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Weng C, Groh AMR, Yaqubi M, Cui QL, Stratton JA, Moore GRW, Antel JP. Heterogeneity of mature oligodendrocytes in the central nervous system. Neural Regen Res 2025; 20:1336-1349. [PMID: 38934385 DOI: 10.4103/nrr.nrr-d-24-00055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 06/06/2024] [Indexed: 06/28/2024] Open
Abstract
Mature oligodendrocytes form myelin sheaths that are crucial for the insulation of axons and efficient signal transmission in the central nervous system. Recent evidence has challenged the classical view of the functionally static mature oligodendrocyte and revealed a gamut of dynamic functions such as the ability to modulate neuronal circuitry and provide metabolic support to axons. Despite the recognition of potential heterogeneity in mature oligodendrocyte function, a comprehensive summary of mature oligodendrocyte diversity is lacking. We delve into early 20 th -century studies by Robertson and Río-Hortega that laid the foundation for the modern identification of regional and morphological heterogeneity in mature oligodendrocytes. Indeed, recent morphologic and functional studies call into question the long-assumed homogeneity of mature oligodendrocyte function through the identification of distinct subtypes with varying myelination preferences. Furthermore, modern molecular investigations, employing techniques such as single cell/nucleus RNA sequencing, consistently unveil at least six mature oligodendrocyte subpopulations in the human central nervous system that are highly transcriptomically diverse and vary with central nervous system region. Age and disease related mature oligodendrocyte variation denotes the impact of pathological conditions such as multiple sclerosis, Alzheimer's disease, and psychiatric disorders. Nevertheless, caution is warranted when subclassifying mature oligodendrocytes because of the simplification needed to make conclusions about cell identity from temporally confined investigations. Future studies leveraging advanced techniques like spatial transcriptomics and single-cell proteomics promise a more nuanced understanding of mature oligodendrocyte heterogeneity. Such research avenues that precisely evaluate mature oligodendrocyte heterogeneity with care to understand the mitigating influence of species, sex, central nervous system region, age, and disease, hold promise for the development of therapeutic interventions targeting varied central nervous system pathology.
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Affiliation(s)
- Chao Weng
- Neuroimmunology Unit, Montreal Neurological Institute-Hospital, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- Department of Neurology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Adam M R Groh
- Neuroimmunology Unit, Montreal Neurological Institute-Hospital, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Moein Yaqubi
- Neuroimmunology Unit, Montreal Neurological Institute-Hospital, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Qiao-Ling Cui
- Neuroimmunology Unit, Montreal Neurological Institute-Hospital, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Jo Anne Stratton
- Neuroimmunology Unit, Montreal Neurological Institute-Hospital, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - G R Wayne Moore
- Neuroimmunology Unit, Montreal Neurological Institute-Hospital, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Jack P Antel
- Neuroimmunology Unit, Montreal Neurological Institute-Hospital, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
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2
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Zhu Y, Wei Y, Yu X, Liu J, Lan R, Guo X, Luo Y. Altered sleep onset transition in depression: Evidence from EEG activity and EEG functional connectivity analyses. Clin Neurophysiol 2024; 166:129-141. [PMID: 39163676 DOI: 10.1016/j.clinph.2024.08.002] [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: 08/14/2023] [Revised: 08/01/2024] [Accepted: 08/03/2024] [Indexed: 08/22/2024]
Abstract
OBJECTIVE Sleep disorders constitute a principal diagnostic criterion for depression, potentially reflecting the abnormal persistence of brain activity during the sleep onset (SO) transition. Here, we sought to explore the differences in the dynamic changes in the EEG activity and the EEG functional connectivity (FC) during the SO transition in depressed patients. METHODS Overnight polysomnography recordings were obtained from thirty-two depressed patients and thirty-three healthy controls. The multiscale permutation entropy (MSPE) and EEG relative power were extracted to characterize EEG activity, and weighted phase lag index (WPLI) was calculated to characterize EEG FC. RESULTS The intergroup differences in EEG activity of relative power and MSPE were reversed near SO, which attributed to slower rates of change among depressed patients. Regarding the characteristics of the EEG FC network, depressed patients exhibited significantly higher inter-hemispheric and interregional WPLI values in both delta and alpha bands throughout the SO transition, concomitant with different dynamic properties in the delta band FC. During the process after SO, patients exhibited increased inter-hemispheric long-range links, whereas controls showed more intra-hemispheric ones. Finally, we found significant correlations in the dynamic changes between different EEG measures. CONCLUSIONS Our research revealed that the abnormal changes during the SO transition in depressed patients were manifested in both homeostatic and dynamic aspects, which were reflected in EEG FC and EEG activity, respectively. SIGNIFICANCE These findings may elucidate the mechanism underlying sleep disorders in depression from the perspective of neural activity.
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Affiliation(s)
- Yongpeng Zhu
- School of Biomedical Engineering, Sun Yat-sen University-Shenzhen Campus, Shenzhen 518000, China
| | - Yu Wei
- School of Biomedical Engineering, Sun Yat-sen University-Shenzhen Campus, Shenzhen 518000, China
| | - Xiaokang Yu
- School of Biomedical Engineering, Sun Yat-sen University-Shenzhen Campus, Shenzhen 518000, China
| | - Jiahao Liu
- School of Biomedical Engineering, Sun Yat-sen University-Shenzhen Campus, Shenzhen 518000, China
| | - Rongxi Lan
- School of Biomedical Engineering, Sun Yat-sen University-Shenzhen Campus, Shenzhen 518000, China
| | - Xinwen Guo
- The Seventh Affiliated Hospital of Southern Medical University, Foshan 528000, China.
| | - Yuxi Luo
- School of Biomedical Engineering, Sun Yat-sen University-Shenzhen Campus, Shenzhen 518000, China; Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, Sun Yat-sen University-Shenzhen Campus, Shenzhen 518000, China.
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3
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Yang L, Peng J, Zhang L, Zhang F, Wu J, Zhang X, Pang J, Jiang Y. Advanced Diffusion Tensor Imaging in White Matter Injury After Subarachnoid Hemorrhage. World Neurosurg 2024; 189:77-88. [PMID: 38789033 DOI: 10.1016/j.wneu.2024.05.107] [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: 04/23/2024] [Accepted: 05/16/2024] [Indexed: 05/26/2024]
Abstract
Subarachnoid hemorrhage (SAH) is recognized as an especially severe stroke variant, notorious for its high mortality and long-term disability rates, in addition to a range of both immediate and enduring neurologic impacts. Over half of the SAH survivors experience varying degrees of neurologic disorders, with many enduring chronic neuropsychiatric conditions. Due to the limitations of traditional imaging techniques in depicting subtle changes within brain tissues posthemorrhage, the accurate detection and diagnosis of white matter (WM) injuries are complicated. Against this backdrop, diffusion tensor imaging (DTI) has emerged as a promising biomarker for structural imaging, renowned for its enhanced sensitivity in identifying axonal damage. This capability positions DTI as an invaluable tool for forming precise and expedient prognoses for SAH survivors. This study synthesizes an assessment of DTI for the diagnosis and prognosis of neurologic dysfunctions in patients with SAH, emphasizing the notable changes observed in DTI metrics and their association with potential pathophysiological processes. Despite challenges associated with scanning technology differences and data processing, DTI demonstrates significant clinical potential for early diagnosis of cognitive impairments following SAH and monitoring therapeutic effects. Future research requires the development of highly standardized imaging paradigms to enhance diagnostic accuracy and devise targeted therapeutic strategies for SAH patients. In sum, DTI technology not only augments our understanding of the impact of SAH but also may offer new avenues for improving patient prognoses.
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Affiliation(s)
- Lei Yang
- Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China; Laboratory of Neurological Diseases and Brain Function, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Jianhua Peng
- Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China; Laboratory of Neurological Diseases and Brain Function, The Affiliated Hospital of Southwest Medical University, Luzhou, China; Academician (Expert) Workstation of Sichuan Province, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Lifang Zhang
- Institute of Brain Science, Southwest Medical University, Luzhou, China; Sichuan Clinical Research Center for Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Fan Zhang
- Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China; Laboratory of Neurological Diseases and Brain Function, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Jinpeng Wu
- Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China; Laboratory of Neurological Diseases and Brain Function, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Xianhui Zhang
- Academician (Expert) Workstation of Sichuan Province, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Jinwei Pang
- Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Yong Jiang
- Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China; Laboratory of Neurological Diseases and Brain Function, The Affiliated Hospital of Southwest Medical University, Luzhou, China; Institute of Brain Science, Southwest Medical University, Luzhou, China; Sichuan Clinical Research Center for Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
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4
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Segal A, Smith RE, Chopra S, Oldham S, Parkes L, Aquino K, Kia SM, Wolfers T, Franke B, Hoogman M, Beckmann CF, Westlye LT, Andreassen OA, Zalesky A, Harrison BJ, Davey CG, Soriano-Mas C, Cardoner N, Tiego J, Yücel M, Braganza L, Suo C, Berk M, Cotton S, Bellgrove MA, Marquand AF, Fornito A. Multiscale heterogeneity of white matter morphometry in psychiatric disorders. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.04.606523. [PMID: 39149253 PMCID: PMC11326206 DOI: 10.1101/2024.08.04.606523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Background Inter-individual variability in neurobiological and clinical characteristics in mental illness is often overlooked by classical group-mean case-control studies. Studies using normative modelling to infer person-specific deviations of grey matter volume have indicated that group means are not representative of most individuals. The extent to which this variability is present in white matter morphometry, which is integral to brain function, remains unclear. Methods We applied Warped Bayesian Linear Regression normative models to T1-weighted magnetic resonance imaging data and mapped inter-individual variability in person-specific white matter volume deviations in 1,294 cases (58% male) diagnosed with one of six disorders (attention-deficit/hyperactivity, autism, bipolar, major depressive, obsessive-compulsive and schizophrenia) and 1,465 matched controls (54% male) recruited across 25 scan sites. We developed a framework to characterize deviation heterogeneity at multiple spatial scales, from individual voxels, through inter-regional connections, specific brain regions, and spatially extended brain networks. Results The specific locations of white matter volume deviations were highly heterogeneous across participants, affecting the same voxel in fewer than 8% of individuals with the same diagnosis. For autism and schizophrenia, negative deviations (i.e., areas where volume is lower than normative expectations) aggregated into common tracts, regions and large-scale networks in up to 35% of individuals. Conclusions The prevalence of white matter volume deviations was lower than previously observed in grey matter, and the specific location of these deviations was highly heterogeneous when considering voxel-wise spatial resolution. Evidence of aggregation within common pathways and networks was apparent in schizophrenia and autism but not other disorders.
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Affiliation(s)
- Ashlea Segal
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Australia
- Wu Tsai Institute, Department of Neuroscience, Yale University, New Haven, United States
| | - Robert E Smith
- The Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia
- Florey Department of Neuroscience and Mental Health, Parkville, Victoria, Australia
| | - Sidhant Chopra
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Stuart Oldham
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Australia
- Developmental Imaging, Murdoch Children’s Research Institute, Melbourne, Australia
| | - Linden Parkes
- Department of Psychiatry, Rutgers University, Piscataway, NJ 08854, USA
| | | | - Seyed Mostafa Kia
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Cognitive Science and Artificial Intelligence, Tilburg School of Humanities and Digital Sciences, Tilburg University, Tilburg, the Netherlands
| | - Thomas Wolfers
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo & Oslo University Hospital, Oslo, Norway
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health (TÜCMH), University of Tübingen, Tübingen, Germany
| | - Barbara Franke
- Department of Cognitive Neuroscience, Donders Institute of Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Human Genetics, Donders Institute of Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Martine Hoogman
- Department of Human Genetics, Donders Institute of Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Psychiatry, Donders Institute of Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Christian F. Beckmann
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands
- Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Lars T. Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo & Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Ole A. Andreassen
- Department of Psychology, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Victoria, Australia
- Department of Biomedical Engineering, The University of Melbourne, Victoria, Australia
| | - Ben J. Harrison
- Department of Psychiatry, The University of Melbourne, Victoria, Australia
| | | | - Carles Soriano-Mas
- Department of Psychiatry, Bellvitge University Hospital. Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Carlos III Health Institute, Madrid, Spain
- Department of Social Psychology and Quantitative Psychology, Universitat de Barcelona-UB, Barcelona, Spain
| | - Narcís Cardoner
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Carlos III Health Institute, Madrid, Spain
- Sant Pau Mental Health Research Group, Institut d’Investigació Biomèdica Sant Pau (IIB-Sant Pau), Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Jeggan Tiego
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Australia
| | - Murat Yücel
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Leah Braganza
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Chao Suo
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Australia
- Australian Characterisation Commons at Scale (ACCS) Project, Monash eResearch Centre, Melbourne, Australia
| | - Michael Berk
- Deakin University, IMPACT – the Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon Health, Geelong, Australia
- Orygen, Melbourne, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia
- Florey Institute for Neuroscience and Mental Health, Parkville, Australia
| | - Sue Cotton
- Orygen, Melbourne, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia
| | - Mark A. Bellgrove
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Andre F. Marquand
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands
- Department of Neuroimaging, Centre of Neuroimaging Sciences, Institute of Psychiatry, King’s College London, London, The United Kingdom
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Australia
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5
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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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6
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Rootes-Murdy K, Panta S, Kelly R, Romero J, Quidé Y, Cairns MJ, Loughland C, Carr VJ, Catts SV, Jablensky A, Green MJ, Henskens F, Kiltschewskij D, Michie PT, Mowry B, Pantelis C, Rasser PE, Reay WR, Schall U, Scott RJ, Watkeys OJ, Roberts G, Mitchell PB, Fullerton JM, Overs BJ, Kikuchi M, Hashimoto R, Matsumoto J, Fukunaga M, Sachdev PS, Brodaty H, Wen W, Jiang J, Fani N, Ely TD, Lorio A, Stevens JS, Ressler K, Jovanovic T, van Rooij SJ, Federmann LM, Jockwitz C, Teumer A, Forstner AJ, Caspers S, Cichon S, Plis SM, Sarwate AD, Calhoun VD. Cortical similarities in psychiatric and mood disorders identified in federated VBM analysis via COINSTAC. PATTERNS (NEW YORK, N.Y.) 2024; 5:100987. [PMID: 39081570 PMCID: PMC11284501 DOI: 10.1016/j.patter.2024.100987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 01/02/2024] [Accepted: 04/10/2024] [Indexed: 08/02/2024]
Abstract
Structural neuroimaging studies have identified a combination of shared and disorder-specific patterns of gray matter (GM) deficits across psychiatric disorders. Pooling large data allows for examination of a possible common neuroanatomical basis that may identify a certain vulnerability for mental illness. Large-scale collaborative research is already facilitated by data repositories, institutionally supported databases, and data archives. However, these data-sharing methodologies can suffer from significant barriers. Federated approaches augment these approaches by enabling access or more sophisticated, shareable and scaled-up analyses of large-scale data. We examined GM alterations using Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation, an open-source, decentralized analysis application. Through federated analysis of eight sites, we identified significant overlap in the GM patterns (n = 4,102) of individuals with schizophrenia, major depressive disorder, and autism spectrum disorder. These results show cortical and subcortical regions that may indicate a shared vulnerability to psychiatric disorders.
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Affiliation(s)
- 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
| | - Sandeep Panta
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Ross Kelly
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Javier Romero
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Yann Quidé
- School of Psychology, University of New South Wales, Sydney, NSW, Australia
- Neuroscience Research Australia, Sydney, NSW, Australia
| | - Murray J. Cairns
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, NSW, Australia
- Centre for Brain and Mental Health Research, University of Newcastle, Callaghan, NSW, Australia
- Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
| | - Carmel Loughland
- Centre for Brain and Mental Health Research, University of Newcastle, Callaghan, NSW, Australia
- Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
| | - Vaughan J. Carr
- Neuroscience Research Australia, Sydney, NSW, Australia
- Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, NSW, Australia
- Department of Psychiatry, Monash University, Clayton, VIC, Australia
| | - Stanley V. Catts
- School of Medicine, University of Queensland, Brisbane, QLD, Australia
| | | | - Melissa J. Green
- Neuroscience Research Australia, Sydney, NSW, Australia
- Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, NSW, Australia
| | - Frans Henskens
- Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
- School of Medicine & Public Health, University of Newcastle, Newcastle, NSW, Australia
- Priority Research Centre for Health Behaviour, University of Newcastle, Newcastle, NSW, Australia
| | - Dylan Kiltschewskij
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, NSW, Australia
- Centre for Brain and Mental Health Research, University of Newcastle, Callaghan, NSW, Australia
- Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
| | - Patricia T. Michie
- Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
- School of Psychological Sciences, University of Newcastle, Callaghan, NSW, Australia
| | - Bryan Mowry
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
- Queensland Centre for Mental Health Research, University of Queensland, Brisbane, QLD, Australia
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne & Melbourne Health, Carlton South, VIC, Australia
- Florey Institute of Neuroscience & Mental Health, Parkville, VIC, Australia
| | - Paul E. Rasser
- Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
- Priority Research Centre for Health Behaviour, University of Newcastle, Newcastle, NSW, Australia
| | - William R. Reay
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, NSW, Australia
- Centre for Brain and Mental Health Research, University of Newcastle, Callaghan, NSW, Australia
- Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
| | - Ulrich Schall
- Centre for Brain and Mental Health Research, University of Newcastle, Callaghan, NSW, Australia
- Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
| | - Rodney J. Scott
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, NSW, Australia
| | - Oliver J. Watkeys
- Neuroscience Research Australia, Sydney, NSW, Australia
- Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, NSW, Australia
| | - Gloria Roberts
- Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, NSW, Australia
| | - Philip B. Mitchell
- Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, NSW, Australia
| | - Janice M. Fullerton
- Neuroscience Research Australia, Sydney, NSW, Australia
- School of Biomedical Sciences, University of New South Wales, Sydney, NSW, Australia
| | | | - Masataka Kikuchi
- Department of Computational Biology and Medical Sciences, University of Tokyo, Chiba, Japan
| | - Ryota Hashimoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Junya Matsumoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Masaki Fukunaga
- Section of Brain Function Information, National Institute for Physiological Sciences, Aichi, Japan
| | - Perminder S. Sachdev
- Centre for Healthy Brain Aging, Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, NSW, Australia
- Neuropsychiatric Institute, Prince of Wales Hospital, Randwick, NSW, Australia
| | - Henry Brodaty
- Centre for Healthy Brain Aging, Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, NSW, Australia
| | - Wei Wen
- Centre for Healthy Brain Aging, Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, NSW, Australia
| | - Jiyang Jiang
- Centre for Healthy Brain Aging, Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, NSW, Australia
| | - Negar Fani
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Timothy D. Ely
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | | | - Jennifer S. Stevens
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
- Atlanta VA Medical Center, Decatur, GA, USA
| | - Kerry Ressler
- McLean Hospital, Harvard Medical School, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Tanja Jovanovic
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, MI, USA
| | - Sanne J.H. van Rooij
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Lydia M. Federmann
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Christiane Jockwitz
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Alexander Teumer
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Andreas J. Forstner
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Sven Cichon
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Department of Biomedicine, University of Basel, Basel, Switzerland
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
| | - Sergey M. Plis
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Anand D. Sarwate
- Department of Electrical and Computer Engineering, Rutgers University-New Brunswick, Piscataway, NJ, 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
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7
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Sun H, Liu N, Qiu C, Tao B, Yang C, Tang B, Li H, Zhan K, Cai C, Zhang W, Lui S. Applications of MRI in Schizophrenia: Current Progress in Establishing Clinical Utility. J Magn Reson Imaging 2024. [PMID: 38946400 DOI: 10.1002/jmri.29470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 05/20/2024] [Accepted: 05/20/2024] [Indexed: 07/02/2024] Open
Abstract
Schizophrenia is a severe mental illness that significantly impacts the lives of affected individuals and with increasing mortality rates. Early detection and intervention are crucial for improving outcomes but the lack of validated biomarkers poses great challenges in such efforts. The use of magnetic resonance imaging (MRI) in schizophrenia enables the investigation of the disorder's etiological and neuropathological substrates in vivo. After decades of research, promising findings of MRI have been shown to aid in screening high-risk individuals and predicting illness onset, and predicting symptoms and treatment outcomes of schizophrenia. The integration of machine learning and deep learning techniques makes it possible to develop intelligent diagnostic and prognostic tools with extracted or selected imaging features. In this review, we aimed to provide an overview of current progress and prospects in establishing clinical utility of MRI in schizophrenia. We first provided an overview of MRI findings of brain abnormalities that might underpin the symptoms or treatment response process in schizophrenia patients. Then, we summarized the ongoing efforts in the computer-aided utility of MRI in schizophrenia and discussed the gap between MRI research findings and real-world applications. Finally, promising pathways to promote clinical translation were provided. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Hui Sun
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Naici Liu
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Changjian Qiu
- Mental Health Center, West China Hospital of Sichuan University, Sichuan Clinical Medical Research Center for Mental Disorders, Chengdu, China
| | - Bo Tao
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Chengmin Yang
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Biqiu Tang
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Hongwei Li
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Department of Radiology, The Third Hospital of Mianyang/Sichuan Mental Health Center, Mianyang, China
| | - Kongcai Zhan
- Department of Radiology, Zigong Affiliated Hospital of Southwest Medical University, Zigong Psychiatric Research Center, Zigong, China
| | - Chunxian Cai
- Department of Radiology, the Second People's Hospital of Neijiang, Neijiang, China
| | - Wenjing Zhang
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Su Lui
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
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8
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Chan SY, Fitzgerald E, Ngoh ZM, Lee J, Chuah J, Chia JSM, Fortier MV, Tham EH, Zhou JH, Silveira PP, Meaney MJ, Tan AP. Examining the associations between microglia genetic capacity, early life exposures and white matter development at the level of the individual. Brain Behav Immun 2024; 119:781-791. [PMID: 38677627 DOI: 10.1016/j.bbi.2024.04.038] [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: 03/15/2024] [Revised: 04/17/2024] [Accepted: 04/23/2024] [Indexed: 04/29/2024] Open
Abstract
There are inter-individual differences in susceptibility to the influence of early life experiences for which the underlying neurobiological mechanisms are poorly understood. Microglia play a role in environmental surveillance and may influence individual susceptibility to environmental factors. As an index of neurodevelopment, we estimated individual slopes of mean white matter fractional anisotropy (WM-FA) across three time-points (age 4.5, 6.0, and 7.5 years) for 351 participants. Individual variation in microglia reactivity was derived from an expression-based polygenic score(ePGS) comprised of Single Nucleotide Polymorphisms (SNPs) functionally related to the expression of microglia-enriched genes.A higher ePGS denotes an increased genetic capacity for the expression of microglia-related genes, and thus may confer a greater capacity to respond to the early environment and to influence brain development. We hypothesized that this ePGS would associate with the WM-FA index of neurodevelopment and moderate the influence of early environmental factors.Our findings show sex dependency, where a significant association between WM-FA and microglia ePGS was only obtained for females.We then examined associations with perinatal factors known to decrease (optimal birth outcomes and familial conditions) or increase (systemic inflammation) the risk for later mental health problems.In females, individuals with high microglia ePGS showed a negative association between systemic inflammation and WM-FA and a positive association between more advantageous environmental conditions and WM-FA. The microglia ePGS in females thus accounted for variations in the influence of the quality of the early environment on WM-FA.Finally, WM-FA slopes mediated the association of microglia ePGS with interpersonal problems and social hostility in females. Our findings suggest the genetic capacity for microglia function as a potential factor underlying differential susceptibility to early life exposuresthrough influences on neurodevelopment.
