1
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García-San-Martín N, Bethlehem RAI, Mihalik A, Seidlitz J, Sebenius I, Alemán-Morillo C, Dorfschmidt L, Shafiei G, Ortiz-García de la Foz V, Merritt K, David A, Morgan SE, Ruiz-Veguilla M, Ayesa-Arriola R, Vázquez-Bourgon J, Alexander-Bloch A, Misic B, Bullmore ET, Suckling J, Crespo-Facorro B, Romero-García R. Molecular and micro-architectural mapping of gray matter alterations in psychosis. Mol Psychiatry 2024:10.1038/s41380-024-02724-0. [PMID: 39266711 DOI: 10.1038/s41380-024-02724-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 08/21/2024] [Accepted: 08/23/2024] [Indexed: 09/14/2024]
Abstract
The psychosis spectrum encompasses a heterogeneous range of clinical conditions associated with abnormal brain development. Detecting patterns of atypical neuroanatomical maturation across psychiatric disorders requires an interpretable metric standardized by age-, sex- and site-effect. The molecular and micro-architectural attributes that account for these deviations in brain structure from typical neurodevelopment are still unknown. Here, we aggregate structural magnetic resonance imaging data from 38,696 healthy controls (HC) and 1256 psychosis-related conditions, including first-degree relatives of schizophrenia (SCZ) and schizoaffective disorder (SAD) patients (n = 160), individuals who had psychotic experiences (n = 157), patients who experienced a first episode of psychosis (FEP, n = 352), and individuals with chronic SCZ or SAD (n = 587). Using a normative modeling approach, we generated centile scores for cortical gray matter (GM) phenotypes, identifying deviations in regional volumes below the expected trajectory for all conditions, with a greater impact on the clinically diagnosed ones, FEP and chronic. Additionally, we mapped 46 neurobiological features from healthy individuals (including neurotransmitters, cell types, layer thickness, microstructure, cortical expansion, and metabolism) to these abnormal centiles using a multivariate approach. Results revealed that neurobiological features were highly co-localized with centile deviations, where metabolism (e.g., cerebral metabolic rate of oxygen (CMRGlu) and cerebral blood flow (CBF)) and neurotransmitter concentrations (e.g., serotonin (5-HT) and acetylcholine (α4β2) receptors) showed the most consistent spatial overlap with abnormal GM trajectories. Taken together these findings shed light on the vulnerability factors that may underlie atypical brain maturation during different stages of psychosis.
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Affiliation(s)
| | | | - Agoston Mihalik
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Jakob Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Isaac Sebenius
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | | | - Lena Dorfschmidt
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Golia Shafiei
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Víctor Ortiz-García de la Foz
- Department of Psychiatry, Marqués de Valdecilla University Hospital, IDIVAL, School of Medicine, University of Cantabria, Santander, Spain
- Biomedical Research Center in Mental Health Network (CIBERSAM), Health Institute Carlos III, Madrid, Spain
| | - Kate Merritt
- Division of Psychiatry, Institute of Mental Health, UCL, London, UK
| | - Anthony David
- Division of Psychiatry, Institute of Mental Health, UCL, London, UK
| | - Sarah E Morgan
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Miguel Ruiz-Veguilla
- Biomedical Research Center in Mental Health Network (CIBERSAM), Health Institute Carlos III, Madrid, Spain
- Mental Health Service, Virgen del Rocío University Hospital, Seville, Spain
- Instituto de Biomedicina de Sevilla (IBiS) HUVR/CSIC, University of Seville, Seville, Spain
| | - Rosa Ayesa-Arriola
- Department of Psychiatry, Marqués de Valdecilla University Hospital, IDIVAL, School of Medicine, University of Cantabria, Santander, Spain
- Biomedical Research Center in Mental Health Network (CIBERSAM), Health Institute Carlos III, Madrid, Spain
| | - Javier Vázquez-Bourgon
- Department of Psychiatry, Marqués de Valdecilla University Hospital, IDIVAL, School of Medicine, University of Cantabria, Santander, Spain
- Biomedical Research Center in Mental Health Network (CIBERSAM), Health Institute Carlos III, Madrid, Spain
| | - Aaron Alexander-Bloch
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Bratislav Misic
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | | | - John Suckling
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Peterborough NHS Foundation Trust, Peterborough, UK
| | - Benedicto Crespo-Facorro
- Department of Psychiatry, Marqués de Valdecilla University Hospital, IDIVAL, School of Medicine, University of Cantabria, Santander, Spain
- Biomedical Research Center in Mental Health Network (CIBERSAM), Health Institute Carlos III, Madrid, Spain
- Mental Health Service, Virgen del Rocío University Hospital, Seville, Spain
- Instituto de Biomedicina de Sevilla (IBiS) HUVR/CSIC, University of Seville, Seville, Spain
| | - Rafael Romero-García
- Department of Medical Physiology and Biophysics, University of Seville, Seville, Spain.
- Department of Psychiatry, University of Cambridge, Cambridge, UK.
- Biomedical Research Center in Mental Health Network (CIBERSAM), Health Institute Carlos III, Madrid, Spain.
- Instituto de Biomedicina de Sevilla (IBiS) HUVR/CSIC, University of Seville, Seville, Spain.
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2
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Ding H, Zhang Y, Xie Y, Du X, Ji Y, Lin L, Chang Z, Zhang B, Liang M, Yu C, Qin W. Individualized Texture Similarity Network in Schizophrenia. Biol Psychiatry 2024; 96:176-187. [PMID: 38218309 DOI: 10.1016/j.biopsych.2023.12.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 12/14/2023] [Accepted: 12/23/2023] [Indexed: 01/15/2024]
Abstract
BACKGROUND Structural covariance network disruption has been considered an important pathophysiological indicator for schizophrenia. Here, we introduced a novel individualized structural covariance network measure, referred to as a texture similarity network (TSN), and hypothesized that the TSN could reliably reveal unique intersubject heterogeneity and complex dysconnectivity patterns in schizophrenia. METHODS The TSN was constructed by measuring the covariance of 180 three-dimensional voxelwise gray-level co-occurrence matrix feature maps between brain areas in each participant. We first tested the validity and reproducibility of the TSN in characterizing the intersubject variability in 2 longitudinal test-retest healthy cohorts. The TSN was further applied to elucidate intersubject variability and dysconnectivity patterns in 10 schizophrenia case-control datasets (609 schizophrenia cases vs. 579 controls) as well as in a first-episode depression dataset (69 patients with depression vs. 69 control participants). RESULTS The test-retest analysis demonstrated higher TSN intersubject than intrasubject variability. Moreover, the TSN reliably revealed higher intersubject variability in both chronic and first-episode schizophrenia, but not in depression. The TSN also reproducibly detected coexistent increased and decreased TSN strength in widespread brain areas, increased global small-worldness, and the coexistence of both structural hyposynchronization in the central networks and hypersynchronization in peripheral networks in patients with schizophrenia but not in patients with depression. Finally, aberrant intersubject variability and covariance strength patterns revealed by the TSN showed a missing or weak correlation with other individualized structural covariance network measures, functional connectivity, and regional volume changes. CONCLUSIONS These findings support the reliability of a TSN in revealing unique structural heterogeneity and complex dysconnectivity in patients with schizophrenia.
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Affiliation(s)
- Hao Ding
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China; School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Yu Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yingying Xie
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Xiaotong Du
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yi Ji
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Liyuan Lin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Zhongyu Chang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Bin Zhang
- Tianjin Anding Hospital, Tianjin Medical University, Tianjin, China; Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Meng Liang
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Chunshui Yu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China; School of Medical Imaging, Tianjin Medical University, Tianjin, China.
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.
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3
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Jiang Y, Luo C, Wang J, Palaniyappan L, Chang X, Xiang S, Zhang J, Duan M, Huang H, Gaser C, Nemoto K, Miura K, Hashimoto R, Westlye LT, Richard G, Fernandez-Cabello S, Parker N, Andreassen OA, Kircher T, Nenadić I, Stein F, Thomas-Odenthal F, Teutenberg L, Usemann P, Dannlowski U, Hahn T, Grotegerd D, Meinert S, Lencer R, Tang Y, Zhang T, Li C, Yue W, Zhang Y, Yu X, Zhou E, Lin CP, Tsai SJ, Rodrigue AL, Glahn D, Pearlson G, Blangero J, Karuk A, Pomarol-Clotet E, Salvador R, Fuentes-Claramonte P, Garcia-León MÁ, Spalletta G, Piras F, Vecchio D, Banaj N, Cheng J, Liu Z, Yang J, Gonul AS, Uslu O, Burhanoglu BB, Uyar Demir A, Rootes-Murdy K, Calhoun VD, Sim K, Green M, Quidé Y, Chung YC, Kim WS, Sponheim SR, Demro C, Ramsay IS, Iasevoli F, de Bartolomeis A, Barone A, Ciccarelli M, Brunetti A, Cocozza S, Pontillo G, Tranfa M, Park MTM, Kirschner M, Georgiadis F, Kaiser S, Van Rheenen TE, Rossell SL, Hughes M, Woods W, Carruthers SP, Sumner P, Ringin E, Spaniel F, Skoch A, Tomecek D, Homan P, Homan S, Omlor W, Cecere G, Nguyen DD, Preda A, Thomopoulos SI, Jahanshad N, Cui LB, Yao D, Thompson PM, Turner JA, van Erp TGM, Cheng W, Feng J. Neurostructural subgroup in 4291 individuals with schizophrenia identified using the subtype and stage inference algorithm. Nat Commun 2024; 15:5996. [PMID: 39013848 PMCID: PMC11252381 DOI: 10.1038/s41467-024-50267-3] [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: 10/17/2023] [Accepted: 07/03/2024] [Indexed: 07/18/2024] Open
Abstract
Machine learning can be used to define subtypes of psychiatric conditions based on shared biological foundations of mental disorders. Here we analyzed cross-sectional brain images from 4,222 individuals with schizophrenia and 7038 healthy subjects pooled across 41 international cohorts from the ENIGMA, non-ENIGMA cohorts and public datasets. Using the Subtype and Stage Inference (SuStaIn) algorithm, we identify two distinct neurostructural subgroups by mapping the spatial and temporal 'trajectory' of gray matter change in schizophrenia. Subgroup 1 was characterized by an early cortical-predominant loss with enlarged striatum, whereas subgroup 2 displayed an early subcortical-predominant loss in the hippocampus, striatum and other subcortical regions. We confirmed the reproducibility of the two neurostructural subtypes across various sample sites, including Europe, North America and East Asia. This imaging-based taxonomy holds the potential to identify individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.
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Affiliation(s)
- Yuchao Jiang
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technology, University of Electronic Science and Technology of China, Chengdu, China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Research Unit of NeuroInformation (2019RU035), Chinese Academy of Medical Sciences, Chengdu, China
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lena Palaniyappan
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montréal, Canada
| | - Xiao Chang
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Shitong Xiang
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Jie Zhang
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Mingjun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Huan Huang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Christian Gaser
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
- Department of Neurology, Jena University Hospital, Jena, Germany
- German Center for Mental Health (DZPG), Site Jena-Magdeburg-Halle, Magdeburg, Germany
| | - Kiyotaka Nemoto
- Department of Psychiatry, Division of Clinical Medicine, Institute of Medicine, University of Tsukuba, Tsukuba, Japan
| | - Kenichiro Miura
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Ryota Hashimoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Genevieve Richard
- Department of Psychology, University of Oslo, Oslo, Norway
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Sara Fernandez-Cabello
- Department of Psychology, University of Oslo, Oslo, Norway
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Nadine Parker
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Philipps Universität Marburg, Rudolf-Bultmann-Str. 8, Marburg, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, Philipps Universität Marburg, Rudolf-Bultmann-Str. 8, Marburg, Germany
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, Philipps Universität Marburg, Rudolf-Bultmann-Str. 8, Marburg, Germany
| | - Florian Thomas-Odenthal
- Department of Psychiatry and Psychotherapy, Philipps Universität Marburg, Rudolf-Bultmann-Str. 8, Marburg, Germany
| | - Lea Teutenberg
- Department of Psychiatry and Psychotherapy, Philipps Universität Marburg, Rudolf-Bultmann-Str. 8, Marburg, Germany
| | - Paula Usemann
- Department of Psychiatry and Psychotherapy, Philipps Universität Marburg, Rudolf-Bultmann-Str. 8, Marburg, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Institute for Translational Neuroscience, University of Münster, Münster, Germany
| | - Rebekka Lencer
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Department of Psychiatry and Psychotherapie and Center for Brain, Behavior and Metabolism, Lübeck University, Lübeck, Germany
- Institute for Transnational Psychiatry and Otto Creutzfeldt Center for Behavioral and Cognitive Neuroscience, University of Münster, Münster, Germany
| | - Yingying Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tianhong Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chunbo Li
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Weihua Yue
- 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), Beijing, PR China
- Chinese Institute for Brain Research, Beijing, PR China
- PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, PR China
| | - Yuyanan Zhang
- 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), Beijing, PR China
| | - Xin Yu
- 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), Beijing, PR China
| | - Enpeng Zhou
- 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), Beijing, PR China
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Shih-Jen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Amanda L Rodrigue
- Department of Psychiatry, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - David Glahn
- Department of Psychiatry, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Godfrey Pearlson
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA
| | - John Blangero
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas of the Rio Grande Valley, Brownsville, TX, USA
| | - Andriana Karuk
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Madrid, Spain
| | - Edith Pomarol-Clotet
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Madrid, Spain
| | - Raymond Salvador
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Madrid, Spain
| | - Paola Fuentes-Claramonte
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Madrid, Spain
| | - María Ángeles Garcia-León
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Madrid, Spain
| | - Gianfranco Spalletta
- Neuropsychiatry Laboratory, Department of Clinical Neuroscience and Neurorehabilitation, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Fabrizio Piras
- Neuropsychiatry Laboratory, Department of Clinical Neuroscience and Neurorehabilitation, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Daniela Vecchio
- Neuropsychiatry Laboratory, Department of Clinical Neuroscience and Neurorehabilitation, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Nerisa Banaj
- Neuropsychiatry Laboratory, Department of Clinical Neuroscience and Neurorehabilitation, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Jingliang Cheng
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhening Liu
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan, PR China
| | - Jie Yang
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan, PR China
| | - Ali Saffet Gonul
- Ege University School of Medicine Department of Psychiatry, SoCAT Lab, Izmir, Turkey
| | - Ozgul Uslu
- Ege University Institute of Health Sciences Department of Neuroscience, Izmir, Turkey
| | | | - Aslihan Uyar Demir
- Ege University School of Medicine Department of Psychiatry, SoCAT Lab, Izmir, Turkey
| | - Kelly Rootes-Murdy
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, GA, USA
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, GA, USA
| | - Kang Sim
- West Region, Institute of Mental Health, Singapore, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Melissa Green
- School of Clinical Medicine, University of New South Wales, SYD, Australia
| | - Yann Quidé
- School of Psychology, University of New South Wales, SYD, Australia
| | - Young Chul Chung
- Department of Psychiatry, Jeonbuk National University Hospital, Jeonju, Korea
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Woo-Sung Kim
- Department of Psychiatry, Jeonbuk National University Hospital, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Scott R Sponheim
- Minneapolis VA Medical Center, University of Minnesota, Minneapolis, MN, USA
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Caroline Demro
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Ian S Ramsay
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Felice Iasevoli
- Section of Psychiatry - Department of Neuroscience, University "Federico II", Naples, Italy
| | - Andrea de Bartolomeis
- Section of Psychiatry - Department of Neuroscience, University "Federico II", Naples, Italy
| | - Annarita Barone
- Section of Psychiatry - Department of Neuroscience, University "Federico II", Naples, Italy
| | - Mariateresa Ciccarelli
- Section of Psychiatry - Department of Neuroscience, University "Federico II", Naples, Italy
| | - Arturo Brunetti
- Department of Advanced Biomedical Sciences, University "Federico II", Naples, Italy
| | - Sirio Cocozza
- Department of Advanced Biomedical Sciences, University "Federico II", Naples, Italy
| | - Giuseppe Pontillo
- Department of Advanced Biomedical Sciences, University "Federico II", Naples, Italy
| | - Mario Tranfa
- Department of Advanced Biomedical Sciences, University "Federico II", Naples, Italy
| | - Min Tae M Park
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, TO, Canada
- Centre for Addiction and Mental Health, TO, Canada
| | - Matthias Kirschner
- Division of Adult Psychiatry, Department of Psychiatry, University Hospitals of Geneva, Geneva, Switzerland
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland
| | - Foivos Georgiadis
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland
| | - Stefan Kaiser
- Division of Adult Psychiatry, Department of Psychiatry, University Hospitals of Geneva, Geneva, Switzerland
| | - Tamsyn E Van Rheenen
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne, MEL, Australia
- Centre for Mental Health and Brain Sciences, School of Health Sciences, Swinburne University, MEL, Australia
| | - Susan L Rossell
- Centre for Mental Health and Brain Sciences, School of Health Sciences, Swinburne University, MEL, Australia
| | - Matthew Hughes
- Centre for Mental Health and Brain Sciences, School of Health Sciences, Swinburne University, MEL, Australia
| | - William Woods
- Centre for Mental Health and Brain Sciences, School of Health Sciences, Swinburne University, MEL, Australia
| | - Sean P Carruthers
- Centre for Mental Health and Brain Sciences, School of Health Sciences, Swinburne University, MEL, Australia
| | - Philip Sumner
- Centre for Mental Health and Brain Sciences, School of Health Sciences, Swinburne University, MEL, Australia
| | - Elysha Ringin
- National Institute of Mental Health, Klecany, Czech Republic
| | - Filip Spaniel
- National Institute of Mental Health, Klecany, Czech Republic
| | - Antonin Skoch
- National Institute of Mental Health, Klecany, Czech Republic
- MR Unit, Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - David Tomecek
- National Institute of Mental Health, Klecany, Czech Republic
- Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic
- Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Philipp Homan
- Psychiatric Hospital, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich & Swiss Federal Institute of Technology Zurich, Zurich, Switzerland
| | - Stephanie Homan
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, Zurich, Switzerland
- Experimental Psychopathology and Psychotherapy, Department of Psychology, University of Zurich, Zurich, Switzerland
| | - Wolfgang Omlor
- Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - Giacomo Cecere
- Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - Dana D Nguyen
- Department of Pediatrics, University of California Irvine, Irvine, CA, USA
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Long-Biao Cui
- Department of Clinical Psychology, Fourth Military Medical University, Xi'an, PR China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technology, University of Electronic Science and Technology of China, Chengdu, China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Research Unit of NeuroInformation (2019RU035), Chinese Academy of Medical Sciences, Chengdu, China
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jessica A Turner
- Psychiatry and Behavioral Health, Ohio State Wexner Medical Center, Columbus, OH, USA
| | - Theo G M van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine Hall, room 109, Irvine, CA, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, 309 Qureshey Research Lab, Irvine, CA, USA
| | - Wei Cheng
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- Shanghai Medical College and Zhongshan Hospital Immunotherapy Technology Transfer Center, Shanghai, China
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China.
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
- Zhangjiang Fudan International Innovation Center, Shanghai, China.
- School of Data Science, Fudan University, Shanghai, China.
- Department of Computer Science, University of Warwick, Coventry, UK.
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4
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Wang G, Jiang N, Ma Y, Chen D, Wu J, Li G, Liang D, Yan T. Connectional-style-guided contextual representation learning for brain disease diagnosis. Neural Netw 2024; 175:106296. [PMID: 38653077 DOI: 10.1016/j.neunet.2024.106296] [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: 08/09/2023] [Revised: 01/26/2024] [Accepted: 04/06/2024] [Indexed: 04/25/2024]
Abstract
Structural magnetic resonance imaging (sMRI) has shown great clinical value and has been widely used in deep learning (DL) based computer-aided brain disease diagnosis. Previous DL-based approaches focused on local shapes and textures in brain sMRI that may be significant only within a particular domain. The learned representations are likely to contain spurious information and have poor generalization ability in other diseases and datasets. To facilitate capturing meaningful and robust features, it is necessary to first comprehensively understand the intrinsic pattern of the brain that is not restricted within a single data/task domain. Considering that the brain is a complex connectome of interlinked neurons, the connectional properties in the brain have strong biological significance, which is shared across multiple domains and covers most pathological information. In this work, we propose a connectional style contextual representation learning model (CS-CRL) to capture the intrinsic pattern of the brain, used for multiple brain disease diagnosis. Specifically, it has a vision transformer (ViT) encoder and leverages mask reconstruction as the proxy task and Gram matrices to guide the representation of connectional information. It facilitates the capture of global context and the aggregation of features with biological plausibility. The results indicate that CS-CRL achieves superior accuracy in multiple brain disease diagnosis tasks across six datasets and three diseases and outperforms state-of-the-art models. Furthermore, we demonstrate that CS-CRL captures more brain-network-like properties, and better aggregates features, is easier to optimize, and is more robust to noise, which explains its superiority in theory.
