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Farrugia C, Galdi P, Irazu IA, Scerri K, Bajada CJ. Local gradient analysis of human brain function using the Vogt-Bailey Index. Brain Struct Funct 2024; 229:497-512. [PMID: 38294531 PMCID: PMC10917869 DOI: 10.1007/s00429-023-02751-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 12/09/2023] [Indexed: 02/01/2024]
Abstract
In this work, we take a closer look at the Vogt-Bailey (VB) index, proposed in Bajada et al. (NeuroImage 221:117140, 2020) as a tool for studying local functional homogeneity in the human cortex. We interpret the VB index in terms of the minimum ratio cut, a scaled cut-set weight that indicates whether a network can easily be disconnected into two parts having a comparable number of nodes. In our case, the nodes of the network consist of a brain vertex/voxel and its neighbours, and a given edge is weighted according to the affinity of the nodes it connects (as reflected by the modified Pearson correlation between their fMRI time series). Consequently, the minimum ratio cut quantifies the degree of small-scale similarity in brain activity: the greater the similarity, the 'heavier' the edges and the more difficult it is to disconnect the network, hence the higher the value of the minimum ratio cut. We compare the performance of the VB index with that of the Regional Homogeneity (ReHo) algorithm, commonly used to assess whether voxels in close proximity have synchronised fMRI signals, and find that the VB index is uniquely placed to detect sharp changes in the (local) functional organization of the human cortex.
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Affiliation(s)
- Christine Farrugia
- Faculty of Engineering, L-Università ta' Malta, Msida, Malta.
- University of Malta Magnetic Resonance Imaging Platform (UMRI), L-Università ta' Malta, Msida, Malta.
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK.
| | - Paola Galdi
- School of Informatics, The University of Edinburgh, Edinburgh, UK
| | | | - Kenneth Scerri
- Faculty of Engineering, L-Università ta' Malta, Msida, Malta
| | - Claude J Bajada
- University of Malta Magnetic Resonance Imaging Platform (UMRI), L-Università ta' Malta, Msida, Malta.
- Faculty of Medicine and Surgery, L-Università ta' Malta, Msida, Malta.
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Bleimeister I, Avni I, Granovetter M, Meiri G, Ilan M, Michaelovski A, Menashe I, Behrmann M, Dinstein I. Idiosyncratic pupil regulation in autistic children. bioRxiv 2024:2024.01.10.575072. [PMID: 38260528 PMCID: PMC10802609 DOI: 10.1101/2024.01.10.575072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Recent neuroimaging and eye tracking studies have suggested that children with autism spectrum disorder (ASD) may exhibit more variable and idiosyncratic brain responses and eye movements than typically developing (TD) children. Here we extended this research for the first time to pupillometry recordings. We successfully completed pupillometry recordings with 103 children (66 with ASD), 4.5-years-old on average, who viewed three 90 second movies, twice. We extracted their pupillary time-course for each movie, capturing their stimulus evoked pupillary responses. We then computed the correlation between the time-course of each child and those of all others in their group. This yielded an average inter-subject correlation value per child, representing how similar their pupillary responses were to all others in their group. ASD participants exhibited significantly weaker inter-subject correlations than TD participants, reliably across all three movies. Differences across groups were largest in responses to a naturalistic movie containing footage of a social interaction between two TD children. This measure enabled classification of ASD and TD children with a sensitivity of 0.82 and specificity of 0.73 when trained and tested on independent datasets. Using the largest ASD pupillometry dataset to date, we demonstrate the utility of a new technique for measuring the idiosyncrasy of pupil regulation, which can be completed even by young children with co-occurring intellectual disability. These findings reveal that a considerable subgroup of ASD children have significantly more unstable, idiosyncratic pupil regulation than TD children, indicative of more variable, weakly regulated, underlying neural activity.
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Affiliation(s)
- Isabel Bleimeister
- Psychology Department, Ben Gurion University of the Negev, Beer Sheva, Israel 84105
- Azrieli National Centre for Autism and Neurodevelopment Research, Ben Gurion University of the Negev, Beer Sheva, Israel
| | - Inbar Avni
- Azrieli National Centre for Autism and Neurodevelopment Research, Ben Gurion University of the Negev, Beer Sheva, Israel
- Cognitive and Brain Sciences Department, Ben Gurion University of the Negev, Beer Sheva, Israel
| | - Michael Granovetter
- Department of Ophthalmology, University of Pittsburgh, Pittsburgh, U.S.A 15213
| | - Gal Meiri
- Azrieli National Centre for Autism and Neurodevelopment Research, Ben Gurion University of the Negev, Beer Sheva, Israel
- Pre-school Psychiatry Unit, Soroka Medical Center, Beer Sheva, Israel 84105
| | - Michal Ilan
- Psychology Department, Ben Gurion University of the Negev, Beer Sheva, Israel 84105
- Azrieli National Centre for Autism and Neurodevelopment Research, Ben Gurion University of the Negev, Beer Sheva, Israel
- Pre-school Psychiatry Unit, Soroka Medical Center, Beer Sheva, Israel 84105
| | - Analya Michaelovski
- Azrieli National Centre for Autism and Neurodevelopment Research, Ben Gurion University of the Negev, Beer Sheva, Israel
- Child Development Institute, Soroka Medical Center, Beer Sheva, Israel 84105
| | - Idan Menashe
- Azrieli National Centre for Autism and Neurodevelopment Research, Ben Gurion University of the Negev, Beer Sheva, Israel
- Public Health Department, Ben-Gurion University, Beer Sheva, Israel 84105
| | - Marlene Behrmann
- Department of Ophthalmology, University of Pittsburgh, Pittsburgh, U.S.A 15213
| | - Ilan Dinstein
- Psychology Department, Ben Gurion University of the Negev, Beer Sheva, Israel 84105
- Azrieli National Centre for Autism and Neurodevelopment Research, Ben Gurion University of the Negev, Beer Sheva, Israel
- Cognitive and Brain Sciences Department, Ben Gurion University of the Negev, Beer Sheva, Israel
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Chen T, You W, Zhang L, Ye W, Feng J, Lu J, Lv J, Tang Y, Wei D, Gui S, Jiang J, Wang Z, Wang Y, Zhao Q, Zhang Y, Qu J, Li C, Jiang Y, Zhang X, Li Y, Guan S. Automated anatomical labeling of the intracranial arteries via deep learning in computed tomography angiography. Front Physiol 2024; 14:1310357. [PMID: 38239880 PMCID: PMC10794642 DOI: 10.3389/fphys.2023.1310357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 11/28/2023] [Indexed: 01/22/2024] Open
Abstract
