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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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Verdi S, Rutherford S, Fraza C, Tosun D, Altmann A, Raket LL, Schott JM, Marquand AF, Cole JH. Personalizing progressive changes to brain structure in Alzheimer's disease using normative modeling. Alzheimers Dement 2024. [PMID: 39234956 DOI: 10.1002/alz.14174] [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: 04/11/2024] [Revised: 07/12/2024] [Accepted: 07/13/2024] [Indexed: 09/06/2024]
Abstract
INTRODUCTION Neuroanatomical normative modeling captures individual variability in Alzheimer's disease (AD). Here we used normative modeling to track individuals' disease progression in people with mild cognitive impairment (MCI) and patients with AD. METHODS Cortical and subcortical normative models were generated using healthy controls (n ≈ 58k). These models were used to calculate regional z scores in 3233 T1-weighted magnetic resonance imaging time-series scans from 1181 participants. Regions with z scores < -1.96 were classified as outliers mapped on the brain and summarized by total outlier count (tOC). RESULTS tOC increased in AD and in people with MCI who converted to AD and also correlated with multiple non-imaging markers. Moreover, a higher annual rate of change in tOC increased the risk of progression from MCI to AD. Brain outlier maps identified the hippocampus as having the highest rate of change. DISCUSSION Individual patients' atrophy rates can be tracked by using regional outlier maps and tOC. HIGHLIGHTS Neuroanatomical normative modeling was applied to serial Alzheimer's disease (AD) magnetic resonance imaging (MRI) data for the first time. Deviation from the norm (outliers) of cortical thickness or brain volume was computed in 3233 scans. The number of brain-structure outliers increased over time in people with AD. Patterns of change in outliers varied markedly between individual patients with AD. People with mild cognitive impairment whose outliers increased over time had a higher risk of progression from AD.
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Affiliation(s)
- Serena Verdi
- Centre for Medical Image Computing, University College London, London, UK
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Saige Rutherford
- 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
| | - Charlotte Fraza
- 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
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
| | - Andre Altmann
- Centre for Medical Image Computing, University College London, London, UK
| | - Lars Lau Raket
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Jonathan M Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - 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
| | - James H Cole
- Centre for Medical Image Computing, University College London, London, UK
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
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Menardi A, Spoa M, Vallesi A. Brain topology underlying executive functions across the lifespan: focus on the default mode network. Front Psychol 2024; 15:1441584. [PMID: 39295768 PMCID: PMC11408365 DOI: 10.3389/fpsyg.2024.1441584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Accepted: 08/19/2024] [Indexed: 09/21/2024] Open
Abstract
Introduction While traditional neuroimaging approaches to the study of executive functions (EFs) have typically employed task-evoked paradigms, resting state studies are gaining popularity as a tool for investigating inter-individual variability in the functional connectome and its relationship to cognitive performance outside of the scanner. Method Using resting state functional magnetic resonance imaging data from the Human Connectome Project Lifespan database, the present study capitalized on graph theory to chart cross-sectional variations in the intrinsic functional organization of the frontoparietal (FPN) and the default mode (DMN) networks in 500 healthy individuals (from 10 to 100 years of age), to investigate the neural underpinnings of EFs across the lifespan. Results Topological properties of both the FPN and DMN were associated with EF performance but not with a control task of picture naming, providing specificity in support for a tight link between neuro-functional and cognitive-behavioral efficiency within the EF domain. The topological organization of the DMN, however, appeared more sensitive to age-related changes relative to that of the FPN. Discussion The DMN matures earlier in life than the FPN and it ıs more susceptible to neurodegenerative changes. Because its activity is stronger in conditions of resting state, the DMN might be easier to measure in noncompliant populations and in those at the extremes of the life-span curve, namely very young or elder participants. Here, we argue that the study of its functional architecture in relation to higher order cognition across the lifespan might, thus, be of greater interest compared with what has been traditionally thought.
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Affiliation(s)
- A Menardi
- Department of Neuroscience, University of Padova, Padova, Italy
- Padova Neuroscience Center, University of Padova, Padova, Italy
| | - M Spoa
- Department of General Psychology, University of Padova, Padova, Italy
| | - A Vallesi
- Department of Neuroscience, University of Padova, Padova, Italy
- Padova Neuroscience Center, University of Padova, Padova, Italy
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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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Young TR, Kumar VJ, Saranathan M. Normative Modeling of Thalamic Nuclear Volumes and Characterization of Lateralized Volume Alterations in Alzheimer's Disease Versus Schizophrenia. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00241-6. [PMID: 39182722 DOI: 10.1016/j.bpsc.2024.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Revised: 08/13/2024] [Accepted: 08/13/2024] [Indexed: 08/27/2024]
Abstract
BACKGROUND Thalamic nuclei facilitate a wide range of complex behaviors, emotions, and cognition and have been implicated in neuropsychiatric disorders including Alzheimer's disease (AD) and schizophrenia. The aim of this work was to establish novel normative models of thalamic nuclear volumes and their laterality indices and investigate their changes in schizophrenia and AD. METHODS Volumes of bilateral whole thalami and 10 thalamic nuclei were generated from T1 MRI data using a state-of-the-art novel segmentation method in healthy control subjects (n=2374) and early mild cognitive impairment (MCI, n=211), late MCI (n=113), AD (n=88), and schizophrenia (n=168). Normative models for each nucleus were generated from healthy control subjects while controlling for sex, intracranial volume, and site. Extreme z-score deviations (|z|>1.96) and z-score distributions were compared across phenotypes. Z-scores were associated with clinical descriptors. RESULTS Increased infranormal and decreased supranormal z-scores were observed in schizophrenia and AD. Z-score shifts representing reduced volumes were observed in most nuclei in schizophrenia and AD with strong overlap in the bilateral pulvinar, medial dorsal, and centromedian nuclei. Shifts were larger in AD with evidence of a left-sided preference in early MCI while a predilection for right thalamic nuclei was observed in schizophrenia. The right medial dorsal nucleus was associated with disorganized thought and daily auditory verbal hallucinations. CONCLUSION In AD, thalamic nuclei are more severely and symmetrically affected while in schizophrenia, the right thalamic nuclei are more affected. We highlight the right medial dorsal nucleus, which may mediate multiple symptoms of schizophrenia and is affected early in the disease course.
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Affiliation(s)
- Taylor R Young
- Department of Psychiatry, University of Massachusetts Chan Medical School, Worcester, MA; Department of Neurology, University of Massachusetts Chan Medical School, Worcester, MA.
| | | | - Manojkumar Saranathan
- Department of Radiology, University of Massachusetts Chan Medical School, Worcester, MA
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Little B, Flowers C, Blamire A, Thelwall P, Taylor JP, Gallagher P, Cousins DA, Wang Y. Multivariate brain-cognition associations in euthymic bipolar disorder. Bipolar Disord 2024. [PMID: 39138611 DOI: 10.1111/bdi.13484] [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: 08/15/2024]
Abstract
BACKGROUND People with bipolar disorder (BD) tend to show widespread cognitive impairment compared to healthy controls. Impairments in processing speed (PS), attention and executive function (EF) may represent 'core' impairments that have a role in wider cognitive dysfunction. Cognitive impairments appear to relate to structural brain abnormalities in BD, but whether core deficits are related to particular brain regions is unclear and much of the research on brain-cognition associations is limited by univariate analysis and small samples. METHODS Euthymic BD patients (n = 56) and matched healthy controls (n = 26) underwent T1-weighted MRI scans and completed neuropsychological tests of PS, attention and EF. We utilised public datasets to develop normative models of cortical thickness (n = 5977) to generate robust estimations of cortical abnormalities in patients. Canonical correlation analysis was used to assess multivariate brain-cognition associations in BD, controlling for age, sex and premorbid IQ. RESULTS BD showed impairments on tests of PS, attention and EF, and abnormal cortical thickness in several brain regions compared to healthy controls. Impairments in tests of PS and EF were most strongly associated with cortical thickness in the left inferior temporal, right entorhinal and right temporal pole areas. CONCLUSION Impairments in PS, attention and EF can be observed in euthymic BD and may be related to abnormal cortical thickness in temporal regions. Future research should continue to leverage normative modelling and multivariate methods to examine complex brain-cognition associations in BD. Future research may benefit from exploring covariance between traditional brain structural morphological metrics such as cortical thickness, cortical volume and surface area.
