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Bi Y, Abrol A, Jia S, Sui J, Calhoun VD. Gray matters: ViT-GAN framework for identifying schizophrenia biomarkers linking structural MRI and functional network connectivity. Neuroimage 2024; 297:120674. [PMID: 38851549 DOI: 10.1016/j.neuroimage.2024.120674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 06/02/2024] [Accepted: 06/06/2024] [Indexed: 06/10/2024] Open
Abstract
Brain disorders are often associated with changes in brain structure and function, where functional changes may be due to underlying structural variations. Gray matter (GM) volume segmentation from 3D structural MRI offers vital structural information for brain disorders like schizophrenia, as it encompasses essential brain tissues such as neuronal cell bodies, dendrites, and synapses, which are crucial for neural signal processing and transmission; changes in GM volume can thus indicate alterations in these tissues, reflecting underlying pathological conditions. In addition, the use of the ICA algorithm to transform high-dimensional fMRI data into functional network connectivity (FNC) matrices serves as an effective carrier of functional information. In our study, we introduce a new generative deep learning architecture, the conditional efficient vision transformer generative adversarial network (cEViT-GAN), which adeptly generates FNC matrices conditioned on GM to facilitate the exploration of potential connections between brain structure and function. We developed a new, lightweight self-attention mechanism for our ViT-based generator, enhancing the generation of refined attention maps critical for identifying structural biomarkers based on GM. Our approach not only generates high quality FNC matrices with a Pearson correlation of 0.74 compared to real FNC data, but also uses attention map technology to identify potential biomarkers in GM structure that could lead to functional abnormalities in schizophrenia patients. Visualization experiments within our study have highlighted these structural biomarkers, including the medial prefrontal cortex (mPFC), dorsolateral prefrontal cortex (DL-PFC), and cerebellum. In addition, through cross-domain analysis comparing generated and real FNC matrices, we have identified functional connections with the highest correlations to structural information, further validating the structure-function connections. This comprehensive analysis helps to understand the intricate relationship between brain structure and its functional manifestations, providing a more refined insight into the neurobiological research of schizophrenia.
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Affiliation(s)
- Yuda Bi
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA 30303, USA.
| | - Anees Abrol
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA 30303, USA
| | - Sihan Jia
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA 30303, USA
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA 30303, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA 30303, USA
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2
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Zhang G, Xiao Q, Wang C, Gao W, Su L, Lu G, Zhong Y. The Different Impact of Depressive or Manic First-episode on Pediatric Bipolar Disorder Patients: Evidence From Resting-state fMRI. Neuroscience 2023; 526:185-195. [PMID: 37385333 DOI: 10.1016/j.neuroscience.2023.06.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 06/14/2023] [Accepted: 06/16/2023] [Indexed: 07/01/2023]
Abstract
Bipolar disorder may begin as depression or mania, which can affect the treatment and prognosis of bipolar disorder. However, the physiological and pathological differences of pediatric bipolar disorder (PBD) patients with different onset symptoms are not clear. The purpose of this study was to investigate the differences of clinical, cognitive function and intrinsic brain networks in PBD patients with first-episode depression and first-episode mania. A total of 63 participants, including 43 patients and 20 healthy controls, underwent resting-state fMRI scans. PBD patients were classified as first-episode depressive and first-episode manic based on their first-episode symptoms. Cognitive tests were used to measure attention and memory of all participants. Independent component analysis (ICA) was used to extract the salience network (SN), default-mode network (DMN), central executive network (ECN) and limbic network (LN) for each participant. Spearman rank correlation analysis was performed between abnormal activation and clinical and cognitive measures. The results showed that there were differences in cognitive functions such as attention and visual memory between first-episode depression and mania, as well as differences activation in anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), precuneus, inferior parietal cortex and parahippocampus. And significant associations of brain activity with clinical assessments or cognition were found in different patients. In conclusion, we found differential impairments in cognitive and brain network activation in first-episode depressive and first-episode manic PBD patients, and correlations were found between these impairments. These evidences may shed light on the different developmental paths of bipolar disorder.