Background and purpose: Anatomical labeling of the cerebral vasculature is a crucial topic in determining the morphological nature and characterizing the vital variations of vessels, yet precise labeling of the intracranial arteries is time-consuming and challenging, given anatomical structural variability and surging imaging data. We present a U-Net-based deep learning (DL) model to automatically label detailed anatomical segments in computed tomography angiography (CTA) for the first time. The trained DL algorithm was further tested on a clinically relevant set for the localization of intracranial aneurysms (IAs). Methods: 457 examinations with varying degrees of arterial stenosis were used to train, validate, and test the model, aiming to automatically label 42 segments of the intracranial arteries [e.g., 7 segments of the internal carotid artery (ICA)]. Evaluation metrics included Dice similarity coefficient (DSC), mean surface distance (MSD), and Hausdorff distance (HD). Additionally, 96 examinations containing at least one IA were enrolled to assess the model's potential in enhancing clinicians' precision in IA localization. A total of 5 clinicians with different experience levels participated as readers in the clinical experiment and identified the precise location of IA without and with algorithm assistance, where there was a washout period of 14 days between two interpretations. The diagnostic accuracy, time, and mean interrater agreement (Fleiss' Kappa) were calculated to assess the differences in clinical performance of clinicians. Results: The proposed model exhibited notable labeling performance on 42 segments that included 7 anatomical segments of ICA, with the mean DSC of 0.88, MSD of 0.82 mm and HD of 6.59 mm. Furthermore, the model demonstrated superior labeling performance in healthy subjects compared to patients with stenosis (DSC: 0.91 vs. 0.89, p < 0.05; HD: 4.75 vs. 6.19, p < 0.05). Concurrently, clinicians with model predictions achieved significant improvements when interpreting the precise location of IA. The clinicians' mean accuracy increased by 0.04 (p = 0.003), mean time to diagnosis reduced by 9.76 s (p < 0.001), and mean interrater agreement (Fleiss' Kappa) increased by 0.07 (p = 0.029). Conclusion: Our model stands proficient for labeling intracranial arteries using the largest CTA dataset. Crucially, it demonstrates clinical utility, helping prioritize the patients with high risks and ease clinical workload.
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Affiliation(s)
- Ting Chen
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital and Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- School of Biomedical Engineering, Capital Medical University, Beijing, China
| | - Wei You
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital and Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Department of Neurointerventional Engineering and Technology, Beijing Engineering Research Center (NO: BG0287), Beijing, China
| | - Liyuan Zhang
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Wanxing Ye
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Junqiang Feng
- Department of Neurosurgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Jing Lu
- Department of Radiology, Third Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Jian Lv
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital and Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Yudi Tang
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital and Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Dachao Wei
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital and Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Siming Gui
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital and Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Jia Jiang
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital and Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Ziyao Wang
- Department of Interventional Neuroradiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Yanwen Wang
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Qi Zhao
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Yifan Zhang
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Junda Qu
- School of Biomedical Engineering, Capital Medical University, Beijing, China
- Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Chunlin Li
- School of Biomedical Engineering, Capital Medical University, Beijing, China
- Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Yuhua Jiang
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital and Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Xu Zhang
- School of Biomedical Engineering, Capital Medical University, Beijing, China
- Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Youxiang Li
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital and Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- School of Biomedical Engineering, Capital Medical University, Beijing, China
- Department of Neurointerventional Engineering and Technology, Beijing Engineering Research Center (NO: BG0287), Beijing, China
| | - Sheng Guan
- Department of Interventional Neuroradiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
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Salloum-Asfar S, Zawia N, Abdulla SA. Retracing our steps: A review on autism research in children, its limitation and impending pharmacological interventions. Pharmacol Ther 2024; 253:108564. [PMID: 38008401 DOI: 10.1016/j.pharmthera.2023.108564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 11/16/2023] [Accepted: 11/19/2023] [Indexed: 11/28/2023]
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by three core impairments: impaired communication, impaired reciprocal social interaction, and restricted, repetitive, and stereotypical behavior patterns. Spectrum refers to the heterogeneity of presentation, severity of symptoms, and medical comorbidities associated with ASD. Among the most common underlying medical conditions are attention-deficit/hyperactivity disorder (ADHD), anxiety, depression, epilepsy, digestive disorders, metabolic disorders, and immune disorders. At present, in the absence of an objective and accurate diagnosis of ASD, such as a blood test, pharmacological management remains a challenge. There are no approved medications to treat the core symptoms of the disorder and behavioral interventions are typically used as first line treatment. Additionally, psychotropic drugs with different mechanisms of action have been approved to reduce associated symptoms and comorbidities, including aripiprazole, risperidone, and haloperidol for irritability and aggression, methylphenidate, atomoxetine, clonidine, and guanfacine for ADHD, and melatonin for sleep disturbances. The purpose of this review is to emphasize that it is imperative to develop objective, personalized diagnostic kits in order to tailor and individualize treatment strategies, as well as to describe the current pharmacological management options available in clinical practice and new prospects that may be helpful in managing ASD's core symptoms.
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Affiliation(s)
- Salam Salloum-Asfar
- Neurological Disorders Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar.
| | - Nasser Zawia
- Neurological Disorders Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Sara A Abdulla
- Neurological Disorders Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar.