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Affiliation(s)
- Bethany Little
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex Biosystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | - Carly Flowers
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Andrew Blamire
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Peter Thelwall
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - John-Paul Taylor
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Peter Gallagher
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - David Andrew Cousins
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- Cumbria, Northumberland, Tyne and Wear NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Yujiang Wang
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex Biosystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, UK
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Kozma C, Schroeder G, Owen T, de Tisi J, McEvoy AW, Miserocchi A, Duncan J, Wang Y, Taylor PN. Identifying epileptogenic abnormality by decomposing intracranial EEG and MEG power spectra. J Neurosci Methods 2024; 408:110180. [PMID: 38795977 DOI: 10.1016/j.jneumeth.2024.110180] [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: 02/15/2024] [Revised: 05/08/2024] [Accepted: 05/22/2024] [Indexed: 05/28/2024]
Abstract
BACKGROUND Accurate identification of abnormal electroencephalographic (EEG) activity is pivotal for diagnosing and treating epilepsy. Recent studies indicate that decomposing brain activity into periodic (oscillatory) and aperiodic (trend across all frequencies) components can illuminate the drivers of spectral activity changes. NEW METHODS We analysed intracranial EEG (iEEG) data from 234 subjects, creating a normative map. This map was compared to a cohort of 63 patients with refractory focal epilepsy under consideration for neurosurgery. The normative map was computed using three approaches: (i) relative complete band power, (ii) relative band power with the aperiodic component removed, and (iii) the aperiodic exponent. Abnormalities were calculated for each approach in the patient cohort. We evaluated the spatial profiles, assessed their ability to localize abnormalities, and replicated the findings using magnetoencephalography (MEG). RESULTS Normative maps of relative complete band power and relative periodic band power exhibited similar spatial profiles, while the aperiodic normative map revealed higher exponent values in the temporal lobe. Abnormalities estimated through complete band power effectively distinguished between good and bad outcome patients. Combining periodic and aperiodic abnormalities enhanced performance, like the complete band power approach. COMPARISON WITH EXISTING METHODS AND CONCLUSIONS Sparing cerebral tissue with abnormalities in both periodic and aperiodic activity may result in poor surgical outcomes. Both periodic and aperiodic components do not carry sufficient information in isolation. The relative complete band power solution proved to be the most reliable method for this purpose. Future studies could investigate how cerebral location or pathology influences periodic or aperiodic abnormalities.
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Affiliation(s)
- Csaba Kozma
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom.
| | - Gabrielle Schroeder
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Tom Owen
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Jane de Tisi
- UCL Queen Square Institute of Neurology, Queen Square, London, United Kingdom
| | - Andrew W McEvoy
- UCL Queen Square Institute of Neurology, Queen Square, London, United Kingdom
| | - Anna Miserocchi
- UCL Queen Square Institute of Neurology, Queen Square, London, United Kingdom
| | - John Duncan
- UCL Queen Square Institute of Neurology, Queen Square, London, United Kingdom
| | - Yujiang Wang
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom; Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom; UCL Queen Square Institute of Neurology, Queen Square, London, United Kingdom
| | - Peter N Taylor
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom; Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom; UCL Queen Square Institute of Neurology, Queen Square, London, United Kingdom
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Gardner M, Shinohara RT, Bethlehem RAI, Romero-Garcia R, Warrier V, Dorfschmidt L, Shanmugan S, Thompson P, Seidlitz J, Alexander-Bloch AF, Chen AA. ComBatLS: A location- and scale-preserving method for multi-site image harmonization. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.21.599875. [PMID: 39131292 PMCID: PMC11312440 DOI: 10.1101/2024.06.21.599875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Recent work has leveraged massive datasets and advanced harmonization methods to construct normative models of neuroanatomical features and benchmark individuals' morphology. However, current harmonization tools do not preserve the effects of biological covariates including sex and age on features' variances; this failure may induce error in normative scores, particularly when such factors are distributed unequally across sites. Here, we introduce a new extension of the popular ComBat harmonization method, ComBatLS, that preserves biological variance in features' locations and scales. We use UK Biobank data to show that ComBatLS robustly replicates individuals' normative scores better than other ComBat methods when subjects are assigned to sex-imbalanced synthetic "sites". Additionally, we demonstrate that ComBatLS significantly reduces sex biases in normative scores compared to traditional methods. Finally, we show that ComBatLS successfully harmonizes consortium data collected across over 50 studies. R implementation of ComBatLS is available at https://github.com/andy1764/ComBatFamily.
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Affiliation(s)
- Margaret Gardner
- Brain-Gene-Development Lab, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Neuroscience Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Center for Biomedical Imaging Computing and Analytics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, USA
| | | | - Rafael Romero-Garcia
- Instituto de Biomedicina de Sevilla (IBiS) HUVR/CSIC/Universidad de Sevilla/CIBERSAM, ISCIII, Dpto. de Fisiología Médica y Biofísica, Seville, ES
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Varun Warrier
- Department of Psychology, University of Cambridge, Cambridge, UK
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Lena Dorfschmidt
- Brain-Gene-Development Lab, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Sheila Shanmugan
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA
| | - Paul Thompson
- Imaging Genetics Center, Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jakob Seidlitz
- Brain-Gene-Development Lab, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Aaron F Alexander-Bloch
- Brain-Gene-Development Lab, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Andrew A Chen
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA
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Rodrigue AL, Hayes RA, Waite E, Corcoran M, Glahn DC, Jalbrzikowski M. Multimodal Neuroimaging Summary Scores as Neurobiological Markers of Psychosis. Schizophr Bull 2024; 50:792-803. [PMID: 37844289 PMCID: PMC11283202 DOI: 10.1093/schbul/sbad149] [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] [Indexed: 10/18/2023]
Abstract
BACKGROUND AND HYPOTHESIS Structural brain alterations are well-established features of schizophrenia but they do not effectively predict disease/disease risk. Similar to polygenic risk scores in genetics, we integrated multifactorial aspects of brain structure into a summary "Neuroscore" and examined its potential as a marker of disease. STUDY DESIGN We extracted measures from T1-weighted scans and diffusion tensor imaging (DTI) models from three studies with schizophrenia and healthy individuals. We calculated individual-level summary scores (Neuroscores) for T1-weighted and DTI measures and a combined score (Multimodal Neuroscore-MM). We assessed each score's ability to differentiate schizophrenia cases from controls and its relationship to clinical symptomatology, intelligence quotient (IQ), and medication dosage. We assessed Neuroscore specificity by performing all analyses in a more inclusive psychosis sample and by using scores generated from MDD effect sizes. STUDY RESULTS All Neuroscores significantly differentiated schizophrenia cases from controls (T1 d = 0.56, DTI d = 0.29, MM d = 0.64) to a greater degree than individual brain regions. Higher Neuroscores (ie, increased liability) were associated with lower IQ (T1 β = -0.26, DTI β = -0.15, MM β = -0.30). Higher T1-weighted Neuroscores were associated with higher positive and negative symptom severity (Positive β = 0.21, Negative β = 0.16); Higher Multimodal Neuroscores were associated with higher positive symptom severity (β = 0.30). SZ Neuroscores outperformed MDD Neuroscores in predicting IQ (T1: z = 3.5, q = 0.0007; MM: z = 1.8, q = 0.05). CONCLUSIONS Neuroscores are a step toward leveraging widespread structural brain alterations in psychosis to identify robust neurobiological markers of disease. Future studies will assess ways to improve neuroscore calculation, including developing the optimal methods to calculate neuroscores and considering disorder overlap.
