factorization. Nat Commun 2023; 14:4053. [PMID: 37422463 PMCID: PMC10329663 DOI: 10.1038/s41467-023-39861-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 06/27/2023] [Indexed: 07/10/2023] Open
Abstract
The high inter-individual heterogeneity in individuals with depression limits neuroimaging studies with case-control approaches to identify promising biomarkers for individualized clinical decision-making. We put forward a framework integrating the normative model and non-negative matrix factorization (NMF) to quantitatively assess altered gray matter morphology in depression from a dimensional perspective. The proposed framework parses altered gray matter morphology into overlapping latent disease factors, and assigns patients distinct factor compositions, thus preserving inter-individual variability. We identified four robust disease factors with distinct clinical symptoms and cognitive processes in depression. In addition, we showed the quantitative relationship between the group-level gray matter morphological differences and disease factors. Furthermore, this framework significantly predicted factor compositions of patients in an independent dataset. The framework provides an approach to resolve neuroanatomical heterogeneity in depression.
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Affiliation(s)
- Shaoqiang Han
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Henan Province, China.
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Henan Province, China.
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Henan Province, China.
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Henan Province, China.
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Henan Province, China.
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Henan Province, China.
- Key Laboratory of Imaging Intelligence Research medicine of Henan Province, Henan Province, China.
- Henan Engineering Research Center of Brain Function Development and Application, Henan Province, China.
| | - Qian Cui
- School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu, China
| | - Ruiping Zheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Henan Province, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Henan Province, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Henan Province, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Henan Province, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Henan Province, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Henan Province, China
- Key Laboratory of Imaging Intelligence Research medicine of Henan Province, Henan Province, China
- Henan Engineering Research Center of Brain Function Development and Application, Henan Province, China
| | - Shuying Li
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Henan Province, China
| | - Bingqian Zhou
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Henan Province, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Henan Province, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Henan Province, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Henan Province, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Henan Province, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Henan Province, China
- Key Laboratory of Imaging Intelligence Research medicine of Henan Province, Henan Province, China
- Henan Engineering Research Center of Brain Function Development and Application, Henan Province, China
| | - Keke Fang
- Department of Pharmacy, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Henan Province, China
| | - Wei Sheng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Baohong Wen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Henan Province, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Henan Province, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Henan Province, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Henan Province, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Henan Province, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Henan Province, China
- Key Laboratory of Imaging Intelligence Research medicine of Henan Province, Henan Province, China
- Henan Engineering Research Center of Brain Function Development and Application, Henan Province, China
| | - Liang Liu
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Henan Province, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Henan Province, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Henan Province, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Henan Province, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Henan Province, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Henan Province, China
- Key Laboratory of Imaging Intelligence Research medicine of Henan Province, Henan Province, China
- Henan Engineering Research Center of Brain Function Development and Application, Henan Province, China
| | - Yarui Wei
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Henan Province, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Henan Province, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Henan Province, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Henan Province, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Henan Province, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Henan Province, China
- Key Laboratory of Imaging Intelligence Research medicine of Henan Province, Henan Province, China
- Henan Engineering Research Center of Brain Function Development and Application, Henan Province, China
| | - Huafu Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Henan Province, China.
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
| | - Yuan Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Henan Province, China.
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Henan Province, China.
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Henan Province, China.
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Henan Province, China.
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Henan Province, China.
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Henan Province, China.
- Key Laboratory of Imaging Intelligence Research medicine of Henan Province, Henan Province, China.
- Henan Engineering Research Center of Brain Function Development and Application, Henan Province, China.
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Henan Province, China.
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Henan Province, China.
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Henan Province, China.
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Henan Province, China.
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Henan Province, China.
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Henan Province, China.
- Key Laboratory of Imaging Intelligence Research medicine of Henan Province, Henan Province, China.
- Henan Engineering Research Center of Brain Function Development and Application, Henan Province, China.
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Henan Province, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Henan Province, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Henan Province, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Henan Province, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Henan Province, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Henan Province, China
- Key Laboratory of Imaging Intelligence Research medicine of Henan Province, Henan Province, China
- Henan Engineering Research Center of Brain Function Development and Application, Henan Province, China
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Han S, Xu Y, Fang K, Guo HR, Wei Y, Liu L, Wen B, Liu H, Zhang Y, Cheng J. Mapping the neuroanatomical heterogeneity of OCD using a framework integrating normative model and non-negative matrix factorization. Cereb Cortex 2023:7153879. [PMID: 37150510 DOI: 10.1093/cercor/bhad149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 04/05/2023] [Accepted: 04/06/2023] [Indexed: 05/09/2023] Open
Abstract
Obsessive-compulsive disorder (OCD) is a spectrum disorder with high interindividual heterogeneity. We propose a comprehensible framework integrating normative model and non-negative matrix factorization (NMF) to quantitatively estimate the neuroanatomical heterogeneity of OCD from a dimensional perspective. T1-weighted magnetic resonance images of 98 first-episode untreated patients with OCD and matched healthy controls (HCs, n = 130) were acquired. We derived individualized differences in gray matter morphometry using normative model and parsed them into latent disease factors using NMF. Four robust disease factors were identified. Each patient expressed multiple factors and exhibited a unique factor composition. Factor compositions of patients were significantly correlated with severity of symptom, age of onset, illness duration, and exhibited sex differences, capturing sources of clinical heterogeneity. In addition, the group-level morphological differences obtained with two-sample t test could be quantitatively derived from the identified disease factors, reconciling the group-level and subject-level findings in neuroimaging studies. Finally, we uncovered two distinct subtypes with opposite morphological differences compared with HCs from factor compositions. Our findings suggest that morphological differences of individuals with OCD are the unique combination of distinct neuroanatomical patterns. The proposed framework quantitatively estimating neuroanatomical heterogeneity paves the way for precision medicine in OCD.