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Park BY, Benkarim O, Weber CF, Kebets V, Fett S, Yoo S, Martino AD, Milham MP, Misic B, Valk SL, Hong SJ, Bernhardt BC. Connectome-wide structure-function coupling models implicate polysynaptic alterations in autism. Neuroimage 2024; 285:120481. [PMID: 38043839 DOI: 10.1016/j.neuroimage.2023.120481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Revised: 11/29/2023] [Accepted: 12/01/2023] [Indexed: 12/05/2023] Open
Abstract
Autism spectrum disorder (ASD) is one of the most common neurodevelopmental diagnoses. Although incompletely understood, structural and functional network alterations are increasingly recognized to be at the core of the condition. We utilized multimodal imaging and connectivity modeling to study structure-function coupling in ASD and probed mono- and polysynaptic mechanisms on structurally-governed network function. We examined multimodal magnetic resonance imaging data in 80 ASD and 61 neurotypical controls from the Autism Brain Imaging Data Exchange (ABIDE) II initiative. We predicted intrinsic functional connectivity from structural connectivity data in each participant using a Riemannian optimization procedure that varies the times that simulated signals can unfold along tractography-derived personalized connectomes. In both ASD and neurotypical controls, we observed improved structure-function prediction at longer diffusion time scales, indicating better modeling of brain function when polysynaptic mechanisms are accounted for. Prediction accuracy differences (∆prediction accuracy) were marked in transmodal association systems, such as the default mode network, in both neurotypical controls and ASD. Differences were, however, lower in ASD in a polysynaptic regime at higher simulated diffusion times. We compared regional differences in ∆prediction accuracy between both groups to assess the impact of polysynaptic communication on structure-function coupling. This analysis revealed that between-group differences in ∆prediction accuracy followed a sensory-to-transmodal cortical hierarchy, with an increased gap between controls and ASD in transmodal compared to sensory/motor systems. Multivariate associative techniques revealed that structure-function differences reflected inter-individual differences in autistic symptoms and verbal as well as non-verbal intelligence. Our network modeling approach sheds light on atypical structure-function coupling in autism, and suggests that polysynaptic network mechanisms are implicated in the condition and that these can help explain its wide range of associated symptoms.
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Affiliation(s)
- Bo-Yong Park
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada; Department of Data Science, Inha University, Incheon, South Korea; Department of Statistics and Data Science, Inha University, Incheon, South Korea; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea.
| | - Oualid Benkarim
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Clara F Weber
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Valeria Kebets
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Serena Fett
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Seulki Yoo
- Convergence Research Institute, Sungkyunkwan University, Suwon, South Korea
| | - Adriana Di Martino
- Center for the Developing Brain, Child Mind Institute, New York, United States
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, United States
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Sofie L Valk
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Seok-Jun Hong
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea; Center for the Developing Brain, Child Mind Institute, New York, United States; Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
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Knodt AR, Elliott ML, Whitman ET, Winn A, Addae A, Ireland D, Poulton R, Ramrakha S, Caspi A, Moffitt TE, Hariri AR. Test-retest reliability and predictive utility of a macroscale principal functional connectivity gradient. Hum Brain Mapp 2023; 44:6399-6417. [PMID: 37851700 PMCID: PMC10681655 DOI: 10.1002/hbm.26517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 09/23/2023] [Accepted: 09/30/2023] [Indexed: 10/20/2023] Open
Abstract
Mapping individual differences in brain function has been hampered by poor reliability as well as limited interpretability. Leveraging patterns of brain-wide functional connectivity (FC) offers some promise in this endeavor. In particular, a macroscale principal FC gradient that recapitulates a hierarchical organization spanning molecular, cellular, and circuit level features along a sensory-to-association cortical axis has emerged as both a parsimonious and interpretable measure of individual differences in behavior. However, the measurement reliabilities of this FC gradient have not been fully evaluated. Here, we assess the reliabilities of both global and regional principal FC gradient measures using test-retest data from the young adult Human Connectome Project (HCP-YA) and the Dunedin Study. Analyses revealed that the reliabilities of principal FC gradient measures were (1) consistently higher than those for traditional edge-wise FC measures, (2) higher for FC measures derived from general FC (GFC) in comparison with resting-state FC, and (3) higher for longer scan lengths. We additionally examined the relative utility of these principal FC gradient measures in predicting cognition and aging in both datasets as well as the HCP-aging dataset. These analyses revealed that regional FC gradient measures and global gradient range were significantly associated with aging in all three datasets, and moderately associated with cognition in the HCP-YA and Dunedin Study datasets, reflecting contractions and expansions of the cortical hierarchy, respectively. Collectively, these results demonstrate that measures of the principal FC gradient, especially derived using GFC, effectively capture a reliable feature of the human brain subject to interpretable and biologically meaningful individual variation, offering some advantages over traditional edge-wise FC measures in the search for brain-behavior associations.
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Affiliation(s)
- Annchen R. Knodt
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth CarolinaUSA
| | - Maxwell L. Elliott
- Department of Psychology, Center for Brain ScienceHarvard UniversityCambridgeMassachusettsUSA
| | - Ethan T. Whitman
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth CarolinaUSA
| | - Alex Winn
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth CarolinaUSA
| | - Angela Addae
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth CarolinaUSA
| | - David Ireland
- Dunedin Multidisciplinary Health and Development Research Unit, Department of PsychologyUniversity of OtagoDunedinNew Zealand
| | - Richie Poulton
- Dunedin Multidisciplinary Health and Development Research Unit, Department of PsychologyUniversity of OtagoDunedinNew Zealand
| | - Sandhya Ramrakha
- Dunedin Multidisciplinary Health and Development Research Unit, Department of PsychologyUniversity of OtagoDunedinNew Zealand
| | - Avshalom Caspi
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth CarolinaUSA
- Department of Psychiatry and Behavioral SciencesDuke UniversityDurhamNorth CarolinaUSA
- Institute of Psychiatry, Psychology, and NeuroscienceKing's College LondonLondonUK
| | - Terrie E. Moffitt
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth CarolinaUSA
- Department of Psychiatry and Behavioral SciencesDuke UniversityDurhamNorth CarolinaUSA
- Institute of Psychiatry, Psychology, and NeuroscienceKing's College LondonLondonUK
| | - Ahmad R. Hariri
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth CarolinaUSA
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Weber CF, Lake EMR, Haider SP, Mozayan A, Bobba PS, Mukherjee P, Scheinost D, Constable RT, Ment L, Payabvash S. Autism spectrum disorder-specific changes in white matter connectome edge density based on functionally defined nodes. Front Neurosci 2023; 17:1285396. [PMID: 38075286 PMCID: PMC10702224 DOI: 10.3389/fnins.2023.1285396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 10/30/2023] [Indexed: 02/12/2024] Open
Abstract
Introduction Autism spectrum disorder (ASD) is associated with both functional and microstructural connectome disruptions. We deployed a novel methodology using functionally defined nodes to guide white matter (WM) tractography and identify ASD-related microstructural connectome changes across the lifespan. Methods We used diffusion tensor imaging and clinical data from four studies in the national database for autism research (NDAR) including 155 infants, 102 toddlers, 230 adolescents, and 96 young adults - of whom 264 (45%) were diagnosed with ASD. We applied cortical nodes from a prior fMRI study identifying regions related to symptom severity scores and used these seeds to construct WM fiber tracts as connectome Edge Density (ED) maps. Resulting ED maps were assessed for between-group differences using voxel-wise and tract-based analysis. We then examined the association of ASD diagnosis with ED driven from functional nodes generated from different sensitivity thresholds. Results In ED derived from functionally guided tractography, we identified ASD-related changes in infants (pFDR ≤ 0.001-0.483). Overall, more wide-spread ASD-related differences were detectable in ED based on functional nodes with positive symptom correlation than negative correlation to ASD, and stricter thresholds for functional nodes resulted in stronger correlation with ASD among infants (z = -6.413 to 6.666, pFDR ≤ 0.001-0.968). Voxel-wise analysis revealed wide-spread ED reductions in central WM tracts of toddlers, adolescents, and adults. Discussion We detected early changes of aberrant WM development in infants developing ASD when generating microstructural connectome ED map with cortical nodes defined by functional imaging. These were not evident when applying structurally defined nodes, suggesting that functionally guided DTI-based tractography can help identify early ASD-related WM disruptions between cortical regions exhibiting abnormal connectivity patterns later in life. Furthermore, our results suggest a benefit of involving functionally informed nodes in diffusion imaging-based probabilistic tractography, and underline that different age cohorts can benefit from age- and brain development-adapted image processing protocols.