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Affiliation(s)
- Amanda L Rodrigue
- Department of Psychiatry, Boston Children’s Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Rebecca A Hayes
- Department of Psychiatry, Boston Children’s Hospital, Boston, MA, USA
| | - Emma Waite
- Department of Psychiatry, Boston Children’s Hospital, Boston, MA, USA
| | - Mary Corcoran
- Department of Psychiatry, Boston Children’s Hospital, Boston, MA, USA
| | - David C Glahn
- Department of Psychiatry, Boston Children’s Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA
| | - Maria Jalbrzikowski
- Department of Psychiatry, Boston Children’s Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
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10
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Savage HS, Mulders PCR, van Eijndhoven PFP, van Oort J, Tendolkar I, Vrijsen JN, Beckmann CF, Marquand AF. Dissecting task-based fMRI activity using normative modelling: an application to the Emotional Face Matching Task. Commun Biol 2024; 7:888. [PMID: 39033247 PMCID: PMC11271583 DOI: 10.1038/s42003-024-06573-z] [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: 04/05/2024] [Accepted: 07/09/2024] [Indexed: 07/23/2024] Open
Abstract
Functional neuroimaging has contributed substantially to understanding brain function but is dominated by group analyses that index only a fraction of the variation in these data. It is increasingly clear that parsing the underlying heterogeneity is crucial to understand individual differences and the impact of different task manipulations. We estimate large-scale (N = 7728) normative models of task-evoked activation during the Emotional Face Matching Task, which enables us to bind heterogeneous datasets to a common reference and dissect heterogeneity underlying group-level analyses. We apply this model to a heterogenous patient cohort, to map individual differences between patients with one or more mental health diagnoses relative to the reference cohort and determine multivariate associations with transdiagnostic symptom domains. For the face>shapes contrast, patients have a higher frequency of extreme deviations which are spatially heterogeneous. In contrast, normative models for faces>baseline have greater predictive value for individuals' transdiagnostic functioning. Taken together, we demonstrate that normative modelling of fMRI task-activation can be used to illustrate the influence of different task choices and map replicable individual differences, and we encourage its application to other neuroimaging tasks in future studies.
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Affiliation(s)
- Hannah S Savage
- Donders Institute of Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands.
| | - Peter C R Mulders
- Donders Institute of Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Philip F P van Eijndhoven
- Donders Institute of Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Jasper van Oort
- Donders Institute of Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Indira Tendolkar
- Donders Institute of Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Janna N Vrijsen
- Donders Institute of Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Centre, Nijmegen, The Netherlands
- Depression Expertise Centre, Pro Persona Mental Health Care, Nijmegen, The Netherlands
| | - Christian F Beckmann
- Donders Institute of 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, UK
| | - Andre F Marquand
- Donders Institute of Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands.
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Yu B, Kaku A, Liu K, Parnandi A, Fokas E, Venkatesan A, Pandit N, Ranganath R, Schambra H, Fernandez-Granda C. Quantifying impairment and disease severity using AI models trained on healthy subjects. NPJ Digit Med 2024; 7:180. [PMID: 38969786 PMCID: PMC11226623 DOI: 10.1038/s41746-024-01173-x] [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: 11/06/2023] [Accepted: 06/21/2024] [Indexed: 07/07/2024] Open
Abstract
Automatic assessment of impairment and disease severity is a key challenge in data-driven medicine. We propose a framework to address this challenge, which leverages AI models trained exclusively on healthy individuals. The COnfidence-Based chaRacterization of Anomalies (COBRA) score exploits the decrease in confidence of these models when presented with impaired or diseased patients to quantify their deviation from the healthy population. We applied the COBRA score to address a key limitation of current clinical evaluation of upper-body impairment in stroke patients. The gold-standard Fugl-Meyer Assessment (FMA) requires in-person administration by a trained assessor for 30-45 minutes, which restricts monitoring frequency and precludes physicians from adapting rehabilitation protocols to the progress of each patient. The COBRA score, computed automatically in under one minute, is shown to be strongly correlated with the FMA on an independent test cohort for two different data modalities: wearable sensors (ρ = 0.814, 95% CI [0.700,0.888]) and video (ρ = 0.736, 95% C.I [0.584, 0.838]). To demonstrate the generalizability of the approach to other conditions, the COBRA score was also applied to quantify severity of knee osteoarthritis from magnetic-resonance imaging scans, again achieving significant correlation with an independent clinical assessment (ρ = 0.644, 95% C.I [0.585,0.696]).
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Affiliation(s)
- Boyang Yu
- Center for Data Science, New York University, 60 Fifth Ave, New York, NY, 10011, USA
| | - Aakash Kaku
- Center for Data Science, New York University, 60 Fifth Ave, New York, NY, 10011, USA
| | - Kangning Liu
- Center for Data Science, New York University, 60 Fifth Ave, New York, NY, 10011, USA
| | - Avinash Parnandi
- Department of Neurology, NYU Grossman School of Medicine, 550 1st Ave, New York, NY, 10016, USA
- Department of Rehabilitation Medicine, NYU Grossman School of Medicine, 550 1st Ave, New York, NY, 10016, USA
| | - Emily Fokas
- Department of Neurology, NYU Grossman School of Medicine, 550 1st Ave, New York, NY, 10016, USA
| | - Anita Venkatesan
- Department of Neurology, NYU Grossman School of Medicine, 550 1st Ave, New York, NY, 10016, USA
| | - Natasha Pandit
- Department of Rehabilitation Medicine, NYU Grossman School of Medicine, 550 1st Ave, New York, NY, 10016, USA
| | - Rajesh Ranganath
- Center for Data Science, New York University, 60 Fifth Ave, New York, NY, 10011, USA
- Courant Institute of Mathematical Sciences, New York University, 251 Mercer St, New York, NY, 10012, USA
| | - Heidi Schambra
- Department of Neurology, NYU Grossman School of Medicine, 550 1st Ave, New York, NY, 10016, USA.
- Department of Rehabilitation Medicine, NYU Grossman School of Medicine, 550 1st Ave, New York, NY, 10016, USA.
| | - Carlos Fernandez-Granda
- Center for Data Science, New York University, 60 Fifth Ave, New York, NY, 10011, USA.
- Courant Institute of Mathematical Sciences, New York University, 251 Mercer St, New York, NY, 10012, USA.