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Affiliation(s)
- Gui Zhang
- School of Psychology, Nanjing Normal University, Nanjing 210097, China
| | - Qian Xiao
- Mental Health Centre of Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Chun Wang
- Department of Psychiatry, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Weijia Gao
- Children's Hospital affiliated to the Medical College of Zhejiang University, Hangzhou 310003, Zhejiang, China
| | - Linyan Su
- Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Guangming Lu
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, 210093 Nanjing, Jiangsu, China
| | - Yuan Zhong
- School of Psychology, Nanjing Normal University, Nanjing 210097, China.
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3
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Luna LP, Radua J, Fortea L, Sugranyes G, Fortea A, Fusar-Poli P, Smith L, Firth J, Shin JI, Brunoni AR, Husain MI, Husian MO, Sair HI, Mendes WO, Uchoa LRA, Berk M, Maes M, Daskalakis ZJ, Frangou S, Fornaro M, Vieta E, Stubbs B, Solmi M, Carvalho AF. A systematic review and meta-analysis of structural and functional brain alterations in individuals with genetic and clinical high-risk for psychosis and bipolar disorder. Prog Neuropsychopharmacol Biol Psychiatry 2022; 117:110540. [PMID: 35240226 DOI: 10.1016/j.pnpbp.2022.110540] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 02/14/2022] [Accepted: 02/19/2022] [Indexed: 11/20/2022]
Abstract
Neuroimaging findings in people at either genetic risk or at clinical high-risk for psychosis (CHR-P) or bipolar disorder (CHR-B) remain unclear. A meta-analytic review of whole-brain voxel-based morphometry (VBM) and functional magnetic resonance imaging (fMRI) studies in individuals with genetic risk or CHR-P or CHR-B and controls identified 94 datasets (N = 7942). Notwithstanding no significant findings were observed following adjustment for multiple comparisons, several findings were noted at a more liberal threshold. Subjects at genetic risk for schizophrenia or bipolar disorder or at CHR-P exhibited lower gray matter (GM) volumes in the gyrus rectus (Hedges' g = -0.19). Genetic risk for psychosis was associated with GM reductions in the right cerebellum and left amygdala. CHR-P was associated with decreased GM volumes in the frontal superior gyrus and hypoactivation in the right precuneus, the superior frontal gyrus and the right inferior frontal gyrus. Genetic and CHR-P were associated with small structural and functional alterations involving regions implicated in psychosis. Further neuroimaging studies in individuals with genetic or CHR-B are warranted.
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Affiliation(s)
- Licia P Luna
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Division of Neuroradiology, Postal Mail: 600 N Wolfe Street Phipps B100F, 21287 Baltimore, USA
| | - Joaquim Radua
- Imaging of Mood- and Anxiety-Related Disorders (IMARD) group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), CIBERSAM, Barcelona, Spain; Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom; Centre for Psychiatric Research and Education, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Lydia Fortea
- Imaging of Mood- and Anxiety-Related Disorders (IMARD) group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), CIBERSAM, Barcelona, Spain
| | - Gisela Sugranyes
- Multimodal neuroimaging in high risk and early psychosis, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), CIBERSAM, Barcelona, Spain; Department of Child and Adolescent Psychiatry and Psychology, Institute of Neuroscience, Hospital Clínic, Barcelona, Spain; Fundació Clínic per a la Recerca Biomèdica (FCRB), Esther Koplowitz Centre, Barcelona, Spain
| | - Adriana Fortea
- Multimodal neuroimaging in high risk and early psychosis, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), CIBERSAM, Barcelona, Spain; Department of Child and Adolescent Psychiatry and Psychology, Institute of Neuroscience, Hospital Clínic, Barcelona, Spain; Fundació Clínic per a la Recerca Biomèdica (FCRB), Esther Koplowitz Centre, Barcelona, Spain; University of Barcelona, Barcelona, Spain