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Affiliation(s)
- Shaoqiang Han
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province
- Henan Engineering Research Center of Brain Function Development and Application
| | - Yinhuan Xu
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province
- Henan Engineering Research Center of Brain Function Development and Application
| | - Keke Fang
- Department of Pharmacy, Henan Cancer Hospital, Affiliated Cancer Hospital of Zhengzhou University
| | - Hui-Rong Guo
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University
| | - Yarui Wei
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province
- Henan Engineering Research Center of Brain Function Development and Application
| | - Liang Liu
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province
- Henan Engineering Research Center of Brain Function Development and Application
| | - Baohong Wen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province
- Henan Engineering Research Center of Brain Function Development and Application
| | - Hao Liu
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province
- Henan Engineering Research Center of Brain Function Development and Application
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province
- Henan Engineering Research Center of Brain Function Development and Application
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province
- Henan Engineering Research Center of Brain Function Development and Application
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Chen J, Ooi LQR, Tan TWK, Zhang S, Li J, Asplund CL, Eickhoff SB, Bzdok D, Holmes AJ, Yeo BTT. Relationship Between Prediction Accuracy and Feature Importance Reliability: an Empirical and Theoretical Study. Neuroimage 2023; 274:120115. [PMID: 37088322 DOI: 10.1016/j.neuroimage.2023.120115] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 03/06/2023] [Accepted: 04/13/2023] [Indexed: 04/25/2023] Open
Abstract
There is significant interest in using neuroimaging data to predict behavior. The predictive models are often interpreted by the computation of feature importance, which quantifies the predictive relevance of an imaging feature. Tian and Zalesky (2021) suggest that feature importance estimates exhibit low split-half reliability, as well as a trade-off between prediction accuracy and feature importance reliability across parcellation resolutions. However, it is unclear whether the trade-off between prediction accuracy and feature importance reliability is universal. Here, we demonstrate that, with a sufficient sample size, feature importance (operationalized as Haufe-transformed weights) can achieve fair to excellent split-half reliability. With a sample size of 2600 participants, Haufe-transformed weights achieve average intra-class correlation coefficients of 0.75, 0.57 and 0.53 for cognitive, personality and mental health measures respectively. Haufe-transformed weights are much more reliable than original regression weights and univariate FC-behavior correlations. Original regression weights are not reliable even with 2600 participants. Intriguingly, feature importance reliability is strongly positively correlated with prediction accuracy across phenotypes. Within a particular behavioral domain, there is no clear relationship between prediction performance and feature importance reliability across regression models. Furthermore, we show mathematically that feature importance reliability is necessary, but not sufficient, for low feature importance error. In the case of linear models, lower feature importance error is mathematically related to lower prediction error. Therefore, higher feature importance reliability might yield lower feature importance error and higher prediction accuracy. Finally, we discuss how our theoretical results relate with the reliability of imaging features and behavioral measures. Overall, the current study provides empirical and theoretical insights into the relationship between prediction accuracy and feature importance reliability.
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Affiliation(s)
- Jianzhong Chen
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Leon Qi Rong Ooi
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore
| | - Trevor Wei Kiat Tan
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore
| | - Shaoshi Zhang
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore
| | - Jingwei Li
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany; Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Christopher L Asplund
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore; Division of Social Sciences, Yale-NUS College, Singapore; Department of Psychology, National University of Singapore, Singapore; Duke-NUS Medical School, Singapore
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany; Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Danilo Bzdok
- Department of Biomedical Engineering, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Mila - Quebec AI Institute, Montreal, Canada
| | - Avram J Holmes
- Yale University, Departments of Psychology and Psychiatry, New Haven, CT, USA
| | - B T Thomas Yeo
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
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Rutherford S, Barkema P, Tso IF, Sripada C, Beckmann CF, Ruhe HG, Marquand AF. Evidence for embracing normative modeling. eLife 2023; 12:e85082. [PMID: 36912775 PMCID: PMC10036120 DOI: 10.7554/elife.85082] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 03/10/2023] [Indexed: 03/14/2023] Open
Abstract
In this work, we expand the normative model repository introduced in Rutherford et al., 2022a to include normative models charting lifespan trajectories of structural surface area and brain functional connectivity, measured using two unique resting-state network atlases (Yeo-17 and Smith-10), and an updated online platform for transferring these models to new data sources. We showcase the value of these models with a head-to-head comparison between the features output by normative modeling and raw data features in several benchmarking tasks: mass univariate group difference testing (schizophrenia versus control), classification (schizophrenia versus control), and regression (predicting general cognitive ability). Across all benchmarks, we show the advantage of using normative modeling features, with the strongest statistically significant results demonstrated in the group difference testing and classification tasks. We intend for these accessible resources to facilitate the wider adoption of normative modeling across the neuroimaging community.
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Affiliation(s)
- Saige Rutherford
- Department of Cognitive Neuroscience, Radboud University Nijmegen Medical CentreNijmegenNetherlands
- Donders Institute, Radboud University NijmegenNijmegenNetherlands
- Department of Psychiatry, University of Michigan-Ann ArborAnn ArborUnited States
| | - Pieter Barkema
- Donders Institute, Radboud University NijmegenNijmegenNetherlands
| | - Ivy F Tso
- Department of Psychiatry, University of Michigan-Ann ArborAnn ArborUnited States
- Department of Psychology, University of Michigan-Ann ArborAnn ArborUnited States
| | - Chandra Sripada
- Department of Psychiatry, University of Michigan-Ann ArborAnn ArborUnited States
- Department of Philosophy, University of Michigan-Ann ArborAnn ArborUnited States
| | - Christian F Beckmann
- Department of Cognitive Neuroscience, Radboud University Nijmegen Medical CentreNijmegenNetherlands
- Donders Institute, Radboud University NijmegenNijmegenNetherlands
- Center for Functional MRI of the Brain (FMRIB), Nuffield Department for Clinical Neuroscience, Welcome Centre for Integrative Neuroimaging, Oxford UniversityOxfordUnited Kingdom
| | - Henricus G Ruhe
- Donders Institute, Radboud University NijmegenNijmegenNetherlands
- Department of Psychiatry, Radboud University Nijmegen Medical CentreNijmegenNetherlands
| | - Andre F Marquand
- Department of Cognitive Neuroscience, Radboud University Nijmegen Medical CentreNijmegenNetherlands
- Donders Institute, Radboud University NijmegenNijmegenNetherlands
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36
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Bozek J, Griffanti L, Lau S, Jenkinson M. Normative models for neuroimaging markers: Impact of model selection, sample size and evaluation criteria. Neuroimage 2023; 268:119864. [PMID: 36621581 DOI: 10.1016/j.neuroimage.2023.119864] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 12/13/2022] [Accepted: 01/03/2023] [Indexed: 01/07/2023] Open
Abstract
Modelling population reference curves or normative modelling is increasingly used with the advent of large neuroimaging studies. In this paper we assess the performance of fitting methods from the perspective of clinical applications and investigate the influence of the sample size. Further, we evaluate linear and non-linear models for percentile curve estimation and highlight how the bias-variance trade-off manifests in typical neuroimaging data. We created plausible ground truth distributions of hippocampal volumes in the age range of 45 to 80 years, as an example application. Based on these distributions we repeatedly simulated samples for sizes between 50 and 50,000 data points, and for each simulated sample we fitted a range of normative models. We compared the fitted models and their variability across repetitions to the ground truth, with specific focus on the outer percentiles (1st, 5th, 10th) as these are the most clinically relevant. Our results quantify the expected decreasing trend in variance of the volume estimates with increasing sample size. However, bias in the volume estimates only decreases a modest amount, without much improvement at large sample sizes. The uncertainty of model performance is substantial for what would often be considered large samples in a neuroimaging context and rises dramatically at the ends of the age range, where fewer data points exist. Flexible models perform better across sample sizes, especially for non-linear ground truth. Surprisingly large samples of several thousand data points are needed to accurately capture outlying percentiles across the age range for applications in research and clinical settings. Performance evaluation methods should assess both bias and variance. Furthermore, caution is needed when attempting to go near the ends of the age range captured by the source data set and, as is a well known general principle, extrapolation beyond the age range should always be avoided. To help with such evaluations of normative models we have made our code available to guide researchers developing or utilising normative models.