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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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Sun H, Liu N, Qiu C, Tao B, Yang C, Tang B, Li H, Zhan K, Cai C, Zhang W, Lui S. Applications of MRI in Schizophrenia: Current Progress in Establishing Clinical Utility. J Magn Reson Imaging 2024. [PMID: 38946400 DOI: 10.1002/jmri.29470] [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: 08/17/2023] [Revised: 05/20/2024] [Accepted: 05/20/2024] [Indexed: 07/02/2024] Open
Abstract
Schizophrenia is a severe mental illness that significantly impacts the lives of affected individuals and with increasing mortality rates. Early detection and intervention are crucial for improving outcomes but the lack of validated biomarkers poses great challenges in such efforts. The use of magnetic resonance imaging (MRI) in schizophrenia enables the investigation of the disorder's etiological and neuropathological substrates in vivo. After decades of research, promising findings of MRI have been shown to aid in screening high-risk individuals and predicting illness onset, and predicting symptoms and treatment outcomes of schizophrenia. The integration of machine learning and deep learning techniques makes it possible to develop intelligent diagnostic and prognostic tools with extracted or selected imaging features. In this review, we aimed to provide an overview of current progress and prospects in establishing clinical utility of MRI in schizophrenia. We first provided an overview of MRI findings of brain abnormalities that might underpin the symptoms or treatment response process in schizophrenia patients. Then, we summarized the ongoing efforts in the computer-aided utility of MRI in schizophrenia and discussed the gap between MRI research findings and real-world applications. Finally, promising pathways to promote clinical translation were provided. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Hui Sun
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Naici Liu
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Changjian Qiu
- Mental Health Center, West China Hospital of Sichuan University, Sichuan Clinical Medical Research Center for Mental Disorders, Chengdu, China
| | - Bo Tao
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Chengmin Yang
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Biqiu Tang
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Hongwei Li
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Department of Radiology, The Third Hospital of Mianyang/Sichuan Mental Health Center, Mianyang, China
| | - Kongcai Zhan
- Department of Radiology, Zigong Affiliated Hospital of Southwest Medical University, Zigong Psychiatric Research Center, Zigong, China
| | - Chunxian Cai
- Department of Radiology, the Second People's Hospital of Neijiang, Neijiang, China
| | - Wenjing Zhang
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Su Lui
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
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14
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Bottenhorn KL, Corbett JD, Ahmadi H, Herting MM. Spatiotemporal patterns in cortical development: Age, puberty, and individual variability from 9 to 13 years of age. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.29.601354. [PMID: 39005403 PMCID: PMC11244861 DOI: 10.1101/2024.06.29.601354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Humans and nonhuman primate studies suggest that timing and tempo of cortical development varies neuroanatomically along a sensorimotor-to-association (S-A) axis. Prior human studies have reported a principal S-A axis across various modalities, but largely rely on cross-sectional samples with wide age-ranges. Here, we investigate developmental changes and individual variability in cortical organization along the S-A axis between the ages of 9-13 years using a large, longitudinal sample (N = 2487-3747, 46-50% female) from the Adolescent Brain Cognitive Development Study (ABCD Study®). This work assesses multiple aspects of neurodevelopment indexed by changes in cortical thickness, cortical microarchitecture, and resting-state functional fluctuations. First, we evaluated S-A organization in age-related changes and, then, computed individual-level S-A alignment in brain changes and assessing differences therein due to age, sex, and puberty. Varying degrees of linear and quadratic age-related brain changes were identified along the S-A axis. Yet, these patterns of cortical development were overshadowed by considerable individual variability in S-A alignment. Even within individuals, there was little correspondence between S-A patterning across the different aspects of neurodevelopment investigated (i.e., cortical morphology, microarchitecture, function). Some of the individual variation in developmental patterning of cortical morphology and microarchitecture was explained by age, sex, and pubertal development. Altogether, this work contextualizes prior findings that regional age differences do progress along an S-A axis at a group level, while highlighting broad variation in developmental change between individuals and between aspects of cortical development, in part due to sex and puberty. Significance Statement Understanding normative patterns of adolescent brain change, and individual variability therein, is crucial for disentangling healthy and abnormal development. We used longitudinal human neuroimaging data to study several aspects of neurodevelopment during early adolescence and assessed their organization along a sensorimotor-to-association (S-A) axis across the cerebral cortex. Age differences in brain changes were linear and curvilinear along this S-A axis. However, individual-level sensorimotor-association alignment varied considerably, driven in part by differences in age, sex, and pubertal development.
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15
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Nárai Á, Hermann P, Rádosi A, Vakli P, Weiss B, Réthelyi JM, Bunford N, Vidnyánszky Z. Amygdala Volume is Associated with ADHD Risk and Severity Beyond Comorbidities in Adolescents: Clinical Testing of Brain Chart Reference Standards. Res Child Adolesc Psychopathol 2024; 52:1063-1074. [PMID: 38483760 PMCID: PMC11217056 DOI: 10.1007/s10802-024-01190-0] [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] [Accepted: 03/01/2024] [Indexed: 07/03/2024]
Abstract
Understanding atypicalities in ADHD brain correlates is a step towards better understanding ADHD etiology. Efforts to map atypicalities at the level of brain structure have been hindered by the absence of normative reference standards. Recent publication of brain charts allows for assessment of individual variation relative to age- and sex-adjusted reference standards and thus estimation not only of case-control differences but also of intraindividual prediction. METHODS Aim was to examine, whether brain charts can be applied in a sample of adolescents (N = 140, 38% female) to determine whether atypical brain subcortical and total volumes are associated with ADHD at-risk status and severity of parent-rated symptoms, accounting for self-rated anxiety and depression, and parent-rated oppositional defiant disorder (ODD) as well as motion. RESULTS Smaller bilateral amygdala volume was associated with ADHD at-risk status, beyond effects of comorbidities and motion, and smaller bilateral amygdala volume was associated with inattention and hyperactivity/impulsivity, beyond effects of comorbidities except for ODD symptoms, and motion. CONCLUSIONS Individual differences in amygdala volume meaningfully add to estimating ADHD risk and severity. Conceptually, amygdalar involvement is consistent with behavioral and functional imaging data on atypical reinforcement sensitivity as a marker of ADHD-related risk. Methodologically, results show that brain chart reference standards can be applied to address clinically informative, focused and specific questions.
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Affiliation(s)
- Ádám Nárai
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary
- Doctoral School of Biology and Sportbiology, Institute of Biology, Faculty of Sciences, University of Pécs, Pécs, Hungary
| | - Petra Hermann
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary
| | - Alexandra Rádosi
- Clinical and Developmental Neuropsychology Research Group, Institute of Cognitive Neuroscience and Psychology, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary
- Doctoral School of Mental Health Sciences, Semmelweis University, Budapest, Hungary
| | - Pál Vakli
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary
| | - Béla Weiss
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary
| | - János M Réthelyi
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Nóra Bunford
- Clinical and Developmental Neuropsychology Research Group, Institute of Cognitive Neuroscience and Psychology, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary.
| | - Zoltán Vidnyánszky
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary.
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16
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Jiang A, Ma X, Li S, Wang L, Yang B, Wang S, Li M, Dong G. Age-atypical brain functional networks in autism spectrum disorder: a normative modeling approach. Psychol Med 2024; 54:2042-2053. [PMID: 38563297 DOI: 10.1017/s0033291724000138] [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] [Indexed: 04/04/2024]
Abstract
BACKGROUND Despite extensive research into the neural basis of autism spectrum disorder (ASD), the presence of substantial biological and clinical heterogeneity among diagnosed individuals remains a major barrier. Commonly used case‒control designs assume homogeneity among subjects, which limits their ability to identify biological heterogeneity, while normative modeling pinpoints deviations from typical functional network development at individual level. METHODS Using a world-wide multi-site database known as Autism Brain Imaging Data Exchange, we analyzed individuals with ASD and typically developed (TD) controls (total n = 1218) aged 5-40 years, generating individualized whole-brain network functional connectivity (FC) maps of age-related atypicality in ASD. We then used local polynomial regression to estimate a networkwise normative model of development and explored correlations between ASD symptoms and brain networks. RESULTS We identified a subset exhibiting highly atypical individual-level FC, exceeding 2 standard deviation from the normative value. We also identified clinically relevant networks (mainly default mode network) at cohort level, since the outlier rates decreased with age in TD participants, but increased in those with autism. Moreover, deviations were linked to severity of repetitive behaviors and social communication symptoms. CONCLUSIONS Individuals with ASD exhibit distinct, highly individualized trajectories of brain functional network development. In addition, distinct developmental trajectories were observed among ASD and TD individuals, suggesting that it may be challenging to identify true differences in network characteristics by comparing young children with ASD to their TD peers. This study enhances understanding of the biological heterogeneity of the disorder and can inform precision medicine.
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Affiliation(s)
- Anhang Jiang
- Department of Psychology, Yunnan Normal University, Kunming, Yunnan Province, P.R. China
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R. China
| | - Xuefeng Ma
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R. China
| | - Shuang Li
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R. China
| | - Lingxiao Wang
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R. China
- Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang Province, China
| | - Bo Yang
- Department of Psychology, Yunnan Normal University, Kunming, Yunnan Province, P.R. China
| | - Shizhen Wang
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R. China
| | - Mei Li
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R. China
- Center for Mental Health Education and Counselling, Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R. China
| | - Guangheng Dong
- Department of Psychology, Yunnan Normal University, Kunming, Yunnan Province, P.R. China
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17
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Harnett NG, Fani N, Rowland G, Kumar P, Rutherford S, Nickerson LD. Population-level normative models reveal race- and socioeconomic-related variability in cortical thickness of threat neurocircuitry. Commun Biol 2024; 7:745. [PMID: 38898062 PMCID: PMC11187116 DOI: 10.1038/s42003-024-06436-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 06/11/2024] [Indexed: 06/21/2024] Open
Abstract
The inequitable distribution of economic resources and exposure to adversity between racial groups contributes to mental health disparities within the United States. Consideration of the potential neurodevelopmental consequences, however, has been limited particularly for neurocircuitry known to regulate the emotional response to threat. Characterizing the consequences of inequity on threat neurocircuitry is critical for robust and generalizable neurobiological models of psychiatric illness. Here we use data from the Adolescent Brain and Cognitive Development Study 4.0 release to investigate the contributions of individual and neighborhood-level economic resources and exposure to discrimination. We investigate the potential appearance of race-related differences using both standard methods and through population-level normative modeling. We show that, in a sample of white and Black adolescents, racial inequities in socioeconomic factors largely contribute to the appearance of race-related differences in cortical thickness of threat neurocircuitry. The race-related differences are preserved through the use of population-level models and such models also preserve associations between cortical thickness and specific socioeconomic factors. The present findings highlight that such socioeconomic inequities largely underlie race-related differences in brain morphology. The present findings provide important new insight for the generation of generalizable neurobiological models of psychiatric illness.