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Affiliation(s)
- Clara F Weber
- Yale University School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, United States
- Social Neuroscience Lab, Department of Psychiatry and Psychotherapy, Lübeck University, Lübeck, Germany
- Center of Brain, Behavior and Metabolism (CBBM), Lübeck University, Lübeck, Germany
| | - Evelyn M R Lake
- Yale University School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, United States
| | - Stefan P Haider
- Yale University School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, United States
- Department of Otorhinolaryngology, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Ali Mozayan
- Yale University School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, United States
| | - Pratheek S Bobba
- Yale University School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, United States
| | - Pratik Mukherjee
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Dustin Scheinost
- Yale University School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, United States
| | - Robert T Constable
- Yale University School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, United States
| | - Laura Ment
- Yale University School of Medicine, Department of Pediatrics and Neurology, New Haven, CT, United States
| | - Seyedmehdi Payabvash
- Yale University School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, United States
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Mitchell B, Thor S, Piper M. Cellular and molecular functions of SETD2 in the central nervous system. J Cell Sci 2023; 136:jcs261406. [PMID: 37921122 DOI: 10.1242/jcs.261406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2023] Open
Abstract
The covalent modification of histones is critical for many biological functions in mammals, including gene regulation and chromatin structure. Posttranslational histone modifications are added and removed by specialised 'writer' and 'eraser' enzymes, respectively. One such writer protein implicated in a wide range of cellular processes is SET domain-containing 2 (SETD2), a histone methyltransferase that catalyses the trimethylation of lysine 36 on histone H3 (H3K36me3). Recently, SETD2 has also been found to modify proteins other than histones, including actin and tubulin. The emerging roles of SETD2 in the development and function of the mammalian central nervous system (CNS) are of particular interest as several SETD2 variants have been implicated in neurodevelopmental disorders, such as autism spectrum disorder and the overgrowth disorder Luscan-Lumish syndrome. Here, we summarise the numerous roles of SETD2 in mammalian cellular functions and development, with a focus on the CNS. We also provide an overview of the consequences of SETD2 variants in human disease and discuss future directions for understanding essential cellular functions of SETD2.
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Affiliation(s)
- Benjamin Mitchell
- The School of Biomedical Sciences, University of Queensland, Brisbane, Queensland 4072, Australia
| | - Stefan Thor
- The School of Biomedical Sciences, University of Queensland, Brisbane, Queensland 4072, Australia
| | - Michael Piper
- The School of Biomedical Sciences, University of Queensland, Brisbane, Queensland 4072, Australia
- Queensland Brain Institute, University of Queensland, Brisbane, Queensland 4072, Australia
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9
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Wan B, Hong SJ, Bethlehem RAI, Floris DL, Bernhardt BC, Valk SL. Diverging asymmetry of intrinsic functional organization in autism. Mol Psychiatry 2023; 28:4331-4341. [PMID: 37587246 PMCID: PMC10827663 DOI: 10.1038/s41380-023-02220-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 08/01/2023] [Accepted: 08/04/2023] [Indexed: 08/18/2023]
Abstract
Autism is a neurodevelopmental condition involving atypical sensory-perceptual functions together with language and socio-cognitive deficits. Previous work has reported subtle alterations in the asymmetry of brain structure and reduced laterality of functional activation in individuals with autism relative to non-autistic individuals (NAI). However, whether functional asymmetries show altered intrinsic systematic organization in autism remains unclear. Here, we examined inter- and intra-hemispheric asymmetry of intrinsic functional gradients capturing connectome organization along three axes, stretching between sensory-default, somatomotor-visual, and default-multiple demand networks, to study system-level hemispheric imbalances in autism. We observed decreased leftward functional asymmetry of language network organization in individuals with autism, relative to NAI. Whereas language network asymmetry varied across age groups in NAI, this was not the case in autism, suggesting atypical functional laterality in autism may result from altered developmental trajectories. Finally, we observed that intra- but not inter-hemispheric features were predictive of the severity of autistic traits. Our findings illustrate how regional and patterned functional lateralization is altered in autism at the system level. Such differences may be rooted in atypical developmental trajectories of functional organization asymmetry in autism.
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Affiliation(s)
- Bin Wan
- Otto Hahn Research Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
- International Max Planck Research School on Neuroscience of Communication: Function, Structure, and Plasticity (IMPRS NeuroCom), Leipzig, Germany.
- Department of Cognitive Neurology, University Hospital Leipzig and Faculty of Medicine, University of Leipzig, Leipzig, Germany.
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany.
| | - Seok-Jun Hong
- Centre for Neuroscience Imaging Research, Institute for Basic Science, Department of Global Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
| | | | - Dorothea L Floris
- Department of Psychology, University of Zürich, Zürich, Switzerland
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montréal Neurological Institute and Hospital, McGill University, Montréal, QC, Canada
| | - Sofie L Valk
- Otto Hahn Research Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany.