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Affiliation(s)
- Gongshu Wang
- School of Medical Technology, Beijing Institute of Technology, Beijing, China.
| | - Ning Jiang
- School of Medical Technology, Beijing Institute of Technology, Beijing, China.
| | - Yunxiao Ma
- School of Medical Technology, Beijing Institute of Technology, Beijing, China.
| | - Duanduan Chen
- School of Medical Technology, Beijing Institute of Technology, Beijing, China.
| | - Jinglong Wu
- School of Medical Technology, Beijing Institute of Technology, Beijing, China.
| | - Guoqi Li
- Institute of Automation, Chinese Academy of Sciences, Beijing, China.
| | - Dong Liang
- Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
| | - Tianyi Yan
- School of Medical Technology, Beijing Institute of Technology, Beijing, China.
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5
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Kennedy E, Liebel SW, Lindsey HM, Vadlamani S, Lei PW, Adamson MM, Alda M, Alonso-Lana S, Anderson TJ, Arango C, Asarnow RF, Avram M, Ayesa-Arriola R, Babikian T, Banaj N, Bird LJ, Borgwardt S, Brodtmann A, Brosch K, Caeyenberghs K, Calhoun VD, Chiaravalloti ND, Cifu DX, Crespo-Facorro B, Dalrymple-Alford JC, Dams-O’Connor K, Dannlowski U, Darby D, Davenport N, DeLuca J, Diaz-Caneja CM, Disner SG, Dobryakova E, Ehrlich S, Esopenko C, Ferrarelli F, Frank LE, Franz CE, Fuentes-Claramonte P, Genova H, Giza CC, Goltermann J, Grotegerd D, Gruber M, Gutierrez-Zotes A, Ha M, Haavik J, Hinkin C, Hoskinson KR, Hubl D, Irimia A, Jansen A, Kaess M, Kang X, Kenney K, Keřková B, Khlif MS, Kim M, Kindler J, Kircher T, Knížková K, Kolskår KK, Krch D, Kremen WS, Kuhn T, Kumari V, Kwon J, Langella R, Laskowitz S, Lee J, Lengenfelder J, Liou-Johnson V, Lippa SM, Løvstad M, Lundervold AJ, Marotta C, Marquardt CA, Mattos P, Mayeli A, McDonald CR, Meinert S, Melzer TR, Merchán-Naranjo J, Michel C, Morey RA, Mwangi B, Myall DJ, Nenadić I, Newsome MR, Nunes A, O’Brien T, Oertel V, Ollinger J, Olsen A, Ortiz García de la Foz V, Ozmen M, Pardoe H, Parent M, Piras F, Piras F, Pomarol-Clotet E, Repple J, Richard G, Rodriguez J, Rodriguez M, Rootes-Murdy K, Rowland J, Ryan NP, Salvador R, Sanders AM, Schmidt A, Soares JC, Spalleta G, Španiel F, Sponheim SR, Stasenko A, Stein F, Straube B, Thames A, Thomas-Odenthal F, Thomopoulos SI, Tone EB, Torres I, Troyanskaya M, Turner JA, Ulrichsen KM, Umpierrez G, Vecchio D, Vilella E, Vivash L, Walker WC, Werden E, Westlye LT, Wild K, Wroblewski A, Wu MJ, Wylie GR, Yatham LN, Zunta-Soares GB, Thompson PM, Pugh MJ, Tate DF, Hillary FG, Wilde EA, Dennis EL. Verbal Learning and Memory Deficits across Neurological and Neuropsychiatric Disorders: Insights from an ENIGMA Mega Analysis. Brain Sci 2024; 14:669. [PMID: 39061410 PMCID: PMC11274572 DOI: 10.3390/brainsci14070669] [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: 06/07/2024] [Revised: 06/20/2024] [Accepted: 06/26/2024] [Indexed: 07/28/2024] Open
Abstract
Deficits in memory performance have been linked to a wide range of neurological and neuropsychiatric conditions. While many studies have assessed the memory impacts of individual conditions, this study considers a broader perspective by evaluating how memory recall is differentially associated with nine common neuropsychiatric conditions using data drawn from 55 international studies, aggregating 15,883 unique participants aged 15-90. The effects of dementia, mild cognitive impairment, Parkinson's disease, traumatic brain injury, stroke, depression, attention-deficit/hyperactivity disorder (ADHD), schizophrenia, and bipolar disorder on immediate, short-, and long-delay verbal learning and memory (VLM) scores were estimated relative to matched healthy individuals. Random forest models identified age, years of education, and site as important VLM covariates. A Bayesian harmonization approach was used to isolate and remove site effects. Regression estimated the adjusted association of each clinical group with VLM scores. Memory deficits were strongly associated with dementia and schizophrenia (p < 0.001), while neither depression nor ADHD showed consistent associations with VLM scores (p > 0.05). Differences associated with clinical conditions were larger for longer delayed recall duration items. By comparing VLM across clinical conditions, this study provides a foundation for enhanced diagnostic precision and offers new insights into disease management of comorbid disorders.
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Affiliation(s)
- Eamonn Kennedy
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA; (E.K.); (S.W.L.); (H.M.L.); (S.V.); (M.R.N.); (M.J.P.); (D.F.T.); (E.A.W.)
- Division of Epidemiology, University of Utah, Salt Lake City, UT 84108, USA;
- George E Wahlen Veterans Affairs Medical Center, Salt Lake City, UT 84148, USA
| | - Spencer W. Liebel
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA; (E.K.); (S.W.L.); (H.M.L.); (S.V.); (M.R.N.); (M.J.P.); (D.F.T.); (E.A.W.)
- George E Wahlen Veterans Affairs Medical Center, Salt Lake City, UT 84148, USA
| | - Hannah M. Lindsey
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA; (E.K.); (S.W.L.); (H.M.L.); (S.V.); (M.R.N.); (M.J.P.); (D.F.T.); (E.A.W.)
- George E Wahlen Veterans Affairs Medical Center, Salt Lake City, UT 84148, USA
| | - Shashank Vadlamani
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA; (E.K.); (S.W.L.); (H.M.L.); (S.V.); (M.R.N.); (M.J.P.); (D.F.T.); (E.A.W.)
| | - Pui-Wa Lei
- Department of Educational Psychology, Counseling, and Special Education, Pennsylvania State University, University Park, PA 16802, USA;
| | - Maheen M. Adamson
- WRIISC-WOMEN & Rehabilitation Department, VA Palo Alto, Palo Alto, CA 94304, USA (X.K.); (V.L.-J.)
- Neurosurgery, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Martin Alda
- Department of Psychiatry, Dalhousie University, Halifax, NS B3H 4R2, Canada; (M.A.); (A.N.)
| | - Silvia Alonso-Lana
- FIDMAG Research Foundation, 08025 Barcelona, Spain; (S.A.-L.); (P.F.-C.); (E.P.-C.); (R.S.)
- Centro Investigación Biomédica en Red Salud Mental (CIBERSAM), 28029 Madrid, Spain; (C.A.); (R.A.-A.); (B.C.-F.); (A.G.-Z.); (E.V.)
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, 08022 Barcelona, Spain
| | - Tim J. Anderson
- Department of Medicine, University of Otago, Christchurch 8011, New Zealand; (T.J.A.); (J.C.D.-A.); (T.R.M.)
- New Zealand Brain Research Institute, Christchurch 8011, New Zealand;
- Department of Neurology, Te Whatu Ora–Health New Zealand Waitaha Canterbury, Christchurch 8011, New Zealand
| | - Celso Arango
- Centro Investigación Biomédica en Red Salud Mental (CIBERSAM), 28029 Madrid, Spain; (C.A.); (R.A.-A.); (B.C.-F.); (A.G.-Z.); (E.V.)
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), School of Medicine, Universidad Complutense, 28040 Madrid, Spain; (C.M.D.-C.); (J.M.-N.)
| | - Robert F. Asarnow
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90095, USA; (R.F.A.); (T.B.); (C.H.); (T.K.); (A.T.)
- Brain Research Institute, University of California Los Angeles, Los Angeles, CA 90095, USA
- Department of Psychology, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Mihai Avram
- Translational Psychiatry, Department of Psychiatry and Psychotherapy, University of Lübeck, 23562 Lübeck, Germany; (M.A.); (S.B.)
| | - Rosa Ayesa-Arriola
- Centro Investigación Biomédica en Red Salud Mental (CIBERSAM), 28029 Madrid, Spain; (C.A.); (R.A.-A.); (B.C.-F.); (A.G.-Z.); (E.V.)
- Department of Psychiatry, Marqués de Valdecilla University Hospital, Instituto de Investigación Sanitaria Valdecilla (IDIVAL), School of Medicine, University of Cantabria, 39008 Santander, Spain;
| | - Talin Babikian
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90095, USA; (R.F.A.); (T.B.); (C.H.); (T.K.); (A.T.)
- UCLA Steve Tisch BrainSPORT Program, University of California Los Angeles, Los Angeles, CA 90095, USA;
| | - Nerisa Banaj
- Laboratory of Neuropsychiatry, Santa Lucia Foundation IRCCS, 00179 Rome, Italy; (N.B.); (R.L.); (F.P.); (F.P.); (G.S.); (D.V.)
| | - Laura J. Bird
- School of Clinical Sciences, Monash University, Clayton, VIC 3800, Australia;
| | - Stefan Borgwardt
- Translational Psychiatry, Department of Psychiatry and Psychotherapy, University of Lübeck, 23562 Lübeck, Germany; (M.A.); (S.B.)
- Center of Brain, Behaviour and Metabolism (CBBM), University of Lübeck, 23562 Lübeck, Germany
| | - Amy Brodtmann
- Cognitive Health Initiative, School of Translational Medicine, Monash University, Melbourne, VIC 3800, Australia;
- Department of Medicine, Royal Melbourne Hospital, Melbourne, VIC 3050, Australia;
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, University of Marburg, 35032 Marburg, Germany; (K.B.); (A.J.); (T.K.); (I.N.); (F.S.); (B.S.); (F.T.-O.); (A.W.)
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA
| | - Karen Caeyenberghs
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Burwood, VIC 3125, Australia;
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory University, Atlanta, GA 30322, USA; (V.D.C.); (K.R.-M.)
| | - Nancy D. Chiaravalloti
- Centers for Neuropsychology, Neuroscience & Traumatic Brain Injury Research, Kessler Foundation, East Hanover, NJ 07936, USA;
- Department of Physical Medicine & Rehabilitation, Rutgers, New Jersey Medical School, Newark, NJ 07103, USA; (J.D.); (E.D.); (H.G.); (D.K.); (J.L.); (G.R.W.)
| | - David X. Cifu
- Rehabilitation Medicine Department, National Institutes of Health Clinical Center, Bethesda, MD 20892, USA;
| | - Benedicto Crespo-Facorro
- Centro Investigación Biomédica en Red Salud Mental (CIBERSAM), 28029 Madrid, Spain; (C.A.); (R.A.-A.); (B.C.-F.); (A.G.-Z.); (E.V.)
- Department of Psychiatry, Virgen del Rocio University Hospital, School of Medicine, University of Seville, IBIS, 41013 Seville, Spain
| | - John C. Dalrymple-Alford
- Department of Medicine, University of Otago, Christchurch 8011, New Zealand; (T.J.A.); (J.C.D.-A.); (T.R.M.)
- New Zealand Brain Research Institute, Christchurch 8011, New Zealand;
- School of Psychology, Speech and Hearing, University of Canterbury, Christchurch 8041, New Zealand
| | - Kristen Dams-O’Connor
- Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA (C.E.)
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany; (U.D.); (J.G.); (D.G.); (M.G.); (S.M.); (J.R.)
| | - David Darby
- Department of Neuroscience, Monash University, Melbourne, VIC 3800, Australia; (D.D.); (C.M.); (L.V.)
- Department of Neurology, Alfred Health, Melbourne, VIC 3004, Australia
- The Florey Institute of Neuroscience and Mental Health, Melbourne, VIC 3052, Australia; (H.P.); (E.W.)
| | - Nicholas Davenport
- Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis, MN 55455, USA; (N.D.); (S.G.D.); (C.A.M.); (S.R.S.)
- Minneapolis VA Health Care System, Minneapolis, MN 55417, USA
| | - John DeLuca
- Department of Physical Medicine & Rehabilitation, Rutgers, New Jersey Medical School, Newark, NJ 07103, USA; (J.D.); (E.D.); (H.G.); (D.K.); (J.L.); (G.R.W.)
- Kessler Foundation, East Hanover, NJ 07936, USA
| | - Covadonga M. Diaz-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), School of Medicine, Universidad Complutense, 28040 Madrid, Spain; (C.M.D.-C.); (J.M.-N.)
| | - Seth G. Disner
- Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis, MN 55455, USA; (N.D.); (S.G.D.); (C.A.M.); (S.R.S.)
- Minneapolis VA Health Care System, Minneapolis, MN 55417, USA
| | - Ekaterina Dobryakova
- Department of Physical Medicine & Rehabilitation, Rutgers, New Jersey Medical School, Newark, NJ 07103, USA; (J.D.); (E.D.); (H.G.); (D.K.); (J.L.); (G.R.W.)
- Center for Traumatic Brain Injury, Kessler Foundation, East Hanover, NJ 07936, USA
| | - Stefan Ehrlich
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, 01307 Dresden, Germany;
- Eating Disorders Research and Treatment Center, Department of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, 01307 Dresden, Germany
| | - Carrie Esopenko
- Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA (C.E.)
| | - Fabio Ferrarelli
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA; (F.F.); (A.M.)
| | - Lea E. Frank
- Department of Psychology, University of Oregon, Eugene, OR 97403, USA
| | - Carol E. Franz
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA; (C.E.F.); (W.S.K.); (J.R.); (A.S.)
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA 92093, USA
| | - Paola Fuentes-Claramonte
- FIDMAG Research Foundation, 08025 Barcelona, Spain; (S.A.-L.); (P.F.-C.); (E.P.-C.); (R.S.)
- Centro Investigación Biomédica en Red Salud Mental (CIBERSAM), 28029 Madrid, Spain; (C.A.); (R.A.-A.); (B.C.-F.); (A.G.-Z.); (E.V.)
| | - Helen Genova
- Department of Physical Medicine & Rehabilitation, Rutgers, New Jersey Medical School, Newark, NJ 07103, USA; (J.D.); (E.D.); (H.G.); (D.K.); (J.L.); (G.R.W.)
- Center for Autism Research, Kessler Foundation, East Hanover, NJ 07936, USA
| | - Christopher C. Giza
- UCLA Steve Tisch BrainSPORT Program, University of California Los Angeles, Los Angeles, CA 90095, USA;
- Department of Pediatrics, Division of Neurology, UCLA Mattel Children’s Hospital, Los Angeles, CA 90095, USA
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Janik Goltermann
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany; (U.D.); (J.G.); (D.G.); (M.G.); (S.M.); (J.R.)
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany; (U.D.); (J.G.); (D.G.); (M.G.); (S.M.); (J.R.)
| | - Marius Gruber
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany; (U.D.); (J.G.); (D.G.); (M.G.); (S.M.); (J.R.)
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Goethe University, 60590 Frankfurt, Germany
| | - Alfonso Gutierrez-Zotes
- Centro Investigación Biomédica en Red Salud Mental (CIBERSAM), 28029 Madrid, Spain; (C.A.); (R.A.-A.); (B.C.-F.); (A.G.-Z.); (E.V.)
- Hospital Universitari Institut Pere Mata, 43007 Tarragona, Spain
- Institut d’Investiació Sanitària Pere Virgili-CERCA, Universitat Rovira i Virgili, 43007 Tarragona, Spain
| | - Minji Ha
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul 08826, Republic of Korea; (M.H.); (J.K.); (J.L.)
| | - Jan Haavik
- Department of Biomedicine, University of Bergen, 5007 Bergen, Norway;
- Division of Psychiatry, Haukeland University Hospital, 5021 Bergen, Norway
| | - Charles Hinkin
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90095, USA; (R.F.A.); (T.B.); (C.H.); (T.K.); (A.T.)
| | - Kristen R. Hoskinson
- Center for Biobehavioral Health, The Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205, USA;
- Section of Pediatrics, The Ohio State University College of Medicine, Columbus, OH 43210, USA
| | - Daniela Hubl
- Translational Research Centre, University Hospital of Psychiatry and Psychotherapy, University of Bern, 3000 Bern, Switzerland;
| | - Andrei Irimia
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA 90089, USA;
- Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA
- Department of Quantitative & Computational Biology, Dornsife College of Arts & Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, University of Marburg, 35032 Marburg, Germany; (K.B.); (A.J.); (T.K.); (I.N.); (F.S.); (B.S.); (F.T.-O.); (A.W.)
| | - Michael Kaess
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, 3000 Bern, Switzerland; (M.K.); (J.K.); (C.M.)
- Clinic of Child and Adolescent Psychiatry, Centre of Psychosocial Medicine, University of Heidelberg, 69120 Heidelberg, Germany
| | - Xiaojian Kang
- WRIISC-WOMEN & Rehabilitation Department, VA Palo Alto, Palo Alto, CA 94304, USA (X.K.); (V.L.-J.)
| | - Kimbra Kenney
- Department of Neurology, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA;
| | - Barbora Keřková
- National Institute of Mental Health, 250 67 Klecany, Czech Republic; (B.K.); (K.K.); (M.R.); (F.Š.)
| | - Mohamed Salah Khlif
- Cognitive Health Initiative, Central Clinical School, Monash University, Melbourne, VIC 3800, Australia;
| | - Minah Kim
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul 03080, Republic of Korea;
- Department of Psychiatry, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Jochen Kindler
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, 3000 Bern, Switzerland; (M.K.); (J.K.); (C.M.)
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, University of Marburg, 35032 Marburg, Germany; (K.B.); (A.J.); (T.K.); (I.N.); (F.S.); (B.S.); (F.T.-O.); (A.W.)
| | - Karolina Knížková
- National Institute of Mental Health, 250 67 Klecany, Czech Republic; (B.K.); (K.K.); (M.R.); (F.Š.)
- Department of Psychiatry, First Faculty of Medicine, Charles University and General University Hospital, 128 00 Prague, Czech Republic
| | - Knut K. Kolskår
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, 0424 Oslo, Norway; (K.K.K.); (G.R.); (A.-M.S.); (K.M.U.); (L.T.W.)
- Department of Psychology, University of Oslo, 0373 Oslo, Norway;
- Department of Research, Sunnaas Rehabilitation Hospital, 1450 Nesodden, Norway
| | - Denise Krch
- Department of Physical Medicine & Rehabilitation, Rutgers, New Jersey Medical School, Newark, NJ 07103, USA; (J.D.); (E.D.); (H.G.); (D.K.); (J.L.); (G.R.W.)
- Center for Traumatic Brain Injury, Kessler Foundation, East Hanover, NJ 07936, USA
| | - William S. Kremen
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA; (C.E.F.); (W.S.K.); (J.R.); (A.S.)
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA 92093, USA
| | - Taylor Kuhn
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90095, USA; (R.F.A.); (T.B.); (C.H.); (T.K.); (A.T.)
| | - Veena Kumari
- Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge UB8 3PH, UK;
| | - Junsoo Kwon
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul 08826, Republic of Korea; (M.H.); (J.K.); (J.L.)
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul 03080, Republic of Korea;
- Department of Psychiatry, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Roberto Langella
- Laboratory of Neuropsychiatry, Santa Lucia Foundation IRCCS, 00179 Rome, Italy; (N.B.); (R.L.); (F.P.); (F.P.); (G.S.); (D.V.)
| | - Sarah Laskowitz
- Brain Imaging and Analysis Center, Duke University, Durham, NC 27710, USA; (S.L.); (R.A.M.)
| | - Jungha Lee
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul 08826, Republic of Korea; (M.H.); (J.K.); (J.L.)
| | - Jean Lengenfelder
- Department of Physical Medicine & Rehabilitation, Rutgers, New Jersey Medical School, Newark, NJ 07103, USA; (J.D.); (E.D.); (H.G.); (D.K.); (J.L.); (G.R.W.)
- Center for Traumatic Brain Injury, Kessler Foundation, East Hanover, NJ 07936, USA
| | - Victoria Liou-Johnson
- WRIISC-WOMEN & Rehabilitation Department, VA Palo Alto, Palo Alto, CA 94304, USA (X.K.); (V.L.-J.)
| | - Sara M. Lippa
- National Intrepid Center of Excellence, Walter Reed National Military Medical Center, Bethesda, MD 20814, USA; (S.M.L.); (J.O.)
- Department of Neuroscience, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA
| | - Marianne Løvstad
- Department of Psychology, University of Oslo, 0373 Oslo, Norway;
- Department of Research, Sunnaas Rehabilitation Hospital, 1450 Nesodden, Norway
| | - Astri J. Lundervold
- Department of Biological and Medical Psychology, University of Bergen, 5007 Bergen, Norway;
| | - Cassandra Marotta
- Department of Neuroscience, Monash University, Melbourne, VIC 3800, Australia; (D.D.); (C.M.); (L.V.)
- Department of Neurology, Alfred Health, Melbourne, VIC 3004, Australia
| | - Craig A. Marquardt
- Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis, MN 55455, USA; (N.D.); (S.G.D.); (C.A.M.); (S.R.S.)