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Affiliation(s)
- Jelena Bozek
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Ludovica Griffanti
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, Warneford Hospital, University of Oxford, United Kingdom; Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, United Kingdom
| | - Stephan Lau
- Australian Institute for Machine Learning, School of Computer and Mathematical Sciences, The University of Adelaide, Adelaide, SA, Australia; South Australian Health and Medical Research Institute (SAHMRI), Adelaide, SA, Australia
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, United Kingdom; Australian Institute for Machine Learning, School of Computer and Mathematical Sciences, The University of Adelaide, Adelaide, SA, Australia; South Australian Health and Medical Research Institute (SAHMRI), Adelaide, SA, Australia.
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Norman LJ, Sudre G, Price J, Shastri GG, Shaw P. Evidence from "big data" for the default-mode hypothesis of ADHD: a mega-analysis of multiple large samples. Neuropsychopharmacology 2023; 48:281-289. [PMID: 36100657 PMCID: PMC9751118 DOI: 10.1038/s41386-022-01408-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 07/10/2022] [Accepted: 07/16/2022] [Indexed: 12/26/2022]
Abstract
We sought to identify resting-state characteristics related to attention deficit/hyperactivity disorder, both as a categorical diagnosis and as a trait feature, using large-scale samples which were processed according to a standardized pipeline. In categorical analyses, we considered 1301 subjects with diagnosed ADHD, contrasted against 1301 unaffected controls (total N = 2602; 1710 males (65.72%); mean age = 10.86 years, sd = 2.05). Cases and controls were 1:1 nearest neighbor matched on in-scanner motion and key demographic variables and drawn from multiple large cohorts. Associations between ADHD-traits and resting-state connectivity were also assessed in a large multi-cohort sample (N = 10,113). ADHD diagnosis was associated with less anticorrelation between the default mode and salience/ventral attention (B = 0.009, t = 3.45, p-FDR = 0.004, d = 0.14, 95% CI = 0.004, 0.014), somatomotor (B = 0.008, t = 3.49, p-FDR = 0.004, d = 0.14, 95% CI = 0.004, 0.013), and dorsal attention networks (B = 0.01, t = 4.28, p-FDR < 0.001, d = 0.17, 95% CI = 0.006, 0.015). These results were robust to sensitivity analyses considering comorbid internalizing problems, externalizing problems and psychostimulant medication. Similar findings were observed when examining ADHD traits, with the largest effect size observed for connectivity between the default mode network and the dorsal attention network (B = 0.0006, t = 5.57, p-FDR < 0.001, partial-r = 0.06, 95% CI = 0.0004, 0.0008). We report significant ADHD-related differences in interactions between the default mode network and task-positive networks, in line with default mode interference models of ADHD. Effect sizes (Cohen's d and partial-r, estimated from the mega-analytic models) were small, indicating subtle group differences. The overlap between the affected brain networks in the clinical and general population samples supports the notion of brain phenotypes operating along an ADHD continuum.
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Affiliation(s)
- Luke J Norman
- Office of the Clinical Director, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA.
- Section on Neurobehavioral and Clinical Research, Social and Behavioral Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
| | - Gustavo Sudre
- Section on Neurobehavioral and Clinical Research, Social and Behavioral Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Jolie Price
- Section on Neurobehavioral and Clinical Research, Social and Behavioral Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Gauri G Shastri
- Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Philip Shaw
- Office of the Clinical Director, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA
- Section on Neurobehavioral and Clinical Research, Social and Behavioral Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
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Kia SM, Huijsdens H, Rutherford S, de Boer A, Dinga R, Wolfers T, Berthet P, Mennes M, Andreassen OA, Westlye LT, Beckmann CF, Marquand AF. Closing the life-cycle of normative modeling using federated hierarchical Bayesian regression. PLoS One 2022; 17:e0278776. [PMID: 36480551 PMCID: PMC9731431 DOI: 10.1371/journal.pone.0278776] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 11/22/2022] [Indexed: 12/13/2022] Open
Abstract
Clinical neuroimaging data availability has grown substantially in the last decade, providing the potential for studying heterogeneity in clinical cohorts on a previously unprecedented scale. Normative modeling is an emerging statistical tool for dissecting heterogeneity in complex brain disorders. However, its application remains technically challenging due to medical data privacy issues and difficulties in dealing with nuisance variation, such as the variability in the image acquisition process. Here, we approach the problem of estimating a reference normative model across a massive population using a massive multi-center neuroimaging dataset. To this end, we introduce a federated probabilistic framework using hierarchical Bayesian regression (HBR) to complete the life-cycle of normative modeling. The proposed model provides the possibilities to learn, update, and adapt the model parameters on decentralized neuroimaging data. Our experimental results confirm the superiority of HBR in deriving more accurate normative ranges on large multi-site neuroimaging datasets compared to the current standard methods. In addition, our approach provides the possibility to recalibrate and reuse the learned model on local datasets and even on datasets with very small sample sizes. The proposed method will facilitate applications of normative modeling as a medical tool for screening the biological deviations in individuals affected by complex illnesses such as mental disorders.
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Affiliation(s)
- Seyed Mostafa Kia
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Hester Huijsdens
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Saige Rutherford
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Augustijn de Boer
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Richard Dinga
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Thomas Wolfers
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Pierre Berthet
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Maarten Mennes
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Ole A. Andreassen
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Lars T. Westlye
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Christian F. Beckmann
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, The Netherlands
- Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom
| | - Andre F. Marquand
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Neuroimaging, Institute of Psychiatry, King’s College London, London, United Kingdom
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Liu N, Liu Q, Yang Z, Xu J, Fu G, Zhou Y, Li H, Wang Y, Liu L, Qian Q. Different functional alteration in attention-deficit/hyperactivity disorder across developmental age groups: A meta-analysis and an independent validation of resting-state functional connectivity studies. CNS Neurosci Ther 2022; 29:60-69. [PMID: 36468409 PMCID: PMC9804052 DOI: 10.1111/cns.14032] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 03/21/2022] [Accepted: 10/09/2022] [Indexed: 12/09/2022] Open
Abstract
BACKGROUND Attention-deficit/hyperactivity disorder (ADHD) is a highly complex and heterogeneous disorder. Abnormal brain connectivity in ADHD might be influenced by developmental ages which might lead to the lacking of significant spatial convergence across studies. However, the developmental patterns and mechanisms of ADHD brain connectivity remain to be fully uncovered. METHODS In the present study, we searched PubMed, Scopus, Web of Science, and Embase for seed-based whole-brain resting-state functional connectivity studies of ADHD published through October 12th, 2020. The seeds meeting inclusion criteria were categorized into the cortex group and subcortex group, as previous studies suggested that the cortex and subcortex have different temporal patterns of development. Activation likelihood estimation meta-analysis was performed to investigate the abnormal connectivity in different age groups (all-age group, younger: <12 years, older: ≥12 years). Moreover, significant convergence of reported foci was used as seeds for validation with our independent dataset. RESULTS As with previous studies, scarce results were found in the all-age group. However, we found that the younger group consistently exhibited hyper-connectivity between different parts of the cortex and left middle frontal gyrus, and hypo-connectivity between different parts of the cortex and left putamen/pallidus/amygdala. Whereas, the older group (mainly for adults) showed hyper-connectivity between the cortex and right precuneus/sub-gyral/cingulate gyrus. Besides, the abnormal cortico-cortical and cortico-subcortical functional connectivity in children, and the abnormal cortico-cortical functional connectivity in adults were verified in our independent dataset. CONCLUSION Our study emphasizes the importance of developmental age effects on the study of brain networks in ADHD. Further, we proposed that cortico-cortical and cortico-subcortical connectivity might play an important role in the pathophysiology of children with ADHD, while abnormal cortico-cortical connections were more important for adults with ADHD. This work provided a potential new insight to understand the neurodevelopmental mechanisms and possible clinical application of ADHD.