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
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10
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Javaheripour N, Wagner G, de la Cruz F, Walter M, Szycik GR, Tietze FA. Altered brain network organization in adults with Asperger's syndrome: decreased connectome transitivity and assortativity with increased global efficiency. Front Psychiatry 2023; 14:1223147. [PMID: 37701094 PMCID: PMC10494541 DOI: 10.3389/fpsyt.2023.1223147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 07/26/2023] [Indexed: 09/14/2023] Open
Abstract
Introduction Autism spectrum disorder (ASD) is a neurodevelopmental disorder that persists into adulthood with both social and cognitive disturbances. Asperger's syndrome (AS) was a distinguished subcategory of autism in the DSM-IV-TR defined by specific symptoms including difficulties in social interactions, inflexible thinking patterns, and repetitive behaviour without any delay in language or cognitive development. Studying the functional brain organization of individuals with these specific symptoms may help to better understand Autism spectrum symptoms. Methods The aim of this study is therefore to investigate functional connectivity as well as functional network organization characteristics using graph-theory measures of the whole brain in male adults with AS compared to healthy controls (HC) (AS: n = 15, age range 21-55 (mean ± sd: 39.5 ± 11.6), HC: n = 15, age range 22-57 [mean ± sd: 33.5 ± 8.5]). Results No significant differences were found when comparing the region-by-region connectivity at the whole-brain level between the AS group and HC. However, measures of "transitivity," which reflect local information processing and functional segregation, and "assortativity," indicating network resilience, were reduced in the AS group compared to HC. On the other hand, global efficiency, which represents the overall effectiveness and speed of information transfer across the entire brain network, was increased in the AS group. Discussion Our findings suggest that individuals with AS may have alterations in the organization and functioning of brain networks, which could contribute to the distinctive cognitive and behavioural features associated with this condition. We suggest further research to explore the association between these altered functional patterns in brain networks and specific behavioral traits observed in individuals with AS, which could provide valuable insights into the underlying mechanisms of its symptomatology.
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Affiliation(s)
- Nooshin Javaheripour
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Gerd Wagner
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
- Center for Intervention and Research on Adaptive and Maladaptive Brain Circuits Underlying Mental Health (C-I-R-C), Jena, Germany
| | - Feliberto de la Cruz
- Department of Psychosomatic Medicine and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Martin Walter
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
- Center for Intervention and Research on Adaptive and Maladaptive Brain Circuits Underlying Mental Health (C-I-R-C), Jena, Germany
- Clinical Affective Neuroimaging Laboratory (CANLAB), Magdeburg, Germany
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
- Leibniz Institute for Neurobiology, Magdeburg, Germany
- Center for Behavioral Brain Sciences, Magdeburg, Germany
- German Center for Mental Health (DZPG), Jena, Germany
| | - Gregor R. Szycik
- Department of Psychiatry and Psychotherapy, Hannover Medical School, Hannover, Germany
| | - Fabian-Alexander Tietze
- Department of Psychiatry and Psychotherapy, Jüdisches Krankenhaus Berlin—Berlin Jewish Hospital, Academic Teaching Hospital of the Charité—Universitätsmedizin Berlin, Berlin, Germany
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11
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Cotter M, Reisli S, Francisco AA, Wakim KM, Oakes L, Crosse MJ, Foxe JJ, Molholm S. Neurophysiological measures of auditory sensory processing are associated with adaptive behavior in children with Autism Spectrum Disorder. J Neurodev Disord 2023; 15:11. [PMID: 37005597 PMCID: PMC10068141 DOI: 10.1186/s11689-023-09480-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 02/27/2023] [Indexed: 04/04/2023] Open
Abstract
BACKGROUND Atypical auditory cortical processing is consistently found in scalp electrophysiological and magnetoencephalographic studies of Autism Spectrum Disorder (ASD), and may provide a marker of neuropathological brain development. However, the relationship between atypical cortical processing of auditory information and adaptive behavior in ASD is not yet well understood. METHODS We sought to test the hypothesis that early (100-175 ms) auditory processing in ASD is related to everyday adaptive behavior through the examination of auditory event-related potentials (AEPs) in response to simple tones and Vineland Adaptive Behavior Scales in a large cohort of children with ASD (N = 84), aged 6-17, and in age- and IQ- matched neurotypically (NT) developing controls (N = 132). RESULTS Statistical analyses revealed significant group differences in early AEPs over temporal scalp regions (150-175 ms), and the expected rightward lateralization of the AEP (100-125 ms and 150-175 ms) to tonal stimuli in both groups. Lateralization of the AEP (150-175 ms) was significantly associated with adaptive functioning in the socialization domain. CONCLUSIONS These results lend support to the hypothesis that atypical processing of sensory information is related to everyday adaptive behavior in autism.
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Affiliation(s)
- Mairin Cotter
- The Cognitive Neurophysiology Laboratory, Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
- Department of Psychology, Fordham University, Bronx, NY, 10458, USA
| | - Seydanur Reisli
- The Cognitive Neurophysiology Laboratory, Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
- Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Ana Alves Francisco
- The Cognitive Neurophysiology Laboratory, Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Kathryn-Mary Wakim
- The Cognitive Neurophysiology Laboratory, Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
- Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Leona Oakes
- The Cognitive Neurophysiology Laboratory, Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
- The Frederick J. and Marion A. Schindler Cognitive Neurophysiology Laboratory, The Ernest J. Del Monte Institute for Neuroscience, Department of Neuroscience, University of Rochester School of Medicine and Dentistry, Rochester, NY, 14642, USA
| | - Michael J Crosse
- Segotia, Galway, Ireland
- Trinity Centre for Biomedical Engineering, Trinity College Dublin, Dublin, Ireland
| | - John J Foxe
- The Cognitive Neurophysiology Laboratory, Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
- Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
- The Frederick J. and Marion A. Schindler Cognitive Neurophysiology Laboratory, The Ernest J. Del Monte Institute for Neuroscience, Department of Neuroscience, University of Rochester School of Medicine and Dentistry, Rochester, NY, 14642, USA
| | - Sophie Molholm
- The Cognitive Neurophysiology Laboratory, Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.
- Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.
- The Frederick J. and Marion A. Schindler Cognitive Neurophysiology Laboratory, The Ernest J. Del Monte Institute for Neuroscience, Department of Neuroscience, University of Rochester School of Medicine and Dentistry, Rochester, NY, 14642, USA.
- Department of Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.