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Affiliation(s)
- Shi Yu Chan
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), 30 Medical Dr, Singapore 117609, Singapore
| | - Eamon Fitzgerald
- Ludmer Centre for Neuroinformatics and Mental Health, McGill University, 1010 Rue Sherbrooke O, QC H3A 2R7, Canada; Douglas Mental Health University Institute, Department of Psychiatry, McGill University, 6875 Bd LaSalle, QC H4H 1R3, Canada
| | - Zhen Ming Ngoh
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), 30 Medical Dr, Singapore 117609, Singapore
| | - Janice Lee
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), 30 Medical Dr, Singapore 117609, Singapore
| | - Jasmine Chuah
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), 30 Medical Dr, Singapore 117609, Singapore
| | - Joanne S M Chia
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), 30 Medical Dr, Singapore 117609, Singapore
| | - Marielle V Fortier
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), 30 Medical Dr, Singapore 117609, Singapore; Department of Diagnostic and Interventional Imaging, KK Women's and Children's Hospital, 100 Bukit Timah Rd, Singapore 229899, Singapore; Duke-NUS Medical School, 8 College Rd, Singapore 169857, Singapore
| | - Elizabeth H Tham
- Yong Loo Lin School of Medicine, National University of Singapore (NUS), 10 Medical Dr, Singapore 117597, Singapore; Khoo Teck Puat-National University Children's Medical Institute, National University Health System (NUHS), 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
| | - Juan H Zhou
- Yong Loo Lin School of Medicine, National University of Singapore (NUS), 10 Medical Dr, Singapore 117597, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore
| | - Patricia P Silveira
- Ludmer Centre for Neuroinformatics and Mental Health, McGill University, 1010 Rue Sherbrooke O, QC H3A 2R7, Canada; Douglas Mental Health University Institute, Department of Psychiatry, McGill University, 6875 Bd LaSalle, QC H4H 1R3, Canada; Yong Loo Lin School of Medicine, National University of Singapore (NUS), 10 Medical Dr, Singapore 117597, Singapore
| | - Michael J Meaney
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), 30 Medical Dr, Singapore 117609, Singapore; Douglas Mental Health University Institute, Department of Psychiatry, McGill University, 6875 Bd LaSalle, QC H4H 1R3, Canada; Yong Loo Lin School of Medicine, National University of Singapore (NUS), 10 Medical Dr, Singapore 117597, Singapore; Brain - Body Initiative Program, Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, Connexis North Tower, Singapore 138632, Singapore
| | - Ai Peng Tan
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), 30 Medical Dr, Singapore 117609, Singapore; Yong Loo Lin School of Medicine, National University of Singapore (NUS), 10 Medical Dr, Singapore 117597, Singapore; Brain - Body Initiative Program, Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, Connexis North Tower, Singapore 138632, Singapore; Department of Diagnostic Imaging, National University Health System, 1E Kent Ridge Rd, Singapore 119228, Singapore.
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龙 仁, 毛 鑫, 高 天, 解 倩, 谈 瀚, 李 子, 韩 鸿, 袁 兰. [Ursolic acid improved demyelination and interstitial fluid drainage disorders in schizophrenia mice]. BEIJING DA XUE XUE BAO. YI XUE BAN = JOURNAL OF PEKING UNIVERSITY. HEALTH SCIENCES 2024; 56:487-494. [PMID: 38864135 PMCID: PMC11167553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Indexed: 06/13/2024]
Abstract
OBJECTIVE To unveil the pathological changes associated with demyelination in schizophrenia (SZ) and its consequential impact on interstitial fluid (ISF) drainage, and to investigate the therapeutic efficacy of ursolic acid (UA) in treating demyelination and the ensuing abnormalities in ISF drainage in SZ. METHODS Female C57BL/6J mice, aged 6-8 weeks and weighing (20±2) g, were randomly divided into three groups: control, SZ model, and UA treatment. The control group received intraperitoneal injection (ip) of physiological saline and intragastric administration (ig) of 1% carboxymethylcellulose sodium (CMC-Na). The SZ model group was subjected to ip injection of 2 mg/kg dizocilpine maleate (MK-801) and ig administration of 1% CMC-Na. The UA treatment group underwent ig administration of 25 mg/kg UA and ip injection of 2 mg/kg MK-801. The treatment group received UA pretreatment via ig administration for one week, followed by a two-week drug intervention for all the three groups. Behavioral assessments, including the open field test and prepulse inhibition experiment, were conducted post-modeling. Subsequently, changes in the ISF partition drainage were investigated through fluorescent tracer injection into specific brain regions. Immunofluorescence analysis was employed to examine alterations in aquaporin 4 (AQP4) polarity distribution in the brain and changes in protein expression. Myelin reflex imaging using Laser Scanning Confocal Microscopy (LSCM) was utilized to study modifications in myelin within the mouse brain. Quantitative data underwent one-way ANOVA, followed by TukeyHSD for post hoc pairwise comparisons between the groups. RESULTS The open field test revealed a significantly longer total distance [(7 949.39±1 140.55) cm vs. (2 831.01±1 212.72) cm, P < 0.001] and increased central area duration [(88.43±22.06) s vs. (56.85±18.58) s, P=0.011] for the SZ model group compared with the controls. The UA treatment group exhibited signifi-cantly reduced total distance [(2 415.80±646.95) cm vs. (7 949.39±1 140.55) cm, P < 0.001] and increased central area duration [(54.78±11.66) s vs. (88.43±22.06) s, P=0.007] compared with the model group. Prepulse inhibition test results demonstrated a markedly lower inhibition rate of the startle reflex in the model group relative to the controls (P < 0.001 for both), with the treatment group displaying significant improvement (P < 0.001 for both). Myelin sheath analysis indicated significant demyelination in the model group, while UA treatment reversed this effect. Fluorescence tracing exhibited a significantly larger tracer diffusion area towards the rostral cortex and reflux area towards the caudal thalamus in the model group relative to the controls [(13.93±3.35) mm2 vs. (2.79±0.94) mm2, P < 0.001 for diffusion area; (2.48±0.38) mm2 vs. (0.05±0.12) mm2, P < 0.001 for reflux area], with significant impairment of drainage in brain regions. The treatment group demonstrated significantly reduced tracer diffusion and reflux areas [(7.93±2.48) mm2 vs. (13.93±3.35) mm2, P < 0.001 for diffusion area; (0.50±0.30) mm2 vs. (2.48±0.38) mm2, P < 0.001 for reflux area]. Immunofluorescence staining revealed disrupted AQP4 polarity distribution and reduced AQP4 protein expression in the model group compared with the controls [(3 663.88±733.77) μm2 vs. (13 354.92±4 054.05) μm2, P < 0.001]. The treatment group exhibited restored AQP4 polarity distribution and elevated AQP4 protein expression [(11 104.68±3 200.04) μm2 vs. (3 663.88±733.77) μm2, P < 0.001]. CONCLUSION UA intervention ameliorates behavioral performance in SZ mice, Thus alleviating hyperactivity and anxiety symptoms and restoring sensorimotor gating function. The underlying mechanism may involve the improvement of demyelination and ISF drainage dysregulation in SZ mice.
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Affiliation(s)
- 仁 龙
- 北京大学医学技术研究院医学影像技术学系,北京市磁共振成像设备与技术重点实验室,北京 100191Department of Medical Imaging Technology, Institute of Medical Technology, Peking University & Beijing Key Lab of Magnetic Resonance Imaging Device and Technique, Beijing 100191, China
| | - 鑫 毛
- 北京大学第三医院放射科,北京 100191Department of Radiology, Peking Univer-sity Third Hispital, Beijing 100191, China
| | - 天姿 高
- 北京大学医学技术研究院医学影像技术学系,北京市磁共振成像设备与技术重点实验室,北京 100191Department of Medical Imaging Technology, Institute of Medical Technology, Peking University & Beijing Key Lab of Magnetic Resonance Imaging Device and Technique, Beijing 100191, China
| | - 倩 解
- 北京大学第三医院放射科,北京 100191Department of Radiology, Peking Univer-sity Third Hispital, Beijing 100191, China
| | - 瀚博 谈
- 北京大学医学技术研究院医学影像技术学系,北京市磁共振成像设备与技术重点实验室,北京 100191Department of Medical Imaging Technology, Institute of Medical Technology, Peking University & Beijing Key Lab of Magnetic Resonance Imaging Device and Technique, Beijing 100191, China
| | - 子寅 李
- 北京大学医学技术研究院医学影像技术学系,北京市磁共振成像设备与技术重点实验室,北京 100191Department of Medical Imaging Technology, Institute of Medical Technology, Peking University & Beijing Key Lab of Magnetic Resonance Imaging Device and Technique, Beijing 100191, China
| | - 鸿宾 韩
- 北京大学医学技术研究院医学影像技术学系,北京市磁共振成像设备与技术重点实验室,北京 100191Department of Medical Imaging Technology, Institute of Medical Technology, Peking University & Beijing Key Lab of Magnetic Resonance Imaging Device and Technique, Beijing 100191, China
- 北京大学第三医院放射科,北京 100191Department of Radiology, Peking Univer-sity Third Hispital, Beijing 100191, China
| | - 兰 袁
- 北京大学医学技术研究院医学影像技术学系,北京市磁共振成像设备与技术重点实验室,北京 100191Department of Medical Imaging Technology, Institute of Medical Technology, Peking University & Beijing Key Lab of Magnetic Resonance Imaging Device and Technique, Beijing 100191, China
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10
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Hayashi Y, Okumura H, Arioka Y, Kushima I, Mori D, Lo T, Otgonbayar G, Kato H, Nawa Y, Kimura H, Aleksic B, Ozaki N. Analysis of human neuronal cells carrying ASTN2 deletion associated with psychiatric disorders. Transl Psychiatry 2024; 14:236. [PMID: 38830862 PMCID: PMC11148150 DOI: 10.1038/s41398-024-02962-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 05/19/2024] [Accepted: 05/23/2024] [Indexed: 06/05/2024] Open
Abstract
Recent genetic studies have found common genomic risk variants among psychiatric disorders, strongly suggesting the overlaps in their molecular and cellular mechanism. Our research group identified the variant in ASTN2 as one of the candidate risk factors across these psychiatric disorders by whole-genome copy number variation analysis. However, the alterations in the human neuronal cells resulting from ASTN2 variants identified in patients remain unknown. To address this, we used patient-derived and genome-edited iPS cells with ASTN2 deletion; cells were further differentiated into neuronal cells. A comprehensive gene expression analysis using genome-edited iPS cells with variants on both alleles revealed that the expression level of ZNF558, a gene specifically expressed in human forebrain neural progenitor cells, was greatly reduced in ASTN2-deleted neuronal cells. Furthermore, the expression of the mitophagy-related gene SPATA18, which is repressed by ZNF558, and mitophagy activity were increased in ASTN2-deleted neuronal cells. These phenotypes were also detected in neuronal cells differentiated from patient-derived iPS cells with heterozygous ASTN2 deletion. Our results suggest that ASTN2 deletion is related to the common pathogenic mechanism of psychiatric disorders by regulating mitophagy via ZNF558.
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Affiliation(s)
- Yu Hayashi
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Hiroki Okumura
- Department of Hospital Pharmacy, Nagoya University Hospital, Nagoya, Japan
- Pathophysiology of Mental Disorders, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Yuko Arioka
- Pathophysiology of Mental Disorders, Nagoya University Graduate School of Medicine, Nagoya, Japan.
- Center for Advanced Medicine and Clinical Research, Nagoya University Hospital, Nagoya, Japan.
| | - Itaru Kushima
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan
- Pathophysiology of Mental Disorders, Nagoya University Graduate School of Medicine, Nagoya, Japan
- Medical Genomics Center, Nagoya University Hospital, Nagoya, Japan
| | - Daisuke Mori
- Pathophysiology of Mental Disorders, Nagoya University Graduate School of Medicine, Nagoya, Japan
- Brain and Mind Research Center, Nagoya University, Nagoya, Japan
| | - Tzuyao Lo
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Gantsooj Otgonbayar
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Hidekazu Kato
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Yoshihiro Nawa
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Hiroki Kimura
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Branko Aleksic
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Norio Ozaki
- Pathophysiology of Mental Disorders, Nagoya University Graduate School of Medicine, Nagoya, Japan
- Institute for Glyco-core Research (iGCORE), Nagoya University, Nagoya, Japan
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11
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Zhang Z, Wei W, Wang S, Li M, Li X, Li X, Wang Q, Yu H, Zhang Y, Guo W, Ma X, Zhao L, Deng W, Sham PC, Sun Y, Li T. Dynamic structure-function coupling across three major psychiatric disorders. Psychol Med 2024; 54:1629-1640. [PMID: 38084608 DOI: 10.1017/s0033291723003525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
BACKGROUND Convergent evidence has suggested atypical relationships between brain structure and function in major psychiatric disorders, yet how the abnormal patterns coincide and/or differ across different disorders remains largely unknown. Here, we aim to investigate the common and/or unique dynamic structure-function coupling patterns across major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ). METHODS We quantified the dynamic structure-function coupling in 452 patients with psychiatric disorders (MDD/BD/SZ = 166/168/118) and 205 unaffected controls at three distinct brain network levels, such as global, meso-, and local levels. We also correlated dynamic structure-function coupling with the topological features of functional networks to examine how the structure-function relationship facilitates brain information communication over time. RESULTS The dynamic structure-function coupling is preserved for the three disorders at the global network level. Similar abnormalities in the rich-club organization are found in two distinct functional configuration states at the meso-level and are associated with the disease severity of MDD, BD, and SZ. At the local level, shared and unique alterations are observed in the brain regions involving the visual, cognitive control, and default mode networks. In addition, the relationships between structure-function coupling and the topological features of functional networks are altered in a manner indicative of state specificity. CONCLUSIONS These findings suggest both transdiagnostic and illness-specific alterations in the dynamic structure-function relationship of large-scale brain networks across MDD, BD, and SZ, providing new insights and potential biomarkers into the neurodevelopmental basis underlying the behavioral and cognitive deficits observed in these disorders.
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Affiliation(s)
- Zhe Zhang
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- School of Physics, Hangzhou Normal University, Hangzhou, China
- Institute of Brain Science, School of Basic Medical Sciences, Hangzhou Normal University, Hangzhou, China
| | - Wei Wei
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- Translational Psychiatry Research Laboratory, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, China
| | - Sujie Wang
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
| | - Mingli Li
- Mental Health Center and Psychiatric Laboratory, West China Hospital of Sichuan University, Chengdu, China
| | - Xiaojing Li
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- Translational Psychiatry Research Laboratory, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, China
| | - Xiaoyu Li
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
| | - Qiang Wang
- Mental Health Center and Psychiatric Laboratory, West China Hospital of Sichuan University, Chengdu, China
| | - Hua Yu
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- Translational Psychiatry Research Laboratory, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, China
| | - Yamin Zhang
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- Translational Psychiatry Research Laboratory, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, China
| | - Wanjun Guo
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- Translational Psychiatry Research Laboratory, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, China
| | - Xiaohong Ma
- Mental Health Center and Psychiatric Laboratory, West China Hospital of Sichuan University, Chengdu, China
| | - Liansheng Zhao
- Mental Health Center and Psychiatric Laboratory, West China Hospital of Sichuan University, Chengdu, China
| | - Wei Deng
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- Translational Psychiatry Research Laboratory, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, China
| | - Pak C Sham
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Centre for PanorOmic Sciences, The University of Hong Kong, Hong Kong SAR, China
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, China
| | - Yu Sun
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- Department of Neurology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Tao Li
- Department of Biomedical Engineering, & Department of Neurobiology, Key Laboratory for Biomedical Engineering of Ministry of Education, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- Translational Psychiatry Research Laboratory, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, China
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12
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Huang D, Wu Y, Yue J, Wang X. Causal relationship between resting-state networks and depression: a bidirectional two-sample mendelian randomization study. BMC Psychiatry 2024; 24:402. [PMID: 38811927 PMCID: PMC11138044 DOI: 10.1186/s12888-024-05857-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 05/20/2024] [Indexed: 05/31/2024] Open
Abstract
BACKGROUND Cerebral resting-state networks were suggested to be strongly associated with depressive disorders. However, the causal relationship between cerebral networks and depressive disorders remains controversial. In this study, we aimed to investigate the effect of resting-state networks on depressive disorders using a bidirectional Mendelian randomization (MR) design. METHODS Updated summary-level genome-wide association study (GWAS) data correlated with resting-state networks were obtained from a meta-analysis of European-descent GWAS from the Complex Trait Genetics Lab. Depression-related GWAS data were obtained from the FinnGen study involving participants with European ancestry. Resting-state functional magnetic resonance imaging and multiband diffusion imaging of the brain were performed to measure functional and structural connectivity in seven well-known networks. Inverse-variance weighting was used as the primary estimate, whereas the MR-Pleiotropy RESidual Sum and Outliers (PRESSO), MR-Egger, and weighted median were used to detect heterogeneity, sensitivity, and pleiotropy. RESULTS In total, 20,928 functional and 20,573 structural connectivity data as well as depression-related GWAS data from 48,847 patients and 225,483 controls were analyzed. Evidence for a causal effect of the structural limbic network on depressive disorders was found in the inverse variance-weighted limbic network (odds ratio, [Formula: see text]; 95% confidence interval, [Formula: see text]; [Formula: see text]), whereas the causal effect of depressive disorders on SC LN was not found(OR=1.0025; CI,1.0005-1.0046; P=0.012). No significant associations between functional connectivity of the resting-state networks and depressive disorders were found in this MR study. CONCLUSIONS These results suggest that genetically determined structural connectivity of the limbic network has a causal effect on depressive disorders and may play a critical role in its neuropathology.
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Affiliation(s)
- Dongmiao Huang
- Department of Psychiatry, the Fifth Affiliated Hospital of Sun Yat-sen University, No. 52, East Meihua Road, Zhuhai City, Guangdong Province, 519000, China
| | - Yuelin Wu
- Department of Psychiatry, the Fifth Affiliated Hospital of Sun Yat-sen University, No. 52, East Meihua Road, Zhuhai City, Guangdong Province, 519000, China
| | - Jihui Yue
- Department of Psychiatry, the Fifth Affiliated Hospital of Sun Yat-sen University, No. 52, East Meihua Road, Zhuhai City, Guangdong Province, 519000, China.
| | - Xianglan Wang
- Department of Psychiatry, the Fifth Affiliated Hospital of Sun Yat-sen University, No. 52, East Meihua Road, Zhuhai City, Guangdong Province, 519000, China.