- Minneapolis VA Health Care System, Minneapolis, MN 55417, USA
| | - Paulo Mattos
- Institute D’Or for Research and Education (IDOR), São Paulo 04501-000, Brazil;
| | - Ahmad Mayeli
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA; (F.F.); (A.M.)
| | - Carrie R. McDonald
- Department of Radiation Medicine and Applied Sciences and Psychiatry, University of California San Diego, La Jolla, CA 92093, USA;
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA 92093, USA
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany; (U.D.); (J.G.); (D.G.); (M.G.); (S.M.); (J.R.)
- Institute for Translational Neuroscience, University of Münster, 48149 Münster, Germany
| | - Tracy R. Melzer
- Department of Medicine, University of Otago, Christchurch 8011, New Zealand; (T.J.A.); (J.C.D.-A.); (T.R.M.)
- New Zealand Brain Research Institute, Christchurch 8011, New Zealand;
- School of Psychology, Speech and Hearing, University of Canterbury, Christchurch 8041, New Zealand
| | - Jessica Merchán-Naranjo
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), School of Medicine, Universidad Complutense, 28040 Madrid, Spain; (C.M.D.-C.); (J.M.-N.)
| | - Chantal Michel
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, 3000 Bern, Switzerland; (M.K.); (J.K.); (C.M.)
| | - Rajendra A. Morey
- Brain Imaging and Analysis Center, Duke University, Durham, NC 27710, USA; (S.L.); (R.A.M.)
- VISN 6 MIRECC, Durham VA, Durham, NC 27705, USA
| | - Benson Mwangi
- Center of Excellence on Mood Disorders, Louis A Faillace, MD Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; (B.M.); (J.C.S.); (M.-J.W.); (G.B.Z.-S.)
| | - Daniel J. Myall
- New Zealand Brain Research Institute, Christchurch 8011, New Zealand;
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, University of Marburg, 35032 Marburg, Germany; (K.B.); (A.J.); (T.K.); (I.N.); (F.S.); (B.S.); (F.T.-O.); (A.W.)
| | - Mary R. Newsome
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA; (E.K.); (S.W.L.); (H.M.L.); (S.V.); (M.R.N.); (M.J.P.); (D.F.T.); (E.A.W.)
- George E Wahlen Veterans Affairs Medical Center, Salt Lake City, UT 84148, USA
| | - Abraham Nunes
- Department of Psychiatry, Dalhousie University, Halifax, NS B3H 4R2, Canada; (M.A.); (A.N.)
- Faculty of Computer Science, Dalhousie University, Halifax, NS B3H 4R2, Canada
| | - Terence O’Brien
- Department of Medicine, Royal Melbourne Hospital, Melbourne, VIC 3050, Australia;
- Department of Neuroscience, The School of Translational Medicine, Alfred Health, Monash University, Melbourne VIC 3004, Australia
| | - Viola Oertel
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Frankfurt University, 60590 Frankfurt, Germany;
| | - John Ollinger
- National Intrepid Center of Excellence, Walter Reed National Military Medical Center, Bethesda, MD 20814, USA; (S.M.L.); (J.O.)
| | - Alexander Olsen
- Department of Psychology, Norwegian University of Science and Technology, 7491 Trondheim, Norway;
- Department of Physical Medicine and Rehabilitation, St Olavs Hospital, Trondheim University Hospital, 7006 Trondheim, Norway
- NorHEAD—Norwegian Centre for Headache Research, 7491 Trondheim, Norway
| | - Victor Ortiz García de la Foz
- Department of Psychiatry, Marqués de Valdecilla University Hospital, Instituto de Investigación Sanitaria Valdecilla (IDIVAL), School of Medicine, University of Cantabria, 39008 Santander, Spain;
| | - Mustafa Ozmen
- Division of Epidemiology, University of Utah, Salt Lake City, UT 84108, USA;
- Department of Electrical and Electronics Engineering, Antalya Bilim University, 07190 Antalya, Turkey
| | - Heath Pardoe
- The Florey Institute of Neuroscience and Mental Health, Melbourne, VIC 3052, Australia; (H.P.); (E.W.)
| | - Marise Parent
- Neuroscience Institute & Department of Psychology, Georgia State University, Atlanta, GA 30303, USA;
| | - Fabrizio Piras
- Laboratory of Neuropsychiatry, Santa Lucia Foundation IRCCS, 00179 Rome, Italy; (N.B.); (R.L.); (F.P.); (F.P.); (G.S.); (D.V.)
| | - Federica Piras
- Laboratory of Neuropsychiatry, Santa Lucia Foundation IRCCS, 00179 Rome, Italy; (N.B.); (R.L.); (F.P.); (F.P.); (G.S.); (D.V.)
| | - Edith Pomarol-Clotet
- FIDMAG Research Foundation, 08025 Barcelona, Spain; (S.A.-L.); (P.F.-C.); (E.P.-C.); (R.S.)
- Centro Investigación Biomédica en Red Salud Mental (CIBERSAM), 28029 Madrid, Spain; (C.A.); (R.A.-A.); (B.C.-F.); (A.G.-Z.); (E.V.)
| | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany; (U.D.); (J.G.); (D.G.); (M.G.); (S.M.); (J.R.)
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Goethe University, 60590 Frankfurt, Germany
| | - Geneviève Richard
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, 0424 Oslo, Norway; (K.K.K.); (G.R.); (A.-M.S.); (K.M.U.); (L.T.W.)
| | - Jonathan Rodriguez
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA; (C.E.F.); (W.S.K.); (J.R.); (A.S.)
| | - Mabel Rodriguez
- National Institute of Mental Health, 250 67 Klecany, Czech Republic; (B.K.); (K.K.); (M.R.); (F.Š.)
| | - Kelly Rootes-Murdy
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory University, Atlanta, GA 30322, USA; (V.D.C.); (K.R.-M.)
| | - Jared Rowland
- WG (Bill) Hefner VA Medical Center, Salisbury, NC 28144, USA;
- Department of Neurobiology & Anatomy, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
- VA Mid-Atlantic Mental Illness Research Education and Clinical Center (MA-MIRECC), Durham, NC 27705, USA
| | - Nicholas P. Ryan
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, VIC 3220, Australia;
- Department of Paediatrics, The University of Melbourne, Parkville, VIC 3052, Australia
| | - Raymond Salvador
- FIDMAG Research Foundation, 08025 Barcelona, Spain; (S.A.-L.); (P.F.-C.); (E.P.-C.); (R.S.)
- Centro Investigación Biomédica en Red Salud Mental (CIBERSAM), 28029 Madrid, Spain; (C.A.); (R.A.-A.); (B.C.-F.); (A.G.-Z.); (E.V.)
| | - Anne-Marthe Sanders
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, 0424 Oslo, Norway; (K.K.K.); (G.R.); (A.-M.S.); (K.M.U.); (L.T.W.)
- Department of Psychology, University of Oslo, 0373 Oslo, Norway;
- Department of Research, Sunnaas Rehabilitation Hospital, 1450 Nesodden, Norway
| | - Andre Schmidt
- Department of Psychiatry (UPK), University of Basel, 4002 Basel, Switzerland;
| | - Jair C. Soares
- Center of Excellence on Mood Disorders, Louis A Faillace, MD Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; (B.M.); (J.C.S.); (M.-J.W.); (G.B.Z.-S.)
| | - Gianfranco Spalleta
- Laboratory of Neuropsychiatry, Santa Lucia Foundation IRCCS, 00179 Rome, Italy; (N.B.); (R.L.); (F.P.); (F.P.); (G.S.); (D.V.)
| | - Filip Španiel
- National Institute of Mental Health, 250 67 Klecany, Czech Republic; (B.K.); (K.K.); (M.R.); (F.Š.)
- 3rd Faculty of Medicine, Charles University, 100 00 Prague, Czech Republic
| | - Scott R. Sponheim
- Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis, MN 55455, USA; (N.D.); (S.G.D.); (C.A.M.); (S.R.S.)
- Minneapolis VA Health Care System, Minneapolis, MN 55417, USA
| | - Alena Stasenko
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA; (C.E.F.); (W.S.K.); (J.R.); (A.S.)
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA 92093, USA
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, University of Marburg, 35032 Marburg, Germany; (K.B.); (A.J.); (T.K.); (I.N.); (F.S.); (B.S.); (F.T.-O.); (A.W.)
| | - Benjamin Straube
- Department of Psychiatry and Psychotherapy, University of Marburg, 35032 Marburg, Germany; (K.B.); (A.J.); (T.K.); (I.N.); (F.S.); (B.S.); (F.T.-O.); (A.W.)
| | - April Thames
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90095, USA; (R.F.A.); (T.B.); (C.H.); (T.K.); (A.T.)
| | - Florian Thomas-Odenthal
- Department of Psychiatry and Psychotherapy, University of Marburg, 35032 Marburg, Germany; (K.B.); (A.J.); (T.K.); (I.N.); (F.S.); (B.S.); (F.T.-O.); (A.W.)
| | - Sophia I. Thomopoulos
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA 90292, USA; (S.I.T.); (P.M.T.)
| | - Erin B. Tone
- Department of Psychology, Georgia State University, Atlanta, GA 30303, USA;
| | - Ivan Torres
- Department of Psychiatry, University of British Columbia, Vancouver, BC V6T 1Z4, Canada; (I.T.); (L.N.Y.)
- British Columbia Mental Health and Substance Use Services Research Institute, Vancouver, BC V5Z 1M9, Canada
| | - Maya Troyanskaya
- Michael E DeBakey Veterans Affairs Medical Center, Houston, TX 77030, USA;
- H Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jessica A. Turner
- Psychiatry and Behavioral Health, Ohio State Wexner Medical Center, Columbus, OH 43210, USA;
| | - Kristine M. Ulrichsen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, 0424 Oslo, Norway; (K.K.K.); (G.R.); (A.-M.S.); (K.M.U.); (L.T.W.)
- Department of Psychology, University of Oslo, 0373 Oslo, Norway;
- Department of Research, Sunnaas Rehabilitation Hospital, 1450 Nesodden, Norway
| | - Guillermo Umpierrez
- Division of Endocrinology, Emory University School of Medicine, Atlanta, GA 30322, USA;
| | - Daniela Vecchio
- Laboratory of Neuropsychiatry, Santa Lucia Foundation IRCCS, 00179 Rome, Italy; (N.B.); (R.L.); (F.P.); (F.P.); (G.S.); (D.V.)
| | - Elisabet Vilella
- Centro Investigación Biomédica en Red Salud Mental (CIBERSAM), 28029 Madrid, Spain; (C.A.); (R.A.-A.); (B.C.-F.); (A.G.-Z.); (E.V.)
- Hospital Universitari Institut Pere Mata, 43007 Tarragona, Spain
- Institut d’Investiació Sanitària Pere Virgili-CERCA, Universitat Rovira i Virgili, 43007 Tarragona, Spain
| | - Lucy Vivash
- Department of Neuroscience, Monash University, Melbourne, VIC 3800, Australia; (D.D.); (C.M.); (L.V.)
- Department of Neurology, Alfred Health, Melbourne, VIC 3004, Australia
| | - William C. Walker
- Department of Physical Medicine and Rehabilitation, Virginia Commonwealth University, Richmond, VA 23298, USA;
- Richmond Veterans Affairs (VA) Medical Center, Central Virginia VA Health Care System, Richmond, VA 23249, USA
| | - Emilio Werden
- The Florey Institute of Neuroscience and Mental Health, Melbourne, VIC 3052, Australia; (H.P.); (E.W.)
| | - Lars T. Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, 0424 Oslo, Norway; (K.K.K.); (G.R.); (A.-M.S.); (K.M.U.); (L.T.W.)
- Department of Psychology, University of Oslo, 0373 Oslo, Norway;
- KG Jebsen Center for Neurodevelopmental Disorders, University of Oslo, 0372 Oslo, Norway
| | - Krista Wild
- Department of Psychology, Phoenix VA Health Care System, Phoenix, AZ 85012, USA;
| | - Adrian Wroblewski
- Department of Psychiatry and Psychotherapy, University of Marburg, 35032 Marburg, Germany; (K.B.); (A.J.); (T.K.); (I.N.); (F.S.); (B.S.); (F.T.-O.); (A.W.)
| | - Mon-Ju Wu
- Center of Excellence on Mood Disorders, Louis A Faillace, MD Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; (B.M.); (J.C.S.); (M.-J.W.); (G.B.Z.-S.)
| | - Glenn R. Wylie
- Department of Physical Medicine & Rehabilitation, Rutgers, New Jersey Medical School, Newark, NJ 07103, USA; (J.D.); (E.D.); (H.G.); (D.K.); (J.L.); (G.R.W.)
- Rocco Ortenzio Neuroimaging Center, Kessler Foundation, East Hanover, NJ 07936, USA
| | - Lakshmi N. Yatham
- Department of Psychiatry, University of British Columbia, Vancouver, BC V6T 1Z4, Canada; (I.T.); (L.N.Y.)
| | - Giovana B. Zunta-Soares
- Center of Excellence on Mood Disorders, Louis A Faillace, MD Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; (B.M.); (J.C.S.); (M.-J.W.); (G.B.Z.-S.)
| | - Paul M. Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA 90292, USA; (S.I.T.); (P.M.T.)
- Departments of Neurology, Pediatrics, Psychiatry, Radiology, Engineering, and Ophthalmology, University of Southern California, Los Angeles, CA 90089, USA
| | - Mary Jo Pugh
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA; (E.K.); (S.W.L.); (H.M.L.); (S.V.); (M.R.N.); (M.J.P.); (D.F.T.); (E.A.W.)
- Division of Epidemiology, University of Utah, Salt Lake City, UT 84108, USA;
| | - David F. Tate
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA; (E.K.); (S.W.L.); (H.M.L.); (S.V.); (M.R.N.); (M.J.P.); (D.F.T.); (E.A.W.)
- George E Wahlen Veterans Affairs Medical Center, Salt Lake City, UT 84148, USA
| | - Frank G. Hillary
- Department of Psychology, Penn State University, State College, PA 16801, USA;
- Department of Neurology, Hershey Medical Center, State College, PA 16801, USA
- Social Life and Engineering Science Imaging Center, Penn State University, State College, PA 16801, USA
| | - Elisabeth A. Wilde
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA; (E.K.); (S.W.L.); (H.M.L.); (S.V.); (M.R.N.); (M.J.P.); (D.F.T.); (E.A.W.)
- George E Wahlen Veterans Affairs Medical Center, Salt Lake City, UT 84148, USA
| | - Emily L. Dennis
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA; (E.K.); (S.W.L.); (H.M.L.); (S.V.); (M.R.N.); (M.J.P.); (D.F.T.); (E.A.W.)
- George E Wahlen Veterans Affairs Medical Center, Salt Lake City, UT 84148, USA
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Weng T, Zheng Y, Xie Y, Qin W, Guo L. Diagnosing schizophrenia using deep learning: Novel interpretation approaches and multi-site validation. Brain Res 2024; 1833:148876. [PMID: 38513996 DOI: 10.1016/j.brainres.2024.148876] [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: 12/04/2023] [Revised: 02/28/2024] [Accepted: 03/18/2024] [Indexed: 03/23/2024]
Abstract
Schizophrenia is a profound and enduring mental disorder that imposes significant negative impacts on individuals, their families, and society at large. The development of more accurate and objective diagnostic tools for schizophrenia can be expedited through the employment of deep learning (DL), that excels at deciphering complex hierarchical non-linear patterns. However, the limited interpretability of deep learning has eroded confidence in the model and restricted its clinical utility. At the same time, if the data source is only derived from a single center, the model's generalizability is difficult to test. To enhance the model's reliability and applicability, leave-one-center-out validation with a large and diverse sample from multiple centers is crucial. In this study, we utilized Nine different global centers to train and test the 3D Resnet model's generalizability, resulting in an 82% classification performance (area under the curve) on all datasets sourced from different countries, employing a leave-one-center-out-validation approach. Per our approximation of the feature significance of each region on the atlas, we identified marked differences in the thalamus, pallidum, and inferior frontal gyrus between individuals with schizophrenia and healthy controls, lending credence to prior research findings. At the same time, in order to translate the model's output into clinically applicable insights, the SHapley Additive exPlanations (SHAP) permutation explainer method with an anatomical atlas have been refined, thereby offering precise neuroanatomical and functional interpretations of different brain regions.
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Affiliation(s)
- Tingting Weng
- School of Medical Imaging, Tianjin Medical University, Tianjin 300203, China
| | - Yuemei Zheng
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Shandong 100038, China
| | - Yingying Xie
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Li Guo
- School of Medical Imaging, Tianjin Medical University, Tianjin 300203, China.
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7
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Wang G, Jiang N, Ma Y, Suo D, Liu T, Funahashi S, Yan T. Using a deep generation network reveals neuroanatomical specificity in hemispheres. PATTERNS (NEW YORK, N.Y.) 2024; 5:100930. [PMID: 38645770 PMCID: PMC11026975 DOI: 10.1016/j.patter.2024.100930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 01/08/2024] [Accepted: 01/15/2024] [Indexed: 04/23/2024]
Abstract
Asymmetry is an important property of brain organization, but its nature is still poorly understood. Capturing the neuroanatomical components specific to each hemisphere facilitates the understanding of the establishment of brain asymmetry. Since deep generative networks (DGNs) have powerful inference and recovery capabilities, we use one hemisphere to predict the opposite hemisphere by training the DGNs, which automatically fit the built-in dependencies between the left and right hemispheres. After training, the reconstructed images approximate the homologous components in the hemisphere. We use the difference between the actual and reconstructed hemispheres to measure hemisphere-specific components due to asymmetric expression of environmental and genetic factors. The results show that our model is biologically plausible and that our proposed metric of hemispheric specialization is reliable, representing a wide range of individual variation. Together, this work provides promising tools for exploring brain asymmetry and new insights into self-supervised DGNs for representing the brain.
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Affiliation(s)
- Gongshu Wang
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Ning Jiang
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Yunxiao Ma
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Dingjie Suo
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Tiantian Liu
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Shintaro Funahashi
- Advanced Research Institute for Multidisciplinary Science, Beijing Institute of Technology, Beijing 100081, China
- Department of Cognitive and Behavioral Sciences, Graduate School of Human and Environmental Science, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan
- Kokoro Research Center, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan
| | - Tianyi Yan
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
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8
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Basodi S, Raja R, Gazula H, Romero JT, Panta S, Maullin-Sapey T, Nichols TE, Calhoun VD. Decentralized Mixed Effects Modeling in COINSTAC. Neuroinformatics 2024; 22:163-175. [PMID: 38424371 DOI: 10.1007/s12021-024-09657-7] [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] [Accepted: 02/15/2024] [Indexed: 03/02/2024]
Abstract
Performing group analysis on magnetic resonance imaging (MRI) data with linear mixed-effects (LME) models is challenging due to its large dimensionality and inherent multi-level covariance structure. In addition, as large-scale collaborative projects become commonplace in neuroimaging, data must increasingly be stored and analyzed from different locations. In such settings, substantial overhead can occur in terms of data transfer and coordination between participating research groups. In some cases, data cannot be pooled together due to privacy or regulatory concerns. In this work, we propose a decentralized LME model to perform a large-scale analysis of data from different collaborations without data pooling. This method is efficient as it overcomes the hurdles of data sharing and has lower bandwidth and memory requirements for analysis than the centralized modeling approach. We evaluate our model using features extracted from structural magnetic resonance imaging (sMRI) data. Results highlight gray matter reductions in the temporal lobe/insula and medial frontal regions in schizophrenia, consistent with prior studies. Our analysis also demonstrates that decentralized LME models achieve similar performance compared to the models trained with all the data in one location. We also implement the decentralized LME approach in COINSTAC, an open source, decentralized platform for federating neuroimaging analysis, providing an easy to use tool for dissemination to the neuroimaging community.
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Affiliation(s)
- Sunitha Basodi
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Rajikha Raja
- St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Harshvardhan Gazula
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Javier Tomas Romero
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Sandeep Panta
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | | | - Thomas E Nichols
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.
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9
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Dönmezler S, Sönmez D, Yılbaş B, Öztürk Hİ, İskender G, Kurt İ. Thalamic nuclei volume differences in schizophrenia patients and healthy controls using probabilistic mapping: A comparative analysis. Schizophr Res 2024; 264:266-271. [PMID: 38198878 DOI: 10.1016/j.schres.2024.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 12/13/2023] [Accepted: 01/01/2024] [Indexed: 01/12/2024]
Abstract
AIM We aimed to investigate potential discrepancies in the volume of thalamic nuclei between individuals with schizophrenia and healthy controls. METHODS The imaging data for this study were obtained from the MCICShare data repository within SchizConnect. We employed probabilistic mapping technique developed by Iglesias et al. (2018). The analytical component entailed volumetric segmentation of the thalamus using the FreeSurfer image analysis suite. Our analysis focused on evaluating the differences in the volumes of various thalamic nuclei groups within the thalami, specifically the anterior, intralaminar, medial, posterior, lateral, and ventral groups in both the right and left thalami, between schizophrenia patients and healthy controls. We employed MANCOVA to analyse these dependent variables (volumes of 12 distinct thalamic nuclei groups), with diagnosis (SCZ vs. HCs) as the main explanatory variable, while controlling for covariates such as eTIV and age. RESULTS The assumptions of MANCOVA, including the homogeneity of covariance matrices, were met. Specific univariate tests for the right thalamus revealed significant differences in the medial (F[1, 200] = 26.360, p < 0.001), and the ventral groups (F[1, 200] = 4.793, p = 0.030). For the left thalamus, the medial (F[1, 200] = 22.527, p < 0.001); posterior (F[1, 200] = 8.227, p = 0.005), lateral (F[1, 200] = 7.004, p = 0.009), and ventral groups (F[1, 200] = 9.309, p = 0.003) showed significant differences. CONCLUSION These findings suggest that particular thalamic nuclei groups in both the right and left thalami may be most affected in schizophrenia, with more pronounced differences observed in the left thalamic nuclei. FUNDINGS The authors received no financial support for the research.