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Affiliation(s)
- Ningning Liu
- Peking University Sixth Hospital, Institute of Mental HealthBeijingChina,NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital)BeijingChina
| | - Qianrong Liu
- Peking University Sixth Hospital, Institute of Mental HealthBeijingChina,NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital)BeijingChina
| | - Ziqi Yang
- Peking University Sixth Hospital, Institute of Mental HealthBeijingChina,NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital)BeijingChina
| | - Jie Xu
- Shenzhen Key Laboratory of Affective and Social Neuroscience, Magnetic Resonance Imaging Center, Center for Brain Disorders and Cognitive Sciences, Shenzhen UniversityShenzhenChina,Department of Biomedical Sciences of Cells & Systems, Section Cognitive Neuroscience, University Medical Center Groningen, University of GroningenGroningenThe Netherlands
| | - Guanghui Fu
- Peking University Sixth Hospital, Institute of Mental HealthBeijingChina,NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital)BeijingChina
| | - Yi Zhou
- Peking University Sixth Hospital, Institute of Mental HealthBeijingChina,NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital)BeijingChina
| | - Haimei Li
- Peking University Sixth Hospital, Institute of Mental HealthBeijingChina,NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital)BeijingChina
| | - Yufeng Wang
- Peking University Sixth Hospital, Institute of Mental HealthBeijingChina,NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital)BeijingChina
| | - Lu Liu
- Peking University Sixth Hospital, Institute of Mental HealthBeijingChina,NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital)BeijingChina
| | - Qiujin Qian
- Peking University Sixth Hospital, Institute of Mental HealthBeijingChina,NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital)BeijingChina
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Nuthmann A, Thibaut M, Tran THC, Boucart M. Impact of neovascular age-related macular degeneration on eye-movement control during scene viewing: Viewing biases and guidance by visual salience. Vision Res 2022; 201:108105. [PMID: 36081228 DOI: 10.1016/j.visres.2022.108105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 06/06/2022] [Accepted: 07/19/2022] [Indexed: 01/25/2023]
Abstract
Human vision requires us to analyze the visual periphery to decide where to fixate next. In the present study, we investigated this process in people with age-related macular degeneration (AMD). In particular, we examined viewing biases and the extent to which visual salience guides fixation selection during free-viewing of naturalistic scenes. We used an approach combining generalized linear mixed modeling (GLMM) with a-priori scene parcellation. This method allows one to investigate group differences in terms of scene coverage and observers' well-known tendency to look at the center of scene images. Moreover, it allows for testing whether image salience influences fixation probability above and beyond what can be accounted for by the central bias. Compared with age-matched normally sighted control subjects (and young subjects), AMD patients' viewing behavior was less exploratory, with a stronger central fixation bias. All three subject groups showed a salience effect on fixation selection-higher-salience scene patches were more likely to be fixated. Importantly, the salience effect for the AMD group was of similar size as the salience effect for the control group, suggesting that guidance by visual salience was still intact. The variances for by-subject random effects in the GLMM indicated substantial individual differences. A separate model exclusively considered the AMD data and included fixation stability as a covariate, with the results suggesting that reduced fixation stability was associated with a reduced impact of visual salience on fixation selection.
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Affiliation(s)
- Antje Nuthmann
- Institute of Psychology, University of Kiel, Kiel, Germany.
| | - Miguel Thibaut
- University of Lille, Lille Neuroscience & Cognition, INSERM, Lille, France
| | - Thi Ha Chau Tran
- University of Lille, Lille Neuroscience & Cognition, INSERM, Lille, France; Ophthalmology Department, Lille Catholic Hospital, Catholic University of Lille, Lille, France
| | - Muriel Boucart
- University of Lille, Lille Neuroscience & Cognition, INSERM, Lille, France.
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Bayer JMM, Dinga R, Kia SM, Kottaram AR, Wolfers T, Lv J, Zalesky A, Schmaal L, Marquand A. Accommodating site variation in neuroimaging data using normative and hierarchical Bayesian models. Neuroimage 2022; 264:119699. [PMID: 36272672 DOI: 10.1016/j.neuroimage.2022.119699] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 10/16/2022] [Accepted: 10/18/2022] [Indexed: 11/06/2022] Open
Abstract
The potential of normative modeling to make individualized predictions from neuroimaging data has enabled inferences that go beyond the case-control approach. However, site effects are often confounded with variables of interest in a complex manner and can bias estimates of normative models, which has impeded the application of normative models to large multi-site neuroimaging data sets. In this study, we suggest accommodating for these site effects by including them as random effects in a hierarchical Bayesian model. We compared the performance of a linear and a non-linear hierarchical Bayesian model in modeling the effect of age on cortical thickness. We used data of 570 healthy individuals from the ABIDE (autism brain imaging data exchange) data set in our experiments. In addition, we used data from individuals with autism to test whether our models are able to retain clinically useful information while removing site effects. We compared the proposed single stage hierarchical Bayesian method to several harmonization techniques commonly used to deal with additive and multiplicative site effects using a two stage regression, including regressing out site and harmonizing for site with ComBat, both with and without explicitly preserving variance caused by age and sex as biological variation of interest, and with a non-linear version of ComBat. In addition, we made predictions from raw data, in which site has not been accommodated for. The proposed hierarchical Bayesian method showed the best predictive performance according to multiple metrics. Beyond that, the resulting z-scores showed little to no residual site effects, yet still retained clinically useful information. In contrast, performance was particularly poor for the regression model and the ComBat model in which age and sex were not explicitly modeled. In all two stage harmonization models, predictions were poorly scaled, suffering from a loss of more than 90% of the original variance. Our results show the value of hierarchical Bayesian regression methods for accommodating site variation in neuroimaging data, which provides an alternative to harmonization techniques. While the approach we propose may have broad utility, our approach is particularly well suited to normative modeling where the primary interest is in accurate modeling of inter-subject variation and statistical quantification of deviations from a reference model.