| | - Paolo Fusar-Poli
- Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Institute of Psychiatry, Psychology & Neuroscience, Department of Psychosis Studies, King's College London, London, United Kingdom; OASIS Service, South London and Maudsley National Health Service (NHS) Foundation Trust, London, United Kingdom; Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy; Maudsley Biomedical Research Centre, National Institute for Health Research, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Lee Smith
- The Cambridge Centre for Sport and Exercise Sciences, Anglia Ruskin University, Cambridge, United Kingdom
| | - Joseph Firth
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK; NICM Health Research Institute, Western Sydney University, Westmead, NSW 2145, Australia
| | - Jae Il Shin
- Department of Pediatrics, Yonsei University College of Medicine, Seodaemun-gu, C.P.O., Seoul, Republic of Korea
| | - Andre R Brunoni
- Laboratory of Neurosciences (LIM-27), Department and Institute of Psychiatry, Faculdade de Medicina da Universidade de São Paulo, R Dr Ovidio Pires de Campos 785, 2o andar, São Paulo 05403-000, Brazil; Department of Internal Medicine, Faculdade de Medicina da Universidade de São Paulo & Hospital Universitário, Universidade de São Paulo, Av. Prof Lineu Prestes 2565, São Paulo 05508-000, Brazil
| | - Muhammad I Husain
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Muhammad O Husian
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Haris I Sair
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Division of Neuroradiology, Postal Mail: 600 N Wolfe Street Phipps B100F, 21287 Baltimore, USA
| | - Walber O Mendes
- Department of Radiology, Hospital Universitário Walter Cantídio, Postal Mail: 1290 Pastor Samuel Munguba St, Rodolfo Teófilo, 60430-372 Fortaleza, Brazil
| | - Luiz Ricardo A Uchoa
- Department of Radiology, Hospital Geral de Fortaleza, Postal Mail: 900 Ávila Goulart Street, Papicu, Fortaleza 60175-295, Brazil
| | - Michael Berk
- Deakin University, IMPACT Strategic Research Centre, Barwon Health, School of Medicine, Geelong, Victoria, Australia; Deakin University, CMMR Strategic Research Centre, School of Medicine, Geelong, Victoria, Australia; Orygen, The National Centre of Excellence in Youth Mental Health, The Department of Psychiatry, the Florey Institute for Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Michael Maes
- Deakin University, IMPACT Strategic Research Centre, Barwon Health, School of Medicine, Geelong, Victoria, Australia; Department of Psychiatry, Chulalongkorn University, Faculty of Medicine, Bangkok, Thailand
| | - Zafiris J Daskalakis
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Sophia Frangou
- Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada; University of Barcelona, Barcelona, Spain; Barcelona Bipolar Disorders and Depressive Unit, Institute of Neurosciences, Hospital Clinic, Barcelona, Spain
| | - Michele Fornaro
- Department of Neuroscience, Reproductive Science and Dentistry, Section of Psychiatr, University School of Medicine Federico II, Naples, Italy
| | - Eduard Vieta
- Bipolar and depressive disorders group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), CIBERSAM, Barcelona, Spain; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Brendon Stubbs
- Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Marco Solmi
- Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Institute of Psychiatry, Psychology & Neuroscience, Department of Psychosis Studies, King's College London, London, United Kingdom; Department of Psychiatry, University of Ottawa, Ontario, Canada.; Department of Mental Health, The Ottawa Hospital, Ontario, Canada.; Ottawa Hospital Research Institute (OHRI), Clinical Epidemiology Program, University of Ottawa, Ottawa Ontario.; School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, Canada
| | - Andre F Carvalho
- IMPACT Strategic Research Centre, Barwon Health, Deakin University School of Medicine, Geelong, Victoria, Australia.