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13
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Arble EP, Steinert SW, Shankar S, Cerjanic A, Sutton BP, Daugherty AM. Ideational Slippage in Middle-Aged and Older Adults: A Preliminary Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2024; 21:656. [PMID: 38928903 PMCID: PMC11203480 DOI: 10.3390/ijerph21060656] [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] [Received: 04/01/2024] [Revised: 05/10/2024] [Accepted: 05/17/2024] [Indexed: 06/28/2024]
Abstract
Ideational slippage-characterized by incorrect word usage and strained logic during dialogue-is common in aging and, at greater frequency, is an indicator of pre-clinical cognitive decline. Performance-based assessment of ideational slippage may be useful in the study of cognitive aging and Alzheimer's-disease-related pathology. In this preliminary study, we examine the association between corpus callosum volume and a performance-based assessment of ideational slippage in middle-aged and older adults (age 61-79 years). Ideational slippage was indexed from cognitive special scores using the Rorschach Inkblot Method (RIM), which are validated indices of deviant verbalization and logical inaccuracy (Sum6, WSum6). Among middle-aged and older adults, smaller splenium volume was associated with greater ideational slippage (ηp2 = 0.48), independent of processing speed and fluid intelligence. The observed negative associations are consistent with visuospatial perception and cognitive functions of the splenium. The effect was strongest with the splenium, and volumes of the genu and total white matter had small effects that were not statistically significant. Conclusions: Results are discussed with future application of RIM special scores for the assessment of pre-clinical cognitive decline and, based on observed effect sizes, power analyses are reported to inform future study planning.
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Affiliation(s)
- Eamonn P. Arble
- Department of Psychology, Eastern Michigan University, Ypsilanti, MI 48197, USA; (S.W.S.); (S.S.)
| | - Steven W. Steinert
- Department of Psychology, Eastern Michigan University, Ypsilanti, MI 48197, USA; (S.W.S.); (S.S.)
| | - Sneha Shankar
- Department of Psychology, Eastern Michigan University, Ypsilanti, MI 48197, USA; (S.W.S.); (S.S.)
| | - Alex Cerjanic
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA; (A.C.); (B.P.S.)
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
| | - Bradley P. Sutton
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA; (A.C.); (B.P.S.)
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
| | - Ana M. Daugherty
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA; (A.C.); (B.P.S.)
- Department of Psychology, Wayne State University, Detroit, MI 48202, USA
- Institute of Gerontology, Wayne State University, Detroit, MI 48202, USA
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14
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Bragg-Gonzalo L, Aguilera A, González-Arias C, De León Reyes NS, Sánchez-Cruz A, Carballeira P, Leroy F, Perea G, Nieto M. Early cortical GABAergic interneurons determine the projection patterns of L4 excitatory neurons. SCIENCE ADVANCES 2024; 10:eadj9911. [PMID: 38728406 PMCID: PMC11086621 DOI: 10.1126/sciadv.adj9911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 04/09/2024] [Indexed: 05/12/2024]
Abstract
During cerebral cortex development, excitatory pyramidal neurons (PNs) establish specific projection patterns while receiving inputs from GABAergic inhibitory interneurons (INs). Whether these inhibitory inputs can shape PNs' projection patterns is, however, unknown. While layer 4 (L4) PNs of the primary somatosensory (S1) cortex are all born as long-range callosal projection neurons (CPNs), most of them acquire local connectivity upon activity-dependent elimination of their interhemispheric axons during postnatal development. Here, we demonstrate that precise developmental regulation of inhibition is key for the retraction of S1L4 PNs' callosal projections. Ablation of somatostatin INs leads to premature inhibition from parvalbumin INs onto S1L4 PNs and prevents them from acquiring their barrel-restricted local connectivity pattern. As a result, adult S1L4 PNs retain interhemispheric projections responding to tactile stimuli, and the mice lose whisker-based texture discrimination. Overall, we show that temporally ordered IN activity during development is key to shaping local ipsilateral S1L4 PNs' projection pattern, which is required for fine somatosensory processing.
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Affiliation(s)
- Lorena Bragg-Gonzalo
- Department of Molecular and Cellular Biology, Centro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas (CNB-CSIC), Madrid 28049, Spain
| | - Alfonso Aguilera
- Department of Molecular and Cellular Biology, Centro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas (CNB-CSIC), Madrid 28049, Spain
| | - Candela González-Arias
- Functional and Systems Neurobiology Department, Instituto Cajal, Consejo Superior de Investigaciones Científicas, Madrid 28002, Spain
| | - Noelia S. De León Reyes
- Instituto de Neurociencias (CSIC-UMH), Av. Ramón y Cajal s/n, San Juan de Alicante, Alicante, Spain
| | - Alonso Sánchez-Cruz
- Department of Molecular and Cellular Biology, Centro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas (CNB-CSIC), Madrid 28049, Spain
| | - Paula Carballeira
- Department of Molecular and Cellular Biology, Centro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas (CNB-CSIC), Madrid 28049, Spain
| | - Félix Leroy
- Instituto de Neurociencias (CSIC-UMH), Av. Ramón y Cajal s/n, San Juan de Alicante, Alicante, Spain
| | - Gertrudis Perea
- Functional and Systems Neurobiology Department, Instituto Cajal, Consejo Superior de Investigaciones Científicas, Madrid 28002, Spain
| | - Marta Nieto
- Department of Molecular and Cellular Biology, Centro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas (CNB-CSIC), Madrid 28049, Spain
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Kotov R, Carpenter WT, Cicero DC, Correll CU, Martin EA, Young JW, Zald DH, Jonas KG. Psychosis superspectrum II: neurobiology, treatment, and implications. Mol Psychiatry 2024; 29:1293-1309. [PMID: 38351173 DOI: 10.1038/s41380-024-02410-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 12/24/2023] [Accepted: 01/04/2024] [Indexed: 02/16/2024]
Abstract
Alternatives to traditional categorical diagnoses have been proposed to improve the validity and utility of psychiatric nosology. This paper continues the companion review of an alternative model, the psychosis superspectrum of the Hierarchical Taxonomy of Psychopathology (HiTOP). The superspectrum model aims to describe psychosis-related psychopathology according to data on distributions and associations among signs and symptoms. The superspectrum includes psychoticism and detachment spectra as well as narrow subdimensions within them. Auxiliary domains of cognitive deficit and functional impairment complete the psychopathology profile. The current paper reviews evidence on this model from neurobiology, treatment response, clinical utility, and measure development. Neurobiology research suggests that psychopathology included in the superspectrum shows similar patterns of neural alterations. Treatment response often mirrors the hierarchy of the superspectrum with some treatments being efficacious for psychoticism, others for detachment, and others for a specific subdimension. Compared to traditional diagnostic systems, the quantitative nosology shows an approximately 2-fold increase in reliability, explanatory power, and prognostic accuracy. Clinicians consistently report that the quantitative nosology has more utility than traditional diagnoses, but studies of patients with frank psychosis are currently lacking. Validated measures are available to implement the superspectrum model in practice. The dimensional conceptualization of psychosis-related psychopathology has implications for research, clinical practice, and public health programs. For example, it encourages use of the cohort study design (rather than case-control), transdiagnostic treatment strategies, and selective prevention based on subclinical symptoms. These approaches are already used in the field, and the superspectrum provides further impetus and guidance for their implementation. Existing knowledge on this model is substantial, but significant gaps remain. We identify outstanding questions and propose testable hypotheses to guide further research. Overall, we predict that the more informative, reliable, and valid characterization of psychopathology offered by the superspectrum model will facilitate progress in research and clinical care.
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Affiliation(s)
- Roman Kotov
- Department of Psychiatry and Behavioral Health, Stony Brook University, Stony Brook, NY, USA.
| | | | - David C Cicero
- Department of Psychology, University of North Texas, Denton, TX, USA
| | - Christoph U Correll
- Department of Psychiatry, The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA
- Department of Psychiatry and Molecular Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Child and Adolescent Psychiatry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Elizabeth A Martin
- Department of Psychological Science, University of California, Irvine, Irvine, CA, USA
| | - Jared W Young
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Research Service, VA San Diego Healthcare System, San Diego, CA, USA
| | - David H Zald
- Rutgers University, The State University of New Jersey, New Brunswick, NJ, USA
| | - Katherine G Jonas
- Department of Psychiatry and Behavioral Health, Stony Brook University, Stony Brook, NY, USA
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16
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Rivera AD, Normanton JR, Butt AM, Azim K. The Genomic Intersection of Oligodendrocyte Dynamics in Schizophrenia and Aging Unravels Novel Pathological Mechanisms and Therapeutic Potentials. Int J Mol Sci 2024; 25:4452. [PMID: 38674040 PMCID: PMC11050044 DOI: 10.3390/ijms25084452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 03/28/2024] [Accepted: 03/30/2024] [Indexed: 04/28/2024] Open
Abstract
Schizophrenia is a significant worldwide health concern, affecting over 20 million individuals and contributing to a potential reduction in life expectancy by up to 14.5 years. Despite its profound impact, the precise pathological mechanisms underlying schizophrenia continue to remain enigmatic, with previous research yielding diverse and occasionally conflicting findings. Nonetheless, one consistently observed phenomenon in brain imaging studies of schizophrenia patients is the disruption of white matter, the bundles of myelinated axons that provide connectivity and rapid signalling between brain regions. Myelin is produced by specialised glial cells known as oligodendrocytes, which have been shown to be disrupted in post-mortem analyses of schizophrenia patients. Oligodendrocytes are generated throughout life by a major population of oligodendrocyte progenitor cells (OPC), which are essential for white matter health and plasticity. Notably, a decline in a specific subpopulation of OPC has been identified as a principal factor in oligodendrocyte disruption and white matter loss in the aging brain, suggesting this may also be a factor in schizophrenia. In this review, we analysed genomic databases to pinpoint intersections between aging and schizophrenia and identify shared mechanisms of white matter disruption and cognitive dysfunction.
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Affiliation(s)
- Andrea D. Rivera
- Department of Neuroscience, Institute of Human Anatomy, University of Padova, Via A. Gabelli 65, 35127 Padua, Italy;
| | - John R. Normanton
- GliaGenesis Limited, Orchard Lea, Horns Lane, Oxfordshire, Witney OX29 8NH, UK; (J.R.N.); (K.A.)
| | - Arthur M. Butt
- GliaGenesis Limited, Orchard Lea, Horns Lane, Oxfordshire, Witney OX29 8NH, UK; (J.R.N.); (K.A.)
- School of Pharmacy and Biomedical Science, University of Portsmouth, Hampshire PO1 2UP, UK
| | - Kasum Azim
- GliaGenesis Limited, Orchard Lea, Horns Lane, Oxfordshire, Witney OX29 8NH, UK; (J.R.N.); (K.A.)
- Independent Data Lab UG, Frauenmantelanger 31, 80937 Munich, Germany
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17
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Cisneros-Mejorado AJ, Ordaz RP, Garay E, Arellano RO. β-carbolines that enhance GABA A receptor response expressed in oligodendrocytes promote remyelination in an in vivo rat model of focal demyelination. Front Cell Neurosci 2024; 18:1369730. [PMID: 38694535 PMCID: PMC11061515 DOI: 10.3389/fncel.2024.1369730] [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: 01/12/2024] [Accepted: 04/09/2024] [Indexed: 05/04/2024] Open
Abstract
Demyelination is typically followed by a remyelination process through mature oligodendrocytes (OLs) differentiated from precursor cells (OPCs) recruited into the lesioned areas, however, this event usually results in uncompleted myelination. Potentiation of the remyelination process is an important target for designing effective therapeutic strategies against white matter loss. Here, it was evaluated the remyelinating effect of different β-carbolines that present differential allosteric modulation on the GABAA receptor expressed in OLs. For this, we used a focalized demyelination model in the inferior cerebellar peduncle (i.c.p.) of rats (DRICP model), in which, demyelination by ethidium bromide (0.05%) stereotaxic injection was confirmed histologically by staining with Black-Gold II (BGII) and toluidine blue. In addition, a longitudinal analysis with diffusion-weighted magnetic resonance imaging (dMRI) was made by computing fractional anisotropy (FA), apparent diffusion coefficient (ADC) and diffusivity parameters to infer i.c.p. microstructural changes. First, dMRI analysis revealed FA decreases together with ADC and radial diffusivity (RD) increases after demyelination, which correlates with histological BGII observations. Then, we evaluated the effect produced by three allosteric GABAA receptor modulators, the N-butyl-β-carboline-3-carboxylate (β-CCB), ethyl 9H-pyrido [3,4-b]indole-3-carboxylate (β-CCE), and 4-ethyl-6,7-dimethoxy-9H-pyrido [3,4-b]indole-3-carboxylic acid methyl ester (DMCM). The results indicated that daily systemic β-CCB (1 mg/Kg) or β-CCE (1 mg/Kg) administration for 2 weeks, but not DMCM (0.35 mg/Kg), in lesioned animals increased FA and decreased ADC or RD, suggesting myelination improvement. This was supported by BGII staining analysis that showed a recovery of myelin content. Also, it was quantified by immunohistochemistry both NG2+ and CC1+ cellular population in the different experimental sceneries. Data indicated that either β-CCB or β-CCE, but not DMCM, produced an increase in the population of CC1+ cells in the lesioned area. Finally, it was also calculated the g-ratio of myelinated axons and observed a similar value in those lesioned animals treated with β-CCB or β-CCE compared to controls. Thus, using the DRICP model, it was observed that either β-CCB or β-CCE, positive modulators of the GABAA receptor in OLs, had a potent promyelinating effect.
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Affiliation(s)
| | | | | | - Rogelio O. Arellano
- Instituto de Neurobiología, Laboratorio de Neurofisiología Celular, Universidad Nacional Autónoma de México, Juriquilla, Mexico
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Thiel K, Lemke H, Winter A, Flinkenflügel K, Waltemate L, Bonnekoh L, Grotegerd D, Dohm K, Hahn T, Förster K, Kanske P, Repple J, Opel N, Redlich R, David F, Forstner AJ, Stein F, Brosch K, Thomas-Odenthal F, Usemann P, Teutenberg L, Straube B, Alexander N, Jamalabadi H, Jansen A, Witt SH, Andlauer TFM, Pfennig A, Bauer M, Nenadić I, Kircher T, Meinert S, Dannlowski U. White and gray matter alterations in bipolar I and bipolar II disorder subtypes compared with healthy controls - exploring associations with disease course and polygenic risk. Neuropsychopharmacology 2024; 49:814-823. [PMID: 38332015 PMCID: PMC10948847 DOI: 10.1038/s41386-024-01812-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 12/28/2023] [Accepted: 01/21/2024] [Indexed: 02/10/2024]
Abstract
Patients with bipolar disorder (BD) show alterations in both gray matter volume (GMV) and white matter (WM) integrity compared with healthy controls (HC). However, it remains unclear whether the phenotypically distinct BD subtypes (BD-I and BD-II) also exhibit brain structural differences. This study investigated GMV and WM differences between HC, BD-I, and BD-II, along with clinical and genetic associations. N = 73 BD-I, n = 63 BD-II patients and n = 136 matched HC were included. Using voxel-based morphometry and tract-based spatial statistics, main effects of group in GMV and fractional anisotropy (FA) were analyzed. Associations between clinical and genetic features and GMV or FA were calculated using regression models. For FA but not GMV, we found significant differences between groups. BD-I patients showed lower FA compared with BD-II patients (ptfce-FWE = 0.006), primarily in the anterior corpus callosum. Compared with HC, BD-I patients exhibited lower FA in widespread clusters (ptfce-FWE < 0.001), including almost all major projection, association, and commissural fiber tracts. BD-II patients also demonstrated lower FA compared with HC, although less pronounced (ptfce-FWE = 0.049). The results remained unchanged after controlling for clinical and genetic features, for which no independent associations with FA or GMV emerged. Our findings suggest that, at a neurobiological level, BD subtypes may reflect distinct degrees of disease expression, with increasing WM microstructure disruption from BD-II to BD-I. This differential magnitude of microstructural alterations was not clearly linked to clinical and genetic variables. These findings should be considered when discussing the classification of BD subtypes within the spectrum of affective disorders.
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Affiliation(s)
- Katharina Thiel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Hannah Lemke
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Alexandra Winter
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Kira Flinkenflügel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Lena Waltemate
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Translational Psychotherapy, Institute of Psychology, University of Göttingen, Göttingen, Germany
| | - Linda Bonnekoh
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Katharina Dohm
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Katharina Förster
- Clinical Psychology and Behavioral Neuroscience, Faculty of Psychology, Technische Universität Dresden, Dresden, Germany
| | - Philipp Kanske
- Clinical Psychology and Behavioral Neuroscience, Faculty of Psychology, Technische Universität Dresden, Dresden, Germany
| | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Department for Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
| | - Nils Opel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Department of Psychiatry, Jena University Hospital/Friedrich-Schiller-University Jena, Jena, Germany
- German Center for Mental Health (DZPG), Halle-Jena-Magdeburg, Germany
| | - Ronny Redlich
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- German Center for Mental Health (DZPG), Halle-Jena-Magdeburg, Germany
- Department of Psychology, University of Halle, Halle, Germany
- Center for Intervention and Research on adaptive and maladaptive brain circuits underlying mental health (C-I-R-C), Jena-Magdeburg-Halle, Halle, Germany
| | - Friederike David
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Andreas J Forstner
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
- Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Florian Thomas-Odenthal
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Paula Usemann
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Lea Teutenberg
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Benjamin Straube
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Nina Alexander
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Hamidreza Jamalabadi
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
- Core-Facility Brainimaging, Faculty of Medicine, University of Marburg, Marburg, Germany
| | - Stephanie H Witt
- Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Till F M Andlauer
- Department of Neurology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Andrea Pfennig
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, TU Dresden University of Technology, Dresden, Germany
| | - Michael Bauer
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, TU Dresden University of Technology, Dresden, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Institute of Translational Neuroscience, University of Münster, Münster, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany.
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19
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Sun H, Yan R, Hua L, Xia Y, Huang Y, Wang X, Yao Z, Lu Q. Based on white matter microstructure to early identify bipolar disorder from patients with depressive episode. J Affect Disord 2024; 350:428-434. [PMID: 38244786 DOI: 10.1016/j.jad.2024.01.147] [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: 07/04/2023] [Revised: 01/10/2024] [Accepted: 01/14/2024] [Indexed: 01/22/2024]
Abstract
OBJECTIVE Because of similar clinical manifestations, bipolar disorder (BD) patients are often misdiagnosed as major depressive disorder (MDD). This study aimed to compare the difference between depressed patients later converting to BD and unipolar depression (UD) according to diffusion tensor imaging (DTI). METHOD Patients with MDD (562 participants) in depressive episode states and healthy controls (HCs) (145 participants) were recruited over 10 years. Demographic and magnetic resonance imaging (MRI) data were collected at the time of recruitment. All patients with MDD were followed up for 5 years and classified into the transfer to BD (tBD) group (83 participants) and UD group (160 participants) according to the follow-up results. DTI and functional magnetic resonance imaging at baseline were compared. RESULTS Common abnormalities were found in both tBD and UD groups, including left superior cerebellar peduncle (SCP.L), right anterior limb of the internal capsule (ALIC.R), right superior fronto-occipital fasciculus (SFOF.R), and right inferior fronto-occipital fasciculus (IFOF.R). The tBD showed more extensive abnormalities than the UD in the body of corpus callosum, fornix, left superior corona radiata, left posterior corona radiata, left superior longitudinal fasciculus, and left superior fronto-occipital fasciculus. CONCLUSION The study demonstrated the common and distinct abnormalities of tBD and UD when compared to HC. The tBD group showed more extensive disruptions of white matter integrity, which could be a potential biomarker for the early identification of BD.
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Affiliation(s)
- Hao Sun
- Nanjing Brain Hospital, Clinical Teaching Hospital of Medical School, Nanjing University, Nanjing, China; Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, 249 Guangzhou Road, Nanjing 210029, China
| | - Rui Yan
- Nanjing Brain Hospital, Clinical Teaching Hospital of Medical School, Nanjing University, Nanjing, China; Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, 249 Guangzhou Road, Nanjing 210029, China
| | - Lingling Hua
- Nanjing Brain Hospital, Clinical Teaching Hospital of Medical School, Nanjing University, Nanjing, China; Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, 249 Guangzhou Road, Nanjing 210029, China
| | - Yi Xia
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, 249 Guangzhou Road, Nanjing 210029, China
| | - Yinghong Huang
- Nanjing Brain Hospital, Clinical Teaching Hospital of Medical School, Nanjing University, Nanjing, China; Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, 249 Guangzhou Road, Nanjing 210029, China
| | - Xiaoqin Wang
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, 249 Guangzhou Road, Nanjing 210029, China
| | - Zhijian Yao
- Nanjing Brain Hospital, Clinical Teaching Hospital of Medical School, Nanjing University, Nanjing, China; Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, 249 Guangzhou Road, Nanjing 210029, China; School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, China.
| | - Qing Lu
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing 210096, China.