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Affiliation(s)
- Johanna M M Bayer
- Orygen, Parkville, Australia; Centre for Youth mental Health, The University of Melbourne, Australia.
| | - Richard Dinga
- Donders Institute, Radboud University, Nijmegen, the Netherlands; Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Seyed Mostafa Kia
- Donders Institute, Radboud University, Nijmegen, the Netherlands; Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Akhil R Kottaram
- Orygen, Parkville, Australia; Centre for Youth mental Health, The University of Melbourne, Australia
| | | | - Jinglei Lv
- School of Biomedical Engineering & Brain and Mind Center, University of Sydney, Sydney, Australia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, The University of Melbourne & Melbourne Health, Australia; Department of Biomedical Engineering, The University of Melbourne, Australia
| | - Lianne Schmaal
- Orygen, Parkville, Australia; Centre for Youth mental Health, The University of Melbourne, Australia
| | - Andre Marquand
- Radboud University Medical Centre, Nijmegen, the Netherlands; Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, the Netherlands; Institute of Psychiatry, Kings College London, London, UK
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42
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Kjelkenes R, Wolfers T, Alnæs D, Norbom LB, Voldsbekk I, Holm M, Dahl A, Berthet P, Tamnes CK, Marquand AF, Westlye LT. Deviations from normative brain white and gray matter structure are associated with psychopathology in youth. Dev Cogn Neurosci 2022; 58:101173. [PMID: 36332329 PMCID: PMC9637865 DOI: 10.1016/j.dcn.2022.101173] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 10/10/2022] [Accepted: 10/31/2022] [Indexed: 11/30/2022] Open
Abstract
Combining imaging modalities and metrics that are sensitive to various aspects of brain structure and maturation may help identify individuals that show deviations in relation to same-aged peers, and thus benefit early-risk-assessment for mental disorders. We used one timepoint multimodal brain imaging, cognitive, and questionnaire data from 1280 eight- to twenty-one-year-olds from the Philadelphia Neurodevelopmental Cohort. We estimated age-related gray and white matter properties and estimated individual deviation scores using normative modeling. Next, we tested for associations between the estimated deviation scores, and with psychopathology domain scores and cognition. More negative deviations in DTI-based fractional anisotropy (FA) and the first principal eigenvalue of the diffusion tensor (L1) were associated with higher scores on psychosis positive and prodromal symptoms and general psychopathology. A more negative deviation in cortical thickness (CT) was associated with a higher general psychopathology score. Negative deviations in global FA, surface area, L1 and CT were also associated with poorer cognitive performance. No robust associations were found between the deviation scores based on CT and DTI. The low correlations between the different multimodal magnetic resonance imaging-based deviation scores suggest that psychopathological burden in adolescence can be mapped onto partly distinct neurobiological features.
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Affiliation(s)
- Rikka Kjelkenes
- Department of Psychology, University of Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo, & Oslo University Hospital, Oslo, Norway.
| | - Thomas Wolfers
- Department of Psychology, University of Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo, & Oslo University Hospital, Oslo, Norway; Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Dag Alnæs
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo, & Oslo University Hospital, Oslo, Norway; Oslo New University College, Oslo, Norway
| | - Linn B Norbom
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo, & Oslo University Hospital, Oslo, Norway; PROMENTA Research Center, Department of Psychology, University of Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Irene Voldsbekk
- Department of Psychology, University of Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo, & Oslo University Hospital, Oslo, Norway
| | - Madelene Holm
- Department of Psychology, University of Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo, & Oslo University Hospital, Oslo, Norway
| | - Andreas Dahl
- Department of Psychology, University of Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo, & Oslo University Hospital, Oslo, Norway
| | - Pierre Berthet
- Department of Psychology, University of Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo, & Oslo University Hospital, Oslo, Norway
| | - Christian K Tamnes
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo, & Oslo University Hospital, Oslo, Norway; PROMENTA Research Center, Department of Psychology, University of Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Andre F Marquand
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands; Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, the Netherlands; Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, King's College London, London, UK
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo, & Oslo University Hospital, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Norway.
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Bayer JMM, Dinga R, Kia SM, Kottaram AR, Wolfers T, Lv J, Zalesky A, Schmaal L, Marquand A. Accommodating site variation in neuroimaging data using normative and hierarchical Bayesian models. Neuroimage 2022; 264:119699. [PMID: 36272672 PMCID: PMC7614761 DOI: 10.1101/2021.02.09.430363] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 10/16/2022] [Accepted: 10/18/2022] [Indexed: 09/12/2023] Open
Abstract
The potential of normative modeling to make individualized predictions from neuroimaging data has enabled inferences that go beyond the case-control approach. However, site effects are often confounded with variables of interest in a complex manner and can bias estimates of normative models, which has impeded the application of normative models to large multi-site neuroimaging data sets. In this study, we suggest accommodating for these site effects by including them as random effects in a hierarchical Bayesian model. We compared the performance of a linear and a non-linear hierarchical Bayesian model in modeling the effect of age on cortical thickness. We used data of 570 healthy individuals from the ABIDE (autism brain imaging data exchange) data set in our experiments. In addition, we used data from individuals with autism to test whether our models are able to retain clinically useful information while removing site effects. We compared the proposed single stage hierarchical Bayesian method to several harmonization techniques commonly used to deal with additive and multiplicative site effects using a two stage regression, including regressing out site and harmonizing for site with ComBat, both with and without explicitly preserving variance caused by age and sex as biological variation of interest, and with a non-linear version of ComBat. In addition, we made predictions from raw data, in which site has not been accommodated for. The proposed hierarchical Bayesian method showed the best predictive performance according to multiple metrics. Beyond that, the resulting z-scores showed little to no residual site effects, yet still retained clinically useful information. In contrast, performance was particularly poor for the regression model and the ComBat model in which age and sex were not explicitly modeled. In all two stage harmonization models, predictions were poorly scaled, suffering from a loss of more than 90% of the original variance. Our results show the value of hierarchical Bayesian regression methods for accommodating site variation in neuroimaging data, which provides an alternative to harmonization techniques. While the approach we propose may have broad utility, our approach is particularly well suited to normative modeling where the primary interest is in accurate modeling of inter-subject variation and statistical quantification of deviations from a reference model.
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Affiliation(s)
- Johanna M M Bayer
- Orygen, Parkville, Australia; Centre for Youth mental Health, The University of Melbourne, Australia.