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Affiliation(s)
- Nathaniel G Harnett
- Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA.
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
| | - Negar Fani
- Department of Psychiatry and Behavioral Neuroscience, Emory University, Atlanta, GA, USA
| | - Grace Rowland
- Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA
| | - Poornima Kumar
- Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Imaging Center, McLean Hospital, Belmont, MA, USA
| | - Saige Rutherford
- Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
- Donders Institute, Radboud University Nijmegen, Nijmegen, Netherlands
- Department of Psychiatry, University of Michigan-Ann Arbor, Ann Arbor, MI, USA
| | - Lisa D Nickerson
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Imaging Center, McLean Hospital, Belmont, MA, USA
- Applied Neuroimaging Statistics Research Laboratory, McLean Hospital, Belmont, MA, USA
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18
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Janssen J, Gallego AG, Díaz-Caneja CM, Lois NG, Janssen N, González-Peñas J, Gordaliza PM, Buimer EE, van Haren NE, Arango C, Kahn RS, Hulshoff Pol HE, Schnack HG. Heterogeneity of morphometric similarity networks in health and schizophrenia. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.26.586768. [PMID: 38948832 PMCID: PMC11212887 DOI: 10.1101/2024.03.26.586768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Introduction Morphometric similarity is a recently developed neuroimaging phenotype of inter-regional connectivity by quantifying the similarity of a region to other regions based on multiple MRI parameters. Altered average morphometric similarity has been reported in psychotic disorders at the group level, with considerable heterogeneity across individuals. We used normative modeling to address cross-sectional and longitudinal inter-individual heterogeneity of morphometric similarity in health and schizophrenia. Methods Morphometric similarity for 62 cortical regions was obtained from baseline and follow-up T1-weighted scans of healthy individuals and patients with chronic schizophrenia. Cortical regions were classified into seven predefined brain functional networks. Using Bayesian Linear Regression and taking into account age, sex, image quality and scanner, we trained and validated normative models in healthy controls from eleven datasets (n = 4310). Individual deviations from the norm (z-scores) in morphometric similarity were computed for each participant for each network and region at both timepoints. A z-score ≧ than 1.96 was considered supra-normal and a z-score ≦ -1.96 infra-normal. As a longitudinal metric, we calculated the change over time of the total number of infra- or supra-normal regions per participant. Results At baseline, patients with schizophrenia had decreased morphometric similarity of the default mode network and increased morphometric similarity of the somatomotor network when compared with healthy controls. The percentage of patients with infra- or supra-normal values for any region at baseline and follow-up was low (<6%) and did not differ from healthy controls. Mean intra-group changes over time in the total number of infra- or supra-normal regions were small in schizophrenia and healthy control groups (<1) and there were no significant between-group differences. Conclusions In a case-control setting, a decrease of morphometric similarity within the default mode network may be a robust finding implicated in schizophrenia. However, normative modeling suggests that significant reductions and changes over time of regional morphometric similarity are evident only in a minority of patients.
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Affiliation(s)
- Joost Janssen
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- Ciber del Área de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
| | - Ana Guil Gallego
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
| | - Covadonga M. Díaz-Caneja
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- Ciber del Área de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- School of Medicine, Universidad Complutense, Madrid, Spain
| | - Noemi González Lois
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
| | - Niels Janssen
- Department of Psychology, Universidad de la Laguna, Tenerife, Spain
- Institute of Biomedical Technologies, Universidad de La Laguna, Tenerife, Spain
- Institute of Neurosciences, Universidad de la Laguna, Santa Cruz de Tenerife, Spain
| | - Javier González-Peñas
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- Ciber del Área de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
| | - Pedro M. Gordaliza
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Radiology Department, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Elizabeth E.L. Buimer
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Neeltje E.M. van Haren
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Centre, Sophia Children’s Hospital, Rotterdam, The Netherlands
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- Ciber del Área de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- School of Medicine, Universidad Complutense, Madrid, Spain
| | - René S. Kahn
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, United States
| | - Hilleke E. Hulshoff Pol
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Hugo G. Schnack
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Centre, Sophia Children’s Hospital, Rotterdam, The Netherlands
- Department of Languages, Literature, and Communication, Faculty of Humanities, Utrecht University, Utrecht, The Netherlands
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19
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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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20
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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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21
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Wang YS, Su XT, Ke L, He QH, Chang D, Nie J, Luo X, Chen F, Xu J, Zhang C, Zhang S, Zhang S, An H, Guo R, Yue S, Duan W, Jia S, Yang S, Yu Y, Zhao Y, Zhou Y, Chen LZ, Fan XR, Gao P, Lv C, Wu Z, Zhao Y, Quan X, Zhao F, Mu Y, Yan Y, Xu W, Liu J, Xing L, Chen X, Wu X, Zhao L, Huang Z, Ren Y, Hao H, Li H, Wang J, Dong Q, Chen L, Huang R, Liu S, Wang Y, Dong Q, Zuo XN. Initiating PeriCBD to probe perinatal influences on neurodevelopment during 3-10 years in China. Sci Data 2024; 11:463. [PMID: 38714688 PMCID: PMC11076487 DOI: 10.1038/s41597-024-03211-5] [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: 10/02/2023] [Accepted: 04/02/2024] [Indexed: 05/10/2024] Open
Abstract
Adverse perinatal factors can interfere with the normal development of the brain, potentially resulting in long-term effects on the comprehensive development of children. Presently, the understanding of cognitive and neurodevelopmental processes under conditions of adverse perinatal factors is substantially limited. There is a critical need for an open resource that integrates various perinatal factors with the development of the brain and mental health to facilitate a deeper understanding of these developmental trajectories. In this Data Descriptor, we introduce a multicenter database containing information on perinatal factors that can potentially influence children's brain-mind development, namely, periCBD, that combines neuroimaging and behavioural phenotypes with perinatal factors at county/region/central district hospitals. PeriCBD was designed to establish a platform for the investigation of individual differences in brain-mind development associated with perinatal factors among children aged 3-10 years. Ultimately, our goal is to help understand how different adverse perinatal factors specifically impact cognitive development and neurodevelopment. Herein, we provide a systematic overview of the data acquisition/cleaning/quality control/sharing, processes of periCBD.
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Affiliation(s)
- Yin-Shan Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Xue-Ting Su
- Department of Military Operational Medical Protection, Chinese PLA Center for Disease Control and Prevention, Beijing, 100850, China
| | - Li Ke
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
- Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, Beijing, 100875, China.