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12
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Larivière S, Bayrak Ş, Vos de Wael R, Benkarim O, Herholz P, Rodriguez-Cruces R, Paquola C, Hong SJ, Misic B, Evans AC, Valk SL, Bernhardt BC. BrainStat: A toolbox for brain-wide statistics and multimodal feature associations. Neuroimage 2023; 266:119807. [PMID: 36513290 DOI: 10.1016/j.neuroimage.2022.119807] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 11/28/2022] [Accepted: 12/09/2022] [Indexed: 12/14/2022] Open
Abstract
Analysis and interpretation of neuroimaging datasets has become a multidisciplinary endeavor, relying not only on statistical methods, but increasingly on associations with respect to other brain-derived features such as gene expression, histological data, and functional as well as cognitive architectures. Here, we introduce BrainStat - a toolbox for (i) univariate and multivariate linear models in volumetric and surface-based brain imaging datasets, and (ii) multidomain feature association of results with respect to spatial maps of post-mortem gene expression and histology, task-based fMRI meta-analysis, as well as resting-state fMRI motifs across several common surface templates. The combination of statistics and feature associations into a turnkey toolbox streamlines analytical processes and accelerates cross-modal research. The toolbox is implemented in both Python and MATLAB, two widely used programming languages in the neuroimaging and neuroinformatics communities. BrainStat is openly available and complemented by an expandable documentation.
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Affiliation(s)
- Sara Larivière
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Şeyma Bayrak
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Reinder Vos de Wael
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Oualid Benkarim
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Peer Herholz
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Raul Rodriguez-Cruces
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Casey Paquola
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Germany
| | - Seok-Jun Hong
- Child Mind Institute, New York, USA; Center for Neuroscience Imaging Research, Institute for Basic Science, and Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Alan C Evans
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Sofie L Valk
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Germany.
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.
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13
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Aglinskas A, Schwartz E, Anzellotti S. Disentangling disorder-specific variation is key for precision psychiatry in autism. Front Behav Neurosci 2023; 17:1121017. [PMID: 37025108 PMCID: PMC10070721 DOI: 10.3389/fnbeh.2023.1121017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 03/03/2023] [Indexed: 04/08/2023] Open
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14
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Abstract
Gradient-based analyses have contributed to the description of cerebellar functional neuroanatomy. More recently, functional gradients of the cerebellum have been used as a multi-purpose tool for neuroimaging research. Here, we provide an overview of the many practical applications of cerebellar functional gradient analyses. These practical applications include examination of intra-cerebellar and cerebellar-extracerebellar organization; transformation of functional gradients into parcellations with discrete borders; projection of functional gradients calculated within cerebellar structures to other extracerebellar structures; interpretation of cerebellar neuroimaging findings using qualitative and quantitative methods; detection of differences in patient populations; and other more complex practical applications of cerebellar gradient-based analyses. This review may serve as an introduction and catalog of options for neuroscientists who wish to design and analyze imaging studies using functional gradients of the cerebellum.
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Affiliation(s)
- Xavier Guell
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, MA, 20114, USA.
- Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research at MIT, Massachusetts Institute of Technology, Cambridge, MA, USA.
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15
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Horien C, Floris DL, Greene AS, Noble S, Rolison M, Tejavibulya L, O'Connor D, McPartland JC, Scheinost D, Chawarska K, Lake EMR, Constable RT. Functional Connectome-Based Predictive Modeling in Autism. Biol Psychiatry 2022; 92:626-642. [PMID: 35690495 PMCID: PMC10948028 DOI: 10.1016/j.biopsych.2022.04.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 04/14/2022] [Accepted: 04/17/2022] [Indexed: 01/08/2023]
Abstract
Autism is a heterogeneous neurodevelopmental condition, and functional magnetic resonance imaging-based studies have helped advance our understanding of its effects on brain network activity. We review how predictive modeling, using measures of functional connectivity and symptoms, has helped reveal key insights into this condition. We discuss how different prediction frameworks can further our understanding of the brain-based features that underlie complex autism symptomatology and consider how predictive models may be used in clinical settings. Throughout, we highlight aspects of study interpretation, such as data decay and sampling biases, that require consideration within the context of this condition. We close by suggesting exciting future directions for predictive modeling in autism.
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Affiliation(s)
- Corey Horien
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, Connecticut; MD-PhD Program, Yale School of Medicine, New Haven, Connecticut.
| | - Dorothea L Floris
- Methods of Plasticity Research, Department of Psychology, University of Zürich, Zurich, Switzerland; Donders Center for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Abigail S Greene
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, Connecticut; MD-PhD Program, Yale School of Medicine, New Haven, Connecticut
| | - Stephanie Noble
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Max Rolison
- Yale Child Study Center, New Haven, Connecticut
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, Connecticut
| | - David O'Connor
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut
| | - James C McPartland
- Department of Psychology, Yale University, New Haven, Connecticut; Yale Child Study Center, New Haven, Connecticut
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, Connecticut; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut; Department of Biomedical Engineering, Yale University, New Haven, Connecticut; Department of Statistics and Data Science, Yale University, New Haven, Connecticut; Yale Child Study Center, New Haven, Connecticut
| | - Katarzyna Chawarska
- Department of Pediatrics, Yale School of Medicine, New Haven, Connecticut; Department of Statistics and Data Science, Yale University, New Haven, Connecticut; Yale Child Study Center, New Haven, Connecticut
| | - Evelyn M R Lake
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, Connecticut; Department of Neurosurgery, Yale School of Medicine, New Haven, Connecticut; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut; Department of Biomedical Engineering, Yale University, New Haven, Connecticut.