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4
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Acar E, Roald M, Hossain KM, Calhoun VD, Adali T. Tracing Evolving Networks Using Tensor Factorizations vs. ICA-Based Approaches. Front Neurosci 2022; 16:861402. [PMID: 35546891 PMCID: PMC9081795 DOI: 10.3389/fnins.2022.861402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 03/07/2022] [Indexed: 11/13/2022] Open
Abstract
Analysis of time-evolving data is crucial to understand the functioning of dynamic systems such as the brain. For instance, analysis of functional magnetic resonance imaging (fMRI) data collected during a task may reveal spatial regions of interest, and how they evolve during the task. However, capturing underlying spatial patterns as well as their change in time is challenging. The traditional approach in fMRI data analysis is to assume that underlying spatial regions of interest are static. In this article, using fractional amplitude of low-frequency fluctuations (fALFF) as an effective way to summarize the variability in fMRI data collected during a task, we arrange time-evolving fMRI data as a subjects by voxels by time windows tensor, and analyze the tensor using a tensor factorization-based approach called a PARAFAC2 model to reveal spatial dynamics. The PARAFAC2 model jointly analyzes data from multiple time windows revealing subject-mode patterns, evolving spatial regions (also referred to as networks) and temporal patterns. We compare the PARAFAC2 model with matrix factorization-based approaches relying on independent components, namely, joint independent component analysis (ICA) and independent vector analysis (IVA), commonly used in neuroimaging data analysis. We assess the performance of the methods in terms of capturing evolving networks through extensive numerical experiments demonstrating their modeling assumptions. In particular, we show that (i) PARAFAC2 provides a compact representation in all modes, i.e., subjects, time, and voxels, revealing temporal patterns as well as evolving spatial networks, (ii) joint ICA is as effective as PARAFAC2 in terms of revealing evolving networks but does not reveal temporal patterns, (iii) IVA's performance depends on sample size, data distribution and covariance structure of underlying networks. When these assumptions are satisfied, IVA is as accurate as the other methods, (iv) when subject-mode patterns differ from one time window to another, IVA is the most accurate. Furthermore, we analyze real fMRI data collected during a sensory motor task, and demonstrate that a component indicating statistically significant group difference between patients with schizophrenia and healthy controls is captured, which includes primary and secondary motor regions, cerebellum, and temporal lobe, revealing a meaningful spatial map and its temporal change.
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Affiliation(s)
- Evrim Acar
- Simula Metropolitan Center for Digital Engineering, Oslo, Norway
| | - Marie Roald
- Simula Metropolitan Center for Digital Engineering, Oslo, Norway.,Oslo Metropolitan University, Oslo, Norway
| | - Khondoker M Hossain
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD, United States
| | - Vince D Calhoun
- Department of Psychology, Georgia State University, Atlanta, GA, United States
| | - Tülay Adali
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD, United States
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5
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Gutman BA, van Erp TG, Alpert K, Ching CRK, Isaev D, Ragothaman A, Jahanshad N, Saremi A, Zavaliangos‐Petropulu A, Glahn DC, Shen L, Cong S, Alnæs D, Andreassen OA, Doan NT, Westlye LT, Kochunov P, Satterthwaite TD, Wolf DH, Huang AJ, Kessler C, Weideman A, Nguyen D, Mueller BA, Faziola L, Potkin SG, Preda A, Mathalon DH, Bustillo J, Calhoun V, Ford JM, Walton E, Ehrlich S, Ducci G, Banaj N, Piras F, Piras F, Spalletta G, Canales‐Rodríguez EJ, Fuentes‐Claramonte P, Pomarol‐Clotet E, Radua J, Salvador R, Sarró S, Dickie EW, Voineskos A, Tordesillas‐Gutiérrez D, Crespo‐Facorro B, Setién‐Suero E, van Son JM, Borgwardt S, Schönborn‐Harrisberger F, Morris D, Donohoe G, Holleran L, Cannon D, McDonald C, Corvin A, Gill M, Filho GB, Rosa PGP, Serpa MH, Zanetti MV, Lebedeva I, Kaleda V, Tomyshev A, Crow T, James A, Cervenka S, Sellgren CM, Fatouros‐Bergman H, Agartz I, Howells F, Stein DJ, Temmingh H, Uhlmann A, de Zubicaray GI, McMahon KL, Wright M, Cobia D, Csernansky JG, Thompson PM, Turner JA, Wang L. A meta-analysis of deep brain structural shape and asymmetry abnormalities in 2,833 individuals with schizophrenia compared with 3,929 healthy volunteers via the ENIGMA Consortium. Hum Brain Mapp 2022; 43:352-372. [PMID: 34498337 PMCID: PMC8675416 DOI: 10.1002/hbm.25625] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 07/13/2021] [Accepted: 07/14/2021] [Indexed: 01/06/2023] Open