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20
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Ding S, Shi Z, Huang K, Fan X, Li X, Zheng H, Wang L, Yan Z, Cai J. Aberrant white matter microstructure detected by automatic fiber quantification in pediatric myelin oligodendrocyte glycoprotein antibody-associated disease. Mult Scler Relat Disord 2024; 84:105483. [PMID: 38354445 DOI: 10.1016/j.msard.2024.105483] [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: 06/06/2023] [Revised: 02/04/2024] [Accepted: 02/08/2024] [Indexed: 02/16/2024]
Abstract
BACKGROUND AND OBJECTIVES Myelin oligodendrocyte glycoprotein antibody-associated diseases (MOGAD) is an idiopathic inflammatory demyelinating disorder in children, for which the precise damage patterns of the white matter (WM) fibers remain unclear. Herein, we utilized diffusion tensor imaging (DTI)-based automated fiber quantification (AFQ) to identify patterns of fiber damage and to investigate the clinical significance of MOGAD-affected fiber tracts. METHODS A total of 28 children with MOGAD and 31 healthy controls were included in this study. The AFQ approach was employed to track WM fiber with 100 equidistant nodes defined along each tract for statistical analysis of DTI metrics in both the entire and nodal manner. The feature selection method was used to further screen significantly aberrant DTI metrics of the affected fiber tracts or segments for eight common machine learning (ML) to evaluate their potential in identifying MOGAD. These metrics were then correlated with clinical scales to assess their potential as imaging biomarkers. RESULTS In the entire manner, significantly reduced fractional anisotropy (FA) was shown in the left anterior thalamic radiation, arcuate fasciculus, and the posterior and anterior forceps of corpus callosum in MOGAD (all p < 0.05). In the nodal manner, significant DTI metrics alterations were widely observed across 37 segments in 10 fiber tracts (all p < 0.05), mainly characterized by decreased FA and increased radial diffusivity (RD). Among them, 14 DTI metrics in seven fiber tracts were selected as important features to establish ML models, and satisfactory discrimination of MOGAD was obtained in all models (all AUC > 0.85), with the best performance in the logistic regression model (AUC = 0.952). For those features, the FA of left cingulum cingulate and the RD of right inferior frontal-occipital fasciculus were negatively and positively correlated with the expanded disability status scale (r = -0.54, p = 0.014; r = 0.43, p = 0.03), respectively. CONCLUSION Pediatric MOGAD exhibits extensive WM fiber tract aberration detected by AFQ. Certain fiber tracts exhibit specific patterns of DTI metrics that hold promising potential as biomarkers.
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Affiliation(s)
- Shuang Ding
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing 400014, China
| | - Zhuowei Shi
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400000, China
| | - Kaiping Huang
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing 400014, China
| | - Xiao Fan
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing 400014, China
| | - Xiujuan Li
- Department of Neurology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing 400014, China
| | - Helin Zheng
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing 400014, China
| | - Longlun Wang
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing 400014, China
| | - Zichun Yan
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400000, China
| | - Jinhua Cai
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing 400014, China.
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21
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Deloulme JC, Leclercq M, Deschaux O, Flore G, Capellano L, Tocco C, Braz BY, Studer M, Lahrech H. Structural interhemispheric connectivity defects in mouse models of BBSOAS: Insights from high spatial resolution 3D white matter tractography. Neurobiol Dis 2024; 193:106455. [PMID: 38408685 DOI: 10.1016/j.nbd.2024.106455] [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: 11/10/2023] [Revised: 02/23/2024] [Accepted: 02/23/2024] [Indexed: 02/28/2024] Open
Abstract
White matter (WM) tract formation and axonal pathfinding are major processes in brain development allowing to establish precise connections between targeted structures. Disruptions in axon pathfinding and connectivity impairments will lead to neural circuitry abnormalities, often associated with various neurodevelopmental disorders (NDDs). Among several neuroimaging methodologies, Diffusion Tensor Imaging (DTI) is a magnetic resonance imaging (MRI) technique that has the advantage of visualizing in 3D the WM tractography of the whole brain non-invasively. DTI is particularly valuable in unpinning structural tract connectivity defects of neural networks in NDDs. In this study, we used 3D DTI to unveil brain-specific tract defects in two mouse models lacking the Nr2f1 gene, which mutations in patients have been proven to cause an emerging NDD, called Bosch-Boonstra-Schaaf Optic Atrophy (BBSOAS). We aimed to investigate the impact of the lack of cortical Nr2f1 function on WM morphometry and tract microstructure quantifications. We found in both mutant mice partial loss of fibers and severe misrouting of the two major cortical commissural tracts, the corpus callosum, and the anterior commissure, as well as the two major hippocampal efferent tracts, the post-commissural fornix, and the ventral hippocampal commissure. DTI tract malformations were supported by 2D histology, 3D fluorescent imaging, and behavioral analyses. We propose that these interhemispheric connectivity impairments are consistent in explaining some cognitive defects described in BBSOAS patients, particularly altered information processing between the two brain hemispheres. Finally, our results highlight 3DDTI as a relevant neuroimaging modality that can provide appropriate morphometric biomarkers for further diagnosis of BBSOAS patients.
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Affiliation(s)
| | | | - Olivier Deschaux
- University Côte d'Azur (UCA), CNRS, Inserm, Institute of Biology Valrose (iBV), Nice, France
| | - Gemma Flore
- Institute of Genetics and Biophysics "Adriano Buzzati Traverso", CNR, Napoli, Italy
| | - Laetitia Capellano
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institute Neurosciences, 38000 Grenoble, France
| | - Chiara Tocco
- University Côte d'Azur (UCA), CNRS, Inserm, Institute of Biology Valrose (iBV), Nice, France
| | - Barbara Yael Braz
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institute Neurosciences, 38000 Grenoble, France
| | - Michèle Studer
- University Côte d'Azur (UCA), CNRS, Inserm, Institute of Biology Valrose (iBV), Nice, France.
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Gai Q, Chu T, Li Q, Guo Y, Ma H, Shi Y, Che K, Zhao F, Dong F, Li Y, Xie H, Mao N. Altered intersubject functional variability of brain white-matter in major depressive disorder and its association with gene expression profiles. Hum Brain Mapp 2024; 45:e26670. [PMID: 38553866 PMCID: PMC10980843 DOI: 10.1002/hbm.26670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 03/02/2024] [Accepted: 03/13/2024] [Indexed: 04/02/2024] Open
Abstract
Major depressive disorder (MDD) is a clinically heterogeneous disorder. Its mechanism is still unknown. Although the altered intersubject variability in functional connectivity (IVFC) within gray-matter has been reported in MDD, the alterations to IVFC within white-matter (WM-IVFC) remain unknown. Based on the resting-state functional MRI data of discovery (145 MDD patients and 119 healthy controls [HCs]) and validation cohorts (54 MDD patients, and 78 HCs), we compared the WM-IVFC between the two groups. We further assessed the meta-analytic cognitive functions related to the alterations. The discriminant WM-IVFC values were used to classify MDD patients and predict clinical symptoms in patients. In combination with the Allen Human Brain Atlas, transcriptome-neuroimaging association analyses were further conducted to investigate gene expression profiles associated with WM-IVFC alterations in MDD, followed by a set of gene functional characteristic analyses. We found extensive WM-IVFC alterations in MDD compared to HCs, which were associated with multiple behavioral domains, including sensorimotor processes and higher-order functions. The discriminant WM-IVFC could not only effectively distinguish MDD patients from HCs with an area under curve ranging from 0.889 to 0.901 across three classifiers, but significantly predict depression severity (r = 0.575, p = 0.002) and suicide risk (r = 0.384, p = 0.040) in patients. Furthermore, the variability-related genes were enriched for synapse, neuronal system, and ion channel, and predominantly expressed in excitatory and inhibitory neurons. Our results obtained good reproducibility in the validation cohort. These findings revealed intersubject functional variability changes of brain WM in MDD and its linkage with gene expression profiles, providing potential implications for understanding the high clinical heterogeneity of MDD.
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Affiliation(s)
- Qun Gai
- Department of Radiology, Yantai Yuhuangding HospitalQingdao UniversityYantaiShandongChina
- Big Data & Artificial Intelligence LaboratoryYantai Yuhuangding HospitalYantaiShandongChina
- Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women's DiseasesYantai Yuhuangding HospitalYantaiShandongChina
| | - Tongpeng Chu
- Department of Radiology, Yantai Yuhuangding HospitalQingdao UniversityYantaiShandongChina
- Big Data & Artificial Intelligence LaboratoryYantai Yuhuangding HospitalYantaiShandongChina
- Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women's DiseasesYantai Yuhuangding HospitalYantaiShandongChina
| | - Qinghe Li
- School of Medical ImagingBinzhou Medical UniversityYantaiShandongChina
| | - Yuting Guo
- School of Medical ImagingBinzhou Medical UniversityYantaiShandongChina
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding HospitalQingdao UniversityYantaiShandongChina
| | - Yinghong Shi
- Department of Radiology, Yantai Yuhuangding HospitalQingdao UniversityYantaiShandongChina
| | - Kaili Che
- Department of Radiology, Yantai Yuhuangding HospitalQingdao UniversityYantaiShandongChina
| | - Feng Zhao
- School of Computer Science and TechnologyShandong Technology and Business UniversityYantaiShandongChina
| | - Fanghui Dong
- School of Medical ImagingBinzhou Medical UniversityYantaiShandongChina
| | - Yuna Li
- Department of Radiology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
| | - Haizhu Xie
- Department of Radiology, Yantai Yuhuangding HospitalQingdao UniversityYantaiShandongChina
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding HospitalQingdao UniversityYantaiShandongChina
- Big Data & Artificial Intelligence LaboratoryYantai Yuhuangding HospitalYantaiShandongChina
- Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women's DiseasesYantai Yuhuangding HospitalYantaiShandongChina
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Chen M, Xia X, Kang Z, Li Z, Dai J, Wu J, Chen C, Qiu Y, Liu T, Liu Y, Zhang Z, Shen Q, Tao S, Deng Z, Lin Y, Wei Q. Distinguishing schizophrenia and bipolar disorder through a Multiclass Classification model based on multimodal neuroimaging data. J Psychiatr Res 2024; 172:119-128. [PMID: 38377667 DOI: 10.1016/j.jpsychires.2024.02.024] [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: 11/15/2023] [Revised: 02/05/2024] [Accepted: 02/07/2024] [Indexed: 02/22/2024]
Abstract
This study aimed to identify neural biomarkers for schizophrenia (SZ) and bipolar disorder (BP) by analyzing multimodal neuroimaging. Utilizing data from structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), and resting-state functional magnetic resonance imaging (rs-fMRI), multiclass classification models were created for SZ, BP, and healthy controls (HC). A total of 113 participants (BP: 31, SZ: 39, and HC: 43) were recruited under strict enrollment control, from which 272, 200, and 1875 features were extracted from sMRI, DTI, and rs-fMRI data, respectively. A support vector machine (SVM) with recursive feature elimination (RFE) was employed to build the models using a one-against-one approach and leave-one-out cross-validation, achieving a classification accuracy of 70.8%. The most discriminative features were primarily from rs-fMRI, along with significant findings in sMRI and DTI. Key biomarkers identified included the increased thickness of the left cuneus cortex and decreased regional functional connectivity strength (rFCS) in the left supramarginal gyrus as shared indicators for BP and SZ. Additionally, decreased fractional anisotropy in the left superior fronto-occipital fasciculus was suggested as specific to BP, while decreased rFCS in the left inferior parietal area might serve as a specific biomarker for SZ. These findings underscore the potential of multimodal neuroimaging in distinguishing between BP and SZ and contribute to the understanding of their neural underpinnings.
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Affiliation(s)
- Ming Chen
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Guangdong Mental Health Institute, Guangdong ProvincialPeople's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Xiaowei Xia
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zhuang Kang
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zhinan Li
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Jiamin Dai
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Junyan Wu
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Cai Chen
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yong Qiu
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Department of Psychiatry, Mindfront Caring Medical, Guangzhou, China
| | - Tong Liu
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Department of Psychiatry, The First Affiliated Hospital of Xi'an Jiaotong University, Shaanxi, China
| | - Yanxi Liu
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Ziyi Zhang
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Department of Medical Division, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Qingni Shen
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Sichu Tao
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zixin Deng
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Ying Lin
- Department of Psychology, Sun Yat-sen University, Guangzhou, China.
| | - Qinling Wei
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
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24
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Stevens M, Ní Mhurchú S, Corley E, Egan C, Hallahan B, McDonald C, Donohoe G, Burke T. Uncinate fasciculus microstructural organisation and emotion recognition in schizophrenia: controlling for hit rate bias. Front Behav Neurosci 2024; 18:1302916. [PMID: 38566859 PMCID: PMC10985192 DOI: 10.3389/fnbeh.2024.1302916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 02/02/2024] [Indexed: 04/04/2024] Open
Abstract
Introduction Schizophrenia (SCZ) is a complex neurodevelopmental disorder characterised by functional and structural brain dysconnectivity and disturbances in perception, cognition, emotion, and social functioning. In the present study, we investigated whether the microstructural organisation of the uncinate fasciculus (UF) was associated with emotion recognition (ER) performance. Additionally, we investigated the usefulness of an unbiased hit rate (UHR) score to control for response biases (i.e., participant guessing) during an emotion recognition task (ERT). Methods Fifty-eight individuals diagnosed with SCZ were included. The CANTAB ERT was used to measure social cognition. Specific ROI manual tract segmentation was completed using ExploreDTI and followed the protocol previously outlined by Coad et al. (2020). Results We found that the microstructural organisation of the UF was significantly correlated with physical neglect and ER outcomes. Furthermore, we found that the UHR score was more sensitive to ERT subscale emotion items than the standard HR score. Finally, given the association between childhood trauma (in particular childhood neglect) and social cognition in SCZ, a mediation analysis found evidence that microstructural alterations of the UF mediated an association between childhood trauma and social cognitive performance. Discussion The mediating role of microstructural alterations in the UF on the association between childhood trauma and social cognitive performance suggests that early life adversity impacts both brain development and social cognitive outcomes for people with SCZ. Limitations of the present study include the restricted ability of the tensor model to correctly assess multi-directionality at regions where fibre populations intersect.
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Affiliation(s)
- Matthew Stevens
- School of Psychology, University of Galway, Galway, Ireland
- Centre for Neuroimaging Cognition and Genomics (NICOG), University of Galway, Galway, Ireland
| | - Síle Ní Mhurchú
- School of Psychology, University of Galway, Galway, Ireland
- Centre for Neuroimaging Cognition and Genomics (NICOG), University of Galway, Galway, Ireland
| | - Emma Corley
- School of Psychology, University of Galway, Galway, Ireland
- Centre for Neuroimaging Cognition and Genomics (NICOG), University of Galway, Galway, Ireland
| | - Ciara Egan
- School of Psychology, University of Galway, Galway, Ireland
- Centre for Neuroimaging Cognition and Genomics (NICOG), University of Galway, Galway, Ireland
| | - Brian Hallahan
- Clinical Neuroimaging Laboratory, Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, University of Galway, Galway, Ireland
| | - Colm McDonald
- Clinical Neuroimaging Laboratory, Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, University of Galway, Galway, Ireland
| | - Gary Donohoe
- School of Psychology, University of Galway, Galway, Ireland
- Centre for Neuroimaging Cognition and Genomics (NICOG), University of Galway, Galway, Ireland
| | - Tom Burke
- School of Psychology, University of Galway, Galway, Ireland
- Centre for Neuroimaging Cognition and Genomics (NICOG), University of Galway, Galway, Ireland
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25
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Bavato F, Barro C, Schnider LK, Simrén J, Zetterberg H, Seifritz E, Quednow BB. Introducing neurofilament light chain measure in psychiatry: current evidence, opportunities, and pitfalls. Mol Psychiatry 2024:10.1038/s41380-024-02524-6. [PMID: 38503931 DOI: 10.1038/s41380-024-02524-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 02/29/2024] [Accepted: 03/07/2024] [Indexed: 03/21/2024]
Abstract
The recent introduction of new-generation immunoassay methods allows the reliable quantification of structural brain markers in peripheral matrices. Neurofilament light chain (NfL), a neuron-specific cytoskeletal component released in extracellular matrices after neuroaxonal impairment, is considered a promising blood marker of active brain pathology. Given its sensitivity to a wide range of neuropathological alterations, NfL has been suggested for the use in clinical practice as a highly sensitive, but unspecific tool to quantify active brain pathology. While large efforts have been put in characterizing its clinical profile in many neurological conditions, NfL has received far less attention as a potential biomarker in major psychiatric disorders. Therefore, we briefly introduce NfL as a marker of neuroaxonal injury, systematically review recent findings on cerebrospinal fluid and blood NfL levels in patients with primary psychiatric conditions and highlight the opportunities and pitfalls. Current evidence suggests an elevation of blood NfL levels in patients with major depression, bipolar disorder, psychotic disorders, anorexia nervosa, and substance use disorders compared to physiological states. However, blood NfL levels strongly vary across diagnostic entities, clinical stage, and patient subgroups, and are influenced by several demographic, clinical, and analytical factors, which require accurate characterization. Potential clinical applications of NfL measure in psychiatry are seen in diagnostic and prognostic algorithms, to exclude neurodegenerative disease, in the assessment of brain toxicity for different pharmacological compounds, and in the longitudinal monitoring of treatment response. The high inter-individual variability of NfL levels and the lack of neurobiological understanding of its release are some of the main current limitations. Overall, this primer aims to introduce researchers and clinicians to NfL measure in the psychiatric field and to provide a conceptual framework for future research directions.
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Affiliation(s)
- Francesco Bavato
- Experimental and Clinical Pharmacopsychology, Department of Psychiatry, Psychotherapy and Psychosomatics; Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland.
| | - Christian Barro
- Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Laura K Schnider
- Experimental and Clinical Pharmacopsychology, Department of Psychiatry, Psychotherapy and Psychosomatics; Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Joel Simrén
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Erich Seifritz
- Department of Psychiatry, Psychotherapy and Psychosomatics; Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and Swiss Federal Institute of Technology Zurich, Zurich, Switzerland
| | - Boris B Quednow
- Experimental and Clinical Pharmacopsychology, Department of Psychiatry, Psychotherapy and Psychosomatics; Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and Swiss Federal Institute of Technology Zurich, Zurich, Switzerland
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26
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Koshiyama D, Nishimura R, Usui K, Fujioka M, Tada M, Kirihara K, Araki T, Kawakami S, Okada N, Koike S, Yamasue H, Abe O, Kasai K. Cortical white matter microstructural alterations underlying the impaired gamma-band auditory steady-state response in schizophrenia. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2024; 10:32. [PMID: 38472253 DOI: 10.1038/s41537-024-00454-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 02/29/2024] [Indexed: 03/14/2024]
Abstract
The gamma-band auditory steady-state response (ASSR), primarily generated from the auditory cortex, has received substantial attention as a potential brain marker indicating the pathophysiology of schizophrenia. Previous studies have shown reduced gamma-band ASSR in patients with schizophrenia and demonstrated correlations with impaired neurocognition and psychosocial functioning. Recent studies in clinical and healthy populations have suggested that the neural substrates of reduced gamma-band ASSR may be distributed throughout the cortices surrounding the auditory cortex, especially in the right hemisphere. This study aimed to investigate associations between the gamma-band ASSR and white matter alterations in the bundles broadly connecting the right frontal, parietal and occipital cortices to clarify the networks underlying reduced gamma-band ASSR in patients with schizophrenia. We measured the 40 Hz ASSR using electroencephalography and diffusion tensor imaging in 42 patients with schizophrenia and 22 healthy comparison subjects. The results showed that the gamma-band ASSR was positively correlated with fractional anisotropy (an index of white matter integrity) in the regions connecting the right frontal, parietal and occipital cortices in healthy subjects (β = 0.41, corrected p = 0.075, uncorrected p = 0.038) but not in patients with schizophrenia (β = 0.17, corrected p = 0.46, uncorrected p = 0.23). These findings support our hypothesis that the generation of gamma-band ASSR is supported by white matter bundles that broadly connect the cortices and that these relationships may be disrupted in schizophrenia. Our study may help characterize and interpret reduced gamma-band ASSR as a useful brain marker of schizophrenia.
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Affiliation(s)
- Daisuke Koshiyama
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Ryoichi Nishimura
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Kaori Usui
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Department of Community Mental Health and Law, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Mao Fujioka
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Mariko Tada
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- The International Research Center for Neurointelligence (WPI-IRCN) at Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo, Japan
| | - Kenji Kirihara
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Disablity Services Office, The University of Tokyo, Tokyo, Japan
| | - Tsuyoshi Araki
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Department of Psychiatry, Teikyo University Hospital, Kawasaki, Japan
| | - Shintaro Kawakami
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Naohiro Okada
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- The International Research Center for Neurointelligence (WPI-IRCN) at Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo, Japan
| | - Shinsuke Koike
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- The International Research Center for Neurointelligence (WPI-IRCN) at Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo, Japan
- University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), Tokyo, Japan
- Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo, Tokyo, Japan
| | - Hidenori Yamasue
- Department of Psychiatry, Hamamatsu University School of Medicine, Hamamatsu, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Kiyoto Kasai
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
- The International Research Center for Neurointelligence (WPI-IRCN) at Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo, Japan.