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16
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Keeratitanont K, Theerakulpisut D, Auvichayapat N, Suphakunpinyo C, Patjanasoontorn N, Tiamkao S, Tepmongkol S, Khiewvan B, Raruenrom Y, Srisuruk P, Paholpak S, Auvichayapat P. Brain laterality evaluated by F-18 fluorodeoxyglucose positron emission computed tomography in autism spectrum disorders. Front Mol Neurosci 2022; 15:901016. [PMID: 36034502 PMCID: PMC9399910 DOI: 10.3389/fnmol.2022.901016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 07/13/2022] [Indexed: 12/04/2022] Open
Abstract
Background and rationale Autism spectrum disorder (ASD) is a neuropsychiatric disorder that has no curative treatment. Little is known about the brain laterality in patients with ASD. F-18 fluorodeoxyglucose positron emission computed tomography (F-18 FDG PET/CT) is a neuroimaging technique that is suitable for ASD owing to its ability to detect whole brain functional abnormalities in a short time and is feasible in ASD patients. The purpose of this study was to evaluate brain laterality using F-18 FDG PET/CT in patients with high-functioning ASD. Materials and methods This case-control study recruited eight ASD patients who met the DSM-5 criteria, the recorded data of eight controls matched for age, sex, and handedness were also enrolled. The resting state of brain glucose metabolism in the regions of interest (ROIs) was analyzed using the Q.Brain software. Brain glucose metabolism and laterality index in each ROI of ASD patients were compared with those of the controls. The pattern of brain metabolism was analyzed using visual analysis and is reported in the data description. Results The ASD group’s overall brain glucose metabolism was lower than that of the control group in both the left and right hemispheres, with mean differences of 1.54 and 1.21, respectively. We found statistically lower mean glucose metabolism for ASD patients than controls in the left prefrontal lateral (Z = 1.96, p = 0.049). The left laterality index was found in nine ROIs for ASD and 11 ROIs for the control. The left laterality index in the ASD group was significantly lower than that in the control group in the prefrontal lateral (Z = 2.52, p = 0.012), precuneus (Z = 2.10, p = 0.036), and parietal inferior (Z = 1.96, p = 0.049) regions. Conclusion Individuals with ASD have lower brain glucose metabolism than control. In addition, the number of ROIs for left laterality index in the ASD group was lower than control. Left laterality defects may be one of the causes of ASD. This knowledge can be useful in the treatment of ASD by increasing the left-brain metabolism. This trial was registered in the Thai Clinical Trials Registry (TCTR20210705005).
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Affiliation(s)
- Keattichai Keeratitanont
- Division of Nuclear Medicine, Department of Radiology, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
- Noninvasive Brain Stimulation Research Group of Thailand, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
| | - Daris Theerakulpisut
- Division of Nuclear Medicine, Department of Radiology, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
| | - Narong Auvichayapat
- Noninvasive Brain Stimulation Research Group of Thailand, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
- Department of Pediatrics, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
| | - Chanyut Suphakunpinyo
- Noninvasive Brain Stimulation Research Group of Thailand, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
- Department of Pediatrics, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
| | - Niramol Patjanasoontorn
- Noninvasive Brain Stimulation Research Group of Thailand, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
- Department of Psychiatry, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
| | - Somsak Tiamkao
- Noninvasive Brain Stimulation Research Group of Thailand, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
- Department of Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
| | - Supatporn Tepmongkol
- Division of Nuclear Medicine, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Chulalongkorn University Biomedical Imaging Group (CUBIG), Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Benjapa Khiewvan
- Division of Nuclear Medicine, Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Yutapong Raruenrom
- Division of Nuclear Medicine, Department of Radiology, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
| | - Piyawan Srisuruk
- Department of Educational Psychology and Counseling, Faculty of Education, Khon Kaen University, Khon Kaen, Thailand
- Research and Service Institute for Autism, Khon Kaen University, Khon Kaen, Thailand
| | - Suchat Paholpak
- Department of Psychiatry, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
- Research and Service Institute for Autism, Khon Kaen University, Khon Kaen, Thailand
| | - Paradee Auvichayapat
- Noninvasive Brain Stimulation Research Group of Thailand, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
- Research and Service Institute for Autism, Khon Kaen University, Khon Kaen, Thailand
- *Correspondence: Paradee Auvichayapat,
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17
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York NS, Sanchez-Arias JC, McAdam ACH, Rivera JE, Arbour LT, Swayne LA. Mechanisms underlying the role of ankyrin-B in cardiac and neurological health and disease. Front Cardiovasc Med 2022; 9:964675. [PMID: 35990955 PMCID: PMC9386378 DOI: 10.3389/fcvm.2022.964675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 07/11/2022] [Indexed: 11/13/2022] Open
Abstract
The ANK2 gene encodes for ankyrin-B (ANKB), one of 3 members of the ankyrin family of proteins, whose name is derived from the Greek word for anchor. ANKB was originally identified in the brain (B denotes “brain”) but has become most widely known for its role in cardiomyocytes as a scaffolding protein for ion channels and transporters, as well as an interacting protein for structural and signaling proteins. Certain loss-of-function ANK2 variants are associated with a primarily cardiac-presenting autosomal-dominant condition with incomplete penetrance and variable expressivity characterized by a predisposition to supraventricular and ventricular arrhythmias, arrhythmogenic cardiomyopathy, congenital and adult-onset structural heart disease, and sudden death. Another independent group of ANK2 variants are associated with increased risk for distinct neurological phenotypes, including epilepsy and autism spectrum disorders. The mechanisms underlying ANKB's roles in cells in health and disease are not fully understood; however, several clues from a range of molecular and cell biological studies have emerged. Notably, ANKB exhibits several isoforms that have different cell-type–, tissue–, and developmental stage– expression profiles. Given the conservation within ankyrins across evolution, model organism studies have enabled the discovery of several ankyrin roles that could shed important light on ANKB protein-protein interactions in heart and brain cells related to the regulation of cellular polarity, organization, calcium homeostasis, and glucose and fat metabolism. Along with this accumulation of evidence suggesting a diversity of important ANKB cellular functions, there is an on-going debate on the role of ANKB in disease. We currently have limited understanding of how these cellular functions link to disease risk. To this end, this review will examine evidence for the cellular roles of ANKB and the potential contribution of ANKB functional variants to disease risk and presentation. This contribution will highlight the impact of ANKB dysfunction on cardiac and neuronal cells and the significance of understanding the role of ANKB variants in disease.