Abstract
Schizophrenia is associated with widespread alterations in subcortical brain structure. While analytic methods have enabled more detailed morphometric characterization, findings are often equivocal. In this meta-analysis, we employed the harmonized ENIGMA shape analysis protocols to collaboratively investigate subcortical brain structure shape differences between individuals with schizophrenia and healthy control participants. The study analyzed data from 2,833 individuals with schizophrenia and 3,929 healthy control participants contributed by 21 worldwide research groups participating in the ENIGMA Schizophrenia Working Group. Harmonized shape analysis protocols were applied to each site's data independently for bilateral hippocampus, amygdala, caudate, accumbens, putamen, pallidum, and thalamus obtained from T1-weighted structural MRI scans. Mass univariate meta-analyses revealed more-concave-than-convex shape differences in the hippocampus, amygdala, accumbens, and thalamus in individuals with schizophrenia compared with control participants, more-convex-than-concave shape differences in the putamen and pallidum, and both concave and convex shape differences in the caudate. Patterns of exaggerated asymmetry were observed across the hippocampus, amygdala, and thalamus in individuals with schizophrenia compared to control participants, while diminished asymmetry encompassed ventral striatum and ventral and dorsal thalamus. Our analyses also revealed that higher chlorpromazine dose equivalents and increased positive symptom levels were associated with patterns of contiguous convex shape differences across multiple subcortical structures. Findings from our shape meta-analysis suggest that common neurobiological mechanisms may contribute to gray matter reduction across multiple subcortical regions, thus enhancing our understanding of the nature of network disorganization in schizophrenia.
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Affiliation(s)
- Boris A. Gutman
- Department of Biomedical EngineeringIllinois Institute of TechnologyChicagoIllinoisUSA
- Institute for Information Transmission Problems (Kharkevich Institute)MoscowRussia
| | - Theo G.M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
- Center for the Neurobiology of Learning and MemoryUniversity of California IrvineIrvineCaliforniaUSA
| | - Kathryn Alpert
- Department of Psychiatry and Behavioral SciencesNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Christopher R. K. Ching
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Dmitry Isaev
- Department of Biomedical EngineeringDuke UniversityDurhamNorth CarolinaUSA
| | - Anjani Ragothaman
- Department of biomedical engineeringOregon Health and Science universityPortlandOregonUSA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Arvin Saremi
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Artemis Zavaliangos‐Petropulu
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - David C. Glahn
- Department of PsychiatryBoston Children's Hospital and Harvard Medical SchoolBostonMassachusettsUSA
| | - Li Shen
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Shan Cong
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Dag Alnæs
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Ole Andreas Andreassen
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Nhat Trung Doan
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Lars T. Westlye
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Peter Kochunov
- Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Theodore D. Satterthwaite
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Daniel H. Wolf
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Alexander J. Huang
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - Charles Kessler
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - Andrea Weideman
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - Dana Nguyen
- Department of PediatricsUniversity of California IrvineIrvineCaliforniaUSA
| | - Bryon A. Mueller
- Department of Psychiatry and Behavioral SciencesUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Lawrence Faziola
- Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - Steven G. Potkin
- Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - Adrian Preda
- Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - Daniel H. Mathalon
- Department of Psychiatry and Weill Institute for NeurosciencesUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Judith Ford Mental HealthVA San Francisco Healthcare SystemSan FranciscoCaliforniaUSA
| | - Juan Bustillo
- Departments of Psychiatry & NeuroscienceUniversity of New MexicoAlbuquerqueNew MexicoUSA
| | - Vince Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) [Georgia State University, Georgia Institute of Technology]Emory UniversityAtlantaGeorgiaUSA
- Department of Electrical and Computer EngineeringThe University of New MexicoAlbuquerqueNew MexicoUSA
| | - Judith M. Ford
- Judith Ford Mental HealthVA San Francisco Healthcare SystemSan FranciscoCaliforniaUSA
- Department of Psychiatry and Behavioral SciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | | | - Stefan Ehrlich
- Division of Psychological & Social Medicine and Developmental NeurosciencesFaculty of Medicine, TU‐DresdenDresdenGermany
| | | | - Nerisa Banaj
- Laboratory of NeuropsychiatryIRCCS Santa Lucia FoundationRomeItaly
| | - Fabrizio Piras
- Laboratory of NeuropsychiatryIRCCS Santa Lucia FoundationRomeItaly
| | - Federica Piras
- Laboratory of NeuropsychiatryIRCCS Santa Lucia FoundationRomeItaly
| | - Gianfranco Spalletta
- Laboratory of NeuropsychiatryIRCCS Santa Lucia FoundationRomeItaly
- Menninger Department of Psychiatry and Behavioral SciencesBaylor College of MedicineHoustonTexasUSA
| | | | | | | | - Joaquim Radua
- FIDMAG Germanes Hospitalàries Research FoundationCIBERSAMBarcelonaSpain
- Institut d'Investigacions Biomdiques August Pi i Sunyer (IDIBAPS)BarcelonaSpain
| | - Raymond Salvador
- FIDMAG Germanes Hospitalàries Research FoundationCIBERSAMBarcelonaSpain
| | - Salvador Sarró
- FIDMAG Germanes Hospitalàries Research FoundationCIBERSAMBarcelonaSpain
| | - Erin W. Dickie
- Centre for Addiction and Mental Health (CAMH)TorontoCanada
| | | | | | | | | | | | - Stefan Borgwardt
- Department of PsychiatryUniversity of BaselBaselSwitzerland
- Department of Psychiatry and PsychotherapyUniversity of LübeckLübeckGermany
| | | | - Derek Morris
- Centre for Neuroimaging and Cognitive Genomics, Discipline of BiochemistryNational University of Ireland GalwayGalwayIreland
| | - Gary Donohoe
- Centre for Neuroimaging and Cognitive Genomics, School of PsychologyNational University of Ireland GalwayGalwayIreland
| | - Laurena Holleran
- Centre for Neuroimaging and Cognitive Genomics, School of PsychologyNational University of Ireland GalwayGalwayIreland
| | - Dara Cannon
- Clinical Neuroimaging Laboratory, Centre for Neuroimaging and Cognitive GenomicsNational University of Ireland GalwayGalwayIreland
| | - Colm McDonald
- Clinical Neuroimaging Laboratory, Centre for Neuroimaging and Cognitive GenomicsNational University of Ireland GalwayGalwayIreland
| | - Aiden Corvin
- Neuropsychiatric Genetics Research Group, Department of PsychiatryTrinity College DublinDublinIreland
- Trinity College Institute of NeuroscienceTrinity College DublinDublinIreland
| | - Michael Gill
- Neuropsychiatric Genetics Research Group, Department of PsychiatryTrinity College DublinDublinIreland
- Trinity College Institute of NeuroscienceTrinity College DublinDublinIreland
| | - Geraldo Busatto Filho
- Laboratory of Psychiatric Neuroimaging (LIM‐21), Departamento e Instituto de PsiquiatriaHospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao PauloSao PauloSPBrazil
| | - Pedro G. P. Rosa
- Laboratory of Psychiatric Neuroimaging (LIM‐21), Departamento e Instituto de PsiquiatriaHospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao PauloSao PauloSPBrazil
| | - Mauricio H. Serpa
- Laboratory of Psychiatric Neuroimaging (LIM‐21), Departamento e Instituto de PsiquiatriaHospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao PauloSao PauloSPBrazil
| | - Marcus V. Zanetti
- Laboratory of Psychiatric Neuroimaging (LIM‐21), Departamento e Instituto de PsiquiatriaHospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao PauloSao PauloSPBrazil
- Hospital Sirio‐LibanesSao PauloSPBrazil
| | - Irina Lebedeva
- Laboratory of Neuroimaging and Multimodal AnalysisMental Health Research CenterMoscowRussia
| | - Vasily Kaleda
- Department of Endogenous Mental DisordersMental Health Research CenterMoscowRussia
| | - Alexander Tomyshev
- Laboratory of Neuroimaging and Multimodal AnalysisMental Health Research CenterMoscowRussia