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Ishida T, Yamada S, Yasuda K, Uenishi S, Tamaki A, Tabata M, Ikeda N, Takahashi S, Kimoto S. Aberrant brain dynamics of large-scale functional networks across schizophrenia and mood disorder. Neuroimage Clin 2024; 41:103574. [PMID: 38346380 PMCID: PMC10944194 DOI: 10.1016/j.nicl.2024.103574] [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: 11/23/2023] [Revised: 02/05/2024] [Accepted: 02/05/2024] [Indexed: 03/16/2024]
Abstract
INTRODUCTION The dynamics of large-scale networks, which are known as distributed sets of functionally synchronized brain regions and include the visual network (VIN), somatomotor network (SMN), dorsal attention network (DAN), salience network (SAN), limbic network (LIN), frontoparietal network (FPN), and default mode network (DMN), play important roles in emotional and cognitive processes in humans. Although disruptions in these large-scale networks are considered critical for the pathophysiological mechanisms of psychiatric disorders, their role in psychiatric disorders remains unknown. We aimed to elucidate the aberrant dynamics across large-scale networks in patients with schizophrenia (SZ) and mood disorders. METHODS We performed energy-landscape analysis to investigate the aberrant brain dynamics of seven large-scale networks across 50 healthy controls (HCs), 36 patients with SZ, and 42 patients with major depressive disorder (MDD) recruited at Wakayama Medical University. We identified major patterns of brain activity using energy-landscape analysis and estimated their duration, occurrence, and ease of transition. RESULTS We identified four major brain activity patterns that were characterized by the activation patterns of the DMN and VIN (state 1, DMN (-) VIN (-); state 2, DMN (+) VIN (+); state 3, DMN (-) VIN (+); and state 4, DMN (+) VIN (-)). The duration of state 1 and the occurrence of states 1 and 2 were shorter in the SZ group than in HCs and the MDD group, and the duration of state 3 was longer in the SZ group. The ease of transition between states 3 and 4 was larger in the SZ group than in the HCs and the MDD group. The ease of transition from state 3 to state 4 was negatively associated with verbal fluency in patients with SZ. The current study showed that the brain dynamics was more disrupted in SZ than in MDD. CONCLUSIONS Energy-landscape analysis revealed aberrant brain dynamics across large-scale networks between SZ and MDD and their associations with cognitive abilities in SZ, which cannot be captured by conventional functional connectivity analyses. These results provide new insights into the pathophysiological mechanisms underlying SZ and mood disorders.
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Affiliation(s)
- Takuya Ishida
- Department of Neuropsychiatry, Graduate School of Wakayama Medical University, Wakayama 641-8509, Japan.
| | - Shinichi Yamada
- Department of Neuropsychiatry, Graduate School of Wakayama Medical University, Wakayama 641-8509, Japan
| | - Kasumi Yasuda
- Department of Neuropsychiatry, Graduate School of Wakayama Medical University, Wakayama 641-8509, Japan; Department of Neuropsychiatry, Hanwa Izumi Hospital, Osaka 594-1157, Japan
| | - Shinya Uenishi
- Department of Neuropsychiatry, Graduate School of Wakayama Medical University, Wakayama 641-8509, Japan; Department of Psychiatry, Hidaka Hospital, Wakayama 644-0002, Japan
| | - Atsushi Tamaki
- Department of Neuropsychiatry, Graduate School of Wakayama Medical University, Wakayama 641-8509, Japan; Department of Psychiatry, Wakayama Prefectural Mental Health Care Center, Wakayama 643-0811, Japan
| | - Michiyo Tabata
- Department of Neuropsychiatry, Graduate School of Wakayama Medical University, Wakayama 641-8509, Japan; Department of Neuropsychiatry, Nokamikosei Hospital, Wakayama 640-1141, Japan
| | - Natsuko Ikeda
- Department of Neuropsychiatry, Graduate School of Wakayama Medical University, Wakayama 641-8509, Japan
| | - Shun Takahashi
- Department of Neuropsychiatry, Graduate School of Wakayama Medical University, Wakayama 641-8509, Japan; Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka 565-0871, Japan; Clinical Research and Education Center, Asakayama General Hospital, Osaka 590-0018, Japan; Graduate School of Rehabilitation Science, Osaka Metropolitan University, Osaka 583-8555, Japan
| | - Sohei Kimoto
- Department of Neuropsychiatry, Graduate School of Wakayama Medical University, Wakayama 641-8509, Japan
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28
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Reyes-Lizaola S, Luna-Zarate U, Tendilla-Beltrán H, Morales-Medina JC, Flores G. Structural and biochemical alterations in dendritic spines as key mechanisms for severe mental illnesses. Prog Neuropsychopharmacol Biol Psychiatry 2024; 129:110876. [PMID: 37863171 DOI: 10.1016/j.pnpbp.2023.110876] [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: 08/01/2023] [Revised: 10/11/2023] [Accepted: 10/12/2023] [Indexed: 10/22/2023]
Abstract
Severe mental illnesses (SMI) collectively affect approximately 20% of the global population, as estimated by the World Health Organization (WHO). Despite having diverse etiologies, clinical symptoms, and pharmacotherapies, these diseases share a common pathophysiological characteristic: the misconnection of brain areas involved in reality perception, executive control, and cognition, including the corticolimbic system. Dendritic spines play a crucial role in excitatory neurotransmission within the central nervous system. These small structures exhibit remarkable plasticity, regulated by factors such as neurotransmitter tone, neurotrophic factors, and innate immunity-related molecules, and other mechanisms - all of which are associated with the pathophysiology of SMI. However, studying dendritic spine mechanisms in both healthy and pathological conditions in patients is fraught with technical limitations. This is where animal models related to these diseases become indispensable. They have played a pivotal role in elucidating the significance of dendritic spines in SMI. In this review, the information regarding the potential role of dendritic spines in SMI was summarized, drawing from clinical and animal model reports. Also, the implications of targeting dendritic spine-related molecules for SMI treatment were explored. Specifically, our focus is on major depressive disorder and the neurodevelopmental disorders schizophrenia and autism spectrum disorder. Abundant clinical and basic research has studied the functional and structural plasticity of dendritic spines in these diseases, along with potential pharmacological targets that modulate the dynamics of these structures. These targets may be associated with the clinical efficacy of the pharmacotherapy.
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Affiliation(s)
- Sebastian Reyes-Lizaola
- Departamento de Ciencias de la Salud, Licenciatura en Medicina, Universidad Popular del Estado de Puebla (UPAEP), Puebla, Mexico
| | - Ulises Luna-Zarate
- Departamento de Ciencias de la Salud, Licenciatura en Medicina, Universidad de las Américas Puebla (UDLAP), Puebla, Mexico
| | - Hiram Tendilla-Beltrán
- Laboratorio de Neuropsiquiatría, Instituto de Fisiología, Benemérita Universidad Autónoma de Puebla (BUAP), Puebla, Mexico
| | - Julio César Morales-Medina
- Centro de Investigación en Reproducción Animal, CINVESTAV-Universidad Autónoma de Tlaxcala, Tlaxcala, Mexico
| | - Gonzalo Flores
- Laboratorio de Neuropsiquiatría, Instituto de Fisiología, Benemérita Universidad Autónoma de Puebla (BUAP), Puebla, Mexico.
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29
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Nenadić I, Meller T, Evermann U, Pfarr JK, Federspiel A, Walther S, Grezellschak S, Abu-Akel A. Modelling the overlap and divergence of autistic and schizotypal traits on hippocampal subfield volumes and regional cerebral blood flow. Mol Psychiatry 2024; 29:74-84. [PMID: 37891246 PMCID: PMC11078729 DOI: 10.1038/s41380-023-02302-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 09/22/2023] [Accepted: 10/11/2023] [Indexed: 10/29/2023]
Abstract
Psychiatric disorders show high co-morbidity, including co-morbid expressions of subclinical psychopathology across multiple disease spectra. Given the limitations of classical case-control designs in elucidating this overlap, new approaches are needed to identify biological underpinnings of spectra and their interaction. We assessed autistic-like traits (using the Autism Quotient, AQ) and schizotypy - as models of subclinical expressions of disease phenotypes and examined their association with volumes and regional cerebral blood flow (rCBF) of anterior, mid- and posterior hippocampus segments from structural MRI scans in 318 and arterial spin labelling (ASL) in 346 nonclinical subjects, which overlapped with the structural imaging sample (N = 298). We demonstrate significant interactive effects of positive schizotypy and AQ social skills as well as of positive schizotypy and AQ imagination on hippocampal subfield volume variation. Moreover, we show that AQ attention switching modulated hippocampal head rCBF, while positive schizotypy by AQ attention to detail interactions modulated hippocampal tail rCBF. In addition, we show significant correlation of hippocampal volume and rCBF in both region-of-interest and voxel-wise analyses, which were robust after removal of variance related to schizotypy and autistic traits. These findings provide empirical evidence for both the modulation of hippocampal subfield structure and function through subclinical traits, and in particular how only the interaction of phenotype facets leads to significant reductions or variations in these parameters. This makes a case for considering the synergistic impact of different (subclinical) disease spectra on transdiagnostic biological parameters in psychiatry.
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Affiliation(s)
- Igor Nenadić
- Cognitive Neuropsychiatry Lab, Department of Psychiatry and Psychotherapy, Philipps Universität Marburg, Marburg, Germany.
- Center for Mind, Brain, and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Marburg, Germany.
- Marburg University Hospital - UKGM, Marburg, Germany.
| | - Tina Meller
- Cognitive Neuropsychiatry Lab, Department of Psychiatry and Psychotherapy, Philipps Universität Marburg, Marburg, Germany
- Center for Mind, Brain, and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Marburg, Germany
| | - Ulrika Evermann
- Cognitive Neuropsychiatry Lab, Department of Psychiatry and Psychotherapy, Philipps Universität Marburg, Marburg, Germany
- Center for Mind, Brain, and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Marburg, Germany
| | - Julia-Katharina Pfarr
- Cognitive Neuropsychiatry Lab, Department of Psychiatry and Psychotherapy, Philipps Universität Marburg, Marburg, Germany
- Center for Mind, Brain, and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Marburg, Germany
| | - Andrea Federspiel
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
- Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, and University of Bern, Bern, Switzerland
| | - Sebastian Walther
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Sarah Grezellschak
- Cognitive Neuropsychiatry Lab, Department of Psychiatry and Psychotherapy, Philipps Universität Marburg, Marburg, Germany
- Center for Mind, Brain, and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Marburg, Germany
- Marburg University Hospital - UKGM, Marburg, Germany
| | - Ahmad Abu-Akel
- School of Psychological Sciences, University of Haifa, Mount Carmel, Haifa, Israel
- The Haifa Brain and Behavior Hub, University of Haifa, Mount Carmel, Haifa, Israel
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Daniel E, Deng F, Patel SK, Sedrak MS, Kim H, Razavi M, Sun C, Root JC, Ahles TA, Dale W, Chen BT. Brain white matter microstructural changes in chemotherapy-treated older long-term breast cancer survivors. Cancer Med 2024; 13:e6881. [PMID: 38152038 PMCID: PMC10807556 DOI: 10.1002/cam4.6881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 12/11/2023] [Accepted: 12/17/2023] [Indexed: 12/29/2023] Open
Abstract
PURPOSE To assess white matter microstructural changes in older long-term breast cancer survivors 5-15 years post-chemotherapy treatment. METHODS Breast cancer survivors aged 65 years or older who underwent chemotherapy (C+) and who did not undergo chemotherapy (C-) and age- and sex-matched healthy controls (HC) were enrolled at time point 1 (TP1) and followed for 2 years for time point 2 (TP2). All participants underwent brain MRI with diffusion tensor images and neuropsychological (NP) testing with the NIH Toolbox Cognition Battery. Tract-based spatial statistics (TBSS) analysis was performed on the diffusion tensor images to assess white matter microstructural changes with the fractional anisotropy (FA) parameter. RESULTS There were significant longitudinal alterations in FA within the C+ group over time. The C+ group showed diminished FA in the body and genu of corpus callosum, anterior corona radiate, and external capsule on both the whole brain and region of interest (ROI) based analyses after p < 0.05 family-wise error (FWE) correction. However, there were no significant group differences between the groups at TP1. Additionally, at TP1, a positive correlation (R = 0.58, p = 0.04) was observed between the FA value of the anterior corona radiata and the crystallized composite score in the C+ group. CONCLUSIONS Brain white matter microstructural alterations may be the underlying neural correlates of cognitive changes in older breast cancer survivors who had chemotherapy treatment years ago.
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Affiliation(s)
- Ebenezer Daniel
- Department of Diagnostic RadiologyCity of Hope National Medical CenterDuarteCAUSA
| | - Frank Deng
- Department of Diagnostic RadiologyCity of Hope National Medical CenterDuarteCAUSA
| | - Sunita K. Patel
- Department of Population ScienceCity of Hope National Medical CenterDuarteCAUSA
| | - Mina S. Sedrak
- Department of Medical OncologyCity of Hope National Medical CenterDuarteCAUSA
| | - Heeyoung Kim
- Center for Cancer and AgingCity of Hope National Medical CenterDuarteCAUSA
| | - Marianne Razavi
- Department of Supportive Care MedicineCity of Hope National Medical CenterDuarteCAUSA
| | - Can‐Lan Sun
- Center for Cancer and AgingCity of Hope National Medical CenterDuarteCAUSA
| | - James C. Root
- Neurocognitive Research LabMemorial Sloan Kettering Cancer CenterNew YorkNYUSA
| | - Tim A. Ahles
- Neurocognitive Research LabMemorial Sloan Kettering Cancer CenterNew YorkNYUSA
| | - William Dale
- Center for Cancer and AgingCity of Hope National Medical CenterDuarteCAUSA
- Department of Supportive Care MedicineCity of Hope National Medical CenterDuarteCAUSA
| | - Bihong T. Chen
- Department of Diagnostic RadiologyCity of Hope National Medical CenterDuarteCAUSA
- Center for Cancer and AgingCity of Hope National Medical CenterDuarteCAUSA
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31
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Bagautdinova J, Bourque J, Sydnor VJ, Cieslak M, Alexander-Bloch AF, Bertolero MA, Cook PA, Gur RE, Gur RC, Hu F, Larsen B, Moore TM, Radhakrishnan H, Roalf DR, Shinohara RT, Tapera TM, Zhao C, Sotiras A, Davatzikos C, Satterthwaite TD. Development of white matter fiber covariance networks supports executive function in youth. Cell Rep 2023; 42:113487. [PMID: 37995188 PMCID: PMC10795769 DOI: 10.1016/j.celrep.2023.113487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 10/05/2023] [Accepted: 11/09/2023] [Indexed: 11/25/2023] Open
Abstract
During adolescence, the brain undergoes extensive changes in white matter structure that support cognition. Data-driven approaches applied to cortical surface properties have led the field to understand brain development as a spatially and temporally coordinated mechanism that follows hierarchically organized gradients of change. Although white matter development also appears asynchronous, previous studies have relied largely on anatomical tract-based atlases, precluding a direct assessment of how white matter structure is spatially and temporally coordinated. Harnessing advances in diffusion modeling and machine learning, we identified 14 data-driven patterns of covarying white matter structure in a large sample of youth. Fiber covariance networks aligned with known major tracts, while also capturing distinct patterns of spatial covariance across distributed white matter locations. Most networks showed age-related increases in fiber network properties, which were also related to developmental changes in executive function. This study delineates data-driven patterns of white matter development that support cognition.
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Affiliation(s)
- Joëlle Bagautdinova
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Josiane Bourque
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Valerie J Sydnor
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Matthew Cieslak
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Aaron F Alexander-Bloch
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Maxwell A Bertolero
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Philip A Cook
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Fengling Hu
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Tyler M Moore
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Hamsanandini Radhakrishnan
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - David R Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Russel T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Tinashe M Tapera
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Chenying Zhao
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Aristeidis Sotiras
- Department of Radiology and Institute for Informatics, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63130, USA
| | - Christos Davatzikos
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
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Rosemann S, Rauschecker JP. Increased fiber density of the fornix in patients with chronic tinnitus revealed by diffusion-weighted MRI. Front Neurosci 2023; 17:1293133. [PMID: 38192511 PMCID: PMC10773749 DOI: 10.3389/fnins.2023.1293133] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 12/05/2023] [Indexed: 01/10/2024] Open
Abstract
Up to 45% of the elderly population suffer from chronic tinnitus - the phantom perception of sound that is often perceived as ringing, whistling, or hissing "in the ear" without external stimulation. Previous research investigated white matter changes in tinnitus patients using diffusion-weighted magnetic resonance imaging (DWI) to assess measures such as fractional anisotropy (a measure of microstructural integrity of fiber tracts) or mean diffusivity (a measure for general water diffusion). However, findings overlap only minimally and are sometimes even contradictory. We here present the first study encompassing higher diffusion data that allow to focus on changes in tissue microstructure, such as number of axons (fiber density) and macroscopic alterations, including axon diameter, and a combination of both. In order to deal with the crossing-fibers problem, we applied a fixel-based analysis using a constrained spherical deconvolution signal modeling approach. We investigated differences between tinnitus patients and control participants as well as how cognitive abilities and tinnitus distress are related to changes in white matter morphology in chronic tinnitus. For that aim, 20 tinnitus patients and 20 control participants, matched in age, sex and whether they had hearing loss or not, underwent DWI, audiometric and cognitive assessments, and filled in questionnaires targeting anxiety and depression. Our results showed increased fiber density in the fornix in tinnitus patients compared to control participants. The observed changes might, reflect compensatory structural alterations related to the processing of negative emotions or maladaptive changes related to the reinforced learning of the chronic tinnitus sensation. Due to the low sample size, the study should be seen as a pilot study that motivates further research to investigate underlying white matter morphology alterations in tinnitus.
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Affiliation(s)
- Stephanie Rosemann
- Laboratory of Integrative Neuroscience and Cognition, Department of Neuroscience, Georgetown University Medical Center, Washington, DC, United States
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Lan H, Suo X, Zuo C, Ni W, Wang S, Kemp GJ, Gong Q. Shared and distinct abnormalities of brain magnetization transfer ratio in schizophrenia and major depressive disorder: a comparative voxel-based meta-analysis. Chin Med J (Engl) 2023; 136:2824-2833. [PMID: 37697951 PMCID: PMC10686600 DOI: 10.1097/cm9.0000000000002538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Indexed: 09/13/2023] Open
Abstract
BACKGROUND Patients with schizophrenia (SCZ) and major depressive disorder (MDD) share significant clinical overlap, although it remains unknown to what extent this overlap reflects shared neural profiles. To identify the shared and specific abnormalities in SCZ and MDD, we performed a whole-brain voxel-based meta-analysis using magnetization transfer imaging, a technique that characterizes the macromolecular structural integrity of brain tissue in terms of the magnetization transfer ratio (MTR). METHODS A systematic search based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was conducted in PubMed, EMBASE, International Scientific Index (ISI) Web of Science, and MEDLINE for relevant studies up to March 2022. Two researchers independently screened the articles. Rigorous scrutiny and data extraction were performed for the studies that met the inclusion criteria. Voxel-wise meta-analyses were conducted using anisotropic effect size-signed differential mapping with a unified template. Meta-regression was used to explore the potential effects of demographic and clinical characteristics. RESULTS A total of 15 studies with 17 datasets describing 365 SCZ patients, 224 MDD patients, and 550 healthy controls (HCs) were identified. The conjunction analysis showed that both disorders shared higher MTR than HC in the left cerebellum ( P =0.0006) and left fusiform gyrus ( P =0.0004). Additionally, SCZ patients showed disorder-specific lower MTR in the anterior cingulate/paracingulate gyrus, right superior temporal gyrus, and right superior frontal gyrus, and higher MTR in the left thalamus, precuneus/cuneus, posterior cingulate gyrus, and paracentral lobule; and MDD patients showed higher MTR in the left middle occipital region. Meta-regression showed no statistical significance in either group. CONCLUSIONS The results revealed a structural neural basis shared between SCZ and MDD patients, emphasizing the importance of shared neural substrates across psychopathology. Meanwhile, distinct disease-specific characteristics could have implications for future differential diagnosis and targeted treatment.