| | - Richard Dinga
- Donders Institute, Radboud University, Nijmegen, the Netherlands; Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Seyed Mostafa Kia
- Donders Institute, Radboud University, Nijmegen, the Netherlands; Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Akhil R Kottaram
- Orygen, Parkville, Australia; Centre for Youth mental Health, The University of Melbourne, Australia
| | | | - Jinglei Lv
- School of Biomedical Engineering & Brain and Mind Center, University of Sydney, Sydney, Australia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, The University of Melbourne & Melbourne Health, Australia; Department of Biomedical Engineering, The University of Melbourne, Australia
| | - Lianne Schmaal
- Orygen, Parkville, Australia; Centre for Youth mental Health, The University of Melbourne, Australia
| | - Andre Marquand
- Radboud University Medical Centre, Nijmegen, the Netherlands; Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, the Netherlands; Institute of Psychiatry, Kings College London, London, UK
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Remiszewski N, Bryant JE, Rutherford SE, Marquand AF, Nelson E, Askar I, Lahti AC, Kraguljac NV. Contrasting Case-Control and Normative Reference Approaches to Capture Clinically Relevant Structural Brain Abnormalities in Patients With First-Episode Psychosis Who Are Antipsychotic Naive. JAMA Psychiatry 2022; 79:1133-1138. [PMID: 36169987 PMCID: PMC9520436 DOI: 10.1001/jamapsychiatry.2022.3010] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 08/08/2022] [Indexed: 11/14/2022]
Abstract
Importance To make progress toward precision psychiatry, it is crucial to move beyond case-control studies and instead capture individual variations and interpret them in the context of a normal range of biological systems. Objective To evaluate whether baseline deviations from a normative reference range in subcortical volumes are better predictors of antipsychotic treatment response than raw volumes in patients with first-episode psychosis (FEP) who were naive to antipsychotic medication. Design, Setting, and Participants In this prospective longitudinal study, patients with first-episode psychosis who were referred from different clinical settings (emergency department, inpatient units, and outpatient clinics) at the University of Alabama at Birmingham were included. A total of 286 patients were screened, 114 consented, 104 enrolled in the treatment trial, and 85 completed the trial. Patients were observed for 16 weeks. Controls were matched by age and sex. Data were collected between June 2016 and July 2021, and data were analyzed from August 2021 to June 2022. Interventions Risperidone on a flexible dosing scheme for 16 weeks. There was an option to switch to aripiprazole for excessive adverse effects. Main Outcomes and Measures The main outcome of this study was to evaluate, in patients with FEP who were naive to antipsychotic medication, the association of baseline raw volumes and volume deviations in subcortical brain regions with response to antipsychotic medication. Raw brain volumes or volume deviation changes after treatment were not examined. Results Of 190 included participants, 111 (58.4%) were male, and the mean (SD) age was 23.7 (5.5) years. Volumes and deviations were quantified in 98 patients with FEP, and data from 92 controls were used as comparison for case-control contrasts and reference curve calibration. In case-control contrasts, patients with FEP had lower raw thalamus (P = .002; F = 9.63; df = 1), hippocampus (P = .009; F = 17.23; df = 1), amygdala (P = .01; F = 6.55; df = 1), ventral diencephalon (P = .03; F = 4.84; df = 1), and brainstem volumes (P = .004; F = 8.39; df = 1). Of 98 patients, 36 patients with FEP (36%) displayed extreme deviations. Associations with treatment response significantly differed between raw volume and deviation measures in the caudate (z = -2.17; P = .03) and putamen (z = -2.15; P = .03). Conclusions and Relevance These data suggest that normative modeling allows capture of interindividual heterogeneity of regional brain volumes in patients with FEP and characterize structural pathology in a clinically relevant fashion. This holds promise for progress in precision medicine in psychiatry, where group-level studies have failed to derive reliable maps of structural pathology.
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Affiliation(s)
- Natalie Remiszewski
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham
| | - James Edward Bryant
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham
| | - Saige E. Rutherford
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, the Netherlands
| | - Andre F. Marquand
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, the Netherlands
| | - Eric Nelson
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham
| | - Ibrahim Askar
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham
| | - Adrienne Carol Lahti
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham
| | - Nina Vanessa Kraguljac
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham
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Bayer JMM, Thompson PM, Ching CRK, Liu M, Chen A, Panzenhagen AC, Jahanshad N, Marquand A, Schmaal L, Sämann PG. Site effects how-to and when: An overview of retrospective techniques to accommodate site effects in multi-site neuroimaging analyses. Front Neurol 2022; 13:923988. [PMID: 36388214 PMCID: PMC9661923 DOI: 10.3389/fneur.2022.923988] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 08/12/2022] [Indexed: 09/12/2023] Open
Abstract
Site differences, or systematic differences in feature distributions across multiple data-acquisition sites, are a known source of heterogeneity that may adversely affect large-scale meta- and mega-analyses of independently collected neuroimaging data. They influence nearly all multi-site imaging modalities and biomarkers, and methods to compensate for them can improve reliability and generalizability in the analysis of genetics, omics, and clinical data. The origins of statistical site effects are complex and involve both technical differences (scanner vendor, head coil, acquisition parameters, imaging processing) and differences in sample characteristics (inclusion/exclusion criteria, sample size, ancestry) between sites. In an age of expanding international consortium research, there is a growing need to disentangle technical site effects from sample characteristics of interest. Numerous statistical and machine learning methods have been developed to control for, model, or attenuate site effects - yet to date, no comprehensive review has discussed the benefits and drawbacks of each for different use cases. Here, we provide an overview of the different existing statistical and machine learning methods developed to remove unwanted site effects from independently collected neuroimaging samples. We focus on linear mixed effect models, the ComBat technique and its variants, adjustments based on image quality metrics, normative modeling, and deep learning approaches such as generative adversarial networks. For each method, we outline the statistical foundation and summarize strengths and weaknesses, including their assumptions and conditions of use. We provide information on software availability and comment on the ease of use and the applicability of these methods to different types of data. We discuss validation and comparative reports, mention caveats and provide guidance on when to use each method, depending on context and specific research questions.
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Affiliation(s)
- Johanna M. M. Bayer
- Centre for Youth Mental Health, University of Melbourne, Melbourne, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Christopher R. K. Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Mengting Liu
- School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China
| | - Andrew Chen
- Department of Biostatistics, Epidemiology, and Informatics, Penn Statistics in Imaging and Visualization Center, University of Pennsylvania, Philadelphia, PA, United States
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, United States
| | - Alana C. Panzenhagen
- Programa de Pós-graduação em Ciências Biológicas: Bioquímica, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- Department of Translational Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Neda Jahanshad
- Laboratory of Brain eScience, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, CA, United States
| | - Andre Marquand
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behavior, Radboudumc, Nijmegen, Netherlands
| | - Lianne Schmaal
- Centre for Youth Mental Health, University of Melbourne, Melbourne, VIC, Australia
- Orygen, Parkville, VIC, Australia
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Li L, Su X, Zheng Q, Xiao J, Huang XY, Chen W, Yang K, Nie L, Yang X, Chen H, Shi S, Duan X. Cofluctuation analysis reveals aberrant default mode network patterns in adolescents and youths with autism spectrum disorder. Hum Brain Mapp 2022; 43:4722-4732. [PMID: 35781734 PMCID: PMC9491294 DOI: 10.1002/hbm.25986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 05/30/2022] [Accepted: 06/02/2022] [Indexed: 11/24/2022] Open
Abstract
Resting-state functional connectivity (rsFC) approaches provide informative estimates of the functional architecture of the brain, and recently-proposed cofluctuation analysis temporally unwraps FC at every moment in time, providing refined information for quantifying brain dynamics. As a brain network disorder, autism spectrum disorder (ASD) was characterized by substantial alteration in FC, but the contribution of moment-to-moment-activity cofluctuations to the overall dysfunctional connectivity pattern in ASD remains poorly understood. Here, we used the cofluctuation approach to explore the underlying dynamic properties of FC in ASD, using a large multisite resting-state functional magnetic resonance imaging (rs-fMRI) dataset (ASD = 354, typically developing controls [TD] = 446). Our results verified that the networks estimated using high-amplitude frames were highly correlated with the traditional rsFC. Moreover, these frames showed higher average amplitudes in participants with ASD than those in the TD group. Principal component analysis was performed on the activity patterns in these frames and aggregated over all subjects. The first principal component (PC1) corresponds to the default mode network (DMN), and the PC1 coefficients were greater in participants with ASD than those in the TD group. Additionally, increased ASD symptom severity was associated with the increased coefficients, which may result in excessive internally oriented cognition and social cognition deficits in individuals with ASD. Our finding highlights the utility of cofluctuation approaches in prevalent neurodevelopmental disorders and verifies that the aberrant contribution of DMN to rsFC may underline the symptomatology in adolescents and youths with ASD.