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Affiliation(s)
- Süleyman Dönmezler
- Sanko University, School of Medicine, Department of Psychiatry, Gaziantep, Turkey.
| | - Doğuş Sönmez
- Bakirkoy Training and Research Hospital for Psychiatry, Neurology and Neurosurgery, Department of Psychiatry, Istanbul, Turkey
| | - Barış Yılbaş
- Sanko University, School of Medicine, Department of Psychiatry, Gaziantep, Turkey
| | - Halil İbrahim Öztürk
- Sanko University, School of Medicine, Department of Psychiatry, Gaziantep, Turkey
| | - Gizem İskender
- Istanbul Prof. Dr. Cemil Tascioglu City Hospital, Department of Psychiatry, Istanbul, Turkey
| | - İmren Kurt
- Başakşehir Çam and Sakura City Hospital, Department of Psychiatry, Istanbul, Turkey
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10
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Joo SW, Jo YT, Kim Y, Lee WH, Chung YC, Lee J. Structural variability of the cerebral cortex in schizophrenia and its association with clinical symptoms. Psychol Med 2024; 54:399-408. [PMID: 37485703 DOI: 10.1017/s0033291723001988] [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: 07/25/2023]
Abstract
BACKGROUND Substantial evidence indicates structural abnormalities in the cerebral cortex of patients with schizophrenia (SCZ), although their clinical implications remain unclear. Previous case-control studies have investigated group-level differences in structural abnormalities, although the study design cannot account for interindividual differences. Recent research has focused on the association between the heterogeneity of the cerebral cortex morphometric features and clinical heterogeneity. METHODS We used neuroimaging data from 420 healthy controls and 695 patients with SCZ from seven studies. Four cerebral cortex measures were obtained: surface area, gray matter volume, thickness, and local gyrification index. We calculated the coefficient of variation (CV) and person-based similarity index (PBSI) scores and performed group comparisons. Associations between the PBSI scores and cognitive functions were evaluated using Spearman's rho test and normative modeling. RESULTS Patients with SCZ had a greater CV of surface area and cortical thickness than those of healthy controls. All PBSI scores across cortical measures were lower in patients with SCZ than in HCs. In the patient group, the PBSI scores for gray matter volume and all cortical measures taken together positively correlated with the full-scale IQ scores. Patients with deviant PBSI scores for gray matter volume and all cortical measures taken together had lower full-scale IQ scores than those of other patients. CONCLUSIONS The cerebral cortex in patients with SCZ showed greater regional and global structural variability than that in healthy controls. Patients with deviant similarity of cortical structural profiles exhibited a lower general intelligence than those exhibited by the other patients.
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Affiliation(s)
- Sung Woo Joo
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Young Tak Jo
- Department of Psychiatry, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Republic of Korea
| | - Yangsik Kim
- Department of Psychiatry, Inha University Hospital, Incheon, Republic of Korea
| | - Won Hee Lee
- Department of Software Convergence, Kyung Hee University, Yongin, Republic of Korea
| | - Young-Chul Chung
- Department of Psychiatry, Chonbuk National University Medical School, Jeonju, Republic of Korea
| | - Jungsun Lee
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
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11
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Jiang Y, Luo C, Wang J, Palaniyappan L, Chang X, Xiang S, Zhang J, Duan M, Huang H, Gaser C, Nemoto K, Miura K, Hashimoto R, Westlye LT, Richard G, Fernandez-Cabello S, Parker N, Andreassen OA, Kircher T, Nenadić I, Stein F, Thomas-Odenthal F, Teutenberg L, Usemann P, Dannlowski U, Hahn T, Grotegerd D, Meinert S, Lencer R, Tang Y, Zhang T, Li C, Yue W, Zhang Y, Yu X, Zhou E, Lin CP, Tsai SJ, Rodrigue AL, Glahn D, Pearlson G, Blangero J, Karuk A, Pomarol-Clotet E, Salvador R, Fuentes-Claramonte P, Garcia-León MÁ, Spalletta G, Piras F, Vecchio D, Banaj N, Cheng J, Liu Z, Yang J, Gonul AS, Uslu O, Burhanoglu BB, Demir AU, Rootes-Murdy K, Calhoun VD, Sim K, Green M, Quidé Y, Chung YC, Kim WS, Sponheim SR, Demro C, Ramsay IS, Iasevoli F, de Bartolomeis A, Barone A, Ciccarelli M, Brunetti A, Cocozza S, Pontillo G, Tranfa M, Park MTM, Kirschner M, Georgiadis F, Kaiser S, Rheenen TEV, Rossell SL, Hughes M, Woods W, Carruthers SP, Sumner P, Ringin E, Spaniel F, Skoch A, Tomecek D, Homan P, Homan S, Omlor W, Cecere G, Nguyen DD, Preda A, Thomopoulos S, Jahanshad N, Cui LB, Yao D, Thompson PM, Turner JA, van Erp TG, Cheng W, Feng J. Two neurostructural subtypes: results of machine learning on brain images from 4,291 individuals with schizophrenia. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.10.11.23296862. [PMID: 37873296 PMCID: PMC10593004 DOI: 10.1101/2023.10.11.23296862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Machine learning can be used to define subtypes of psychiatric conditions based on shared clinical and biological foundations, presenting a crucial step toward establishing biologically based subtypes of mental disorders. With the goal of identifying subtypes of disease progression in schizophrenia, here we analyzed cross-sectional brain structural magnetic resonance imaging (MRI) data from 4,291 individuals with schizophrenia (1,709 females, age=32.5 years±11.9) and 7,078 healthy controls (3,461 females, age=33.0 years±12.7) pooled across 41 international cohorts from the ENIGMA Schizophrenia Working Group, non-ENIGMA cohorts and public datasets. Using a machine learning approach known as Subtype and Stage Inference (SuStaIn), we implemented a brain imaging-driven classification that identifies two distinct neurostructural subgroups by mapping the spatial and temporal trajectory of gray matter (GM) loss in schizophrenia. Subgroup 1 (n=2,622) was characterized by an early cortical-predominant loss (ECL) with enlarged striatum, whereas subgroup 2 (n=1,600) displayed an early subcortical-predominant loss (ESL) in the hippocampus, amygdala, thalamus, brain stem and striatum. These reconstructed trajectories suggest that the GM volume reduction originates in the Broca's area/adjacent fronto-insular cortex for ECL and in the hippocampus/adjacent medial temporal structures for ESL. With longer disease duration, the ECL subtype exhibited a gradual worsening of negative symptoms and depression/anxiety, and less of a decline in positive symptoms. We confirmed the reproducibility of these imaging-based subtypes across various sample sites, independent of macroeconomic and ethnic factors that differed across these geographic locations, which include Europe, North America and East Asia. These findings underscore the presence of distinct pathobiological foundations underlying schizophrenia. This new imaging-based taxonomy holds the potential to identify a more homogeneous sub-population of individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.
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Affiliation(s)
- Yuchao Jiang
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technology, University of Electronic Science and Technology of China, Chengdu, China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Research Unit of NeuroInformation (2019RU035), Chinese Academy of Medical Sciences, Chengdu, China
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lena Palaniyappan
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montréal, Canada
| | - Xiao Chang
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Shitong Xiang
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Jie Zhang
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Mingjun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Huan Huang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Christian Gaser
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
- Department of Neurology, Jena University Hospital, Jena, Germany
- German Center for Mental Health (DZPG), Site Jena-Magdeburg-Halle, Germany
| | - Kiyotaka Nemoto
- Department of Psychiatry, Division of Clinical Medicine, Institute of Medicine, University of Tsukuba, Tsukuba, 305-8575, Japan
| | - Kenichiro Miura
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, 187-8553, Japan
| | - Ryota Hashimoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, 187-8553, Japan
| | - Lars T. Westlye
- Department of Psychology, University of Oslo, Oslo, Norway
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Genevieve Richard
- Department of Psychology, University of Oslo, Oslo, Norway
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Sara Fernandez-Cabello
- Department of Psychology, University of Oslo, Oslo, Norway
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Nadine Parker
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ole A. Andreassen
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Philipps Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, Philipps Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, Philipps Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
| | - Florian Thomas-Odenthal
- Department of Psychiatry and Psychotherapy, Philipps Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
| | - Lea Teutenberg
- Department of Psychiatry and Psychotherapy, Philipps Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
| | - Paula Usemann
- Department of Psychiatry and Psychotherapy, Philipps Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Rebekka Lencer
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Department of Psychiatry and Psychotherapie and Center for Brain, Behavior and Metabolism, Lübeck University, Lübeck, Germany
- Institute for Transnational Psychiatry and Otto Creutzfeldt Center for Behavioral and Cognitive Neuroscience, University of Münster, Münster, Germany
| | - Yingying Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tianhong Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chunbo Li
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Weihua Yue
- 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), Beijing, PR China
- Chinese Institute for Brain Research, Beijing, PR China
- PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, PR China
| | - Yuyanan Zhang
- 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), Beijing, PR China
| | - Xin Yu
- 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), Beijing, PR China
| | - Enpeng Zhou
- 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), Beijing, PR China
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Shih-Jen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Amanda L. Rodrigue
- Department of Psychiatry, Boston Children’s Hospital, Harvard Medical School, Boston MA, USA
| | - David Glahn
- Department of Psychiatry, Boston Children’s Hospital, Harvard Medical School, Boston MA, USA
| | - Godfrey Pearlson
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA
| | - John Blangero
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas of the Rio Grande Valley, Brownsville, TX, USA
| | - Andriana Karuk
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona 08035, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Spain
| | - Edith Pomarol-Clotet
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona 08035, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Spain
| | - Raymond Salvador
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona 08035, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Spain
| | - Paola Fuentes-Claramonte
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona 08035, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Spain
| | - María Ángeles Garcia-León
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona 08035, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Spain
| | - Gianfranco Spalletta
- Neuropsychiatry Laboratory, Department of Clinical Neuroscience and Neurorehabilitation, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Fabrizio Piras
- Neuropsychiatry Laboratory, Department of Clinical Neuroscience and Neurorehabilitation, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Daniela Vecchio
- Neuropsychiatry Laboratory, Department of Clinical Neuroscience and Neurorehabilitation, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Nerisa Banaj
- Neuropsychiatry Laboratory, Department of Clinical Neuroscience and Neurorehabilitation, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Jingliang Cheng
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhening Liu
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan, PR China
| | - Jie Yang
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan, PR China
| | - Ali Saffet Gonul
- Ege University School of Medicine Department of Psychiatry, SoCAT Lab, Izmir, Turkey
| | - Ozgul Uslu
- Ege University Institute of Health Sciences Department of Neuroscience, Izmir, Turkey
| | | | - Aslihan Uyar Demir
- Ege University School of Medicine Department of Psychiatry, SoCAT Lab, Izmir, Turkey
| | - Kelly Rootes-Murdy
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, GA, USA
| | - Vince D. Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, GA, USA
| | - Kang Sim
- West Region, Institute of Mental Health, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Melissa Green
- School of Clinical Medicine, University of New South Wales, Sydney, Australia
| | - Yann Quidé
- School of Psychology, University of New South Wales, Sydney, Australia
| | - Young Chul Chung
- Department of Psychiatry, Jeonbuk National University Hospital, Jeonju, Korea
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Woo-Sung Kim
- Department of Psychiatry, Jeonbuk National University Hospital, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Scott R. Sponheim
- Minneapolis VA Medical Center, University of Minnesota, Minneapolis, MN, USA
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Caroline Demro
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Ian S. Ramsay
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Felice Iasevoli
- Section of Psychiatry - Department of Neuroscience - University “Federico II”, Naples, Italy
| | - Andrea de Bartolomeis
- Section of Psychiatry - Department of Neuroscience - University “Federico II”, Naples, Italy
| | - Annarita Barone
- Section of Psychiatry - Department of Neuroscience - University “Federico II”, Naples, Italy
| | - Mariateresa Ciccarelli
- Section of Psychiatry - Department of Neuroscience - University “Federico II”, Naples, Italy
| | - Arturo Brunetti
- Department of Advanced Biomedical Sciences - University “Federico II”, Naples, Italy
| | - Sirio Cocozza
- Department of Advanced Biomedical Sciences - University “Federico II”, Naples, Italy
| | - Giuseppe Pontillo
- Department of Advanced Biomedical Sciences - University “Federico II”, Naples, Italy
| | - Mario Tranfa
- Department of Advanced Biomedical Sciences - University “Federico II”, Naples, Italy
| | - Min Tae M. Park
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
- Centre for Addiction and Mental Health, Toronto, Canada
| | - Matthias Kirschner
- Division of Adult Psychiatry, Department of Psychiatry, University Hospitals of Geneva, Switzerland
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Switzerland
| | - Foivos Georgiadis
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Switzerland
| | - Stefan Kaiser
- Division of Adult Psychiatry, Department of Psychiatry, University Hospitals of Geneva, Switzerland
| | - Tamsyn E Van Rheenen
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne, Melbourne, Australia
- Centre for Mental Health and Brain Sciences, School of Health Sciences, Swinburne University, Melbourne, Australia
| | - Susan L Rossell
- Centre for Mental Health and Brain Sciences, School of Health Sciences, Swinburne University, Melbourne, Australia
| | - Matthew Hughes
- Centre for Mental Health and Brain Sciences, School of Health Sciences, Swinburne University, Melbourne, Australia
| | - William Woods
- Centre for Mental Health and Brain Sciences, School of Health Sciences, Swinburne University, Melbourne, Australia
| | - Sean P Carruthers
- Centre for Mental Health and Brain Sciences, School of Health Sciences, Swinburne University, Melbourne, Australia
| | - Philip Sumner
- Centre for Mental Health and Brain Sciences, School of Health Sciences, Swinburne University, Melbourne, Australia
| | - Elysha Ringin
- National Institute of Mental Health, Klecany, Czech Republic
| | - Filip Spaniel
- National Institute of Mental Health, Klecany, Czech Republic
| | - Antonin Skoch
- National Institute of Mental Health, Klecany, Czech Republic
- MR Unit, Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - David Tomecek
- National Institute of Mental Health, Klecany, Czech Republic
- Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic
- Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Philipp Homan
- Psychiatric Hospital, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich & Swiss Federal Institute of Technology Zurich, Zurich, Switzerland
| | - Stephanie Homan
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, Switzerland
- Experimental Psychopathology and Psychotherapy, Department of Psychology, University of Zurich, Switzerland
| | - Wolfgang Omlor
- Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - Giacomo Cecere
- Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - Dana D Nguyen
- Department of Pediatrics, University of California Irvine, Irvine, California, USA
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California, USA
| | - Sophia Thomopoulos
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Long-Biao Cui
- Department of Clinical Psychology, Fourth Military Medical University, Xi’an, PR China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technology, University of Electronic Science and Technology of China, Chengdu, China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Research Unit of NeuroInformation (2019RU035), Chinese Academy of Medical Sciences, Chengdu, China
| | - Paul M. Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jessica A. Turner
- Psychiatry and Behavioral Health, Ohio State Wexner Medical Center, Columbus, OH, USA
| | - Theo G.M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine Hall, room 109, Irvine, CA, 92697-3950, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, 309 Qureshey Research Lab, Irvine, CA, 92697, USA
| | - Wei Cheng
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- Shanghai Medical College and Zhongshan Hospital Immunotherapy Technology Transfer Center, Shanghai, China
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- Fudan ISTBI—ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China
| | | | | | - Jianfeng Feng
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- Fudan ISTBI—ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
- School of Data Science, Fudan University, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
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12
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Puzniak RJ, Prabhakaran GT, McLean RJ, Stober S, Ather S, Proudlock FA, Gottlob I, Dineen RA, Hoffmann MB. CHIASM-Net: Artificial Intelligence-Based Direct Identification of Chiasmal Abnormalities in Albinism. Invest Ophthalmol Vis Sci 2023; 64:14. [PMID: 37815506 PMCID: PMC10573586 DOI: 10.1167/iovs.64.13.14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 09/06/2023] [Indexed: 10/11/2023] Open
Abstract
Purpose Albinism is a congenital disorder affecting pigmentation levels, structure, and function of the visual system. The identification of anatomical changes typical for people with albinism (PWA), such as optic chiasm malformations, could become an important component of diagnostics. Here, we tested an application of convolutional neural networks (CNNs) for this purpose. Methods We established and evaluated a CNN, referred to as CHIASM-Net, for the detection of chiasmal malformations from anatomic magnetic resonance (MR) images of the brain. CHIASM-Net, composed of encoding and classification modules, was developed using MR images of controls (n = 1708) and PWA (n = 32). Evaluation involved 8-fold cross validation involving accuracy, precision, recall, and F1-score metrics and was performed on a subset of controls and PWA samples excluded from the training. In addition to quantitative metrics, we used Explainable AI (XAI) methods that granted insights into factors driving the predictions of CHIASM-Net. Results The results for the scenario indicated an accuracy of 85 ± 14%, precision of 90 ± 14% and recall of 81 ± 18%. XAI methods revealed that the predictions of CHIASM-Net are driven by optic-chiasm white matter and by the optic tracts. Conclusions CHIASM-Net was demonstrated to use relevant regions of the optic chiasm for albinism detection from magnetic resonance imaging (MRI) brain anatomies. This indicates the strong potential of CNN-based approaches for visual pathway analysis and ultimately diagnostics.
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Affiliation(s)
- Robert J Puzniak
- Visual Processing Lab, Department of Ophthalmology, Otto-von-Guericke-University, Magdeburg, Germany
- Department of Neuroscience, Psychology, and Behaviour, University of Leicester, Leicester, United Kingdom
| | - Gokulraj T Prabhakaran
- Visual Processing Lab, Department of Ophthalmology, Otto-von-Guericke-University, Magdeburg, Germany
| | - Rebecca J McLean
- University of Leicester Ulverscroft Eye Unit, University of Leicester, Leicester Royal Infirmary, Leicester, United Kingdom
| | - Sebastian Stober
- Artificial Intelligence Lab, Institute for Intelligent Cooperating Systems, Otto-von-Guericke-University, Magdeburg, Germany
| | - Sarim Ather
- Department of Radiology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Headington, Oxford, United Kingdom
| | - Frank A Proudlock
- University of Leicester Ulverscroft Eye Unit, University of Leicester, Leicester Royal Infirmary, Leicester, United Kingdom
| | - Irene Gottlob
- University of Leicester Ulverscroft Eye Unit, University of Leicester, Leicester Royal Infirmary, Leicester, United Kingdom
- Cooper Neurological Institute and Cooper Medical School of Rowan University, Camden, New Jersey, United States
| | - Robert A Dineen
- Mental Health and Clinical Neuroscience, Faculty of Medicine and Health Sciences, University of Nottingham, Nottingham, United Kingdom
- NIHR Nottingham Biomedical Research Centre, Nottingham, United Kingdom
| | - Michael B Hoffmann
- Visual Processing Lab, Department of Ophthalmology, Otto-von-Guericke-University, Magdeburg, Germany
- Center for Behavioral Brain Sciences, Otto-von-Guericke-Universität, Magdeburg, Germany
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13
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Schabdach JM, Schmitt JE, Sotardi S, Vossough A, Andronikou S, Roberts TP, Huang H, Padmanabhan V, Ortiz-Rosa A, Gardner M, Covitz S, Bedford SA, Mandal AS, Chaiyachati BH, White SR, Bullmore E, Bethlehem RAI, Shinohara RT, Billot B, Iglesias JE, Ghosh S, Gur RE, Satterthwaite TD, Roalf D, Seidlitz J, Alexander-Bloch A. Brain Growth Charts for Quantitative Analysis of Pediatric Clinical Brain MRI Scans with Limited Imaging Pathology. Radiology 2023; 309:e230096. [PMID: 37906015 PMCID: PMC10623207 DOI: 10.1148/radiol.230096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 08/21/2023] [Accepted: 09/12/2023] [Indexed: 11/02/2023]
Abstract
Background Clinically acquired brain MRI scans represent a valuable but underused resource for investigating neurodevelopment due to their technical heterogeneity and lack of appropriate controls. These barriers have curtailed retrospective studies of clinical brain MRI scans compared with more costly prospectively acquired research-quality brain MRI scans. Purpose To provide a benchmark for neuroanatomic variability in clinically acquired brain MRI scans with limited imaging pathology (SLIPs) and to evaluate if growth charts from curated clinical MRI scans differed from research-quality MRI scans or were influenced by clinical indication for the scan. Materials and Methods In this secondary analysis of preexisting data, clinical brain MRI SLIPs from an urban pediatric health care system (individuals aged ≤22 years) were scanned across nine 3.0-T MRI scanners. The curation process included manual review of signed radiology reports and automated and manual quality review of images without gross pathology. Global and regional volumetric imaging phenotypes were measured using two image segmentation pipelines, and clinical brain growth charts were quantitatively compared with charts derived from a large set of research controls in the same age range by means of Pearson correlation and age at peak volume. Results The curated clinical data set included 532 patients (277 male; median age, 10 years [IQR, 5-14 years]; age range, 28 days after birth to 22 years) scanned between 2005 and 2020. Clinical brain growth charts were highly correlated with growth charts derived from research data sets (22 studies, 8346 individuals [4947 male]; age range, 152 days after birth to 22 years) in terms of normative developmental trajectories predicted by the models (median r = 0.979). Conclusion The clinical indication of the scans did not significantly bias the output of clinical brain charts. Brain growth charts derived from clinical controls with limited imaging pathology were highly correlated with brain charts from research controls, suggesting the potential of curated clinical MRI scans to supplement research data sets. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Ertl-Wagner and Pai in this issue.