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Affiliation(s)
- Nicole S. York
- Division of Medical Sciences, University of Victoria, Victoria, BC, Canada
| | | | - Alexa C. H. McAdam
- Department of Medical Genetics, University of British Columbia, Victoria, BC, Canada
| | - Joel E. Rivera
- Division of Medical Sciences, University of Victoria, Victoria, BC, Canada
| | - Laura T. Arbour
- Division of Medical Sciences, University of Victoria, Victoria, BC, Canada
- Department of Medical Genetics, University of British Columbia, Victoria, BC, Canada
- *Correspondence: Laura T. Arbour
| | - Leigh Anne Swayne
- Division of Medical Sciences, University of Victoria, Victoria, BC, Canada
- Department of Cellular and Physiological Sciences and Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
- Leigh Anne Swayne
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18
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Pines AR, Larsen B, Cui Z, Sydnor VJ, Bertolero MA, Adebimpe A, Alexander-Bloch AF, Davatzikos C, Fair DA, Gur RC, Gur RE, Li H, Milham MP, Moore TM, Murtha K, Parkes L, Thompson-Schill SL, Shanmugan S, Shinohara RT, Weinstein SM, Bassett DS, Fan Y, Satterthwaite TD. Dissociable multi-scale patterns of development in personalized brain networks. Nat Commun 2022; 13:2647. [PMID: 35551181 PMCID: PMC9098559 DOI: 10.1038/s41467-022-30244-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 04/21/2022] [Indexed: 11/24/2022] Open
Abstract
The brain is organized into networks at multiple resolutions, or scales, yet studies of functional network development typically focus on a single scale. Here, we derive personalized functional networks across 29 scales in a large sample of youths (n = 693, ages 8-23 years) to identify multi-scale patterns of network re-organization related to neurocognitive development. We found that developmental shifts in inter-network coupling reflect and strengthen a functional hierarchy of cortical organization. Furthermore, we observed that scale-dependent effects were present in lower-order, unimodal networks, but not higher-order, transmodal networks. Finally, we found that network maturation had clear behavioral relevance: the development of coupling in unimodal and transmodal networks are dissociably related to the emergence of executive function. These results suggest that the development of functional brain networks align with and refine a hierarchy linked to cognition.
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Affiliation(s)
- Adam R Pines
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Bart Larsen
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Zaixu Cui
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Chinese Institute for Brain Research, 102206, Beijing, China
| | - Valerie J Sydnor
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Maxwell A Bertolero
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Azeez Adebimpe
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Aaron F Alexander-Bloch
- Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Christos Davatzikos
- Department of Radiology, the University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Damien A Fair
- Department of Pediatrics, College of Education and Human Development, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Ruben C Gur
- Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Radiology, the University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Raquel E Gur
- Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Hongming Li
- Department of Radiology, the University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Michael P Milham
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, 10962, USA.,Center for the Developing Brain, Child Mind Institute, New York City, NY, USA
| | - Tyler M Moore
- Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Kristin Murtha
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Linden Parkes
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | | | - Sheila Shanmugan
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sarah M Weinstein
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Danielle S Bassett
- Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Santa Fe Institute, Santa Fe, NM, 87051, USA
| | - Yong Fan
- Department of Radiology, the University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Theodore D Satterthwaite
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA. .,Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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19
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Benkarim O, Paquola C, Park BY, Kebets V, Hong SJ, Vos de Wael R, Zhang S, Yeo BTT, Eickenberg M, Ge T, Poline JB, Bernhardt BC, Bzdok D. Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging. PLoS Biol 2022; 20:e3001627. [PMID: 35486643 PMCID: PMC9094526 DOI: 10.1371/journal.pbio.3001627] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 05/11/2022] [Accepted: 04/11/2022] [Indexed: 12/18/2022] Open
Abstract
Brain imaging research enjoys increasing adoption of supervised machine learning for single-participant disease classification. Yet, the success of these algorithms likely depends on population diversity, including demographic differences and other factors that may be outside of primary scientific interest. Here, we capitalize on propensity scores as a composite confound index to quantify diversity due to major sources of population variation. We delineate the impact of population heterogeneity on the predictive accuracy and pattern stability in 2 separate clinical cohorts: the Autism Brain Imaging Data Exchange (ABIDE, n = 297) and the Healthy Brain Network (HBN, n = 551). Across various analysis scenarios, our results uncover the extent to which cross-validated prediction performances are interlocked with diversity. The instability of extracted brain patterns attributable to diversity is located preferentially in regions part of the default mode network. Collectively, our findings highlight the limitations of prevailing deconfounding practices in mitigating the full consequences of population diversity.
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Affiliation(s)
- Oualid Benkarim
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Canada
| | - Casey Paquola
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Canada
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Jülich, Germany
| | - Bo-yong Park
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Canada
- Department of Data Science, Inha University, Incheon, South Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Sungkyunkwan University, Suwon, South Korea
| | - Valeria Kebets
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Canada
| | - Seok-Jun Hong
- Center for Neuroscience Imaging Research, Institute for Basic Science, Sungkyunkwan University, Suwon, South Korea
- Center for the Developing Brain, Child Mind Institute, New York, New York, United States of America
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
| | - Reinder Vos de Wael
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Canada
| | - Shaoshi Zhang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | - B. T. Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | | | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Jean-Baptiste Poline
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Canada
| | - Boris C. Bernhardt
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Canada
| | - Danilo Bzdok
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Canada
- Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, Canada
- School of Computer Science, McGill University, Montreal, Canada
- Mila—Quebec Artificial Intelligence Institute, Montreal, Canada
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20
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Affiliation(s)
- Boris C Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
| | | | - Shella Keilholz
- Biomedical Engineering, Emory University / Georgia Institute of Technology, Atlanta, Georgia
| | - Daniel S Margulies
- Integrative Neuroscience and Cognition Center, Centre National de la Recherche Scientifique (CNRS) and Université de Paris, Paris, France
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21
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Beversdorf DQ, Anagnostou E, Hardan A, Wang P, Erickson CA, Frazier TW, Veenstra-VanderWeele J. Editorial: Precision medicine approaches for heterogeneous conditions such as autism spectrum disorders (The need for a biomarker exploration phase in clinical trials - Phase 2m). Front Psychiatry 2022; 13:1079006. [PMID: 36741580 PMCID: PMC9893852 DOI: 10.3389/fpsyt.2022.1079006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 12/13/2022] [Indexed: 01/20/2023] Open
Affiliation(s)
- David Q Beversdorf
- Departments of Radiology, Neurology, and Psychological Sciences, William and Nancy Thompson Endowed Chair in Radiology, University of Missouri, Columbia, MO, United States
| | - Evdokia Anagnostou
- Holland Bloorview Kids Rehabilitation Hospital, University of Toronto, Toronto, ON, Canada
| | - Antonio Hardan
- Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, United States
| | - Paul Wang
- Clinical Research Associates LLC, Simons Foundation, Department of Pediatrics, Yale University School of Medicine, New Haven, CT, United States
| | - Craig A Erickson
- Division of Child and Adolescent Psychiatry, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Thomas W Frazier
- Department of Psychology, John Carroll University, University Heights, OH, United States.,Department of Pediatrics, State University of New York Upstate Medical University, Syracuse, NY, United States
| | - Jeremy Veenstra-VanderWeele
- Departments of Psychiatry and Pediatrics, New York State Psychiatric Institute, Columbia University, New York, NY, United States.,NewYork-Presbyterian Center for Autism and the Developing Brain, New York, NY, United States
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