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Affiliation(s)
- Lei Li
- Department of RadiologyFirst Affiliated Hospital to Army Medical UniversityChongqingChina
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduChina
- School of Life Science and Technology, Center for Information in MedicineUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Xiaoran Su
- Medical Imaging Department, Henan Children's Hospital, Zhengzhou Children's HospitalChildren's Hospital Affiliated to Zhengzhou UniversityZhengzhouChina
- Department of MRThe First Affiliated Hospital of Xinxiang Medical UniversityWeihuiChina
| | - Qingyu Zheng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduChina
- School of Life Science and Technology, Center for Information in MedicineUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Jinming Xiao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduChina
- School of Life Science and Technology, Center for Information in MedicineUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Xin Yue Huang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduChina
- School of Life Science and Technology, Center for Information in MedicineUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Wan Chen
- Medical Imaging Department, Henan Children's Hospital, Zhengzhou Children's HospitalChildren's Hospital Affiliated to Zhengzhou UniversityZhengzhouChina
| | - Kaihua Yang
- Medical Imaging Department, Henan Children's Hospital, Zhengzhou Children's HospitalChildren's Hospital Affiliated to Zhengzhou UniversityZhengzhouChina
| | - Lei Nie
- Medical Imaging Department, Henan Children's Hospital, Zhengzhou Children's HospitalChildren's Hospital Affiliated to Zhengzhou UniversityZhengzhouChina
| | - Xin Yang
- Medical Imaging Department, Henan Children's Hospital, Zhengzhou Children's HospitalChildren's Hospital Affiliated to Zhengzhou UniversityZhengzhouChina
| | - Huafu Chen
- Department of RadiologyFirst Affiliated Hospital to Army Medical UniversityChongqingChina
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduChina
- School of Life Science and Technology, Center for Information in MedicineUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Shengli Shi
- Medical Imaging Department, Henan Children's Hospital, Zhengzhou Children's HospitalChildren's Hospital Affiliated to Zhengzhou UniversityZhengzhouChina
| | - Xujun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduChina
- School of Life Science and Technology, Center for Information in MedicineUniversity of Electronic Science and Technology of ChinaChengduChina
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47
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Mattheisen M, Grove J, Als TD, Martin J, Voloudakis G, Meier S, Demontis D, Bendl J, Walters R, Carey CE, Rosengren A, Strom NI, Hauberg ME, Zeng B, Hoffman G, Zhang W, Bybjerg-Grauholm J, Bækvad-Hansen M, Agerbo E, Cormand B, Nordentoft M, Werge T, Mors O, Hougaard DM, Buxbaum JD, Faraone SV, Franke B, Dalsgaard S, Mortensen PB, Robinson EB, Roussos P, Neale BM, Daly MJ, Børglum AD. Identification of shared and differentiating genetic architecture for autism spectrum disorder, attention-deficit hyperactivity disorder and case subgroups. Nat Genet 2022; 54:1470-1478. [PMID: 36163277 PMCID: PMC10848300 DOI: 10.1038/s41588-022-01171-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 06/20/2022] [Indexed: 02/02/2023]
Abstract
Attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are highly heritable neurodevelopmental conditions, with considerable overlap in their genetic etiology. We dissected their shared and distinct genetic etiology by cross-disorder analyses of large datasets. We identified seven loci shared by the disorders and five loci differentiating them. All five differentiating loci showed opposite allelic directions in the two disorders and significant associations with other traits, including educational attainment, neuroticism and regional brain volume. Integration with brain transcriptome data enabled us to identify and prioritize several significantly associated genes. The shared genomic fraction contributing to both disorders was strongly correlated with other psychiatric phenotypes, whereas the differentiating portion was correlated most strongly with cognitive traits. Additional analyses revealed that individuals diagnosed with both ASD and ADHD were double-loaded with genetic predispositions for both disorders and showed distinctive patterns of genetic association with other traits compared with the ASD-only and ADHD-only subgroups. These results provide insights into the biological foundation of the development of one or both conditions and of the factors driving psychopathology discriminatively toward either ADHD or ASD.
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Affiliation(s)
- Manuel Mattheisen
- Department of Biomedicine - Human Genetics and the iSEQ Center, Aarhus University, Aarhus, Denmark.
- Department of Community Health and Epidemiology & Department of Psychiatry, Dalhousie University, Halifax, NS, Canada.
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany.
| | - Jakob Grove
- Department of Biomedicine - Human Genetics and the iSEQ Center, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
| | - Thomas D Als
- Department of Biomedicine - Human Genetics and the iSEQ Center, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Joanna Martin
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - Georgios Voloudakis
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sandra Meier
- Department of Biomedicine - Human Genetics and the iSEQ Center, Aarhus University, Aarhus, Denmark
- Department of Community Health and Epidemiology & Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Ditte Demontis
- Department of Biomedicine - Human Genetics and the iSEQ Center, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Raymond Walters
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Caitlin E Carey
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Anders Rosengren
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Institute of Biological Psychiatry, Mental Health Services Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
| | - Nora I Strom
- Department of Biomedicine - Human Genetics and the iSEQ Center, Aarhus University, Aarhus, Denmark
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Mads Engel Hauberg
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Biao Zeng
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Gabriel Hoffman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Wen Zhang
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jonas Bybjerg-Grauholm
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Marie Bækvad-Hansen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Esben Agerbo
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
- Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark
| | - Bru Cormand
- Department of Genetics, Microbiology and Statistics, Faculty of Biology, University of Barcelona, Barcelona, Catalonia, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
- Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Catalonia, Spain
- Institut de Recerca Sant Joan de Déu (IR-SJD), Esplugues de Llobregat, Catalonia, Spain
| | - Merete Nordentoft
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Department of Clinical Medicine, Faculty of Health Science, University of Copenhagen, Copenhagen, Denmark
- Copenhagen Research Centre for Mental Health (CORE), Mental Health Centre Copenhagen, Copenhagen, Denmark
- University Hospital, Hellerup, Denmark
| | - Thomas Werge
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Institute of Biological Psychiatry, Mental Health Services Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health Science, University of Copenhagen, Copenhagen, Denmark
- GLOBE Institute, Center for GeoGenetics, University of Copenhagen, Copenhagen, Denmark
| | - Ole Mors
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Psychosis Research Unit, Aarhus University Hospital-Psychiatry, Aarhus, Denmark
| | - David M Hougaard
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Joseph D Buxbaum
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stephen V Faraone
- Department of Psychiatry, State University of New York Upstate Medical University, Syracuse, NY, USA
- Department of Neuroscience and Physiology, State University of New York Upstate Medical University, Syracuse, NY, USA
| | - Barbara Franke
- Department of Psychiatry, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Søren Dalsgaard
- National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
| | - Preben B Mortensen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
- National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
- Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark
| | - Elise B Robinson
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, JJ Peters VA Medical Center, Bronx, NY, USA
| | - Benjamin M Neale
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Mark J Daly
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Anders D Børglum
- Department of Biomedicine - Human Genetics and the iSEQ Center, Aarhus University, Aarhus, Denmark.