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Affiliation(s)
- Jenna M. Schabdach
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - J. Eric Schmitt
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Susan Sotardi
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Arastoo Vossough
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Savvas Andronikou
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Timothy P. Roberts
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Hao Huang
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Viveknarayanan Padmanabhan
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Alfredo Ortiz-Rosa
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Margaret Gardner
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Sydney Covitz
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Saashi A. Bedford
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Ayan S. Mandal
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Barbara H. Chaiyachati
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Simon R. White
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Edward Bullmore
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Richard A. I. Bethlehem
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Russell T. Shinohara
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Benjamin Billot
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - J. Eugenio Iglesias
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Satrajit Ghosh
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Raquel E. Gur
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Theodore D. Satterthwaite
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - David Roalf
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Jakob Seidlitz
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Aaron Alexander-Bloch
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - for the Lifespan Brain Chart Consortium
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
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14
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Liu J, Xiao H, Fan J, Hu W, Yang Y, Dong P, Xing L, Cai J. An overview of artificial intelligence in medical physics and radiation oncology. JOURNAL OF THE NATIONAL CANCER CENTER 2023; 3:211-221. [PMID: 39035195 PMCID: PMC11256546 DOI: 10.1016/j.jncc.2023.08.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 05/03/2023] [Accepted: 08/08/2023] [Indexed: 07/23/2024] Open
Abstract
Artificial intelligence (AI) is developing rapidly and has found widespread applications in medicine, especially radiotherapy. This paper provides a brief overview of AI applications in radiotherapy, and highlights the research directions of AI that can potentially make significant impacts and relevant ongoing research works in these directions. Challenging issues related to the clinical applications of AI, such as robustness and interpretability of AI models, are also discussed. The future research directions of AI in the field of medical physics and radiotherapy are highlighted.
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Affiliation(s)
- Jiali Liu
- Department of Clinical Oncology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
- Department of Clinical Oncology, Hong Kong University Li Ka Shing Medical School, Hong Kong, China
| | - Haonan Xiao
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jiawei Fan
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
| | - Weigang Hu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
| | - Yong Yang
- Department of Radiation Oncology, Stanford University, CA, USA
| | - Peng Dong
- Department of Radiation Oncology, Stanford University, CA, USA
| | - Lei Xing
- Department of Radiation Oncology, Stanford University, CA, USA
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
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15
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Martin D, Basodi S, Panta S, Rootes-Murdy K, Prae P, Sarwate AD, Kelly R, Romero J, Baker BT, Gazula H, Bockholt J, Turner JA, Esper NB, Franco AR, Plis S, Calhoun VD. Enhancing collaborative neuroimaging research: introducing COINSTAC Vaults for federated analysis and reproducibility. Front Neuroinform 2023; 17:1207721. [PMID: 37404336 PMCID: PMC10315678 DOI: 10.3389/fninf.2023.1207721] [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: 04/18/2023] [Accepted: 06/02/2023] [Indexed: 07/06/2023] Open
Abstract
Collaborative neuroimaging research is often hindered by technological, policy, administrative, and methodological barriers, despite the abundance of available data. COINSTAC (The Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation) is a platform that successfully tackles these challenges through federated analysis, allowing researchers to analyze datasets without publicly sharing their data. This paper presents a significant enhancement to the COINSTAC platform: COINSTAC Vaults (CVs). CVs are designed to further reduce barriers by hosting standardized, persistent, and highly-available datasets, while seamlessly integrating with COINSTAC's federated analysis capabilities. CVs offer a user-friendly interface for self-service analysis, streamlining collaboration, and eliminating the need for manual coordination with data owners. Importantly, CVs can also be used in conjunction with open data as well, by simply creating a CV hosting the open data one would like to include in the analysis, thus filling an important gap in the data sharing ecosystem. We demonstrate the impact of CVs through several functional and structural neuroimaging studies utilizing federated analysis showcasing their potential to improve the reproducibility of research and increase sample sizes in neuroimaging studies.
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Affiliation(s)
- Dylan Martin
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
| | - Sunitha Basodi
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
| | - Sandeep Panta
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
| | - Kelly Rootes-Murdy
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
| | - Paul Prae
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
| | - Anand D. Sarwate
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
- Department of Electrical and Computer Engineering, Rutgers University–New Brunswick, Piscataway, NJ, United States
| | - Ross Kelly
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
| | - Javier Romero
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
| | - Bradley T. Baker
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
| | - Harshvardhan Gazula
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Jeremy Bockholt
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
| | - Jessica A. Turner
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
| | - Nathalia B. Esper
- Center for the Developing Brain, Child Mind Institute, New York, NY, United States
| | - Alexandre R. Franco
- Center for the Developing Brain, Child Mind Institute, New York, NY, United States
- Center for Brain Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, United States
- Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, United States
| | - Sergey Plis
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
| | - Vince D. Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
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16
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Martin D, Basodi S, Panta S, Rootes-Murdy K, Prae P, Sarwate AD, Kelly R, Romero J, Baker BT, Gazula H, Bockholt J, Turner J, Esper NB, Franco AR, Plis S, Calhoun VD. Enhancing Collaborative Neuroimaging Research: Introducing COINSTAC Vaults for Federated Analysis and Reproducibility. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.08.539852. [PMID: 37214791 PMCID: PMC10197552 DOI: 10.1101/2023.05.08.539852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Collaborative neuroimaging research is often hindered by technological, policy, administrative, and methodological barriers, despite the abundance of available data. COINSTAC is a platform that successfully tackles these challenges through federated analysis, allowing researchers to analyze datasets without publicly sharing their data. This paper presents a significant enhancement to the COINSTAC platform: COINSTAC Vaults (CVs). CVs are designed to further reduce barriers by hosting standardized, persistent, and highly-available datasets, while seamlessly integrating with COINSTAC's federated analysis capabilities. CVs offer a user-friendly interface for self-service analysis, streamlining collaboration and eliminating the need for manual coordination with data owners. Importantly, CVs can also be used in conjunction with open data as well, by simply creating a CV hosting the open data one would like to include in the analysis, thus filling an important gap in the data sharing ecosystem. We demonstrate the impact of CVs through several functional and structural neuroimaging studies utilizing federated analysis showcasing their potential to improve the reproducibility of research and increase sample sizes in neuroimaging studies.
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17
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Billot B, Greve DN, Puonti O, Thielscher A, Van Leemput K, Fischl B, Dalca AV, Iglesias JE. SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining. Med Image Anal 2023; 86:102789. [PMID: 36857946 PMCID: PMC10154424 DOI: 10.1016/j.media.2023.102789] [Citation(s) in RCA: 76] [Impact Index Per Article: 76.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 01/20/2023] [Accepted: 02/22/2023] [Indexed: 03/03/2023]
Abstract
Despite advances in data augmentation and transfer learning, convolutional neural networks (CNNs) difficultly generalise to unseen domains. When segmenting brain scans, CNNs are highly sensitive to changes in resolution and contrast: even within the same MRI modality, performance can decrease across datasets. Here we introduce SynthSeg, the first segmentation CNN robust against changes in contrast and resolution. SynthSeg is trained with synthetic data sampled from a generative model conditioned on segmentations. Crucially, we adopt a domain randomisation strategy where we fully randomise the contrast and resolution of the synthetic training data. Consequently, SynthSeg can segment real scans from a wide range of target domains without retraining or fine-tuning, which enables straightforward analysis of huge amounts of heterogeneous clinical data. Because SynthSeg only requires segmentations to be trained (no images), it can learn from labels obtained by automated methods on diverse populations (e.g., ageing and diseased), thus achieving robustness to a wide range of morphological variability. We demonstrate SynthSeg on 5,000 scans of six modalities (including CT) and ten resolutions, where it exhibits unparallelled generalisation compared with supervised CNNs, state-of-the-art domain adaptation, and Bayesian segmentation. Finally, we demonstrate the generalisability of SynthSeg by applying it to cardiac MRI and CT scans.
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Affiliation(s)
- Benjamin Billot
- Centre for Medical Image Computing, University College London, UK.
| | - Douglas N Greve
- Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, USA
| | - Oula Puonti
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital, Denmark
| | - Axel Thielscher
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital, Denmark; Department of Health Technology, Technical University of, Denmark
| | - Koen Van Leemput
- Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, USA; Department of Health Technology, Technical University of, Denmark
| | - Bruce Fischl
- Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA; Program in Health Sciences and Technology, Massachusetts Institute of Technology, USA
| | - Adrian V Dalca
- Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA
| | - Juan Eugenio Iglesias
- Centre for Medical Image Computing, University College London, UK; Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA
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18
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Song X, Li R, Wang K, Bai Y, Xiao Y, Wang YP. Joint Sparse Collaborative Regression on Imaging Genetics Study of Schizophrenia. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:1137-1146. [PMID: 35503837 PMCID: PMC10321021 DOI: 10.1109/tcbb.2022.3172289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The imaging genetics approach generates large amount of high dimensional and multi-modal data, providing complementary information for comprehensive study of Schizophrenia, a complex mental disease. However, at the same time, the variety of these data in structures, resolutions, and formats makes their integrative study a forbidding task. In this paper, we propose a novel model called Joint Sparse Collaborative Regression (JSCoReg), which can extract class-specific features from different health conditions/disease classes. We first evaluate the performance of feature selection in terms of Receiver operating characteristic curve and the area under the ROC curve in the simulation experiment. We demonstrate that the JSCoReg model can achieve higher accuracy compared with similar models including Joint Sparse Canonical Correlation Analysis and Sparse Collaborative Regression. We then applied the JSCoReg model to the analysis of schizophrenia dataset collected from the Mind Clinical Imaging Consortium. The JSCoReg enables us to better identify biomarkers associated with schizophrenia, which are verified to be both biologically and statistically significant.
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Affiliation(s)
- Xueli Song
- School of Sciences, Chang’an University, Xi’an, 710064, China
| | - Rongpeng Li
- School of Sciences, Chang’an University, Xi’an, 710064, China
| | - Kaiming Wang
- School of Sciences, Chang’an University, Xi’an, 710064, China
| | - Yuntong Bai
- Biomedical Engineering Department, Tulane University, New Orleans, LA 70118, USA
| | - Yuzhu Xiao
- School of Sciences, Chang’an University, Xi’an, 710064, China
| | - Yu-ping Wang
- Biomedical Engineering Department, Tulane University, New Orleans, LA 70118, USA
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19
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Ballester PL, Suh JS, Ho NCW, Liang L, Hassel S, Strother SC, Arnott SR, Minuzzi L, Sassi RB, Lam RW, Milev R, Müller DJ, Taylor VH, Kennedy SH, Reilly JP, Palaniyappan L, Dunlop K, Frey BN. Gray matter volume drives the brain age gap in schizophrenia: a SHAP study. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2023; 9:3. [PMID: 36624107 PMCID: PMC9829754 DOI: 10.1038/s41537-022-00330-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 12/28/2022] [Indexed: 01/11/2023]
Abstract
Neuroimaging-based brain age is a biomarker that is generated by machine learning (ML) predictions. The brain age gap (BAG) is typically defined as the difference between the predicted brain age and chronological age. Studies have consistently reported a positive BAG in individuals with schizophrenia (SCZ). However, there is little understanding of which specific factors drive the ML-based brain age predictions, leading to limited biological interpretations of the BAG. We gathered data from three publicly available databases - COBRE, MCIC, and UCLA - and an additional dataset (TOPSY) of early-stage schizophrenia (82.5% untreated first-episode sample) and calculated brain age with pre-trained gradient-boosted trees. Then, we applied SHapley Additive Explanations (SHAP) to identify which brain features influence brain age predictions. We investigated the interaction between the SHAP score for each feature and group as a function of the BAG. These analyses identified total gray matter volume (group × SHAP interaction term β = 1.71 [0.53; 3.23]; pcorr < 0.03) as the feature that influences the BAG observed in SCZ among the brain features that are most predictive of brain age. Other brain features also presented differences in SHAP values between SCZ and HC, but they were not significantly associated with the BAG. We compared the findings with a non-psychotic depression dataset (CAN-BIND), where the interaction was not significant. This study has important implications for the understanding of brain age prediction models and the BAG in SCZ and, potentially, in other psychiatric disorders.
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Affiliation(s)
- Pedro L. Ballester
- grid.25073.330000 0004 1936 8227Neuroscience Graduate Program, McMaster University, Hamilton, ON Canada
| | - Jee Su Suh
- grid.25073.330000 0004 1936 8227Neuroscience Graduate Program, McMaster University, Hamilton, ON Canada
| | - Natalie C. W. Ho
- grid.17063.330000 0001 2157 2938Faculty of Arts & Science, University of Toronto, Toronto, ON Canada ,grid.415502.7Keenan Research Centre for Biomedical Science, Unity Health Toronto, Toronto, Canada
| | - Liangbing Liang
- grid.39381.300000 0004 1936 8884Graduate Program in Neuroscience, Western University, London, ON Canada ,grid.39381.300000 0004 1936 8884Robarts Research Institute, Western University, London, ON Canada
| | - Stefanie Hassel
- grid.22072.350000 0004 1936 7697Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB Canada
| | - Stephen C. Strother
- grid.17063.330000 0001 2157 2938Rotman Research Institute, Baycrest, Toronto, ON Canada ,grid.17063.330000 0001 2157 2938Institute of Medical Science, University of Toronto, Toronto, ON Canada ,grid.17063.330000 0001 2157 2938Department of Medical Biophysics, University of Toronto, Toronto, ON Canada
| | - Stephen R. Arnott
- grid.17063.330000 0001 2157 2938Rotman Research Institute, Baycrest, Toronto, ON Canada
| | - Luciano Minuzzi
- Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, Hamilton, ON Canada ,grid.416721.70000 0001 0742 7355Women’s Health Concerns Clinic, St. Joseph’s Healthcare Hamilton, Hamilton, ON Canada
| | - Roberto B. Sassi
- grid.17091.3e0000 0001 2288 9830Department of Psychiatry, University of British Columbia, Vancouver, BC Canada
| | - Raymond W. Lam
- grid.17091.3e0000 0001 2288 9830Department of Psychiatry, University of British Columbia, Vancouver, BC Canada
| | - Roumen Milev
- grid.410356.50000 0004 1936 8331Departments of Psychiatry and Psychology, Queen’s University, and Providence Care, Kingston, ON Canada
| | - Daniel J. Müller
- grid.17063.330000 0001 2157 2938Department of Psychiatry, University of Toronto, Toronto, ON Canada ,grid.155956.b0000 0000 8793 5925Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON Canada
| | - Valerie H. Taylor
- grid.22072.350000 0004 1936 7697Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB Canada
| | - Sidney H. Kennedy
- grid.17063.330000 0001 2157 2938Institute of Medical Science, University of Toronto, Toronto, ON Canada ,grid.17063.330000 0001 2157 2938Department of Psychiatry, University of Toronto, Toronto, ON Canada ,grid.231844.80000 0004 0474 0428Centre for Mental Health, University Health Network, Toronto, ON Canada ,grid.231844.80000 0004 0474 0428Krembil Research Institute, University Health Network, Toronto, ON Canada ,grid.415502.7Centre for Depression and Suicide Studies, and Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, ON Canada
| | - James P. Reilly
- grid.25073.330000 0004 1936 8227Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON Canada
| | - Lena Palaniyappan
- grid.39381.300000 0004 1936 8884Robarts Research Institute, Western University, London, ON Canada ,grid.39381.300000 0004 1936 8884Department of Medical Biophysics, Western University, London, ON Canada ,grid.415847.b0000 0001 0556 2414Lawson Health Research Institute, London, ON Canada ,grid.39381.300000 0004 1936 8884Department of Psychiatry, Western University, London, ON Canada ,grid.14709.3b0000 0004 1936 8649Department of Psychiatry, Douglas Mental Health University Institute, McGill, Douglas, QC Canada
| | - Katharine Dunlop
- grid.415502.7Keenan Research Centre for Biomedical Science, Unity Health Toronto, Toronto, Canada ,grid.17063.330000 0001 2157 2938Institute of Medical Science, University of Toronto, Toronto, ON Canada ,grid.17063.330000 0001 2157 2938Department of Psychiatry, University of Toronto, Toronto, ON Canada ,Centre for Depression & Suicide Studies, Unity Health Toronto, Toronto, ON Canada
| | - Benicio N. Frey
- Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, Hamilton, ON Canada ,grid.416721.70000 0001 0742 7355Women’s Health Concerns Clinic, St. Joseph’s Healthcare Hamilton, Hamilton, ON Canada ,grid.25073.330000 0004 1936 8227Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON Canada
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20
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Zou J, Gao B, Song Y, Qin J. A review of deep learning-based deformable medical image registration. Front Oncol 2022; 12:1047215. [PMID: 36568171 PMCID: PMC9768226 DOI: 10.3389/fonc.2022.1047215] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Accepted: 11/08/2022] [Indexed: 12/12/2022] Open
Abstract
The alignment of images through deformable image registration is vital to clinical applications (e.g., atlas creation, image fusion, and tumor targeting in image-guided navigation systems) and is still a challenging problem. Recent progress in the field of deep learning has significantly advanced the performance of medical image registration. In this review, we present a comprehensive survey on deep learning-based deformable medical image registration methods. These methods are classified into five categories: Deep Iterative Methods, Supervised Methods, Unsupervised Methods, Weakly Supervised Methods, and Latest Methods. A detailed review of each category is provided with discussions about contributions, tasks, and inadequacies. We also provide statistical analysis for the selected papers from the point of view of image modality, the region of interest (ROI), evaluation metrics, and method categories. In addition, we summarize 33 publicly available datasets that are used for benchmarking the registration algorithms. Finally, the remaining challenges, future directions, and potential trends are discussed in our review.
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Affiliation(s)
- Jing Zou
- Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
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21
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Meyer TD, Godfrey CJ, Walss-Bass C. The UT health Psychological Autopsy Interview Schedule (UTH- PAIS) - Description and reliability of diagnoses and transdiagnostic personality measures. J Psychiatr Res 2022; 156:221-227. [PMID: 36270060 DOI: 10.1016/j.jpsychires.2022.10.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 09/20/2022] [Accepted: 10/03/2022] [Indexed: 11/05/2022]
Abstract
Few studies have used psychological autopsies to evaluate large and diverse populations on transdiagnostically relevant variables such as personality, temperament, and trauma exposure; rather, they tend to focus on specific psychiatric disorders or manner of death. We therefore developed the UT Health Psychological Autopsy Interview Schedule (UTH-PAIS). The measure is described, and our results show that the PAIS diagnoses and dimensions can be reliably assessed. Furthermore, we were able to show that our sample of donated brains overall matches the demographic characteristics of a larger pool of individuals receiving a medical autopsy. In the Discussion we review the strengths and potential limitations of the study and outline in which context the PAIS will prove to be useful.