| | - Qing-Hua He
- Faculty of Psychology, Southwest University, Chongqing, 400715, China
| | - Da Chang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - JingJing Nie
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - XinLi Luo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Fumei Chen
- Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, Beijing, 100875, China
| | - Jihong Xu
- National Research Institute for Health Commission, Beijing, 100081, China
| | - Cai Zhang
- Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, Beijing, 100875, China
| | - Shudong Zhang
- Faculty of Education, Beijing Normal University, Beijing, 100875, China
| | - Shuyue Zhang
- Department of Psychology, Faculty of Education, Guangxi Normal University, Guilin, 541001, China
| | - Huiping An
- Anyang Maternal and Child Health Care Hospital, Anyang, 455000, China
| | - Rui Guo
- People's Hospital of Liangping District, Chongqing, 405200, China
| | - Suping Yue
- Anyang Preschool Education College, Anyang, 456150, China
| | - Wen Duan
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, Beijing, 100875, China
| | - Shichao Jia
- Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, Beijing, 100875, China
| | - Sijia Yang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, Beijing, 100875, China
| | - Yankun Yu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Yang Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, Beijing, 100875, China
| | - Yang Zhou
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Li-Zhen Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Xue-Ru Fan
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Peng Gao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Chenyu Lv
- Faculty of Psychology, Southwest University, Chongqing, 400715, China
| | - Ziyun Wu
- Faculty of Psychology, Southwest University, Chongqing, 400715, China
| | - Yunyan Zhao
- Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, Beijing, 100875, China
| | - Xi Quan
- Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, Beijing, 100875, China
| | - Feng Zhao
- Department of Psychology, Faculty of Education, Guangxi Normal University, Guilin, 541001, China
| | - Yanchao Mu
- Anyang Maternal and Child Health Care Hospital, Anyang, 455000, China
| | - Yu Yan
- Anyang Maternal and Child Health Care Hospital, Anyang, 455000, China
| | - Wenchao Xu
- Anyang Maternal and Child Health Care Hospital, Anyang, 455000, China
| | - Jie Liu
- Anyang Maternal and Child Health Care Hospital, Anyang, 455000, China
| | - Lixia Xing
- Anyang Maternal and Child Health Care Hospital, Anyang, 455000, China
| | - Xiaoqin Chen
- People's Hospital of Liangping District, Chongqing, 405200, China
| | - Xiang Wu
- People's Hospital of Liangping District, Chongqing, 405200, China
| | - Lanfeng Zhao
- People's Hospital of Liangping District, Chongqing, 405200, China
| | - Zhijuan Huang
- People's Hospital of Liangping District, Chongqing, 405200, China
| | - Yanzhou Ren
- Anyang Preschool Education College, Anyang, 456150, China
| | - Hongyan Hao
- Anyang Preschool Education College, Anyang, 456150, China
| | - Hui Li
- Anyang Preschool Education College, Anyang, 456150, China
| | - Jing Wang
- Anyang Preschool Education College, Anyang, 456150, China
| | - Qing Dong
- Anyang Preschool Education College, Anyang, 456150, China
| | - Liyan Chen
- Anyang Preschool Education College, Anyang, 456150, China
| | - Ruiwang Huang
- School of Psychology, South China Normal University, Guangzhou, 510631, China
| | - Siman Liu
- School of Humanities and Social Sciences, Beijing Institute of Technology, Beijing, 100081, China
| | - Yun Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Xi-Nian Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
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22
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Young T, Kumar VJ, Saranathan M. Normative modeling of thalamic nuclear volumes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.06.24303871. [PMID: 38496426 PMCID: PMC10942522 DOI: 10.1101/2024.03.06.24303871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Thalamic nuclei have been implicated in neurodegenerative and neuropsychiatric disorders. Normative models for thalamic nuclear volumes have not been proposed thus far. The aim of this work was to establish normative models of thalamic nuclear volumes and subsequently investigate changes in thalamic nuclei in cognitive and psychiatric disorders. Volumes of the bilateral thalami and 12 nuclear regions were generated from T1 MRI data using a novel segmentation method (HIPS-THOMAS) in healthy control subjects (n=2374) and non-control subjects (n=695) with early and late mild cognitive impairment (EMCI, LMCI), Alzheimer's disease (AD), Early psychosis and Schizophrenia, Bipolar disorder, and Attention deficit hyperactivity disorder. Three different normative modelling methods were evaluated while controlling for sex, intracranial volume, and site. Z-scores and extreme z-score deviations were calculated and compared across phenotypes. GAMLSS models performed the best. Statistically significant shifts in z-score distributions consistent with atrophy were observed for most phenotypes. Shifts of progressively increasing magnitude were observed bilaterally from EMCI to AD with larger shifts in the left thalamic regions. Heterogeneous shifts were observed in psychiatric diagnoses with a predilection for the right thalamic regions. Here we present the first normative models of thalamic nuclear volumes and highlight their utility in evaluating heterogenous disorders such as Schizophrenia.
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Affiliation(s)
- Taylor Young
- Department of Psychiatry, University of Massachusetts Chan Medical School, Worcester, MA
- Department of Neurology, University of Massachusetts Chan Medical School, Worcester, MA
| | | | - Manojkumar Saranathan
- Department of Radiology, University of Massachusetts Chan Medical School, Worcester, MA
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23
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Poirot MG, Ruhe HG, Mutsaerts HJMM, Maximov II, Groote IR, Bjørnerud A, Marquering HA, Reneman L, Caan MWA. Treatment Response Prediction in Major Depressive Disorder Using Multimodal MRI and Clinical Data: Secondary Analysis of a Randomized Clinical Trial. Am J Psychiatry 2024; 181:223-233. [PMID: 38321916 DOI: 10.1176/appi.ajp.20230206] [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] [Indexed: 02/08/2024]
Abstract
OBJECTIVE Response to antidepressant treatment in major depressive disorder varies substantially between individuals, which lengthens the process of finding effective treatment. The authors sought to determine whether a multimodal machine learning approach could predict early sertraline response in patients with major depressive disorder. They assessed the predictive contribution of MR neuroimaging and clinical assessments at baseline and after 1 week of treatment. METHODS This was a preregistered secondary analysis of data from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, a multisite double-blind, placebo-controlled randomized clinical trial that included 296 adult outpatients with unmedicated recurrent or chronic major depressive disorder. MR neuroimaging and clinical data were collected before and after 1 week of treatment. Performance in predicting response and remission, collected after 8 weeks, was quantified using balanced accuracy (bAcc) and area under the receiver operating characteristic curve (AUROC) scores. RESULTS A total of 229 patients were included in the analyses (mean age, 38 years [SD=13]; 66% female). Internal cross-validation performance in predicting response to sertraline (bAcc=68% [SD=10], AUROC=0.73 [SD=0.03]) was significantly better than chance. External cross-validation on data from placebo nonresponders (bAcc=62%, AUROC=0.66) and placebo nonresponders who were switched to sertraline (bAcc=65%, AUROC=0.68) resulted in differences that suggest specificity for sertraline treatment compared with placebo treatment. Finally, multimodal models outperformed unimodal models. CONCLUSIONS The study results confirm that early sertraline treatment response can be predicted; that the models are sertraline specific compared with placebo; that prediction benefits from integrating multimodal MRI data with clinical data; and that perfusion imaging contributes most to these predictions. Using this approach, a lean and effective protocol could individualize sertraline treatment planning to improve psychiatric care.