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Affiliation(s)
- Huan Lan
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Xueling Suo
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian 361000, China
| | - Chao Zuo
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan 610041, China
| | - Weishi Ni
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, China
| | - Song Wang
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Graham J. Kemp
- Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L693BX, United Kingdom
| | - Qiyong Gong
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian 361000, China
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Mio M, Kennedy KG, Grigorian A, Zou Y, Dimick MK, Selkirk B, Kertes PJ, Swardfager W, Hahn MK, Black SE, MacIntosh BJ, Goldstein BI. White matter microstructural integrity is associated with retinal vascular caliber in adolescents with bipolar disorder. J Psychosom Res 2023; 175:111529. [PMID: 37856933 DOI: 10.1016/j.jpsychores.2023.111529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 10/12/2023] [Accepted: 10/13/2023] [Indexed: 10/21/2023]
Abstract
OBJECTIVE Reduced white matter integrity is observed in bipolar disorder (BD), and is associated with cardiovascular risk in adults. This topic is underexplored in youth, and in BD, where novel microvascular measures may help to inform understanding of the vascular-brain connection. We therefore examined the association of retinal vascular caliber with white matter integrity in a cross-sectional sample of adolescents with and without BD. METHODS Eighty-four adolescents (n = 42 BD, n = 42 controls) completed retinal imaging, yielding arteriolar and venular caliber. Diffusion tensor imaging measured white matter fractional anisotropy (FA). Multiple linear regression tested associations between retinal vascular caliber and FA in regions-of-interest; corpus callosum, anterior thalamic radiation, uncinate fasciculus, and superior longitudinal fasciculus. Complementary voxel-wise analyses were performed. RESULTS Arteriolar caliber was elevated in adolescents with BD relative to controls (F(1,79) = 6.15, p = 0.02, η2p = 0.07). In the overall sample, higher venular caliber was significantly associated with lower corpus callosum FA (β = -0.24, puncorrected = 0.04). In voxel-wise analyses, higher arteriolar caliber was significantly associated with lower corpus callosum and forceps minor FA in the overall sample (β = -0.46, p = 0.03). A significant diagnosis-by-venular caliber interaction on FA was noted in 5 clusters including the right retrolenticular internal capsule (β = 0.72, p = 0.03), corticospinal tract (β = 0.72, p = 0.04), and anterior corona radiata (β = 0.63, p = 0.04). In each instance, venular caliber was more positively associated with FA in BD vs. controls. CONCLUSION Retinal microvascular measures are associated with white matter integrity in BD, particularly in the corpus callosum. This study was proof-of-concept, designed to guide future studies focused on the vascular-brain interface in BD.
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Affiliation(s)
- Megan Mio
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Canada.
| | - Kody G Kennedy
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Canada
| | - Anahit Grigorian
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Canada
| | - Yi Zou
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Canada
| | - Mikaela K Dimick
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Canada
| | - Beth Selkirk
- John and Liz Tory Eye Centre, Department of Ophthalmology and Vision Sciences, Sunnybrook Health Sciences Centre, Canada
| | - Peter J Kertes
- John and Liz Tory Eye Centre, Department of Ophthalmology and Vision Sciences, Sunnybrook Health Sciences Centre, Canada; University of Toronto, Ophthalmology and Vision Sciences, Toronto, Canada
| | - Walter Swardfager
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, Canada; Hurvitz Brain Sciences Research Program, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Margaret K Hahn
- Schizophrenia Department, Centre for Addiction and Mental Health, Toronto, Canada; Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada
| | - Sandra E Black
- Hurvitz Brain Sciences Research Program, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Bradley J MacIntosh
- Hurvitz Brain Sciences Research Program, Sunnybrook Health Sciences Centre, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Benjamin I Goldstein
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Canada; Department of Pharmacology and Toxicology, University of Toronto, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada
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Okada N, Fukunaga M, Miura K, Nemoto K, Matsumoto J, Hashimoto N, Kiyota M, Morita K, Koshiyama D, Ohi K, Takahashi T, Koeda M, Yamamori H, Fujimoto M, Yasuda Y, Hasegawa N, Narita H, Yokoyama S, Mishima R, Kawashima T, Kobayashi Y, Sasabayashi D, Harada K, Yamamoto M, Hirano Y, Itahashi T, Nakataki M, Hashimoto RI, Tha KK, Koike S, Matsubara T, Okada G, van Erp TGM, Jahanshad N, Yoshimura R, Abe O, Onitsuka T, Watanabe Y, Matsuo K, Yamasue H, Okamoto Y, Suzuki M, Turner JA, Thompson PM, Ozaki N, Kasai K, Hashimoto R. Subcortical volumetric alterations in four major psychiatric disorders: a mega-analysis study of 5604 subjects and a volumetric data-driven approach for classification. Mol Psychiatry 2023; 28:5206-5216. [PMID: 37537281 DOI: 10.1038/s41380-023-02141-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 05/18/2023] [Accepted: 06/16/2023] [Indexed: 08/05/2023]
Abstract
Differential diagnosis is sometimes difficult in practical psychiatric settings, in terms of using the current diagnostic system based on presenting symptoms and signs. The creation of a novel diagnostic system using objective biomarkers is expected to take place. Neuroimaging studies and others reported that subcortical brain structures are the hubs for various psycho-behavioral functions, while there are so far no neuroimaging data-driven clinical criteria overcoming limitations of the current diagnostic system, which would reflect cognitive/social functioning. Prior to the main analysis, we conducted a large-scale multisite study of subcortical volumetric and lateralization alterations in schizophrenia, bipolar disorder, major depressive disorder, and autism spectrum disorder using T1-weighted images of 5604 subjects (3078 controls and 2526 patients). We demonstrated larger lateral ventricles volume in schizophrenia, bipolar disorder, and major depressive disorder, smaller hippocampus volume in schizophrenia and bipolar disorder, and schizophrenia-specific smaller amygdala, thalamus, and accumbens volumes and larger caudate, putamen, and pallidum volumes. In addition, we observed a leftward alteration of lateralization for pallidum volume specifically in schizophrenia. Moreover, as our main objective, we clustered the 5,604 subjects based on subcortical volumes, and explored whether data-driven clustering results can explain cognitive/social functioning in the subcohorts. We showed a four-biotype classification, namely extremely (Brain Biotype [BB] 1) and moderately smaller limbic regions (BB2), larger basal ganglia (BB3), and normal volumes (BB4), being associated with cognitive/social functioning. Specifically, BB1 and BB2-3 were associated with severe and mild cognitive/social impairment, respectively, while BB4 was characterized by normal cognitive/social functioning. Our results may lead to the future creation of novel biological data-driven psychiatric diagnostic criteria, which may be expected to be useful for prediction or therapeutic selection.
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Affiliation(s)
- Naohiro Okada
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- The International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study (UTIAS), Tokyo, Japan
| | - Masaki Fukunaga
- Division of Cerebral Integration, National Institute for Physiological Sciences, Aichi, Japan
| | - Kenichiro Miura
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Kiyotaka Nemoto
- Department of Psychiatry, Institute of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Junya Matsumoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Naoki Hashimoto
- Department of Psychiatry, Hokkaido University Graduate School of Medicine, Hokkaido, Japan
| | - Masahiro Kiyota
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Kentaro Morita
- Department of Rehabilitation, University of Tokyo Hospital, Tokyo, Japan
| | - Daisuke Koshiyama
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Kazutaka Ohi
- Department of Psychiatry, Gifu University Graduate School of Medicine, Gifu, Japan
- Department of General Internal Medicine, Kanazawa Medical University, Ishikawa, Japan
| | - Tsutomu Takahashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan
- Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Michihiko Koeda
- Department of Neuropsychiatry, Graduate School of Medicine, Nippon Medical School, Tokyo, Japan
| | - Hidenaga Yamamori
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan
- Japan Community Health Care Organization Osaka Hospital, Osaka, Japan
| | - Michiko Fujimoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Yuka Yasuda
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
- Life Grow Brilliant Mental Clinic, Medical Corporation Foster, Osaka, Japan
| | - Naomi Hasegawa
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Hisashi Narita
- Department of Psychiatry, Hokkaido University Graduate School of Medicine, Hokkaido, Japan
| | - Satoshi Yokoyama
- Department of Psychiatry and Neuroscience, Hiroshima University, Hiroshima, Japan
| | - Ryo Mishima
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Takahiko Kawashima
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yuko Kobayashi
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Daiki Sasabayashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan
- Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Kenichiro Harada
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Yamaguchi, Japan
| | - Maeri Yamamoto
- Department of Psychiatry, Graduate School of Medicine, Nagoya University, Aichi, Japan
| | - Yoji Hirano
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
- Department of Psychiatry, Division of Clinical Neuroscience, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan
| | - Takashi Itahashi
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Masahito Nakataki
- Department of Psychiatry, Graduate School of Biomedical Sciences, Tokushima University, Tokushima, Japan
| | - Ryu-Ichiro Hashimoto
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
- Department of Language Sciences, Graduate School of Humanities, Tokyo Metropolitan University, Tokyo, Japan
| | - Khin K Tha
- Department of Diagnostic Imaging, Hokkaido University Faculty of Medicine, Hokkaido, Japan
- Global Center for Biomedical Science and Engineering, Hokkaido University Faculty of Medicine, Hokkaido, Japan
| | - Shinsuke Koike
- The International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study (UTIAS), Tokyo, Japan
- University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), Tokyo, Japan
- Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan
| | - Toshio Matsubara
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Yamaguchi, Japan
| | - Go Okada
- Department of Psychiatry and Neuroscience, Hiroshima University, Hiroshima, Japan
| | - Theo G M van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine, CA, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Reiji Yoshimura
- Department of Psychiatry, University of Occupational and Environmental Health, Fukuoka, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | | | - Yoshiyuki Watanabe
- Department of Radiology, Shiga University of Medical Science, Shiga, Japan
| | - Koji Matsuo
- Department of Psychiatry, Faculty of Medicine, Saitama Medical University, Saitama, Japan
| | - Hidenori Yamasue
- Department of Psychiatry, Hamamatsu University School of Medicine, Shizuoka, Japan
| | - Yasumasa Okamoto
- Department of Psychiatry and Neuroscience, Hiroshima University, Hiroshima, Japan
| | - Michio Suzuki
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan
- Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Jessica A Turner
- Department of Psychiatry and Behavioral Health, Wexner Medical Center, The Ohio State University, Columbus, OH, USA
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Norio Ozaki
- Department of Psychiatry, Graduate School of Medicine, Nagoya University, Aichi, Japan
- Pathophysiology of Mental Disorders, Graduate School of Medicine, Nagoya University, Aichi, Japan
| | - Kiyoto Kasai
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- The International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study (UTIAS), Tokyo, Japan
- University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), Tokyo, Japan
| | - Ryota Hashimoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan.
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan.
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36
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Türk Y, Devecioğlu İ, Küskün A, Öge C, Beyazyüz E, Albayrak Y. ROI-based analysis of diffusion indices in healthy subjects and subjects with deficit or non-deficit syndrome schizophrenia. Psychiatry Res Neuroimaging 2023; 336:111726. [PMID: 37925764 DOI: 10.1016/j.pscychresns.2023.111726] [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: 01/19/2023] [Revised: 09/29/2023] [Accepted: 10/14/2023] [Indexed: 11/07/2023]
Abstract
We analyzed DTI data involving 22 healthy subjects (HC), 15 patients with deficit syndrome schizophrenia (DSZ), and 25 patients with non-deficit syndrome schizophrenia (NDSZ). We used a 1.5-T MRI scanner to collect diffusion-weighted images and T1 images, which were employed to correct distortions and deformations within the diffusion-weighted images. For 156 regions of interest (ROI), we calculated the average fractional anisotropy (FA), mean diffusion (MD), and radial diffusion (RD). Each ROI underwent a group-wise comparison using permutation F-test, followed by post hoc pairwise comparisons with Bonferroni correction. In general, we observed lower FA in both schizophrenia groups compared to HC (i.e., HC>(DSZ=NDSZ)), while MD and RD showed the opposite pattern. Notably, specific ROIs with reduced FA in schizophrenia patients included bilateral nucleus accumbens, left fusiform area, brain stem, anterior corpus callosum, left rostral and caudal anterior cingulate, right posterior cingulate, left thalamus, left hippocampus, left inferior temporal cortex, right superior temporal cortex, left pars triangularis and right lingual gyrus. Significantly, the right cuneus exhibited lower FA in the DSZ group compared to other groups ((HC=NDSZ)>DSZ), without affecting MD and RD. These results indicate that compromised neural integrity in the cuneus may contribute to the pathophysiological distinctions between DSZ and NDSZ.
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Affiliation(s)
- Yaşar Türk
- Radiology Department, Medical Faculty, Tekirdağ Namık Kemal University. Namik Kemal Mh., Kampus Cd., Suleymanpasa, Tekirdag 59100, Turkey; Radiology Department, İstanbul Health and Technology University Hospital, Kaptanpasa Mh., Darulaceze Cd., Sisli, İstanbul 34384, Turkey
| | - İsmail Devecioğlu
- Biomedical Engineering Department, Çorlu Faculty of Engineering, Tekirdağ Namık Kemal University, NKU Corlu Muhendislik Fakultesi, Silahtaraga Mh., Çorlu, Tekirdağ 59860, Turkey.
| | - Atakan Küskün
- Radiology Department, Medical Faculty, Kırklareli University, Cumhuriyet Mh., Kofcaz Yolu, Kayali Yerleskesi, Merkezi Derslikler 2, No 39/L, Merkez, Kırklareli, Turkey
| | - Cem Öge
- Psychiatry Department, Çorlu State Hospital, Zafer, Mah. Bülent Ecevit Blv. No:33, Çorlu, Tekirdağ 59850, Turkey
| | - Elmas Beyazyüz
- Psychiatry Department, Medical Faculty, Tekirdağ Namık Kemal University. Namik Kemal Mh., Kampus Cd., Suleymanpasa, Tekirdag 59100, Turkey
| | - Yakup Albayrak
- Psychiatry Department, Medical Faculty, Tekirdağ Namık Kemal University. Namik Kemal Mh., Kampus Cd., Suleymanpasa, Tekirdag 59100, Turkey
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37
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Kristensen TD, Raghava JM, Skjerbæk MW, Dhollander T, Syeda W, Ambrosen KS, Bojesen KB, Nielsen MØ, Pantelis C, Glenthøj BY, Ebdrup BH. Fibre density and fibre-bundle cross-section of the corticospinal tract are distinctly linked to psychosis-specific symptoms in antipsychotic-naïve patients with first-episode schizophrenia. Eur Arch Psychiatry Clin Neurosci 2023; 273:1797-1812. [PMID: 37012463 PMCID: PMC10713712 DOI: 10.1007/s00406-023-01598-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 03/20/2023] [Indexed: 04/05/2023]
Abstract
Multiple lines of research support the dysconnectivity hypothesis of schizophrenia. However, findings on white matter (WM) alterations in patients with schizophrenia are widespread and non-specific. Confounding factors from magnetic resonance image (MRI) processing, clinical diversity, antipsychotic exposure, and substance use may underlie some of the variability. By application of refined methodology and careful sampling, we rectified common confounders investigating WM and symptom correlates in a sample of strictly antipsychotic-naïve first-episode patients with schizophrenia. Eighty-six patients and 112 matched controls underwent diffusion MRI. Using fixel-based analysis (FBA), we extracted fibre-specific measures such as fibre density and fibre-bundle cross-section. Group differences on fixel-wise measures were examined with multivariate general linear modelling. Psychopathology was assessed with the Positive and Negative Syndrome Scale. We separately tested multivariate correlations between fixel-wise measures and predefined psychosis-specific versus anxio-depressive symptoms. Results were corrected for multiple comparisons. Patients displayed reduced fibre density in the body of corpus callosum and in the middle cerebellar peduncle. Fibre density and fibre-bundle cross-section of the corticospinal tract were positively correlated with suspiciousness/persecution, and negatively correlated with delusions. Fibre-bundle cross-section of isthmus of corpus callosum and hallucinatory behaviour were negatively correlated. Fibre density and fibre-bundle cross-section of genu and splenium of corpus callosum were negative correlated with anxio-depressive symptoms. FBA revealed fibre-specific properties of WM abnormalities in patients and differentiated associations between WM and psychosis-specific versus anxio-depressive symptoms. Our findings encourage an itemised approach to investigate the relationship between WM microstructure and clinical symptoms in patients with schizophrenia.
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Affiliation(s)
- Tina D Kristensen
- Center for Neuropsychiatric Schizophrenia Research and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Centre Glostrup, Copenhagen University Hospital, Nordstjernevej 41, 2600, Glostrup, Denmark.
| | - Jayachandra M Raghava
- Center for Neuropsychiatric Schizophrenia Research and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Centre Glostrup, Copenhagen University Hospital, Nordstjernevej 41, 2600, Glostrup, Denmark
- Functional Imaging Unit, Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Glostrup, Denmark
| | - Martin W Skjerbæk
- Center for Neuropsychiatric Schizophrenia Research and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Centre Glostrup, Copenhagen University Hospital, Nordstjernevej 41, 2600, Glostrup, Denmark
| | - Thijs Dhollander
- Developmental Imaging, Murdoch Children's Research Institute, Victoria, Australia
| | - Warda Syeda
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Victoria, Australia
| | - Karen S Ambrosen
- Center for Neuropsychiatric Schizophrenia Research and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Centre Glostrup, Copenhagen University Hospital, Nordstjernevej 41, 2600, Glostrup, Denmark
| | - Kirsten B Bojesen
- Center for Neuropsychiatric Schizophrenia Research and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Centre Glostrup, Copenhagen University Hospital, Nordstjernevej 41, 2600, Glostrup, Denmark
| | - Mette Ø Nielsen
- Center for Neuropsychiatric Schizophrenia Research and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Centre Glostrup, Copenhagen University Hospital, Nordstjernevej 41, 2600, Glostrup, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Christos Pantelis
- Developmental Imaging, Murdoch Children's Research Institute, Victoria, Australia
| | - Birte Y Glenthøj
- Center for Neuropsychiatric Schizophrenia Research and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Centre Glostrup, Copenhagen University Hospital, Nordstjernevej 41, 2600, Glostrup, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Bjørn H Ebdrup
- Center for Neuropsychiatric Schizophrenia Research and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Centre Glostrup, Copenhagen University Hospital, Nordstjernevej 41, 2600, Glostrup, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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38
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Petruso F, Giff A, Milano B, De Rossi M, Saccaro L. Inflammation and emotion regulation: a narrative review of evidence and mechanisms in emotion dysregulation disorders. Neuronal Signal 2023; 7:NS20220077. [PMID: 38026703 PMCID: PMC10653990 DOI: 10.1042/ns20220077] [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: 03/21/2023] [Revised: 10/25/2023] [Accepted: 10/27/2023] [Indexed: 12/01/2023] Open
Abstract
Emotion dysregulation (ED) describes a difficulty with the modulation of which emotions are felt, as well as when and how these emotions are experienced or expressed. It is a focal overarching symptom in many severe and prevalent neuropsychiatric diseases, including bipolar disorders (BD), attention deficit/hyperactivity disorder (ADHD), and borderline personality disorder (BPD). In all these disorders, ED can manifest through symptoms of depression, anxiety, or affective lability. Considering the many symptomatic similarities between BD, ADHD, and BPD, a transdiagnostic approach is a promising lens of investigation. Mounting evidence supports the role of peripheral inflammatory markers and stress in the multifactorial aetiology and physiopathology of BD, ADHD, and BPD. Of note, neural circuits that regulate emotions appear particularly vulnerable to inflammatory insults and peripheral inflammation, which can impact the neuroimmune milieu of the central nervous system. Thus far, few studies have examined the link between ED and inflammation in BD, ADHD, and BPD. To our knowledge, no specific work has provided a critical comparison of the results from these disorders. To fill this gap in the literature, we review the known associations and mechanisms linking ED and inflammation in general, and clinically, in BD, ADHD, and BD. Our narrative review begins with an examination of the routes linking ED and inflammation, followed by a discussion of disorder-specific results accounting for methodological limitations and relevant confounding factors. Finally, we critically discuss both correspondences and discrepancies in the results and comment on potential vulnerability markers and promising therapeutic interventions.
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Affiliation(s)
| | - Alexis E. Giff
- Department of Neuroscience, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Switzerland
| | - Beatrice A. Milano
- Sant’Anna School of Advanced Studies, Pisa, Italy
- University of Pisa, Pisa, Italy
| | | | - Luigi Francesco Saccaro
- Department of Psychiatry, Faculty of Medicine, University of Geneva, Switzerland
- Department of Psychiatry, Geneva University Hospital, Switzerland
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39
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Singh A, Pandey HR, Arya A, Agarwal V, Shree R, Kumar U. Altered white matter integrity in euthymic children with bipolar disorder: A tract-based spatial statistical analysis of diffusion tensor imaging. J Affect Disord 2023; 340:820-827. [PMID: 37597779 DOI: 10.1016/j.jad.2023.08.066] [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: 03/28/2023] [Revised: 08/11/2023] [Accepted: 08/14/2023] [Indexed: 08/21/2023]
Abstract
Pediatric Bipolar Disorder (BD) is a serious mental illness that affects children and adolescents, characterized by episodes of mania, depression, and mixed episodes. Recent studies have suggested that abnormalities in the white matter (WM) may be a contributing factor. The neuropathogenesis of BD in children is not well-described, and research in this area is limited. Euthymic phase is a period in which clinical symptoms are present but not severe enough to significantly impact mood and daily behavior. In order to better understand the WM changes associated with BD in children, this study utilized Diffusion Tensor Imaging (DTI), to investigate alterations in WM microstructure. 20 confirmed euthymic BD children (aged 7-16) and 20 typically developing children were included in the study. DTI scans were obtained using a 3 T Magnetom Skyra and were analyzed using tract-based spatial statistics (TBSS) to examine changes in fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD). Results showed that compared to the healthy control group, the euthymic BD group exhibited increased FA, AD, RD, and MD values in several brain regions, including the thalamus, precentral corticospinal tract, and superior longitudinal fasciculus. Conversely, decreased values were observed in the body of the corpus callosum and inferior fronto-occipital fasciculus. These findings suggest that alterations in WM microstructure are a hallmark of pediatric bipolar disorder. These findings provide important insights into the brain changes associated with pediatric bipolar disorder and open the door for new avenues of research.