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Affiliation(s)
- Thomas D Meyer
- McGovern Medical School, Faillace Department of Psychiatry & Behavioral Sciences, University of Texas Health Science Center at Houston, Houston, TX, USA.
| | - Christopher J Godfrey
- McGovern Medical School, Faillace Department of Psychiatry & Behavioral Sciences, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Consuelo Walss-Bass
- McGovern Medical School, Faillace Department of Psychiatry & Behavioral Sciences, University of Texas Health Science Center at Houston, Houston, TX, USA
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22
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Rootes-Murdy K, Edmond JT, Jiang W, Rahaman MA, Chen J, Perrone-Bizzozero NI, Calhoun VD, van Erp TGM, Ehrlich S, Agartz I, Jönsson EG, Andreassen OA, Westlye LT, Wang L, Pearlson GD, Glahn DC, Hong E, Buchanan RW, Kochunov P, Voineskos A, Malhotra A, Tamminga CA, Liu J, Turner JA. Clinical and cortical similarities identified between bipolar disorder I and schizophrenia: A multivariate approach. Front Hum Neurosci 2022; 16:1001692. [PMID: 36438633 PMCID: PMC9684186 DOI: 10.3389/fnhum.2022.1001692] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Accepted: 10/17/2022] [Indexed: 01/06/2024] Open
Abstract
BACKGROUND Structural neuroimaging studies have identified similarities in the brains of individuals diagnosed with schizophrenia (SZ) and bipolar I disorder (BP), with overlap in regions of gray matter (GM) deficits between the two disorders. Recent studies have also shown that the symptom phenotypes associated with SZ and BP may allow for a more precise categorization than the current diagnostic criteria. In this study, we sought to identify GM alterations that were unique to each disorder and whether those alterations were also related to unique symptom profiles. MATERIALS AND METHODS We analyzed the GM patterns and clinical symptom presentations using independent component analysis (ICA), hierarchical clustering, and n-way biclustering in a large (N ∼ 3,000), merged dataset of neuroimaging data from healthy volunteers (HV), and individuals with either SZ or BP. RESULTS Component A showed a SZ and BP < HV GM pattern in the bilateral insula and cingulate gyrus. Component B showed a SZ and BP < HV GM pattern in the cerebellum and vermis. There were no significant differences between diagnostic groups in these components. Component C showed a SZ < HV and BP GM pattern bilaterally in the temporal poles. Hierarchical clustering of the PANSS scores and the ICA components did not yield new subgroups. N-way biclustering identified three unique subgroups of individuals within the sample that mapped onto different combinations of ICA components and symptom profiles categorized by the PANSS but no distinct diagnostic group differences. CONCLUSION These multivariate results show that diagnostic boundaries are not clearly related to structural differences or distinct symptom profiles. Our findings add support that (1) BP tend to have less severe symptom profiles when compared to SZ on the PANSS without a clear distinction, and (2) all the gray matter alterations follow the pattern of SZ < BP < HV without a clear distinction between SZ and BP.
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Affiliation(s)
- Kelly Rootes-Murdy
- Department of Psychology, Georgia State University, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Jesse T. Edmond
- Department of Psychology, Georgia State University, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Wenhao Jiang
- Department of Psychosomatics and Psychiatry, Medical School, Zhongda Hospital, Institute of Psychosomatics, Southeast University, Nanjing, China
| | - Md A. Rahaman
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Jiayu Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | | | - Vince D. Calhoun
- Department of Psychology, Georgia State University, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Theo G. M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, United States
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, CA, United States
| | - Stefan Ehrlich
- Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Ingrid Agartz
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo, Norway
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institute and Stockholm Health Care Services, Stockholm, Sweden
- K. G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Erik G. Jönsson
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo, Norway
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institute and Stockholm Health Care Services, Stockholm, Sweden
| | - Ole A. Andreassen
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo, Norway
- K. G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Lars T. Westlye
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo, Norway
- K. G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Lei Wang
- Psychiatry and Behavioral Health, Ohio State Wexner Medical Center, Columbus, OH, United States
| | - Godfrey D. Pearlson
- Department of Psychiatry, Yale University, New Haven, CT, United States
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, Hartford, CT, United States
| | - David C. Glahn
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, Hartford, CT, United States
- Boston Children’s Hospital and Harvard Medical School, Boston, MA, United States
| | - Elliot Hong
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Robert W. Buchanan
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Peter Kochunov
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Aristotle Voineskos
- Department of Psychiatry, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada
| | - Anil Malhotra
- Division of Psychiatry Research, Zucker Hillside Hospital, Queens, NY, United States
| | - Carol A. Tamminga
- Department of Psychiatry, University of Texas Southwestern Medical School, Dallas, TX, United States
| | - Jingyu Liu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Jessica A. Turner
- Psychiatry and Behavioral Health, Ohio State Wexner Medical Center, Columbus, OH, United States
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23
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Zhang Y, Zhang H, Xiao L, Bai Y, Calhoun VD, Wang YP. Multi-Modal Imaging Genetics Data Fusion via a Hypergraph-Based Manifold Regularization: Application to Schizophrenia Study. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:2263-2272. [PMID: 35320094 PMCID: PMC9661879 DOI: 10.1109/tmi.2022.3161828] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Recent studies show that multi-modal data fusion techniques combine information from diverse sources for comprehensive diagnosis and prognosis of complex brain disorder, often resulting in improved accuracy compared to single-modality approaches. However, many existing data fusion methods extract features from homogeneous networs, ignoring heterogeneous structural information among multiple modalities. To this end, we propose a Hypergraph-based Multi-modal data Fusion algorithm, namely HMF. Specifically, we first generate a hypergraph similarity matrix to represent the high-order relationships among subjects, and then enforce the regularization term based upon both the inter- and intra-modality relationships of the subjects. Finally, we apply HMF to integrate imaging and genetics datasets. Validation of the proposed method is performed on both synthetic data and real samples from schizophrenia study. Results show that our algorithm outperforms several competing methods, and reveals significant interactions among risk genes, environmental factors and abnormal brain regions.
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24
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Levman J, Jennings M, Rouse E, Berger D, Kabaria P, Nangaku M, Gondra I, Takahashi E. A morphological study of schizophrenia with magnetic resonance imaging, advanced analytics, and machine learning. Front Neurosci 2022; 16:926426. [PMID: 36046472 PMCID: PMC9420897 DOI: 10.3389/fnins.2022.926426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 07/05/2022] [Indexed: 11/16/2022] Open
Abstract
We have performed a morphological analysis of patients with schizophrenia and compared them with healthy controls. Our analysis includes the use of publicly available automated extraction tools to assess regional cortical thickness (inclusive of within region cortical thickness variability) from structural magnetic resonance imaging (MRI), to characterize group-wise abnormalities associated with schizophrenia based on a publicly available dataset. We have also performed a correlation analysis between the automatically extracted biomarkers and a variety of patient clinical variables available. Finally, we also present the results of a machine learning analysis. Results demonstrate regional cortical thickness abnormalities in schizophrenia. We observed a correlation (rho = 0.474) between patients’ depression and the average cortical thickness of the right medial orbitofrontal cortex. Our leading machine learning technology evaluated was the support vector machine with stepwise feature selection, yielding a sensitivity of 92% and a specificity of 74%, based on regional brain measurements, including from the insula, superior frontal, caudate, calcarine sulcus, gyrus rectus, and rostral middle frontal regions. These results imply that advanced analytic techniques combining MRI with automated biomarker extraction can be helpful in characterizing patients with schizophrenia.
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Affiliation(s)
- Jacob Levman
- Department of Computer Science, St. Francis Xavier University, Antigonish, NS, Canada
- Center for Clinical Research, Nova Scotia Health Authority - Research, Innovation and Discovery, Halifax, NS, Canada
- Department of Radiology, Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts Institute of Technology, Boston, MA, United States
- *Correspondence: Jacob Levman,
| | - Maxwell Jennings
- Department of Computer Science, St. Francis Xavier University, Antigonish, NS, Canada
- Department of Mathematics and Statistics, St. Francis Xavier University, Antigonish, NS, Canada
| | - Ethan Rouse
- Department of Computer Science, St. Francis Xavier University, Antigonish, NS, Canada
| | - Derek Berger
- Department of Computer Science, St. Francis Xavier University, Antigonish, NS, Canada
| | - Priya Kabaria
- Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Masahito Nangaku
- Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Iker Gondra
- Department of Computer Science, St. Francis Xavier University, Antigonish, NS, Canada
| | - Emi Takahashi
- Department of Radiology, Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts Institute of Technology, Boston, MA, United States
- Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
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25
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Xie Y, Ding H, Du X, Chai C, Wei X, Sun J, Zhuo C, Wang L, Li J, Tian H, Liang M, Zhang S, Yu C, Qin W. Morphometric Integrated Classification Index: A Multisite Model-Based, Interpretable, Shareable and Evolvable Biomarker for Schizophrenia. Schizophr Bull 2022; 48:1217-1227. [PMID: 35925032 PMCID: PMC9673259 DOI: 10.1093/schbul/sbac096] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND AND HYPOTHESIS Multisite massive schizophrenia neuroimaging data sharing is becoming critical in understanding the pathophysiological mechanism and making an objective diagnosis of schizophrenia; it remains challenging to obtain a generalizable and interpretable, shareable, and evolvable neuroimaging biomarker for schizophrenia diagnosis. STUDY DESIGN A Morphometric Integrated Classification Index (MICI) was proposed as a potential biomarker for schizophrenia diagnosis based on structural magnetic resonance imaging data of 1270 subjects from 10 sites (588 schizophrenia patients and 682 normal controls). An optimal XGBoost classifier plus sample-weighted SHapley Additive explanation algorithms were used to construct the MICI measure. STUDY RESULTS The MICI measure achieved comparable performance with the sample-weighted ensembling model and merged model based on raw data (Delong test, P > 0.82) while outperformed the single-site models (Delong test, P < 0.05) in either the independent-sample testing datasets from the 9 sites or the independent-site dataset (generalizable). Besides, when new sites were embedded in, the performance of this measure was gradually increasing (evolvable). Finally, MICI was strongly associated with the severity of schizophrenia brain structural abnormality, with the patients' positive and negative symptoms, and with the brain expression profiles of schizophrenia risk genes (interpretable). CONCLUSIONS In summary, the proposed MICI biomarker may provide a simple and explainable way to support clinicians for objectively diagnosing schizophrenia. Finally, we developed an online model share platform to promote biomarker generalization and provide free individual prediction services (http://micc.tmu.edu.cn/mici/index.html).
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Affiliation(s)
- Yingying Xie
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Hao Ding
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China,School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Xiaotong Du
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Chao Chai
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Xiaotong Wei
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Jie Sun
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Chuanjun Zhuo
- Department of Psychiatry Functional Neuroimaging Laboratory, Tianjin Mental Health Center, Tianjin Anding Hospital, Tianjin, China
| | - Lina Wang
- Department of Psychiatry Functional Neuroimaging Laboratory, Tianjin Mental Health Center, Tianjin Anding Hospital, Tianjin, China
| | - Jie Li
- Department of Psychiatry Functional Neuroimaging Laboratory, Tianjin Mental Health Center, Tianjin Anding Hospital, Tianjin, China
| | | | - Meng Liang
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China,School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | | | | | - Wen Qin
- To whom correspondence should be addressed; Department of Radiology, and Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital. Anshan Road No 154, Heping District, Tianjin 300052, China.
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26
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Detecting abnormal connectivity in schizophrenia via a joint directed acyclic graph estimation model. Neuroimage 2022; 260:119451. [PMID: 35842099 DOI: 10.1016/j.neuroimage.2022.119451] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 06/14/2022] [Accepted: 07/03/2022] [Indexed: 01/10/2023] Open
Abstract
Functional connectivity (FC) between brain region has been widely studied and linked with cognition and behavior of an individual. FC is usually defined as the correlation or partial correlation of fMRI blood oxygen level-dependent (BOLD) signals between two brain regions. Although FC has been effective to understand brain organization, it cannot reveal the direction of interactions. Many directed acyclic graph (DAG) based methods have been applied to study the directed interactions but their performance was limited by the small sample size while high dimensionality of the available data. By enforcing group regularization and utilizing samples from both case and control groups, we propose a joint DAG model to estimate the directed FC. We first demonstrate that the proposed model is efficient and accurate through a series of simulation studies. We then apply it to the case-control study of schizophrenia (SZ) with data collected from the MIND Clinical Imaging Consortium (MCIC). We have successfully identified decreased functional integration, disrupted hub structures and characteristic edges (CtEs) in SZ patients. Those findings have been confirmed by previous studies with some identified to be potential markers for SZ patients. A comparison of the results between the directed FC and undirected FC showed substantial differences in the selected features. In addition, we used the identified features based on directed FC for the classification of SZ patients and achieved better accuracy than using undirected FC or raw features, demonstrating the advantage of using directed FC for brain network analysis.
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27
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Hoopes A, Iglesias JE, Fischl B, Greve D, Dalca AV. TopoFit: Rapid Reconstruction of Topologically-Correct Cortical Surfaces. PROCEEDINGS OF MACHINE LEARNING RESEARCH 2022; 172:508-520. [PMID: 37220495 PMCID: PMC10201930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Mesh-based reconstruction of the cerebral cortex is a fundamental component in brain image analysis. Classical, iterative pipelines for cortical modeling are robust but often time-consuming, mostly due to expensive procedures that involve topology correction and spherical mapping. Recent attempts to address reconstruction with machine learning methods have accelerated some components in these pipelines, but these methods still require slow processing steps to enforce topological constraints that comply with known anatomical structure. In this work, we introduce a novel learning-based strategy, TopoFit, which rapidly fits a topologically-correct surface to the white-matter tissue boundary. We design a joint network, employing image and graph convolutions and an efficient symmetric distance loss, to learn to predict accurate deformations that map a template mesh to subject-specific anatomy. This technique encompasses the work of current mesh correction, fine-tuning, and inflation processes and, as a result, offers a 150× faster solution to cortical surface reconstruction compared to traditional approaches. We demonstrate that TopoFit is 1.8× more accurate than the current state-of-the-art deep-learning strategy, and it is robust to common failure modes, such as white-matter tissue hypointensities.
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Affiliation(s)
- Andrew Hoopes
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
| | - Juan Eugenio Iglesias
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
- Department of Radiology, Harvard Medical School
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology
- Centre for Medical Image Computing, University College London
| | - Bruce Fischl
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
- Department of Radiology, Harvard Medical School
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology
- Harvard-MIT Division of Health, Sciences, and Technology
| | - Douglas Greve
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
- Department of Radiology, Harvard Medical School
| | - Adrian V Dalca
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
- Department of Radiology, Harvard Medical School
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology
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28
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Acar E, Roald M, Hossain KM, Calhoun VD, Adali T. Tracing Evolving Networks Using Tensor Factorizations vs. ICA-Based Approaches. Front Neurosci 2022; 16:861402. [PMID: 35546891 PMCID: PMC9081795 DOI: 10.3389/fnins.2022.861402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 03/07/2022] [Indexed: 11/13/2022] Open
Abstract
Analysis of time-evolving data is crucial to understand the functioning of dynamic systems such as the brain. For instance, analysis of functional magnetic resonance imaging (fMRI) data collected during a task may reveal spatial regions of interest, and how they evolve during the task. However, capturing underlying spatial patterns as well as their change in time is challenging. The traditional approach in fMRI data analysis is to assume that underlying spatial regions of interest are static. In this article, using fractional amplitude of low-frequency fluctuations (fALFF) as an effective way to summarize the variability in fMRI data collected during a task, we arrange time-evolving fMRI data as a subjects by voxels by time windows tensor, and analyze the tensor using a tensor factorization-based approach called a PARAFAC2 model to reveal spatial dynamics. The PARAFAC2 model jointly analyzes data from multiple time windows revealing subject-mode patterns, evolving spatial regions (also referred to as networks) and temporal patterns. We compare the PARAFAC2 model with matrix factorization-based approaches relying on independent components, namely, joint independent component analysis (ICA) and independent vector analysis (IVA), commonly used in neuroimaging data analysis. We assess the performance of the methods in terms of capturing evolving networks through extensive numerical experiments demonstrating their modeling assumptions. In particular, we show that (i) PARAFAC2 provides a compact representation in all modes, i.e., subjects, time, and voxels, revealing temporal patterns as well as evolving spatial networks, (ii) joint ICA is as effective as PARAFAC2 in terms of revealing evolving networks but does not reveal temporal patterns, (iii) IVA's performance depends on sample size, data distribution and covariance structure of underlying networks. When these assumptions are satisfied, IVA is as accurate as the other methods, (iv) when subject-mode patterns differ from one time window to another, IVA is the most accurate. Furthermore, we analyze real fMRI data collected during a sensory motor task, and demonstrate that a component indicating statistically significant group difference between patients with schizophrenia and healthy controls is captured, which includes primary and secondary motor regions, cerebellum, and temporal lobe, revealing a meaningful spatial map and its temporal change.
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Affiliation(s)
- Evrim Acar
- Simula Metropolitan Center for Digital Engineering, Oslo, Norway
| | - Marie Roald
- Simula Metropolitan Center for Digital Engineering, Oslo, Norway.,Oslo Metropolitan University, Oslo, Norway
| | - Khondoker M Hossain
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD, United States
| | - Vince D Calhoun
- Department of Psychology, Georgia State University, Atlanta, GA, United States
| | - Tülay Adali
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD, United States
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29
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Hoopes A, Hoffmann M, Greve DN, Fischl B, Guttag J, Dalca AV. Learning the Effect of Registration Hyperparameters with HyperMorph. THE JOURNAL OF MACHINE LEARNING FOR BIOMEDICAL IMAGING 2022; 1:003. [PMID: 36147449 PMCID: PMC9491317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
We introduce HyperMorph, a framework that facilitates efficient hyperparameter tuning in learning-based deformable image registration. Classical registration algorithms perform an iterative pair-wise optimization to compute a deformation field that aligns two images. Recent learning-based approaches leverage large image datasets to learn a function that rapidly estimates a deformation for a given image pair. In both strategies, the accuracy of the resulting spatial correspondences is strongly influenced by the choice of certain hyperparameter values. However, an effective hyperparameter search consumes substantial time and human effort as it often involves training multiple models for different fixed hyperparameter values and may lead to suboptimal registration. We propose an amortized hyperparameter learning strategy to alleviate this burden by learning the impact of hyperparameters on deformation fields. We design a meta network, or hypernetwork, that predicts the parameters of a registration network for input hyperparameters, thereby comprising a single model that generates the optimal deformation field corresponding to given hyperparameter values. This strategy enables fast, high-resolution hyperparameter search at test-time, reducing the inefficiency of traditional approaches while increasing flexibility. We also demonstrate additional benefits of HyperMorph, including enhanced robustness to model initialization and the ability to rapidly identify optimal hyperparameter values specific to a dataset, image contrast, task, or even anatomical region, all without the need to retrain models. We make our code publicly available at http://hypermorph.voxelmorph.net.
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Affiliation(s)
- Andrew Hoopes
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
| | - Malte Hoffmann
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
- Department of Radiology, Harvard Medical School
| | - Douglas N Greve
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
- Department of Radiology, Harvard Medical School
| | - Bruce Fischl
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
- Department of Radiology, Harvard Medical School
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology
| | - John Guttag
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology
| | - Adrian V Dalca
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
- Department of Radiology, Harvard Medical School
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology
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30
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Scan Once, Analyse Many: Using Large Open-Access Neuroimaging Datasets to Understand the Brain. Neuroinformatics 2022; 20:109-137. [PMID: 33974213 PMCID: PMC8111663 DOI: 10.1007/s12021-021-09519-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/07/2021] [Indexed: 02/06/2023]
Abstract
We are now in a time of readily available brain imaging data. Not only are researchers now sharing data more than ever before, but additionally large-scale data collecting initiatives are underway with the vision that many future researchers will use the data for secondary analyses. Here I provide an overview of available datasets and some example use cases. Example use cases include examining individual differences, more robust findings, reproducibility-both in public input data and availability as a replication sample, and methods development. I further discuss a variety of considerations associated with using existing data and the opportunities associated with large datasets. Suggestions for further readings on general neuroimaging and topic-specific discussions are also provided.
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31
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White T, Blok E, Calhoun VD. Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed. Hum Brain Mapp 2022; 43:278-291. [PMID: 32621651 PMCID: PMC8675413 DOI: 10.1002/hbm.25120] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 05/12/2020] [Accepted: 06/22/2020] [Indexed: 12/19/2022] Open
Abstract
Collaborative networks and data sharing initiatives are broadening the opportunities for the advancement of science. These initiatives offer greater transparency in science, with the opportunity for external research groups to reproduce, replicate, and extend research findings. Further, larger datasets offer the opportunity to identify homogeneous patterns within subgroups of individuals, where these patterns may be obscured by the heterogeneity of the neurobiological measure in smaller samples. However, data sharing and data pooling initiatives are not without their challenges, especially with new laws that may at first glance appear quite restrictive for open science initiatives. Interestingly, what is key to some of these new laws (i.e, the European Union's general data protection regulation) is that they provide greater control of data to those who "give" their data for research purposes. Thus, the most important element in data sharing is allowing the participants to make informed decisions about how they want their data to be used, and, within the law of the specific country, to follow the participants' wishes. This framework encompasses obtaining thorough informed consent and allowing the participant to determine the extent that they want their data shared, many of the ethical and legal obstacles are reduced to just monsters under the bed. In this manuscript we discuss the many options and obstacles for data sharing, from fully open, to federated learning, to fully closed. Importantly, we highlight the intersection of data sharing, privacy, and data ownership and highlight specific examples that we believe are informative to the neuroimaging community.