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark.
- Center for Genomics and Personalized Medicine, Aarhus, Denmark.
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48
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Srivastava P, Fotiadis P, Parkes L, Bassett DS. The expanding horizons of network neuroscience: From description to prediction and control. Neuroimage 2022; 258:119250. [PMID: 35659996 PMCID: PMC11164099 DOI: 10.1016/j.neuroimage.2022.119250] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 04/15/2022] [Accepted: 04/25/2022] [Indexed: 01/11/2023] Open
Abstract
The field of network neuroscience has emerged as a natural framework for the study of the brain and has been increasingly applied across divergent problems in neuroscience. From a disciplinary perspective, network neuroscience originally emerged as a formal integration of graph theory (from mathematics) and neuroscience (from biology). This early integration afforded marked utility in describing the interconnected nature of neural units, both structurally and functionally, and underscored the relevance of that interconnection for cognition and behavior. But since its inception, the field has not remained static in its methodological composition. Instead, it has grown to use increasingly advanced graph-theoretic tools and to bring in several other disciplinary perspectives-including machine learning and systems engineering-that have proven complementary. In doing so, the problem space amenable to the discipline has expanded markedly. In this review, we discuss three distinct flavors of investigation in state-of-the-art network neuroscience: (i) descriptive network neuroscience, (ii) predictive network neuroscience, and (iii) a perturbative network neuroscience that draws on recent advances in network control theory. In considering each area, we provide a brief summary of the approaches, discuss the nature of the insights obtained, and highlight future directions.
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Affiliation(s)
- Pragya Srivastava
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Panagiotis Fotiadis
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Neuroscience, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Linden Parkes
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Physics & Astronomy, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia PA 19104, USA; Santa Fe Institute, Santa Fe NM 87501, USA.
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49
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Janahi M, Aksman L, Schott JM, Mokrab Y, Altmann A. Nomograms of human hippocampal volume shifted by polygenic scores. eLife 2022; 11:e78232. [PMID: 35938915 PMCID: PMC9391046 DOI: 10.7554/elife.78232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 08/06/2022] [Indexed: 11/25/2022] Open
Abstract
Nomograms are important clinical tools applied widely in both developing and aging populations. They are generally constructed as normative models identifying cases as outliers to a distribution of healthy controls. Currently used normative models do not account for genetic heterogeneity. Hippocampal volume (HV) is a key endophenotype for many brain disorders. Here, we examine the impact of genetic adjustment on HV nomograms and the translational ability to detect dementia patients. Using imaging data from 35,686 healthy subjects aged 44-82 from the UK Biobank (UKB), we built HV nomograms using Gaussian process regression (GPR), which - compared to a previous method - extended the application age by 20 years, including dementia critical age ranges. Using HV polygenic scores (HV-PGS), we built genetically adjusted nomograms from participants stratified into the top and bottom 30% of HV-PGS. This shifted the nomograms in the expected directions by ~100 mm3 (2.3% of the average HV), which equates to 3 years of normal aging for a person aged ~65. Clinical impact of genetically adjusted nomograms was investigated by comparing 818 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database diagnosed as either cognitively normal (CN), having mild cognitive impairment (MCI) or Alzheimer's disease (AD) patients. While no significant change in the survival analysis was found for MCI-to-AD conversion, an average of 68% relative decrease was found in intra-diagnostic-group variance, highlighting the importance of genetic adjustment in untangling phenotypic heterogeneity.
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Affiliation(s)
- Mohammed Janahi
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College LondonLondonUnited Kingdom
- Medical and Population Genomics Lab, Human Genetics Department, Research Branch, Sidra MedicineDohaQatar
| | - Leon Aksman
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern CaliforniaLos AngelesUnited States
| | - Jonathan M Schott
- Dementia Research Centre (DRC), Queen Square Institute of Neurology, University College LondonLondonUnited Kingdom
| | - Younes Mokrab
- Medical and Population Genomics Lab, Human Genetics Department, Research Branch, Sidra MedicineDohaQatar
- Department of Genetic Medicine, Weill Cornell Medicine-QatarDohaQatar
| | - Andre Altmann
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College LondonLondonUnited Kingdom
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Rutherford S, Kia SM, Wolfers T, Fraza C, Zabihi M, Dinga R, Berthet P, Worker A, Verdi S, Ruhe HG, Beckmann CF, Marquand AF. The normative modeling framework for computational psychiatry. Nat Protoc 2022; 17:1711-1734. [PMID: 35650452 PMCID: PMC7613648 DOI: 10.1038/s41596-022-00696-5] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 03/17/2022] [Indexed: 11/09/2022]
Abstract
Normative modeling is an emerging and innovative framework for mapping individual differences at the level of a single subject or observation in relation to a reference model. It involves charting centiles of variation across a population in terms of mappings between biology and behavior, which can then be used to make statistical inferences at the level of the individual. The fields of computational psychiatry and clinical neuroscience have been slow to transition away from patient versus 'healthy' control analytic approaches, probably owing to a lack of tools designed to properly model biological heterogeneity of mental disorders. Normative modeling provides a solution to address this issue and moves analysis away from case-control comparisons that rely on potentially noisy clinical labels. Here we define a standardized protocol to guide users through, from start to finish, normative modeling analysis using the Predictive Clinical Neuroscience toolkit (PCNtoolkit). We describe the input data selection process, provide intuition behind the various modeling choices and conclude by demonstrating several examples of downstream analyses that the normative model may facilitate, such as stratification of high-risk individuals, subtyping and behavioral predictive modeling. The protocol takes ~1-3 h to complete.
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Affiliation(s)
- Saige Rutherford
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands.
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands.
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA.
| | - Seyed Mostafa Kia
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Psychiatry, Utrecht University Medical Center, Utrecht, the Netherlands
| | - Thomas Wolfers
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research, University of Oslo, Oslo, Norway
| | - Charlotte Fraza
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Mariam Zabihi
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Richard Dinga
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Pierre Berthet
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research, University of Oslo, Oslo, Norway
| | - Amanda Worker
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Serena Verdi
- Centre for Medical Image Computing, Medical Physics and Biomedical Engineering, University College London, London, UK
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Henricus G Ruhe
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands
- Department of Psychiatry, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Christian F Beckmann
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands
- Centre for Functional MRI of the Brain, University of Oxford, Oxford, UK
| | - Andre F Marquand
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
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