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Affiliation(s)
- Anshita Singh
- Centre of Bio-Medical Research, Sanjay Gandhi Postgraduate Institute of Medical Sciences Campus, Lucknow, India; Department of Information Technology, Babasaheb Bhimrao Ambedkar University, Lucknow, India
| | - Himanshu R Pandey
- Centre of Bio-Medical Research, Sanjay Gandhi Postgraduate Institute of Medical Sciences Campus, Lucknow, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Amit Arya
- Department of Psychiatry, King George Medical University, Lucknow, India
| | - Vivek Agarwal
- Department of Psychiatry, King George Medical University, Lucknow, India
| | - Raj Shree
- Department of Information Technology, Babasaheb Bhimrao Ambedkar University, Lucknow, India
| | - Uttam Kumar
- Centre of Bio-Medical Research, Sanjay Gandhi Postgraduate Institute of Medical Sciences Campus, Lucknow, India.
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40
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Matsumoto J, Miura K, Fukunaga M, Nemoto K, Koshiyama D, Okada N, Morita K, Yamamori H, Yasuda Y, Fujimoto M, Ito S, Hasegawa N, Watanabe Y, Kasai K, Hashimoto R. Association Study Between White Matter Microstructure and Intelligence Decline in Schizophrenia. Clin EEG Neurosci 2023; 54:567-573. [PMID: 34889128 DOI: 10.1177/15500594211063314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Patients with schizophrenia can exhibit intelligence decline, which is an important element of cognitive impairment. Previous magnetic resonance imaging (MRI) studies have demonstrated that patients with schizophrenia have altered gray matter structures and functional connectivity associated with intelligence decline defined by a difference between premorbid and current intelligence quotients (IQs). However, it has remained unclear whether white matter microstructures are related to intelligence decline. In the present study, the indices of diffusion tensor imaging (DTI) obtained from 138 patients with schizophrenia and 554 healthy controls were analyzed. The patients were classified into three subgroups based on intelligence decline: deteriorated (94 patients), preserved (42 patients), and compromised IQ (2 patients) groups. Given that the DTI of each subject was acquired using either one of two different MRI scanners, we analyzed DTI indices separately for each scanner group. In the comparison between the deteriorated IQ group and the healthy controls, differences in some DTI indices were noted in three regions of interest irrespective of the MRI scanners, whereas differences in only one region of interest were noted between the preserved IQ group and the healthy controls. However, the comparisons between the deteriorated and preserved IQ groups did not show any reproducible differences. Together with the previous findings, it is thought that gray matter structures and functional connectivity are more promising as markers of intelligence decline in schizophrenia than white matter microstructures.
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Affiliation(s)
- Junya Matsumoto
- National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan
| | - Kenichiro Miura
- National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan
| | - Masaki Fukunaga
- National Institute for Physiological Sciences, Okazaki, Aichi, Japan
| | | | - Daisuke Koshiyama
- Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Naohiro Okada
- Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
- The International Research Center for Neurointelligence (WPI-IRCN), University of Tokyo Institutes for Advanced Study (UTIAS), Bunkyo-ku, Tokyo, Japan
| | | | - Hidenaga Yamamori
- National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan
- Japan Community Health Care Organization Osaka Hospital, Osaka, Osaka, Japan
- Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
| | - Yuka Yasuda
- National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan
- Medical Corporation Foster, Osaka, Osaka, Japan
| | - Michiko Fujimoto
- National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan
- Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
| | - Satsuki Ito
- National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan
- Ochanomizu University, Bunkyo-ku, Tokyo, Japan
| | - Naomi Hasegawa
- National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan
| | | | - Kiyoto Kasai
- Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
- The International Research Center for Neurointelligence (WPI-IRCN), University of Tokyo Institutes for Advanced Study (UTIAS), Bunkyo-ku, Tokyo, Japan
| | - Ryota Hashimoto
- National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan
- Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
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41
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Matsumoto J, Fukunaga M, Miura K, Nemoto K, Okada N, Hashimoto N, Morita K, Koshiyama D, Ohi K, Takahashi T, Koeda M, Yamamori H, Fujimoto M, Yasuda Y, Ito S, Yamazaki R, Hasegawa N, Narita H, Yokoyama S, Mishima R, Miyata J, Kobayashi Y, Sasabayashi D, Harada K, Yamamoto M, Hirano Y, Itahashi T, Nakataki M, Hashimoto RI, Tha KK, Koike S, Matsubara T, Okada G, Yoshimura R, Abe O, van Erp TGM, Turner JA, Jahanshad N, Thompson PM, Onitsuka T, Watanabe Y, Matsuo K, Yamasue H, Okamoto Y, Suzuki M, Ozaki N, Kasai K, Hashimoto R. Cerebral cortical structural alteration patterns across four major psychiatric disorders in 5549 individuals. Mol Psychiatry 2023; 28:4915-4923. [PMID: 37596354 PMCID: PMC10914601 DOI: 10.1038/s41380-023-02224-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 07/30/2023] [Accepted: 08/07/2023] [Indexed: 08/20/2023]
Abstract
According to the operational diagnostic criteria, psychiatric disorders such as schizophrenia (SZ), bipolar disorder (BD), major depressive disorder (MDD), and autism spectrum disorder (ASD) are classified based on symptoms. While its cluster of symptoms defines each of these psychiatric disorders, there is also an overlap in symptoms between the disorders. We hypothesized that there are also similarities and differences in cortical structural neuroimaging features among these psychiatric disorders. T1-weighted magnetic resonance imaging scans were performed for 5,549 subjects recruited from 14 sites. Effect sizes were determined using a linear regression model within each protocol, and these effect sizes were meta-analyzed. The similarity of the differences in cortical thickness and surface area of each disorder group was calculated using cosine similarity, which was calculated from the effect sizes of each cortical regions. The thinnest cortex was found in SZ, followed by BD and MDD. The cosine similarity values between disorders were 0.943 for SZ and BD, 0.959 for SZ and MDD, and 0.943 for BD and MDD, which indicated that a common pattern of cortical thickness alterations was found among SZ, BD, and MDD. Additionally, a generally smaller cortical surface area was found in SZ and MDD than in BD, and the effect was larger in SZ. The cosine similarity values between disorders were 0.945 for SZ and MDD, 0.867 for SZ and ASD, and 0.811 for MDD and ASD, which indicated a common pattern of cortical surface area alterations among SZ, MDD, and ASD. Patterns of alterations in cortical thickness and surface area were revealed in the four major psychiatric disorders. To our knowledge, this is the first report of a cross-disorder analysis conducted on four major psychiatric disorders. Cross-disorder brain imaging research can help to advance our understanding of the pathogenesis of psychiatric disorders and common symptoms.
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Affiliation(s)
- Junya Matsumoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, 187-8553, Japan
| | - Masaki Fukunaga
- Section of Brain Function Information, National Institute for Physiological Sciences, Okazaki, 444-8585, Japan
| | - Kenichiro Miura
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, 187-8553, Japan
| | - Kiyotaka Nemoto
- Department of Psychiatry, Institute of Medicine, University of Tsukuba, Tsukuba, 305-8575, Japan
| | - Naohiro Okada
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-8655, Japan
- The International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study (UTIAS), Tokyo, 113-0033, Japan
| | - Naoki Hashimoto
- Department of Psychiatry, Hokkaido University Graduate School of Medicine, Sapporo, 060-8638, Japan
| | - Kentaro Morita
- Department of Rehabilitation, University of Tokyo Hospital, Tokyo, 113-8655, Japan
| | - Daisuke Koshiyama
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-8655, Japan
| | - Kazutaka Ohi
- Department of Psychiatry, Gifu University Graduate School of Medicine, Gifu, 501-1194, Japan
- Department of General Internal Medicine, Kanazawa Medical University, Ishikawa, 920-0293, Japan
| | - Tsutomu Takahashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, 930-0194, Japan
- Research Center for Idling Brain Science, University of Toyama, Toyama, 930-0194, Japan
| | - Michihiko Koeda
- Department of Neuropsychiatry, Graduate School of Medicine, Nippon Medical School, Tokyo, 113-8602, Japan
| | - Hidenaga Yamamori
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, 187-8553, Japan
- Department of Psychiatry, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
- Japan Community Health Care Organization Osaka Hospital, Osaka, 553-0003, Japan
| | - Michiko Fujimoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, 187-8553, Japan
- Department of Psychiatry, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
| | - Yuka Yasuda
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, 187-8553, Japan
- Life Grow Brilliant Mental Clinic, Medical Corporation Foster, Osaka, 530-0013, Japan
| | - Satsuki Ito
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, 187-8553, Japan
- Department of Developmental and Clinical Psychology, The Division of Human Developmental Sciences, Graduate School of Humanity and Sciences, Ochanomizu University, Tokyo, 112-8610, Japan
| | - Ryuichi Yamazaki
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, 187-8553, Japan
- Department of Psychiatry, The Jikei University School of Medicine, Tokyo, 105-8461, Japan
| | - Naomi Hasegawa
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, 187-8553, Japan
| | - Hisashi Narita
- Department of Psychiatry, Hokkaido University Graduate School of Medicine, Sapporo, 060-8638, Japan
| | - Satoshi Yokoyama
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, 734-8551, Japan
| | - Ryo Mishima
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, 606-8507, Japan
| | - Jun Miyata
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, 606-8507, Japan
| | - Yuko Kobayashi
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, 606-8507, Japan
| | - Daiki Sasabayashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, 930-0194, Japan
- Research Center for Idling Brain Science, University of Toyama, Toyama, 930-0194, Japan
| | - Kenichiro Harada
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Ube, 755-8505, Japan
| | - Maeri Yamamoto
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, 466-8550, Japan
| | - Yoji Hirano
- Department of Psychiatry, Division of Clinical Neuroscience, Faculty of Medicine, University of Miyazaki, Miyazaki, 889-1692, Japan
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, 812-8582, Japan
- Institute of Industrial Science, The University of Tokyo, Tokyo, 153-8505, Japan
| | - Takashi Itahashi
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, 157-8577, Japan
| | - Masahito Nakataki
- Department of Psychiatry, Tokushima University Hospital, Tokushima, 770-8503, Japan
| | - Ryu-Ichiro Hashimoto
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, 157-8577, Japan
- Department of Language Sciences, Graduate School of Humanities, Tokyo Metropolitan University, Hachioji, 192-0397, Japan
| | - Khin K Tha
- Global Center for Biomedical Science and Engineering, Hokkaido University Faculty of Medicine, Sapporo, 060-8638, Japan
| | - Shinsuke Koike
- The International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study (UTIAS), Tokyo, 113-0033, Japan
- University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), Tokyo, 153-8902, Japan
- Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, 153-8902, Japan
| | - Toshio Matsubara
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Ube, 755-8505, Japan
| | - Go Okada
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, 734-8551, Japan
| | - Reiji Yoshimura
- Department of Psychiatry, University of Occupational and Environmental Health, Kitakyushu, 807-8555, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-8655, Japan
| | - Theo G M van Erp
- Clinical Translatational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, 92697, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine, CA, 92697, USA
| | - Jessica A Turner
- Department of Psychiatry and Behavioral Health, Wexner Medical Center, the Ohio State University, Columbus, OH, 43210, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90292, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90292, USA
| | - Toshiaki Onitsuka
- National Hospital Organization Sakakibara Hospital, Tsu, 514-1292, Japan
| | - Yoshiyuki Watanabe
- Department of Radiology, Shiga University of Medical Science, Otsu, 520-2192, Japan
| | - Koji Matsuo
- Department of Psychiatry, Faculty of Medicine, Saitama Medical University, Saitama, 350-0495, Japan
| | - Hidenori Yamasue
- Department of Psychiatry, Hamamatsu University School of Medicine, Hamamatsu, 431-3192, Japan
| | - Yasumasa Okamoto
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, 734-8551, Japan
| | - Michio Suzuki
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, 930-0194, Japan
- Research Center for Idling Brain Science, University of Toyama, Toyama, 930-0194, Japan
| | - Norio Ozaki
- Pathophysiology of Mental Disorders, Nagoya University Graduate School of Medicine, Nagoya, 466-8550, Japan
| | - Kiyoto Kasai
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-8655, Japan
- The International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study (UTIAS), Tokyo, 113-0033, Japan
- University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), Tokyo, 153-8902, Japan
| | - Ryota Hashimoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, 187-8553, Japan.
- Department of Psychiatry, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan.
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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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Ćirović M, Jeličić L, Maksimović S, Fatić S, Marisavljević M, Bošković Matić T, Subotić M. EEG Correlates of Cognitive Functions in a Child with ASD and White Matter Signal Abnormalities: A Case Report with Two-and-a-Half-Year Follow-Up. Diagnostics (Basel) 2023; 13:2878. [PMID: 37761245 PMCID: PMC10529253 DOI: 10.3390/diagnostics13182878] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 08/21/2023] [Accepted: 08/31/2023] [Indexed: 09/29/2023] Open
Abstract
This research aimed to examine the EEG correlates of different stimuli processing instances in a child with ASD and white matter signal abnormalities and to investigate their relationship to the results of behavioral tests. The prospective case study reports two and a half years of follow-up data from a child aged 38 to 66 months. Cognitive, speech-language, sensory, and EEG correlates of auditory-verbal and auditory-visual-verbal information processing were recorded during five test periods, and their mutual interrelation was analyzed. EEG findings revealed no functional theta frequency range redistribution in the frontal regions favoring the left hemisphere during speech processing. The results pointed to a positive linear trend in the relative theta frequency range and a negative linear trend in the relative alpha frequency range when listening to and watching the cartoon. There was a statistically significant correlation between EEG signals and behavioral test results. Based on the obtained results, it may be concluded that EEG signals and their association with the results of behavioral tests should be evaluated with certain restraints considering the characteristics of the stimuli during EEG recording.
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Affiliation(s)
- Milica Ćirović
- Cognitive Neuroscience Department, Research and Development Institute “Life Activities Advancement Institute”, 11000 Belgrade, Serbia; (M.Ć.); (S.M.); (S.F.); (M.M.); (M.S.)
- Department of Speech, Language and Hearing Sciences, Institute for Experimental Phonetics and Speech Pathology, 11000 Belgrade, Serbia
| | - Ljiljana Jeličić
- Cognitive Neuroscience Department, Research and Development Institute “Life Activities Advancement Institute”, 11000 Belgrade, Serbia; (M.Ć.); (S.M.); (S.F.); (M.M.); (M.S.)
- Department of Speech, Language and Hearing Sciences, Institute for Experimental Phonetics and Speech Pathology, 11000 Belgrade, Serbia
| | - Slavica Maksimović
- Cognitive Neuroscience Department, Research and Development Institute “Life Activities Advancement Institute”, 11000 Belgrade, Serbia; (M.Ć.); (S.M.); (S.F.); (M.M.); (M.S.)
- Department of Speech, Language and Hearing Sciences, Institute for Experimental Phonetics and Speech Pathology, 11000 Belgrade, Serbia
| | - Saška Fatić
- Cognitive Neuroscience Department, Research and Development Institute “Life Activities Advancement Institute”, 11000 Belgrade, Serbia; (M.Ć.); (S.M.); (S.F.); (M.M.); (M.S.)
- Department of Speech, Language and Hearing Sciences, Institute for Experimental Phonetics and Speech Pathology, 11000 Belgrade, Serbia
| | - Maša Marisavljević
- Cognitive Neuroscience Department, Research and Development Institute “Life Activities Advancement Institute”, 11000 Belgrade, Serbia; (M.Ć.); (S.M.); (S.F.); (M.M.); (M.S.)
- Department of Speech, Language and Hearing Sciences, Institute for Experimental Phonetics and Speech Pathology, 11000 Belgrade, Serbia
| | - Tatjana Bošković Matić
- Department of Neurology, Faculty of Medical Sciences, University of Kragujevac, 34000 Kragujevac, Serbia;
- Clinic of Neurology, University Clinical Centre of Kragujevac, 34000 Kragujevac, Serbia
| | - Miško Subotić
- Cognitive Neuroscience Department, Research and Development Institute “Life Activities Advancement Institute”, 11000 Belgrade, Serbia; (M.Ć.); (S.M.); (S.F.); (M.M.); (M.S.)
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Geraets AFJ, Köhler S, Vergoossen LWM, Backes WH, Stehouwer CD, Verhey FRJ, Jansen JFA, van Sloten TT, Schram MT. The association of white matter connectivity with prevalence, incidence and course of depressive symptoms: The Maastricht Study. Psychol Med 2023; 53:5558-5568. [PMID: 36069192 PMCID: PMC10493191 DOI: 10.1017/s0033291722002768] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 06/27/2022] [Accepted: 08/08/2022] [Indexed: 11/07/2022]
Abstract
BACKGROUND Altered white matter brain connectivity has been linked to depression. The aim of this study was to investigate the association of markers of white matter connectivity with prevalence, incidence and course of depressive symptoms. METHODS Markers of white matter connectivity (node degree, clustering coefficient, local efficiency, characteristic path length, and global efficiency) were assessed at baseline by 3 T MRI in the population-based Maastricht Study (n = 4866; mean ± standard deviation age 59.6 ± 8.5 years, 49.0% women; 17 406 person-years of follow-up). Depressive symptoms (9-item Patient Health Questionnaire; PHQ-9) were assessed at baseline and annually over seven years of follow-up. Major depressive disorder (MDD) was assessed with the Mini-International Neuropsychiatric Interview at baseline only. We used negative binominal, logistic and Cox regression analyses, and adjusted for demographic, cardiovascular, and lifestyle risk factors. RESULTS A lower global average node degree at baseline was associated with the prevalence and persistence of clinically relevant depressive symptoms [PHQ-9 ⩾ 10; OR (95% confidence interval) per standard deviation = 1.21 (1.05-1.39) and OR = 1.21 (1.02-1.44), respectively], after full adjustment. On the contrary, no associations were found of global average node degree with the MDD at baseline [OR 1.12 (0.94-1.32) nor incidence or remission of clinically relevant depressive symptoms [HR = 1.05 (0.95-1.17) and OR 1.08 (0.83-1.41), respectively]. Other connectivity measures of white matter organization were not associated with depression. CONCLUSIONS Our findings suggest that fewer white matter connections may contribute to prevalent depressive symptoms and its persistence but not to incident depression. Future studies are needed to replicate our findings.