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Affiliation(s)
- Maarten G Poirot
- Department of Radiology and Nuclear Medicine (Poirot, Ruhe, Marquering, Reneman) and Department of Biomedical Engineering and Physics (Poirot, Marquering, Reneman, Caan), Amsterdam UMC Location AMC, University of Amsterdam, Amsterdam; Brain Imaging, Amsterdam Neuroscience, Amsterdam (Poirot, Mutsaerts, Reneman, Caan); Department of Psychiatry, Radboudumc, Nijmegen, the Netherlands (Ruhe); Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands (Ruhe); Department of Radiology and Nuclear Medicine, Amsterdam UMC location, Vrije Universiteit Amsterdam, Amsterdam (Mutsaerts); Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen (Maximov, Bjørnerud); Division of Radiology, Vestfold Hospital Trust, Tønsberg, Norway (Groote, Caan); Computational Radiology and Artificial Intelligence, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo (Groote, Bjørnerud); Department of Psychology, University of Oslo, Oslo (Bjørnerud)
| | - Henricus G Ruhe
- Department of Radiology and Nuclear Medicine (Poirot, Ruhe, Marquering, Reneman) and Department of Biomedical Engineering and Physics (Poirot, Marquering, Reneman, Caan), Amsterdam UMC Location AMC, University of Amsterdam, Amsterdam; Brain Imaging, Amsterdam Neuroscience, Amsterdam (Poirot, Mutsaerts, Reneman, Caan); Department of Psychiatry, Radboudumc, Nijmegen, the Netherlands (Ruhe); Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands (Ruhe); Department of Radiology and Nuclear Medicine, Amsterdam UMC location, Vrije Universiteit Amsterdam, Amsterdam (Mutsaerts); Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen (Maximov, Bjørnerud); Division of Radiology, Vestfold Hospital Trust, Tønsberg, Norway (Groote, Caan); Computational Radiology and Artificial Intelligence, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo (Groote, Bjørnerud); Department of Psychology, University of Oslo, Oslo (Bjørnerud)
| | - Henk-Jan M M Mutsaerts
- Department of Radiology and Nuclear Medicine (Poirot, Ruhe, Marquering, Reneman) and Department of Biomedical Engineering and Physics (Poirot, Marquering, Reneman, Caan), Amsterdam UMC Location AMC, University of Amsterdam, Amsterdam; Brain Imaging, Amsterdam Neuroscience, Amsterdam (Poirot, Mutsaerts, Reneman, Caan); Department of Psychiatry, Radboudumc, Nijmegen, the Netherlands (Ruhe); Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands (Ruhe); Department of Radiology and Nuclear Medicine, Amsterdam UMC location, Vrije Universiteit Amsterdam, Amsterdam (Mutsaerts); Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen (Maximov, Bjørnerud); Division of Radiology, Vestfold Hospital Trust, Tønsberg, Norway (Groote, Caan); Computational Radiology and Artificial Intelligence, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo (Groote, Bjørnerud); Department of Psychology, University of Oslo, Oslo (Bjørnerud)
| | - Ivan I Maximov
- Department of Radiology and Nuclear Medicine (Poirot, Ruhe, Marquering, Reneman) and Department of Biomedical Engineering and Physics (Poirot, Marquering, Reneman, Caan), Amsterdam UMC Location AMC, University of Amsterdam, Amsterdam; Brain Imaging, Amsterdam Neuroscience, Amsterdam (Poirot, Mutsaerts, Reneman, Caan); Department of Psychiatry, Radboudumc, Nijmegen, the Netherlands (Ruhe); Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands (Ruhe); Department of Radiology and Nuclear Medicine, Amsterdam UMC location, Vrije Universiteit Amsterdam, Amsterdam (Mutsaerts); Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen (Maximov, Bjørnerud); Division of Radiology, Vestfold Hospital Trust, Tønsberg, Norway (Groote, Caan); Computational Radiology and Artificial Intelligence, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo (Groote, Bjørnerud); Department of Psychology, University of Oslo, Oslo (Bjørnerud)
| | - Inge R Groote
- Department of Radiology and Nuclear Medicine (Poirot, Ruhe, Marquering, Reneman) and Department of Biomedical Engineering and Physics (Poirot, Marquering, Reneman, Caan), Amsterdam UMC Location AMC, University of Amsterdam, Amsterdam; Brain Imaging, Amsterdam Neuroscience, Amsterdam (Poirot, Mutsaerts, Reneman, Caan); Department of Psychiatry, Radboudumc, Nijmegen, the Netherlands (Ruhe); Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands (Ruhe); Department of Radiology and Nuclear Medicine, Amsterdam UMC location, Vrije Universiteit Amsterdam, Amsterdam (Mutsaerts); Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen (Maximov, Bjørnerud); Division of Radiology, Vestfold Hospital Trust, Tønsberg, Norway (Groote, Caan); Computational Radiology and Artificial Intelligence, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo (Groote, Bjørnerud); Department of Psychology, University of Oslo, Oslo (Bjørnerud)
| | - Atle Bjørnerud
- Department of Radiology and Nuclear Medicine (Poirot, Ruhe, Marquering, Reneman) and Department of Biomedical Engineering and Physics (Poirot, Marquering, Reneman, Caan), Amsterdam UMC Location AMC, University of Amsterdam, Amsterdam; Brain Imaging, Amsterdam Neuroscience, Amsterdam (Poirot, Mutsaerts, Reneman, Caan); Department of Psychiatry, Radboudumc, Nijmegen, the Netherlands (Ruhe); Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands (Ruhe); Department of Radiology and Nuclear Medicine, Amsterdam UMC location, Vrije Universiteit Amsterdam, Amsterdam (Mutsaerts); Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen (Maximov, Bjørnerud); Division of Radiology, Vestfold Hospital Trust, Tønsberg, Norway (Groote, Caan); Computational Radiology and Artificial Intelligence, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo (Groote, Bjørnerud); Department of Psychology, University of Oslo, Oslo (Bjørnerud)
| | - Henk A Marquering
- Department of Radiology and Nuclear Medicine (Poirot, Ruhe, Marquering, Reneman) and Department of Biomedical Engineering and Physics (Poirot, Marquering, Reneman, Caan), Amsterdam UMC Location AMC, University of Amsterdam, Amsterdam; Brain Imaging, Amsterdam Neuroscience, Amsterdam (Poirot, Mutsaerts, Reneman, Caan); Department of Psychiatry, Radboudumc, Nijmegen, the Netherlands (Ruhe); Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands (Ruhe); Department of Radiology and Nuclear Medicine, Amsterdam UMC location, Vrije Universiteit Amsterdam, Amsterdam (Mutsaerts); Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen (Maximov, Bjørnerud); Division of Radiology, Vestfold Hospital Trust, Tønsberg, Norway (Groote, Caan); Computational Radiology and Artificial Intelligence, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo (Groote, Bjørnerud); Department of Psychology, University of Oslo, Oslo (Bjørnerud)
| | - Liesbeth Reneman
- Department of Radiology and Nuclear Medicine (Poirot, Ruhe, Marquering, Reneman) and Department of Biomedical Engineering and Physics (Poirot, Marquering, Reneman, Caan), Amsterdam UMC Location AMC, University of Amsterdam, Amsterdam; Brain Imaging, Amsterdam Neuroscience, Amsterdam (Poirot, Mutsaerts, Reneman, Caan); Department of Psychiatry, Radboudumc, Nijmegen, the Netherlands (Ruhe); Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands (Ruhe); Department of Radiology and Nuclear Medicine, Amsterdam UMC location, Vrije Universiteit Amsterdam, Amsterdam (Mutsaerts); Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen (Maximov, Bjørnerud); Division of Radiology, Vestfold Hospital Trust, Tønsberg, Norway (Groote, Caan); Computational Radiology and Artificial Intelligence, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo (Groote, Bjørnerud); Department of Psychology, University of Oslo, Oslo (Bjørnerud)
| | - Matthan W A Caan
- Department of Radiology and Nuclear Medicine (Poirot, Ruhe, Marquering, Reneman) and Department of Biomedical Engineering and Physics (Poirot, Marquering, Reneman, Caan), Amsterdam UMC Location AMC, University of Amsterdam, Amsterdam; Brain Imaging, Amsterdam Neuroscience, Amsterdam (Poirot, Mutsaerts, Reneman, Caan); Department of Psychiatry, Radboudumc, Nijmegen, the Netherlands (Ruhe); Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands (Ruhe); Department of Radiology and Nuclear Medicine, Amsterdam UMC location, Vrije Universiteit Amsterdam, Amsterdam (Mutsaerts); Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen (Maximov, Bjørnerud); Division of Radiology, Vestfold Hospital Trust, Tønsberg, Norway (Groote, Caan); Computational Radiology and Artificial Intelligence, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo (Groote, Bjørnerud); Department of Psychology, University of Oslo, Oslo (Bjørnerud)
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24
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Feng Y, Chandio BQ, Villalon-Reina JE, Benavidez S, Chattopadhyay T, Chehrzadeh S, Laltoo E, Thomopoulos SI, Joshi H, Venkatasubramanian G, John JP, Jahanshad N, Thompson PM. Deep Normative Tractometry for Identifying Joint White Matter Macro- and Micro-structural Abnormalities in Alzheimer's Disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.05.578943. [PMID: 38370817 PMCID: PMC10871218 DOI: 10.1101/2024.02.05.578943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
This study introduces the Deep Normative Tractometry (DNT) framework, that encodes the joint distribution of both macrostructural and microstructural profiles of the brain white matter tracts through a variational autoencoder (VAE). By training on data from healthy controls, DNT learns the normative distribution of tract data, and can delineate along-tract micro-and macro-structural abnormalities. Leveraging a large sample size via generative pre-training, we assess DNT's generalizability using transfer learning on data from an independent cohort acquired in India. Our findings demonstrate DNT's capacity to detect widespread diffusivity abnormalities along tracts in mild cognitive impairment and Alzheimer's disease, aligning closely with results from the Bundle Analytics (BUAN) tractometry pipeline. By incorporating tract geometry information, DNT may be able to distinguish disease-related abnormalities in anisotropy from tract macrostructure, and shows promise in enhancing fine-scale mapping and detection of white matter alterations in neurodegenerative conditions.