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Affiliation(s)
- Tonya White
- Department of Child and Adolescent Psychiatry/PsychologyErasmus University Medical CenterRotterdamThe Netherlands
- Department of RadiologyErasmus University Medical CenterRotterdamThe Netherlands
| | - Elisabet Blok
- Department of Child and Adolescent Psychiatry/PsychologyErasmus University Medical CenterRotterdamThe Netherlands
| | - Vince D. Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
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Joo SW, Kim H, Jo YT, Ahn S, Choi YJ, Park S, Kang Y, Lee J. White matter impairments in patients with schizophrenia: A multisite diffusion MRI study. Prog Neuropsychopharmacol Biol Psychiatry 2021; 111:110381. [PMID: 34111494 DOI: 10.1016/j.pnpbp.2021.110381] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 05/31/2021] [Accepted: 06/04/2021] [Indexed: 10/21/2022]
Abstract
There is a lack of convincing and replicative findings regarding white matter abnormalities in schizophrenia. Several multisite diffusion magnetic resonance imaging (dMRI) studies have been conducted to increase statistical power and reveal subtle white matter changes. Data pooling methods are crucial in joint analysis to compensate for the use of different scanners and image acquisition parameters. A harmonization method using raw dMRI data was developed to overcome the limited generalizability of previous data pooling methods. We obtained dMRI data of 242 healthy controls and 190 patients with schizophrenia from four different study sites. After applying the harmonization method to the raw dMRI data, a two-tensor whole-brain tractography was performed, and diffusion measures were compared between the two groups. The correlation of fractional anisotropy (FA) with the positive and negative symptoms was evaluated, and the interaction effect of diagnosis-by-age, age-squared, and sex was examined. The following white matter tracts showed significant group differences in the FA: the right superior longitudinal fascicle (SLF), the left-to-right lateral orbitofrontal commissural tract, pars orbitalis (pOr-pOr) commissural tract, and pars triangularis (pTr-pTr) commissural tract. The FA of the right SLF and pTr-pTr commissural tract were significantly associated with the Positive and Negative Syndrome Scale (PANSS) positive and negative scores. No significant interaction effect was observed. These findings add to the evidence on structural brain abnormalities in schizophrenia and can aid in obtaining a better understanding of the biological foundations of schizophrenia.
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Affiliation(s)
- Sung Woo Joo
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Harin Kim
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Young Tak Jo
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Soojin Ahn
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Young Jae Choi
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Soyeon Park
- Department of Psychiatry, Medical Foundation Yongin Mental Hospital, Yongin, Republic of Korea
| | - Yuree Kang
- Department of Psychiatry, Medical Foundation Yongin Mental Hospital, Yongin, Republic of Korea
| | - Jungsun Lee
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
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Resolving heterogeneity in schizophrenia through a novel systems approach to brain structure: individualized structural covariance network analysis. Mol Psychiatry 2021; 26:7719-7731. [PMID: 34316005 DOI: 10.1038/s41380-021-01229-4] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 06/15/2021] [Accepted: 07/05/2021] [Indexed: 12/12/2022]
Abstract
Reliable mapping of system-level individual differences is a critical first step toward precision medicine for complex disorders such as schizophrenia. Disrupted structural covariance indicates a system-level brain maturational disruption in schizophrenia. However, most studies examine structural covariance at the group level. This prevents subject-level inferences. Here, we introduce a Network Template Perturbation approach to construct individual differential structural covariance network (IDSCN) using regional gray-matter volume. IDSCN quantifies how structural covariance between two nodes in a patient deviates from the normative covariance in healthy subjects. We analyzed T1 images from 1287 subjects, including 107 first-episode (drug-naive) patients and 71 controls in the discovery datasets and established robustness in 213 first-episode (drug-naive), 294 chronic, 99 clinical high-risk patients, and 494 controls from the replication datasets. Patients with schizophrenia were highly variable in their altered structural covariance edges; the number of altered edges was related to severity of hallucinations. Despite this variability, a subset of covariance edges, including the left hippocampus-bilateral putamen/globus pallidus edges, clustered patients into two distinct subgroups with opposing changes in covariance compared to controls, and significant differences in their anxiety and depression scores. These subgroup differences were stable across all seven datasets with meaningful genetic associations and functional annotation for the affected edges. We conclude that the underlying physiology of affective symptoms in schizophrenia involves the hippocampus and putamen/pallidum, predates disease onset, and is sufficiently consistent to resolve morphological heterogeneity throughout the illness course. The two schizophrenia subgroups identified thus have implications for the nosology and clinical treatment.
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Jiang W, Rootes-Murdy K, Chen J, Bizzozero NIP, Calhoun VD, van Erp TGM, Ehrlich S, Agartz I, Jönsson EG, Andreassen OA, Wang L, Pearlson GD, Glahn DC, Hong E, Liu J, Turner JA. Multivariate alterations in insula - Medial prefrontal cortex linked to genetics in 12q24 in schizophrenia. Psychiatry Res 2021; 306:114237. [PMID: 34655926 PMCID: PMC8643340 DOI: 10.1016/j.psychres.2021.114237] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 10/06/2021] [Accepted: 10/08/2021] [Indexed: 11/29/2022]
Abstract
The direct effect of genetic variations on clinical phenotypes within schizophrenia (SZ) remains elusive. We examined the previously identified association of reduced gray matter concentration in the insula - medial prefrontal cortex and a quantitative trait locus located in 12q24 in a SZ dataset. The main analysis was performed on 1461 SNPs and 830 participants. The highest contributing SNPs were localized in five genes including TMEM119, which encodes a microglial marker, that is associated with neuroinflammation and Alzheimer's disease. The gene set in 12q4 may partially explain brain alterations in SZ, but they may also relate to other psychiatric and developmental disorders.
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Affiliation(s)
- Wenhao Jiang
- Department of Psychology, Georgia State University, United States of America; Department of Psychosomatics and Psychiatry, Zhongda Hospital, Institute of Psychosomatics, Medical School, Southeast University, Nanjing, China
| | - Kelly Rootes-Murdy
- Department of Psychology, Georgia State University, United States of America
| | - Jiayu Chen
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology and Emory University, United States of America
| | | | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology and Emory University, United States of America
| | - Theo G M van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, United States of America; Qureshey Research Laboratory, Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine, CA,United States of America
| | - Stefan Ehrlich
- Department of Psychiatry, Massachusetts General Hospital, United States of America; Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU Dresden, Germany
| | - Ingrid Agartz
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Stockholm Region, Stockholm, Sweden; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Erik G Jönsson
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Stockholm Region, Stockholm, Sweden
| | - Ole A Andreassen
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway
| | - Lei Wang
- Psychiatry and Behavioral Health, Ohio State Wexner Medical Center, United States of America
| | | | - David C Glahn
- Boston Children's Hospital and Harvard Medical School, United States of America
| | - Elliot Hong
- Maryland Psychiatric Research Center, University of Maryland School of Medicine, United States of America
| | - Jingyu Liu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology and Emory University, United States of America
| | - Jessica A Turner
- Department of Psychology, Georgia State University, United States of America; Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology and Emory University, United States of America
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Xiao H, Teng X, Liu C, Li T, Ren G, Yang R, Shen D, Cai J. A review of deep learning-based three-dimensional medical image registration methods. Quant Imaging Med Surg 2021; 11:4895-4916. [PMID: 34888197 PMCID: PMC8611468 DOI: 10.21037/qims-21-175] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 07/15/2021] [Indexed: 01/10/2023]
Abstract
Medical image registration is a vital component of many medical procedures, such as image-guided radiotherapy (IGRT), as it allows for more accurate dose-delivery and better management of side effects. Recently, the successful implementation of deep learning (DL) in various fields has prompted many research groups to apply DL to three-dimensional (3D) medical image registration. Several of these efforts have led to promising results. This review summarized the progress made in DL-based 3D image registration over the past 5 years and identify existing challenges and potential avenues for further research. The collected studies were statistically analyzed based on the region of interest (ROI), image modality, supervision method, and registration evaluation metrics. The studies were classified into three categories: deep iterative registration, supervised registration, and unsupervised registration. The studies are thoroughly reviewed and their unique contributions are highlighted. A summary is presented following a review of each category of study, discussing its advantages, challenges, and trends. Finally, the common challenges for all categories are discussed, and potential future research topics are identified.
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Affiliation(s)
- Haonan Xiao
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Chenyang Liu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Ge Ren
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Ruijie Yang
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, China
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
- Department of Artificial Intelligence, Korea University, Seoul, Republic of Korea
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
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36
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Demro C, Mueller BA, Kent JS, Burton PC, Olman CA, Schallmo MP, Lim KO, Sponheim SR. The psychosis human connectome project: An overview. Neuroimage 2021; 241:118439. [PMID: 34339830 PMCID: PMC8542422 DOI: 10.1016/j.neuroimage.2021.118439] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 06/25/2021] [Accepted: 07/21/2021] [Indexed: 01/05/2023] Open
Abstract
Investigations within the Human Connectome Project have expanded to include studies focusing on brain disorders. This paper describes one of the investigations focused on psychotic psychopathology: The psychosis Human Connectome Project (P-HCP). The data collected as part of this project were multimodal and derived from clinical assessments of psychopathology, cognitive assessments, instrument-based motor assessments, blood specimens, and magnetic resonance imaging (MRI) data. The dataset will be made publicly available through the NIMH Data Archive. In this report we provide specific information on how the sample of participants was obtained and characterized and describe the experimental tasks and procedures used to probe neural functions involved in psychotic disorders that may also mark genetic liability for psychotic psychopathology. Our goal in this paper is to outline the data acquisition process so that researchers intending to use these publicly available data can plan their analyses. MRI data described in this paper are limited to data acquired at 3 Tesla. A companion paper describes the study's 7 Tesla image acquisition protocol in detail, which is focused on visual perceptual functions in psychotic psychopathology.
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Affiliation(s)
- Caroline Demro
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States; Department of Psychology, University of Minnesota, Minneapolis, MN, United State
| | - Bryon A Mueller
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States
| | - Jerillyn S Kent
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States
| | - Philip C Burton
- College of Liberal Arts, University of Minnesota, Minneapolis, MN, United State
| | - Cheryl A Olman
- Department of Psychology, University of Minnesota, Minneapolis, MN, United State
| | - Michael-Paul Schallmo
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States
| | - Kelvin O Lim
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States; Minneapolis Veterans Affairs Medical Center, 1 Veterans Drive, Minneapolis, MN 55417, United State
| | - Scott R Sponheim
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States; Department of Psychology, University of Minnesota, Minneapolis, MN, United State; Minneapolis Veterans Affairs Medical Center, 1 Veterans Drive, Minneapolis, MN 55417, United State.
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37
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Abdolalizadeh A, Ostadrahimi H, Ohadi MAD, Saneei SA, Bayani Ershadi AS. White matter microstructural associates of apathy-avolition in schizophrenia. J Psychiatr Res 2021; 142:110-116. [PMID: 34332375 DOI: 10.1016/j.jpsychires.2021.07.042] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 06/30/2021] [Accepted: 07/21/2021] [Indexed: 11/25/2022]
Abstract
Apathy is present at the onset in nearly half the patients with schizophrenia. Current therapies lack the efficiency to improve apathy in patients. The presence of apathy is also associated with poorer outcomes. Despite its clinical importance, the underlying mechanism of apathy in schizophrenia is unclear, but it seems frontostriatal connections play a role. In this study, we investigated whole-brain white matter microstructural properties associated with the severity of apathy-avolition in schizophrenia. We included 80 schizophrenia patients (60 Male, 20 Female) from the Mind Clinical Imaging Consortium database and associated Apathy-Avolition score of "Scale for Assessment of Negative Symptoms" with fiber integrity measures derived from diffusion-weighted imaging using Tract-Based Spatial Statistics (TBSS). We also did tractography on eight tracts, including bilateral superior longitudinal fasciculus, uncinate fasciculus, cingulum, genu and splenium of the corpus callosum. Age, gender, years of education, chlorpromazine equivalent cumulative dose, and acquisition site were inserted as covariates. We showed a widespread association between lower fiber integrity (by measures of increased mean diffusivity and decreased fractional anisotropy) and increased apathy-avolition in TBSS, which we also validated in tractography. Moreover, mean diffusivity, and not fractional anisotropy, was associated with apathy independent of disease severity. In conclusion, we propose diffuse white-matter pathology, within the corpus callosum, limbic system, and the frontostriatal circuit is involved in apathy-avolition in schizophrenia. Also, we suggest that diffuse neuroinflammatory processes may play a part in apathy-avolition, independent of disease severity.
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Affiliation(s)
- AmirHussein Abdolalizadeh
- Students' Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran; Interdisciplinary Neuroscience Research Program, Tehran University of Medical Sciences, Tehran, Iran.
| | - Hamidreza Ostadrahimi
- Students' Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran; Interdisciplinary Neuroscience Research Program, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Amin Dabbagh Ohadi
- Students' Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran; Interdisciplinary Neuroscience Research Program, Tehran University of Medical Sciences, Tehran, Iran
| | - Seyed AmirHussein Saneei
- Students' Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran; Interdisciplinary Neuroscience Research Program, Tehran University of Medical Sciences, Tehran, Iran
| | - Amir Sasan Bayani Ershadi
- Students' Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran; Interdisciplinary Neuroscience Research Program, Tehran University of Medical Sciences, Tehran, Iran
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Levman J, Jennings M, Kabaria P, Rouse E, Nangaku M, Berger D, Gondra I, Takahashi E, Tyrrell P. Hold-out validation for the assessment of stability and reliability of multivariable regression demonstrated with magnetic resonance imaging of patients with schizophrenia. Int J Dev Neurosci 2021; 81:655-662. [PMID: 34308560 DOI: 10.1002/jdn.10144] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 07/16/2021] [Accepted: 07/21/2021] [Indexed: 11/11/2022] Open
Abstract
Neuroscience studies are very often tasked with identifying measurable differences between two groups of subjects, typically one group with a pathological condition and one group representing control subjects. It is often expected that the measurements acquired for comparing groups are also affected by a variety of additional patient characteristics such as sex, age, and comorbidities. Multivariable regression (MVR) is a statistical analysis technique commonly employed in neuroscience studies to "control for" or "adjust for" secondary effects (such as sex, age, and comorbidities) in order to ensure that the main study findings are focused on actual differences between the groups of interest associated with the condition under investigation. It is common practice in the neuroscience literature to utilize MVR to control for secondary effects; however, at present, it is not typically possible to assess whether the MVR adjustments correct for more error than they introduce. In common neuroscience practice, MVR models are not validated and no attempt to characterize deficiencies in the MVR model is made. In this article, we demonstrate how standard hold-out validation techniques (commonly used in machine learning analyses) that involve repeatedly randomly dividing datasets into training and testing samples can be adapted to the assessment of stability and reliability of MVR models with a publicly available neurological magnetic resonance imaging (MRI) dataset of patients with schizophrenia. Results demonstrate that MVR can introduce measurement error up to 30.06% and, on average across all considered measurements, introduce 9.84% error on this dataset. When hold-out validated MVR does not agree with the results of the standard use of MVR, the use of MVR in the given application is unstable. Thus, this paper helps evaluate the extent to which the simplistic use of MVR introduces study error in neuroscientific analyses with an analysis of patients with schizophrenia.
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Affiliation(s)
- Jacob Levman
- Department of Computer Science, St. Francis Xavier University, Antigonish, Nova Scotia, Canada.,Canada Research Chair in Bioinformatics, St. Francis Xavier University, Antigonish, Nova Scotia, Canada
| | - Maxwell Jennings
- Department of Computer Science, St. Francis Xavier University, Antigonish, Nova Scotia, Canada.,Department of Mathematics and Statistics, St. Francis Xavier University, Antigonish, Nova Scotia, Canada
| | - Priya Kabaria
- Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Ethan Rouse
- Department of Computer Science, St. Francis Xavier University, Antigonish, Nova Scotia, Canada
| | - Masahito Nangaku
- Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Derek Berger
- Department of Computer Science, St. Francis Xavier University, Antigonish, Nova Scotia, Canada
| | - Iker Gondra
- Department of Computer Science, St. Francis Xavier University, Antigonish, Nova Scotia, Canada
| | - Emi Takahashi
- Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Pascal Tyrrell
- Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada.,Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada.,Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
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Zhang A, Fang J, Hu W, Calhoun VD, Wang YP. A Latent Gaussian Copula Model for Mixed Data Analysis in Brain Imaging Genetics. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1350-1360. [PMID: 31689199 PMCID: PMC7756188 DOI: 10.1109/tcbb.2019.2950904] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Recent advances in imaging genetics make it possible to combine different types of data including medical images like functional magnetic resonance imaging (fMRI) and genetic data like single nucleotide polymorphisms (SNPs) for comprehensive diagnosis of mental disorders. Understanding complex interactions among these heterogeneous data may give rise to a new perspective, while at the same time demand statistical models for their integration. Various graphical models have been proposed for the study of interaction or association networks with continuous, binary, and count data as well as the mixture of them. However, limited efforts have been made for the multinomial case, for instance, SNP data. Our goal is therefore to fill the void by developing a graphical model for the integration of fMRI image and SNP data, which can provide deeper understanding of the unknown neurogenetic mechanism. In this article, we propose a latent Gaussian copula model for mixed data containing multinomial components. We assume that the discrete variable is obtained by discretizing a latent (unobserved) continuous variable and then create a semi-rank based estimator of the graph structure. The simulation results demonstrate that the proposed latent correlation has more steady and accurate performance than several existing methods in detecting graph structure. When applying to a real schizophrenia data consisting of SNP array and fMRI image collected by the Mind Clinical Imaging Consortium (MCIC), the proposed method reveals a set of distinct SNP-brain associations, which are verified to be biologically significant. The proposed model is statistically promising in handling mixed types of data including multinomial components, which can find widespread applications. To promote reproducible research, the R code is available at https://github.com/Aiying0512/LGCM.
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40
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Akhonda MABS, Gabrielson B, Bhinge S, Calhoun VD, Adali T. Disjoint subspaces for common and distinct component analysis: Application to the fusion of multi-task FMRI data. J Neurosci Methods 2021; 358:109214. [PMID: 33957159 DOI: 10.1016/j.jneumeth.2021.109214] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 04/12/2021] [Accepted: 04/29/2021] [Indexed: 11/16/2022]
Abstract
BACKGROUND Data-driven methods such as independent component analysis (ICA) makes very few assumptions on the data and the relationships of multiple datasets, and hence, are attractive for the fusion of medical imaging data. Two important extensions of ICA for multiset fusion are the joint ICA (jICA) and the multiset canonical correlation analysis and joint ICA (MCCA-jICA) techniques. Both approaches assume identical mixing matrices, emphasizing components that are common across the multiple datasets. However, in general, one would expect to have components that are both common across the datasets and distinct to each dataset. NEW METHOD We propose a general framework, disjoint subspace analysis using ICA (DS-ICA), which identifies and extracts not only the common but also the distinct components across multiple datasets. A key component of the method is the identification of these subspaces and their separation before subsequent analyses, which helps establish better model match and provides flexibility in algorithm and order choice. COMPARISON We compare DS-ICA with jICA and MCCA-jICA through both simulations and application to multiset functional magnetic resonance imaging (fMRI) task data collected from healthy controls as well as patients with schizophrenia. RESULTS The results show DS-ICA estimates more components discriminative between healthy controls and patients than jICA and MCCA-jICA, and with higher discriminatory power showing activation differences in meaningful regions. When applied to a classification framework, components estimated by DS-ICA results in higher classification performance for different dataset combinations than the other two methods. CONCLUSION These results demonstrate that DS-ICA is an effective method for fusion of multiple datasets.
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Affiliation(s)
- M A B S Akhonda
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, 21250 MD, USA.
| | - Ben Gabrielson
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, 21250 MD, USA
| | - Suchita Bhinge
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, 21250 MD, USA
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, 30303 GA, USA
| | - Tülay Adali
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, 21250 MD, USA
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Basile GA, Bramanti A, Bertino S, Cutroneo G, Bruno A, Tisano A, Paladina G, Milardi D, Anastasi G. Structural Connectivity-Based Parcellation of the Dopaminergic Midbrain in Healthy Subjects and Schizophrenic Patients. MEDICINA (KAUNAS, LITHUANIA) 2020; 56:E686. [PMID: 33322072 PMCID: PMC7764101 DOI: 10.3390/medicina56120686] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 12/02/2020] [Accepted: 12/07/2020] [Indexed: 12/17/2022]
Abstract
Background and objectives: Functional deregulation of dopaminergic midbrain regions is a core feature of schizophrenia pathophysiology. Anatomical research on primates suggests that these regions may be subdivided into distinct, topographically organized functional territories according to their connectivity to the striatum. The aim of the present work was the reconstruction of dopaminergic midbrain subregions in healthy subjects and schizophrenic patients and the evaluation of their structural connectivity profiles. Materials and Methods: A hypothesis-driven connectivity-based parcellation derived from diffusion tractography was applied on 24 healthy subjects and 30 schizophrenic patients to identify distinct territories within the human dopaminergic midbrain in vivo and non-invasively. Results: We identified a tripartite subdivision of dopaminergic midbrain, including limbic, prefrontal and sensorimotor territories. No significant differences in structural features or connectivity were found between subjects and patients. Conclusions: The parcellation scheme proposed herein may help to achieve detailed characterization of structural and functional anomalies of the dopaminergic midbrain in schizophrenic patients.