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Affiliation(s)
- Anouk F. J. Geraets
- Department of Psychiatry and Neuropsychology, Faculty of Health, Medicine & Life Sciences, Maastricht University, Maastricht, the Netherlands
- Alzheimer Centrum Limburg, Maastricht University Medical Center+ (MUMC+), Maastricht, the Netherlands
- Department of Internal Medicine, Maastricht University Medical Center+ (MUMC+), Maastricht, the Netherlands
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+ (MUMC+), Maastricht, the Netherlands
- Heart and Vascular Center, Maastricht University Medical Center+ (MUMC+), Maastricht, the Netherlands
| | - Sebastian Köhler
- Department of Psychiatry and Neuropsychology, Faculty of Health, Medicine & Life Sciences, Maastricht University, Maastricht, the Netherlands
- Alzheimer Centrum Limburg, Maastricht University Medical Center+ (MUMC+), Maastricht, the Netherlands
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+ (MUMC+), Maastricht, the Netherlands
| | - Laura WM Vergoossen
- Department of Psychiatry and Neuropsychology, Faculty of Health, Medicine & Life Sciences, Maastricht University, Maastricht, the Netherlands
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+ (MUMC+), Maastricht, the Netherlands
- School for Mental Health and Neuroscience, Faculty of Health, Medicine & Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Walter H. Backes
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+ (MUMC+), Maastricht, the Netherlands
- School for Mental Health and Neuroscience, Faculty of Health, Medicine & Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Coen D.A. Stehouwer
- Department of Internal Medicine, Maastricht University Medical Center+ (MUMC+), Maastricht, the Netherlands
- Heart and Vascular Center, Maastricht University Medical Center+ (MUMC+), Maastricht, the Netherlands
| | - Frans RJ Verhey
- Department of Psychiatry and Neuropsychology, Faculty of Health, Medicine & Life Sciences, Maastricht University, Maastricht, the Netherlands
- Alzheimer Centrum Limburg, Maastricht University Medical Center+ (MUMC+), Maastricht, the Netherlands
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+ (MUMC+), Maastricht, the Netherlands
| | - Jacobus FA Jansen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+ (MUMC+), Maastricht, the Netherlands
- School for Mental Health and Neuroscience, Faculty of Health, Medicine & Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Thomas T. van Sloten
- Department of Internal Medicine, Maastricht University Medical Center+ (MUMC+), Maastricht, the Netherlands
- Heart and Vascular Center, Maastricht University Medical Center+ (MUMC+), Maastricht, the Netherlands
| | - Miranda T. Schram
- Department of Psychiatry and Neuropsychology, Faculty of Health, Medicine & Life Sciences, Maastricht University, Maastricht, the Netherlands
- Department of Internal Medicine, Maastricht University Medical Center+ (MUMC+), Maastricht, the Netherlands
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+ (MUMC+), Maastricht, the Netherlands
- Heart and Vascular Center, Maastricht University Medical Center+ (MUMC+), Maastricht, the Netherlands
- School for Cardiovascular Diseases (CARIM), Faculty of Health, Medicine & Life Sciences, Maastricht University, Maastricht, the Netherlands
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Shiohama T, Maikusa N, Kawaguchi M, Natsume J, Hirano Y, Saito K, Takanashi JI, Levman J, Takahashi E, Matsumoto K, Yokota H, Hattori S, Tsujimura K, Sawada D, Uchida T, Takatani T, Fujii K, Naganawa S, Sato N, Hamada H. A Brain Morphometry Study with Across-Site Harmonization Using a ComBat-Generalized Additive Model in Children and Adolescents. Diagnostics (Basel) 2023; 13:2774. [PMID: 37685313 PMCID: PMC10487204 DOI: 10.3390/diagnostics13172774] [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: 07/14/2023] [Revised: 08/23/2023] [Accepted: 08/25/2023] [Indexed: 09/10/2023] Open
Abstract
Regional anatomical structures of the brain are intimately connected to functions corresponding to specific regions and the temporospatial pattern of genetic expression and their functions from the fetal period to old age. Therefore, quantitative brain morphometry has often been employed in neuroscience investigations, while controlling for the scanner effect of the scanner is a critical issue for ensuring accuracy in brain morphometric studies of rare orphan diseases due to the lack of normal reference values available for multicenter studies. This study aimed to provide across-site normal reference values of global and regional brain volumes for each sex and age group in children and adolescents. We collected magnetic resonance imaging (MRI) examinations of 846 neurotypical participants aged 6.0-17.9 years (339 male and 507 female participants) from 5 institutions comprising healthy volunteers or neurotypical patients without neurological disorders, neuropsychological disorders, or epilepsy. Regional-based analysis using the CIVET 2.1.0. pipeline provided regional brain volumes, and the measurements were across-site combined using ComBat-GAM harmonization. The normal reference values of global and regional brain volumes and lateral indices in our study could be helpful for evaluating the characteristics of the brain morphology of each individual in a clinical setting and investigating the brain morphology of ultra-rare diseases.
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Affiliation(s)
- Tadashi Shiohama
- Department of Pediatrics, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chuo-ku, Chiba-shi 260-8677, Chiba, Japan
- Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA
| | - Norihide Maikusa
- Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo, Tokyo 108-8639, Japan
- Department of Radiology, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
| | - Masahiro Kawaguchi
- Department of Pediatrics, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya 466-8550, Aichi, Japan; (M.K.)
| | - Jun Natsume
- Department of Pediatrics, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya 466-8550, Aichi, Japan; (M.K.)
- Department of Developmental Disability Medicine, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya 466-8550, Aichi, Japan
| | - Yoshiyuki Hirano
- Research Center for Child Mental Development, Chiba University, Inohana 1-8-1, Chuo-ku, Chiba-shi 260-8677, Chiba, Japan
- United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University and University of Fukui, Suita 565-0871, Osaka, Japan
| | - Keito Saito
- Department of Pediatrics and Pediatric Neurology, Tokyo Women’s Medical University Yachiyo Medical Center, 477-96 Owadashinden, Yachiyo-shi 276-8524, Chiba, Japan
| | - Jun-ichi Takanashi
- Department of Pediatrics and Pediatric Neurology, Tokyo Women’s Medical University Yachiyo Medical Center, 477-96 Owadashinden, Yachiyo-shi 276-8524, Chiba, Japan
| | - Jacob Levman
- Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA
- Department of Mathematics, Statistics and Computer Science, St. Francis Xavier University, 5005 Chapel Square, Antigonish, NS B2G 2W5, Canada
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 149 13th Street, Charlestown, MA 02129, USA
- Nova Scotia Health Authority—Research, Innovation and Discovery Center for Clinical Research, 5790 University Avenue, Halifax, NS B3H 1V7, Canada
| | - Emi Takahashi
- Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 149 13th Street, Charlestown, MA 02129, USA
| | - Koji Matsumoto
- Department of Radiology, Chiba University Hospital, Inohana 1-8-1, Chuo-ku, Chiba-shi 260-8677, Chiba, Japan
| | - Hajime Yokota
- Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chuo-ku, Chiba-shi 260-8677, Chiba, Japan
| | - Shinya Hattori
- Department of Radiology, Chiba University Hospital, Inohana 1-8-1, Chuo-ku, Chiba-shi 260-8677, Chiba, Japan
| | - Keita Tsujimura
- Group of Brain Function and Development, Neuroscience Institute of the Graduate School of Science, Nagoya University, Nagoya 466-8550, Aichi, Japan
- Research Unit for Developmental Disorders, Institute for Advanced Research, Nagoya University, Nagoya 466-8550, Aichi, Japan
| | - Daisuke Sawada
- Department of Pediatrics, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chuo-ku, Chiba-shi 260-8677, Chiba, Japan
| | - Tomoko Uchida
- Department of Pediatrics, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chuo-ku, Chiba-shi 260-8677, Chiba, Japan
| | - Tomozumi Takatani
- Department of Pediatrics, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chuo-ku, Chiba-shi 260-8677, Chiba, Japan
| | - Katsunori Fujii
- Department of Pediatrics, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chuo-ku, Chiba-shi 260-8677, Chiba, Japan
- Department of Pediatrics, International University of Welfare and Health School of Medicine, Narita 286-8520, Chiba, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya 466-8550, Aichi, Japan
| | - Noriko Sato
- Department of Radiology, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
| | - Hiromichi Hamada
- Department of Pediatrics, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chuo-ku, Chiba-shi 260-8677, Chiba, Japan
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46
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Joo SW, Jo YT, Ahn S, Choi YJ, Choi W, Kim SK, Joe S, Lee J. Structural impairment in superficial and deep white matter in schizophrenia. Acta Neuropsychiatr 2023:1-10. [PMID: 37620164 DOI: 10.1017/neu.2023.44] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/26/2023]
Abstract
OBJECTIVE Although disconnectivity among brain regions has been one of the main hypotheses for schizophrenia, the superficial white matter (SWM) has received less attention in schizophrenia research than the deep white matter (DWM) owing to the challenge of consistent reconstruction across subjects. METHODS We obtained the diffusion magnetic resonance imaging (dMRI) data of 223 healthy controls and 143 patients with schizophrenia. After harmonising the raw dMRIs from three different studies, we performed whole-brain two-tensor tractography and fibre clustering on the tractography data. We compared the fractional anisotropy (FA) of white matter tracts between healthy controls and patients with schizophrenia. Spearman's rho was adopted for the associations with clinical symptoms measured by the Positive and Negative Syndrome Scale (PANSS). The Bonferroni correction was used to adjust multiple testing. RESULTS Among the 33 DWM and 8 SWM tracts, patients with schizophrenia had a lower FA in 14 DWM and 4 SWM tracts than healthy controls, with small effect sizes. In the patient group, the FA deviations of the corticospinal and superficial-occipital tracts were negatively correlated with the PANSS negative score; however, this correlation was not evident after adjusting for multiple testing. CONCLUSION We observed the structural impairments of both the DWM and SWM tracts in patients with schizophrenia. The SWM could be a potential target of interest in future research on neural biomarkers for 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
| | - Young Tak Jo
- Department of Psychiatry, Kangdong Sacred Heart Hospital, Hallym University 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
| | - Woohyeok Choi
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Sang Kyoung Kim
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Soohyun Joe
- Brain Laboratory, Department of Psychiatry, University of California San Diego, School of Medicine, San Diego, CA, USA
| | - Jungsun Lee
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
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47
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Feng Y, Chandio BQ, Villalón-Reina JE, Thomopoulos SI, Joshi H, Nair G, Joshi AA, Venkatasubramanian G, John JP, Thompson PM. BundleCleaner: Unsupervised Denoising and Subsampling of Diffusion MRI-Derived Tractography Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.19.553990. [PMID: 37662361 PMCID: PMC10473583 DOI: 10.1101/2023.08.19.553990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
We present BundleCleaner, an unsupervised multi-step framework that can filter, denoise and subsample bundles derived from diffusion MRI-based whole-brain tractography. Our approach considers both the global bundle structure and local streamline-wise features. We apply BundleCleaner to bundles generated from single-shell diffusion MRI data in an independent clinical sample of older adults from India using probabilistic tractography and the resulting 'cleaned' bundles can better align with the atlas bundles with reduced overreach. In a downstream tractometry analysis, we show that the cleaned bundles, represented with less than 20% of the original set of points, can robustly localize along-tract microstructural differences between 32 healthy controls and 34 participants with Alzheimer's disease ranging in age from 55 to 84 years old. Our approach can help reduce memory burden and improving computational efficiency when working with tractography data, and shows promise for large-scale multi-site tractometry.
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Affiliation(s)
- Yixue Feng
- Imaging Genetics Center, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Bramsh Q Chandio
- Imaging Genetics Center, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Julio E Villalón-Reina
- Imaging Genetics Center, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Himanshu Joshi
- Multimodal Brain Image Analysis Laboratory, Translational Psychiatry Laboratory, National Institute of Mental Health and Neuro Sciences, Bengaluru, Karnataka, India
| | - Gauthami Nair
- Multimodal Brain Image Analysis Laboratory, Translational Psychiatry Laboratory, National Institute of Mental Health and Neuro Sciences, Bengaluru, Karnataka, India
| | - Anand A Joshi
- Signal and Image Processing Institute, Ming Hseih dept of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States
| | - Ganesan Venkatasubramanian
- Multimodal Brain Image Analysis Laboratory, Translational Psychiatry Laboratory, National Institute of Mental Health and Neuro Sciences, Bengaluru, Karnataka, India
| | - John P John
- Multimodal Brain Image Analysis Laboratory, Translational Psychiatry Laboratory, National Institute of Mental Health and Neuro Sciences, Bengaluru, Karnataka, India
| | - Paul M Thompson
- Imaging Genetics Center, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
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Hirai S, Sakuma A, Kunii Y, Shimbo H, Hino M, Izumi R, Nagaoka A, Yabe H, Kojima R, Seki E, Arai N, Komori T, Okado H. Disease specific brain capillary angiopathy in schizophrenia, bipolar disorder, and Alzheimer's disease. J Psychiatr Res 2023; 163:74-79. [PMID: 37207434 DOI: 10.1016/j.jpsychires.2023.04.011] [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: 01/20/2023] [Revised: 03/26/2023] [Accepted: 04/08/2023] [Indexed: 05/21/2023]
Abstract
Schizophrenia (SZ) and bipolar disorder (BD), which are both psychiatric disorders, share some common clinical evidence. We recently discovered that brain capillary angiopathy is another common feature of these psychiatric disorders using fibrin accumulation in vascular endothelial cells as an indicator. This study aimed to characterize the similarities and differences in cerebral capillary injuries in various brain diseases to provide new diagnostic methods for SZ and BD and to develop new therapeutic strategies. We evaluated whether discrepancies exist in the degree of vascular damage among SZ and BD and other brain disorders (amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), and Alzheimer's disease (AD)) using postmortem brains. Our results demonstrate that fibrin was strongly accumulated in the capillaries of the grey matter (GM) of brains of patients with SZ and AD and in the capillaries of the white matter (WM) in those of patients with SZ, BD, and AD when compared with control subjects without any psychiatric or neurological disease history. However, ALS and PD brains did not present a significant increase in the amount of accumulated fibrin, either in the capillaries of WM or GM. Furthermore, significant leakage of fibrin into the brain parenchyma, indicating a vascular physical disruption, was observed in the brains of patients with AD but not in the brains of other patients compared with control subjects. In conclusion, our work reveals that Fibrin-accumulation in the brain capillaries are observed in psychiatric disorders, such as SZ, BD, and AD. Furthermore, fibrin-accumulating, nonbreaking type angiopathy is characteristic of SZ and BD, even though there are regional differences between these diseases.
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Affiliation(s)
- Shinobu Hirai
- Brain Metabolic Regulation Group, Frontier Laboratory, Department of Psychiatry and Behavioral Sciences, Tokyo Metropolitan Institute of Medical Science, Tokyo, 156-8506, Japan.
| | - Atsuhiro Sakuma
- Brain Metabolic Regulation Group, Frontier Laboratory, Department of Psychiatry and Behavioral Sciences, Tokyo Metropolitan Institute of Medical Science, Tokyo, 156-8506, Japan
| | - Yasuto Kunii
- Department of Neuropsychiatry, School of Medicine, Fukushima Medical University, Fukushima, 960-1295, Japan; Department of Disaster Psychiatry, International Research Institute of Disaster Science, Tohoku University, Miyagi, 980-8573, Japan
| | - Hiroko Shimbo
- Brain Metabolic Regulation Group, Frontier Laboratory, Department of Psychiatry and Behavioral Sciences, Tokyo Metropolitan Institute of Medical Science, Tokyo, 156-8506, Japan; Sleep Disorders Project, Department of Psychiatry and Behavioral Sciences, Tokyo Metropolitan Institute of Medical Science, Tokyo, 156-8506, Japan
| | - Mizuki Hino
- Department of Neuropsychiatry, School of Medicine, Fukushima Medical University, Fukushima, 960-1295, Japan; Department of Disaster Psychiatry, International Research Institute of Disaster Science, Tohoku University, Miyagi, 980-8573, Japan
| | - Ryuta Izumi
- Department of Neuropsychiatry, School of Medicine, Fukushima Medical University, Fukushima, 960-1295, Japan
| | - Atsuko Nagaoka
- Department of Neuropsychiatry, School of Medicine, Fukushima Medical University, Fukushima, 960-1295, Japan
| | - Hirooki Yabe
- Department of Neuropsychiatry, School of Medicine, Fukushima Medical University, Fukushima, 960-1295, Japan
| | - Rika Kojima
- Laboratory of Neuropathology, Tokyo Metropolitan Institute of Medical Sciences, Tokyo, 156-8506, Japan
| | - Erika Seki
- Laboratory of Neuropathology, Tokyo Metropolitan Institute of Medical Sciences, Tokyo, 156-8506, Japan
| | - Nobutaka Arai
- Laboratory of Neuropathology, Tokyo Metropolitan Institute of Medical Sciences, Tokyo, 156-8506, Japan
| | - Takashi Komori
- Department of Pathology and Laboratory Medicine, Tokyo Metropolitan Neurological Hospital, Tokyo, 183-0042, Japan
| | - Haruo Okado
- Sleep Disorders Project, Department of Psychiatry and Behavioral Sciences, Tokyo Metropolitan Institute of Medical Science, Tokyo, 156-8506, Japan.
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49
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Chen Z, Bo Q, Zhao L, Wang Y, Zhang Z, Zhou Y, Wang C. White matter microstructural abnormalities in individuals with attenuated positive symptom syndromes. J Psychiatr Res 2023; 163:150-158. [PMID: 37210833 DOI: 10.1016/j.jpsychires.2023.05.050] [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: 11/02/2022] [Revised: 05/03/2023] [Accepted: 05/15/2023] [Indexed: 05/23/2023]
Abstract
White matter (WM) microstructural alterations have been extensively studied in patients with psychosis, but research on the microstructure of WM in individuals with attenuated positive symptom syndrome (APSS) is currently limited. To improve the understanding of the neuropathology in APSS, this study investigated the WM of individuals with APSS using diffusion tensor and T1-weighted imaging. Automated fiber quantification was used to calculate the diffusion index values along the trajectories of 20 major fiber tracts in 42 individuals with APSS and 51 age-and sex-matched healthy control (HC) individuals. The diffusion index values in each of fiber tracts were compared node-by-node between the 2 groups. Compared with the HC group, the APSS group showed differences in the diffusion index values in partial segments of the callosum forceps minor, left and right cingulum cingulate, inferior fronto-occipital fasciculus, right corticospinal tract, left superior longitudinal fasciculus, and arcuate fasciculus. Notably, in the APSS group positive associations were found between the axial diffusivity values of the partial nodes of the left and right cingulum cingulate and the current Global Assessment of Functioning scores, as well as between the axial diffusivity values of the partial nodes of the right corticospinal tract and negative symptoms scores and reasoning and problem-solving scores. These findings suggest that individuals with APSS exhibit reduced WM integrity or possible impaired myelin in certain segments of WM tracts involved in the frontal- and limbic-cortical connections. Additionally, abnormal WM tracts appear to be associated with impaired general function and neurocognitive function. This study provides important new insights into the neurobiology of APSS and highlights potential targets for future intervention and treatment.
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Affiliation(s)
- Zhenzhu Chen
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China; Beijing Institute for Brain Disorders Center of Schizophrenia, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
| | - Qijing Bo
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China; Beijing Institute for Brain Disorders Center of Schizophrenia, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China.
| | - Lei Zhao
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China; Beijing Institute for Brain Disorders Center of Schizophrenia, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
| | - Yimeng Wang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China; Beijing Institute for Brain Disorders Center of Schizophrenia, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
| | - Zhifang Zhang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
| | - Yuan Zhou
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China; CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Chuanyue Wang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China; Beijing Institute for Brain Disorders Center of Schizophrenia, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100069, China.
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50
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Thiel K, Meinert S, Winter A, Lemke H, Waltemate L, Breuer F, Gruber M, Leenings R, Wüste L, Rüb K, Pfarr JK, Stein F, Brosch K, Meller T, Ringwald KG, Nenadić I, Krug A, Repple J, Opel N, Koch K, Leehr EJ, Bauer J, Grotegerd D, Hahn T, Kircher T, Dannlowski U. Reduced fractional anisotropy in bipolar disorder v. major depressive disorder independent of current symptoms. Psychol Med 2023; 53:4592-4602. [PMID: 35833369 PMCID: PMC10388324 DOI: 10.1017/s0033291722001490] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 04/28/2022] [Accepted: 05/05/2022] [Indexed: 11/07/2022]
Abstract
BACKGROUND Patients with bipolar disorder (BD) show reduced fractional anisotropy (FA) compared to patients with major depressive disorder (MDD). Little is known about whether these differences are mood state-independent or influenced by acute symptom severity. Therefore, the aim of this study was (1) to replicate abnormalities in white matter microstructure in BD v. MDD and (2) to investigate whether these vary across depressed, euthymic, and manic mood. METHODS In this cross-sectional diffusion tensor imaging study, n = 136 patients with BD were compared to age- and sex-matched MDD patients and healthy controls (HC) (n = 136 each). Differences in FA were investigated using tract-based spatial statistics. Using interaction models, the influence of acute symptom severity and mood state on the differences between patient groups were tested. RESULTS Analyses revealed a main effect of diagnosis on FA across all three groups (ptfce-FWE = 0.003). BD patients showed reduced FA compared to both MDD (ptfce-FWE = 0.005) and HC (ptfce-FWE < 0.001) in large bilateral clusters. These consisted of several white matter tracts previously described in the literature, including commissural, association, and projection tracts. There were no significant interaction effects between diagnosis and symptom severity or mood state (all ptfce-FWE > 0.704). CONCLUSIONS Results indicated that the difference between BD and MDD was independent of depressive and manic symptom severity and mood state. Disruptions in white matter microstructure in BD might be a trait effect of the disorder. The potential of FA values to be used as a biomarker to differentiate BD from MDD should be further addressed in future studies using longitudinal designs.
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Affiliation(s)
- Katharina Thiel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Institute of Translational Neuroscience, University of Münster, Münster, Germany
| | - Alexandra Winter
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Hannah Lemke
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Lena Waltemate
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Fabian Breuer
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Marius Gruber
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Ramona Leenings
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Lucia Wüste
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Kathrin Rüb
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | | | - Frederike Stein
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Tina Meller
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Kai Gustav Ringwald
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Axel Krug
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Department of Psychiatry and Psychotherapy, University Hospital Bonn, Bonn, Germany
| | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Nils Opel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Katharina Koch
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Elisabeth J. Leehr
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Jochen Bauer
- Department of Clinical Radiology, University of Muenster, Muenster, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
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