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Affiliation(s)
- Yixue Feng
- 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
| | - Bramsh Q Chandio
- 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
| | - Julio E Villalon-Reina
- 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
| | - Sebastian Benavidez
- 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
| | - Tamoghna Chattopadhyay
- 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
| | - Sasha Chehrzadeh
- 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
| | - Emily Laltoo
- 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
| | - Sophia I Thomopoulos
- 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
| | - Himanshu Joshi
- Multimodal Brain Image Analysis Laboratory, Translational Psychiatry Laboratory, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, Karnataka, India
| | - Ganesan Venkatasubramanian
- Multimodal Brain Image Analysis Laboratory, Translational Psychiatry Laboratory, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, Karnataka, India
| | - John P John
- Multimodal Brain Image Analysis Laboratory, Translational Psychiatry Laboratory, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, Karnataka, India
| | - Neda Jahanshad
- 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
| | - 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
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25
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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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26
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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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27
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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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28
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Savage HS, Mulders PCR, van Eijndhoven PFP, van Oort J, Tendolkar I, Vrijsen JN, Beckmann CF, Marquand AF. Unpacking the functional heterogeneity of the Emotional Face Matching Task: a normative modelling approach. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.27.534351. [PMID: 37034628 PMCID: PMC10081244 DOI: 10.1101/2023.03.27.534351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Functional neuroimaging has contributed substantially to understanding brain function but is dominated by group analyses that index only a fraction of the variation in these data. It is increasingly clear that parsing the underlying heterogeneity is crucial to understand individual differences and the impact of different task manipulations. We estimate large-scale (N=7728) normative models of task-evoked activation during the Emotional Face Matching Task, which enables us to bind heterogeneous datasets to a common reference and dissect heterogeneity underlying group-level analyses. We apply this model to a heterogenous patient cohort, to map individual differences between patients with one or more mental health diagnoses relative to the reference cohort and determine multivariate associations with transdiagnostic symptom domains. For the face>shapes contrast, patients have a higher frequency of extreme deviations which are spatially heterogeneous. In contrast, normative models for faces>baseline have greater predictive value for individuals' transdiagnostic functioning.
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Affiliation(s)
- Hannah S. Savage
- Donders Institute of Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Peter C. R. Mulders
- Donders Institute of Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Philip F. P. van Eijndhoven
- Donders Institute of Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Jasper van Oort
- Donders Institute of Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Indira Tendolkar
- Donders Institute of Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Janna N. Vrijsen
- Donders Institute of Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Centre, Nijmegen, The Netherlands
- Depression Expertise Centre, Pro Persona Mental Health Care, Nijmegen, The Netherlands
| | - Christian F. Beckmann
- Donders Institute of 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, UK
| | - Andre F. Marquand
- Donders Institute of Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands
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29
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Leiberg K, de Tisi J, Duncan JS, Little B, Taylor PN, Vos SB, Winston GP, Mota B, Wang Y. Effects of anterior temporal lobe resection on cortical morphology. Cortex 2023; 166:233-242. [PMID: 37399617 DOI: 10.1016/j.cortex.2023.04.018] [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: 01/19/2023] [Revised: 04/11/2023] [Accepted: 04/16/2023] [Indexed: 07/05/2023]
Abstract
Neuroimaging can capture brain restructuring after anterior temporal lobe resection (ATLR), a surgical procedure to treat drug-resistant temporal lobe epilepsy (TLE). Here, we examine the effects of this surgery on brain morphology measured in recently-proposed independent variables. We studied 101 individuals with TLE (55 left, 46 right onset) who underwent ATLR. For each individual we considered one pre-surgical MRI and one follow-up MRI 2-13 months after surgery. We used a surface-based method to locally compute traditional morphological variables, and the independent measures K, I, and S, where K measures white matter tension, I captures isometric scaling, and S contains the remaining information about cortical shape. A normative model trained on data from 924 healthy controls was used to debias the data and account for healthy ageing effects occurring during scans. A SurfStat random field theory clustering approach assessed changes across the cortex caused by ATLR. Compared to preoperative data, surgery had marked effects on all morphological measures. Ipsilateral effects were located in the orbitofrontal and inferior frontal gyri, the pre- and postcentral gyri and supramarginal gyrus, and the lateral occipital gyrus and lingual cortex. Contralateral effects were in the lateral occipital gyrus, and inferior frontal gyrus and frontal pole. The restructuring following ATLR is reflected in widespread morphological changes, mainly in regions near the resection, but also remotely in regions that are structurally connected to the anterior temporal lobe. The causes could include mechanical effects, Wallerian degeneration, or compensatory plasticity. The study of independent measures revealed additional effects compared to traditional measures.
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Affiliation(s)
- Karoline Leiberg
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle Upon Tyne, UK.
| | - Jane de Tisi
- Department of Clinical & Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
| | - John S Duncan
- Department of Clinical & Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK; Chalfont Centre for Epilepsy, Chalfont St Peter, UK
| | - Bethany Little
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle Upon Tyne, UK; Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Peter N Taylor
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle Upon Tyne, UK; Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom; Queen Square Institute of Neurology, University College London, Queen Square, London, UK
| | - Sjoerd B Vos
- Queen Square Institute of Neurology, University College London, Queen Square, London, UK; Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL, UK; Centre for Medical Image Computing, University College London, London, UK; Centre for Microscopy, Characterisation, And Analysis, The University of Western Australia, Nedlands, Australia
| | - Gavin P Winston
- Department of Clinical & Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK; MRI Unit, Epilepsy Society, Buckinghamshire, UK; Division of Neurology, Department of Medicine, Queen's University, Kingston, Ontario, Canada
| | - Bruno Mota
- MetaBIO Lab, Instituto de Física, Universidade Federal Do Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
| | - Yujiang Wang
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle Upon Tyne, UK; Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom; Queen Square Institute of Neurology, University College London, Queen Square, London, UK.
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30
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Verdi S, Rutherford S, Fraza C, Tosun D, Altmann A, Raket LL, Schott JM, Marquand AF, Cole JH. Personalising Alzheimer's Disease progression using brain atrophy markers. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.15.23291418. [PMID: 37398392 PMCID: PMC10312850 DOI: 10.1101/2023.06.15.23291418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
INTRODUCTION Neuroanatomical normative modelling can capture individual variability in Alzheimer's Disease (AD). We used neuroanatomical normative modelling to track individuals' disease progression in people with mild cognitive impairment (MCI) and patients with AD. METHODS Cortical thickness and subcortical volume neuroanatomical normative models were generated using healthy controls (n~58k). These models were used to calculate regional Z-scores in 4361 T1-weighted MRI time-series scans. Regions with Z-scores <-1.96 were classified as outliers and mapped on the brain, and also summarised by total outlier count (tOC). RESULTS Rate of change in tOC increased in AD and in people with MCI who converted to AD and correlated with multiple non-imaging markers. Moreover, a higher annual rate of change in tOC increased the risk of MCI progression to AD. Brain Z-score maps showed that the hippocampus had the highest rate of atrophy change. CONCLUSIONS Individual-level atrophy rates can be tracked by using regional outlier maps and tOC.
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Affiliation(s)
- Serena Verdi
- Centre for Medical Image Computing, University College London, London, UK
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Saige Rutherford
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, 6525EN, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, 6525EN, the Netherlands
| | - Charlotte Fraza
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, 6525EN, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, 6525EN, the Netherlands
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Andre Altmann
- Centre for Medical Image Computing, University College London, London, UK
| | - Lars Lau Raket
- Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Jonathan M Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Andre F Marquand
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, 6525EN, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, 6525EN, the Netherlands
| | - James H Cole
- Centre for Medical Image Computing, University College London, London, UK
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
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