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Affiliation(s)
- Gianpaolo Antonio Basile
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, 98124 Messina, Italy; (S.B.); (G.C.); (G.A.)
| | - Alessia Bramanti
- IRCCS Centro Neurolesi “Bonino Pulejo”, 98124 Messina, Italy; (A.B.); (G.P.)
| | - Salvatore Bertino
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, 98124 Messina, Italy; (S.B.); (G.C.); (G.A.)
| | - Giuseppina Cutroneo
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, 98124 Messina, Italy; (S.B.); (G.C.); (G.A.)
| | - Antonio Bruno
- Psychiatry Unit, Department of Biomedical and Dental Sciences and Morphological and Functional Imaging, University of Messina, 98124 Messina, Italy;
| | - Adriana Tisano
- Physical, Rehabilitation Medicine and Sport Medicine Unit, University Hospital G. Martino, 98124 Messina, Italy;
| | - Giuseppe Paladina
- IRCCS Centro Neurolesi “Bonino Pulejo”, 98124 Messina, Italy; (A.B.); (G.P.)
| | - Demetrio Milardi
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, 98124 Messina, Italy; (S.B.); (G.C.); (G.A.)
- IRCCS Centro Neurolesi “Bonino Pulejo”, 98124 Messina, Italy; (A.B.); (G.P.)
| | - Giuseppe Anastasi
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, 98124 Messina, Italy; (S.B.); (G.C.); (G.A.)
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42
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Ji Y, Zhang X, Wang Z, Qin W, Liu H, Xue K, Tang J, Xu Q, Zhu D, Liu F, Yu C. Genes associated with gray matter volume alterations in schizophrenia. Neuroimage 2020; 225:117526. [PMID: 33147509 DOI: 10.1016/j.neuroimage.2020.117526] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 10/28/2020] [Indexed: 12/11/2022] Open
Abstract
Although both schizophrenia and gray matter volume (GMV) show high heritability, however, genes accounting for GMV alterations in schizophrenia remain largely unknown. Based on risk genes identified in schizophrenia by the genome-wide association study of the Schizophrenia Working Group of the Psychiatric Genomics Consortium, we used transcription-neuroimaging association analysis to test that which of these genes are associated with GMV changes in schizophrenia. For each brain tissue sample, the expression profiles of 196 schizophrenia risk genes were extracted from six donated normal brains of the Allen Human Brain Atlas, and GMV differences between patients with schizophrenia and healthy controls were calculated based on five independent case-control structural MRI datasets (276 patients and 284 controls). Genes associated with GMV changes in schizophrenia were identified by performing cross-sample spatial correlations between expression levels of each gene and case-control GMV difference derived from the five MRI datasets integrated by harmonization and meta-analysis. We found that expression levels of 98 genes consistently showed significant cross-sample spatial correlations with GMV changes in schizophrenia. These genes were functionally enriched for chemical synaptic transmission, central nervous system development, and cell projection. Overall, this study provides a set of genes possibly associated with GMV changes in schizophrenia, which could be used as candidate genes to explore biological mechanisms underlying the structural impairments in schizophrenia.
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Affiliation(s)
- Yuan Ji
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Xue Zhang
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Zirui Wang
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Wen Qin
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Huaigui Liu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Kaizhong Xue
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Jie Tang
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Qiang Xu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Dan Zhu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Feng Liu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China.
| | - Chunshui Yu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.
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43
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Wachinger C, Rieckmann A, Pölsterl S. Detect and correct bias in multi-site neuroimaging datasets. Med Image Anal 2020; 67:101879. [PMID: 33152602 DOI: 10.1016/j.media.2020.101879] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Revised: 07/29/2020] [Accepted: 10/05/2020] [Indexed: 12/20/2022]
Abstract
The desire to train complex machine learning algorithms and to increase the statistical power in association studies drives neuroimaging research to use ever-larger datasets. The most obvious way to increase sample size is by pooling scans from independent studies. However, simple pooling is often ill-advised as selection, measurement, and confounding biases may creep in and yield spurious correlations. In this work, we combine 35,320 magnetic resonance images of the brain from 17 studies to examine bias in neuroimaging. In the first experiment, Name That Dataset, we provide empirical evidence for the presence of bias by showing that scans can be correctly assigned to their respective dataset with 71.5% accuracy. Given such evidence, we take a closer look at confounding bias, which is often viewed as the main shortcoming in observational studies. In practice, we neither know all potential confounders nor do we have data on them. Hence, we model confounders as unknown, latent variables. Kolmogorov complexity is then used to decide whether the confounded or the causal model provides the simplest factorization of the graphical model. Finally, we present methods for dataset harmonization and study their ability to remove bias in imaging features. In particular, we propose an extension of the recently introduced ComBat algorithm to control for global variation across image features, inspired by adjusting for unknown population stratification in genetics. Our results demonstrate that harmonization can reduce dataset-specific information in image features. Further, confounding bias can be reduced and even turned into a causal relationship. However, harmonization also requires caution as it can easily remove relevant subject-specific information. Code is available at https://github.com/ai-med/Dataset-Bias.
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Affiliation(s)
- Christian Wachinger
- Lab for Artificial Intelligence in Medical Imaging (AI-Med), Department of Child and Adolescent Psychiatry, University Hospital, LMU München, Germany.
| | - Anna Rieckmann
- Umeå Center for Functional Brain Imaging, Department of Radiation Sciences, Umeå University
| | - Sebastian Pölsterl
- Lab for Artificial Intelligence in Medical Imaging (AI-Med), Department of Child and Adolescent Psychiatry, University Hospital, LMU München, Germany
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44
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Garcia-Dias R, Scarpazza C, Baecker L, Vieira S, Pinaya WHL, Corvin A, Redolfi A, Nelson B, Crespo-Facorro B, McDonald C, Tordesillas-Gutiérrez D, Cannon D, Mothersill D, Hernaus D, Morris D, Setien-Suero E, Donohoe G, Frisoni G, Tronchin G, Sato J, Marcelis M, Kempton M, van Haren NEM, Gruber O, McGorry P, Amminger P, McGuire P, Gong Q, Kahn RS, Ayesa-Arriola R, van Amelsvoort T, Ortiz-García de la Foz V, Calhoun V, Cahn W, Mechelli A. Neuroharmony: A new tool for harmonizing volumetric MRI data from unseen scanners. Neuroimage 2020; 220:117127. [PMID: 32634595 PMCID: PMC7573655 DOI: 10.1016/j.neuroimage.2020.117127] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 06/08/2020] [Accepted: 06/30/2020] [Indexed: 02/05/2023] Open
Abstract
•We present Neuroharmony, a harmonization tool for images from unseen scanners. •We developed Neuroharmony using a total of 15,026 sMRI images. •The tool was able to reduce scanner-related bias from unseen scans. •Neuroharmony represents a significant step towards imaging-based clinical tools. •Neuroharmony is available at https://github.com/garciadias/Neuroharmony .
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Affiliation(s)
- Rafael Garcia-Dias
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, United Kingdom.
| | - Cristina Scarpazza
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, United Kingdom; Department of General Psychology, University of Padova, Via Venezia 8, Padova, Italy
| | - Lea Baecker
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, United Kingdom
| | - Sandra Vieira
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, United Kingdom
| | - Walter H L Pinaya
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, United Kingdom; Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, Santo André, Brazil
| | - Aiden Corvin
- Department of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Barnaby Nelson
- Orygen, The National Centre of Excellence in Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia; Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Benedicto Crespo-Facorro
- Centro Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spain; Departamento de Psiquiatria, Universidad de Sevilla, Instituto de Biomedicina de Sevilla (IBIS), Spain; Hospital Universitario Virgen del Rocío, Sevilla, Spain; Department of Psychiatry, Marqués de Valdecilla University Hospital, IDIVAL, School of Medicine, University of Cantabria, Santander, Spain
| | - Colm McDonald
- Clinical Neuroimaging Laboratory, School of Medicine & Center for Neuroimaging and Cognitive Genomics, NUI Galway University, Galway, Ireland
| | - Diana Tordesillas-Gutiérrez
- Centro Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spain; Neuroimaging Unit, Technological Facilities, Valdecilla Biomedical Research Institute IDIVAL, Spain
| | - Dara Cannon
- Clinical Neuroimaging Laboratory, School of Medicine & Center for Neuroimaging and Cognitive Genomics, NUI Galway University, Galway, Ireland
| | - David Mothersill
- School of Psychology & Center for Neuroimaging and Cognitive Genomics, NUI Galway University, Galway, Ireland
| | - Dennis Hernaus
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht, the Netherlands
| | - Derek Morris
- Discipline of Biochemistry & Center for Neuroimaging and Cognitive Genomics, NUI Galway University, Galway, Ireland
| | - Esther Setien-Suero
- Centro Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spain; Department of Psychiatry, Marqués de Valdecilla University Hospital, IDIVAL, School of Medicine, University of Cantabria, Santander, Spain
| | - Gary Donohoe
- School of Psychology & Center for Neuroimaging and Cognitive Genomics, NUI Galway University, Galway, Ireland
| | - Giovanni Frisoni
- Memory Clinic and LANVIE-Laboratory of Neuroimaging of Ageing, University Hospitals and University of Geneva, Geneva, Switzerland; Laboratory of Alzheimer's Neuroimaging and Epidemiology - LANE, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Giulia Tronchin
- Clinical Neuroimaging Laboratory, School of Medicine & Center for Neuroimaging and Cognitive Genomics, NUI Galway University, Galway, Ireland
| | - João Sato
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, Santo André, Brazil
| | - Machteld Marcelis
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht, the Netherlands
| | - Matthew Kempton
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, United Kingdom
| | - Neeltje E M van Haren
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus Medical Centre - Sophia Children's Hospital, Rotterdam, Netherlands
| | - Oliver Gruber
- Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, Germany; Center for Translational Research in Systems Neuroscience and Psychiatry, Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Germany
| | - Patrick McGorry
- Orygen, The National Centre of Excellence in Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia; Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Paul Amminger
- Orygen, The National Centre of Excellence in Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia; Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, United Kingdom
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Psychoradiology Research Unit of Chinese Academy of Medical Sciences, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - René S Kahn
- Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht, the Netherlands; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Rosa Ayesa-Arriola
- Centro Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spain; Department of Psychiatry, Marqués de Valdecilla University Hospital, IDIVAL, School of Medicine, University of Cantabria, Santander, Spain
| | - Therese van Amelsvoort
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht, the Netherlands
| | - Victor Ortiz-García de la Foz
- Centro Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spain; Department of Psychiatry, Marqués de Valdecilla University Hospital, IDIVAL, School of Medicine, University of Cantabria, Santander, Spain
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia; State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Wiepke Cahn
- Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht, the Netherlands
| | - Andrea Mechelli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, United Kingdom
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45
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Taylor JA, Larsen KM, Garrido MI. Multi-dimensional predictions of psychotic symptoms via machine learning. Hum Brain Mapp 2020; 41:5151-5163. [PMID: 32870535 PMCID: PMC7670649 DOI: 10.1002/hbm.25181] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 07/09/2020] [Accepted: 08/09/2020] [Indexed: 11/10/2022] Open
Abstract
The diagnostic criteria for schizophrenia comprise a diverse range of heterogeneous symptoms. As a result, individuals each present a distinct set of symptoms despite having the same overall diagnosis. Whilst previous machine learning studies have primarily focused on dichotomous patient-control classification, we predict the severity of each individual symptom on a continuum. We applied machine learning regression within a multi-modal fusion framework to fMRI and behavioural data acquired during an auditory oddball task in 80 schizophrenia patients. Brain activity was highly predictive of some, but not all symptoms, namely hallucinations, avolition, anhedonia and attention. Critically, each of these symptoms was associated with specific functional alterations across different brain regions. We also found that modelling symptoms as an ensemble of subscales was more accurate, specific and informative than models which predict compound scores directly. In principle, this approach is transferrable to any psychiatric condition or multi-dimensional diagnosis.
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Affiliation(s)
- Jeremy A Taylor
- Melbourne School of Psychological Sciences, University of Melbourne, Parkville, Victoria, Australia.,Queensland Brain Institute, University of Queensland, St Lucia, Queensland, Australia
| | - Kit M Larsen
- Queensland Brain Institute, University of Queensland, St Lucia, Queensland, Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Clayton, Victoria, Australia.,Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark.,Child and Adolescent Mental Health Care, Mental Health Services Capital Region Copenhagen, University of Copenhagen, Copenhagen, Denmark
| | - Marta I Garrido
- Melbourne School of Psychological Sciences, University of Melbourne, Parkville, Victoria, Australia.,Queensland Brain Institute, University of Queensland, St Lucia, Queensland, Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Clayton, Victoria, Australia.,Centre for Advanced Imaging, University of Queensland, St Lucia, Queensland, Australia
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46
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Abdolalizadeh A, Ostadrahimi H, Mohajer B, Darvishi A, Sattarian M, Bayani Ershadi AS, Abbasi N. White Matter Microstructural Properties Associated with Impaired Attention in Chronic Schizophrenia: A Multi-Center Study. Psychiatry Res Neuroimaging 2020; 302:111105. [PMID: 32498000 DOI: 10.1016/j.pscychresns.2020.111105] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 04/24/2020] [Accepted: 05/01/2020] [Indexed: 12/17/2022]
Abstract
Attention as a key cognitive function is impaired in schizophrenia, interfering with the normal daily life of the patients. Previous studies on the microstructural correlates of attention in schizophrenia were limited to single fibers, did not include a control group, or did not adjust for drug dosage. In the current study, we investigated the association between microstructural properties of the white matter fibers and attention tests in 81 patients and 79 healthy controls from the Mind Clinical Imaging Consortium database. Integrity measures of superior longitudinal fasciculus, cingulum, genu, and splenium were extracted after tractography. Using an interaction model between diagnosis and microstructural properties, and adjusting for age, gender, acquisition site, education, and cumulative drug usage dose, and after correcting for family-wise error, we showed decreased integrity in the patients and a significant negative association between fractional anisotropy of the tracts and trail making test part A with a greater expected decrease in the attention per unit of decrease of integrity in the patients compared to the healthy controls. Our findings suggest that decreased integrity of the bilateral cingulum, and splenium, are independent of the cumulative drug dosage, age, gender, and site, and may underlie the impaired attention in the schizophrenia.
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Affiliation(s)
| | - Hamidreza Ostadrahimi
- Students' Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Bahram Mohajer
- Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Asma Darvishi
- Students' Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Mahta Sattarian
- Students' Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Nooshin Abbasi
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
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47
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Rashid B, Calhoun V. Towards a brain-based predictome of mental illness. Hum Brain Mapp 2020; 41:3468-3535. [PMID: 32374075 PMCID: PMC7375108 DOI: 10.1002/hbm.25013] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 04/06/2020] [Accepted: 04/06/2020] [Indexed: 01/10/2023] Open
Abstract
Neuroimaging-based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognitively healthy and disordered brain structure and function. Recent advancements in machine learning techniques have shown promising outcomes for individualized prediction and characterization of patients with psychiatric disorders. Studies have utilized features from a variety of neuroimaging modalities, including structural, functional, and diffusion magnetic resonance imaging data, as well as jointly estimated features from multiple modalities, to assess patients with heterogeneous mental disorders, such as schizophrenia and autism. We use the term "predictome" to describe the use of multivariate brain network features from one or more neuroimaging modalities to predict mental illness. In the predictome, multiple brain network-based features (either from the same modality or multiple modalities) are incorporated into a predictive model to jointly estimate features that are unique to a disorder and predict subjects accordingly. To date, more than 650 studies have been published on subject-level prediction focusing on psychiatric disorders. We have surveyed about 250 studies including schizophrenia, major depression, bipolar disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, obsessive-compulsive disorder, social anxiety disorder, posttraumatic stress disorder, and substance dependence. In this review, we present a comprehensive review of recent neuroimaging-based predictomic approaches, current trends, and common shortcomings and share our vision for future directions.
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Affiliation(s)
- Barnaly Rashid
- Department of PsychiatryHarvard Medical SchoolBostonMassachusettsUSA
| | - Vince Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
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48
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PENG PENG, JU YONGFENG, ZHANG YIPU, WANG KAIMING, JIANG SUYING, WANG YUPING. Sparse representation and dictionary learning model incorporating group sparsity and incoherence to extract abnormal brain regions associated with schizophrenia. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:104396-104406. [PMID: 33747675 PMCID: PMC7971409 DOI: 10.1109/access.2020.2999513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Schizophrenia is a complex mental illness, the mechanism of which is currently unclear. Using sparse representation and dictionary learning (SDL) model to analyze functional magnetic resonance imaging (fMRI) dataset of schizophrenia is currently a popular method for exploring the mechanism of the disease. The SDL method decomposed the fMRI data into a sparse coding matrix X and a dictionary matrix D. However, these traditional methods overlooked group structure information in X and the coherence between the atoms in D. To address this problem, we propose a new SDL model incorporating group sparsity and incoherence, namely GS2ISDL to detect abnormal brain regions. Specifically, GS2ISDL uses the group structure information that defined by AAL anatomical template from fMRI dataset as priori to achieve inter-group sparsity in X. At the same time, L 1 - norm is enforced on X to achieve intra-group sparsity. In addition, our algorithm also imposes incoherent constraint on the dictionary matrix D to reduce the coherence between the atoms in D, which can ensure the uniqueness of X and the discriminability of the atoms. To validate our proposed model GS2ISDL, we compared it with both IK-SVD and SDL algorithm for analyzing fMRI dataset collected by Mind Clinical Imaging Consortium (MCIC). The results show that the accuracy, sensitivity, recall and MCC values of GS2ISDL are 93.75%, 95.23%, 80.50% and 88.19%, respectively, which outperforms both IK-SVD and SDL. The ROIs extracted by GS2ISDL model (such as Precentral gyrus, Hippocampus and Caudate nucleus, etc.) are further verified by the literature review on schizophrenia studies, which have significant biological significance.
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Affiliation(s)
- PENG PENG
- The school of Electronics and Control Engineering, Chang’an University, Xi’an, Shaanxi, 710049, China
| | - YONGFENG JU
- The school of Electronics and Control Engineering, Chang’an University, Xi’an, Shaanxi, 710049, China
| | - YIPU ZHANG
- The school of Electronics and Control Engineering, Chang’an University, Xi’an, Shaanxi, 710049, China
| | - KAIMING WANG
- The school of Science, Chang’an University, Xi’an, Shaanxi, 710049, China
| | - SUYING JIANG
- The school of Information Engineering, Chang’an University, Xi’an, Shaanxi, 710049, China
| | - YUPING WANG
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, 70118, USA
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49
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Bai Y, Pascal Z, Calhoun V, Wang YP. Optimized Combination of Multiple Graphs With Application to the Integration of Brain Imaging and (epi)Genomics Data. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:1801-1811. [PMID: 31825864 PMCID: PMC7394342 DOI: 10.1109/tmi.2019.2958256] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
With the rapid development of high-throughput technologies, a growing amount of multi-omics data are collected, giving rise to a great demand for combining such data for biomedical discovery. Due to the cost and time to label the data manually, the number of labelled samples is limited. This motivated the need for semi-supervised learning algorithms. In this work, we applied a graph-based semi-supervised learning (GSSL) to classify a severe chronic mental disorder, schizophrenia (SZ). An advantage of GSSL is that it can simultaneously analyse more than two types of data, while many existing models focus on pairwise data analysis. In particular, we applied GSSL to the analysis of single nucleotide polymorphism (SNP), functional magnetic resonance imaging (fMRI) and DNA methylation data, which accounts for genetics, brain imaging (endophenotypes), and environmental factors (epigenomics) respectively. While parameter selection has been an open challenge for most models, another key contribution of this work is that we explored the parameter space to interpret their meaning and established practical guidelines. Based on the practical significance of each hyper-parameter, a relatively small range of candidate values can be determined in a data-driven way to both optimize and speed up the parameter tuning process. We validated the model through both synthetic data and a real SZ dataset of 184 subjects from the Mental Illness and Neuroscience Discovery (MIND) Clinical Imaging Consortium. In comparison to several existing approaches, our algorithm achieved better performance in terms of classification accuracy. We also confirmed the significance of several brain regions associated with SZ.
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50
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Shen L, Thompson PM. Brain Imaging Genomics: Integrated Analysis and Machine Learning. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2020; 108:125-162. [PMID: 31902950 PMCID: PMC6941751 DOI: 10.1109/jproc.2019.2947272] [Citation(s) in RCA: 76] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Brain imaging genomics is an emerging data science field, where integrated analysis of brain imaging and genomics data, often combined with other biomarker, clinical and environmental data, is performed to gain new insights into the phenotypic, genetic and molecular characteristics of the brain as well as their impact on normal and disordered brain function and behavior. It has enormous potential to contribute significantly to biomedical discoveries in brain science. Given the increasingly important role of statistical and machine learning in biomedicine and rapidly growing literature in brain imaging genomics, we provide an up-to-date and comprehensive review of statistical and machine learning methods for brain imaging genomics, as well as a practical discussion on method selection for various biomedical applications.
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Affiliation(s)
- Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA 90232, USA
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