| | - Tim Crow
- Department of PsychiatryUniversity of OxfordOxfordUK
| | - Anthony James
- Department of PsychiatryUniversity of OxfordOxfordUK
| | - Simon Cervenka
- Centre for Psychiatry Reserach, Department of Clinical NeuroscienceKarolinska Institutet, & Stockholm Health Care Services, Region StockholmStockholmSweden
| | - Carl M Sellgren
- Department of Physiology and PharmacologyKarolinska InstitutetStockholmSweden
| | - Helena Fatouros‐Bergman
- Centre for Psychiatry Reserach, Department of Clinical NeuroscienceKarolinska Institutet, & Stockholm Health Care Services, Region StockholmStockholmSweden
| | - Ingrid Agartz
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Fleur Howells
- Department of Psychiatry and Mental Health, Faculty of Health SciencesUniversity of Cape TownCape TownWCSouth Africa
- Neuroscience InstituteUniversity of Cape Town, Cape TownWCSouth Africa
| | - Dan J. Stein
- Department of Psychiatry and Mental Health, Faculty of Health SciencesUniversity of Cape TownCape TownWCSouth Africa
- Neuroscience InstituteUniversity of Cape Town, Cape TownWCSouth Africa
- SA MRC Unit on Risk & Resilience in Mental DisordersUniversity of Cape TownCape TownWCSouth Africa
| | - Henk Temmingh
- Department of Psychiatry and Mental Health, Faculty of Health SciencesUniversity of Cape TownCape TownWCSouth Africa
| | - Anne Uhlmann
- Department of Psychiatry and Mental Health, Faculty of Health SciencesUniversity of Cape TownCape TownWCSouth Africa
- Department of Child and Adolescent PsychiatryTU DresdenGermany
| | - Greig I. de Zubicaray
- School of Psychology, Faculty of HealthQueensland University of Technology (QUT)BrisbaneQLDAustralia
| | - Katie L. McMahon
- School of Clinical SciencesQueensland University of Technology (QUT)BrisbaneQLDAustralia
| | - Margie Wright
- Queensland Brain InstituteUniversity of QueenslandBrisbaneQLDAustralia
| | - Derin Cobia
- Department of Psychiatry and Behavioral SciencesNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Department of Psychology and Neuroscience CenterBrigham Young UniversityProvoUtahUSA
| | - John G. Csernansky
- Department of Psychiatry and Behavioral SciencesNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Paul M. Thompson
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | | | - Lei Wang
- Department of Psychiatry and Behavioral SciencesNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Department of Psychiatry and Behavioral HealthOhio State University Wexner Medical CenterColumbusOhioUSA
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6
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Aryutova K, Stoyanov D. Pharmaco-Magnetic Resonance as a Tool for Monitoring the Medication-Related Effects in the Brain May Provide Potential Biomarkers for Psychotic Disorders. Int J Mol Sci 2021; 22:9309. [PMID: 34502214 PMCID: PMC8430741 DOI: 10.3390/ijms22179309] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 08/19/2021] [Accepted: 08/25/2021] [Indexed: 01/04/2023] Open
Abstract
The neurodegenerative and neurodevelopmental hypotheses represent the basic etiological framework for the origin of schizophrenia. Additionally, the dopamine hypothesis, adopted more than two decades ago, has repeatedly asserted the position of dopamine as a pathobiochemical substrate through the action of psychostimulants and neuroleptics on the mesolimbic and mesocortical systems, giving insight into the origin of positive and negative schizophrenic symptoms. Meanwhile, cognitive impairments in schizophrenia remain incompletely understood but are thought to be present during all stages of the disease, as well as in the prodromal, interictal and residual phases. On the other hand, observations on the effects of NMDA antagonists, such as ketamine and phencyclidine, reveal that hypoglutamatergic neurotransmission causes not only positive and negative but also cognitive schizophrenic symptoms. This review aims to summarize the different hypotheses about the origin of psychoses and to identify the optimal neuroimaging method that can serve to unite them in an integral etiological framework. We systematically searched Google scholar (with no concern to the date published) to identify studies investigating the etiology of schizophrenia, with a focus on impaired central neurotransmission. The complex interaction between the dopamine and glutamate neurotransmitter systems provides the long-needed etiological concept, which combines the neurodegenerative hypothesis with the hypothesis of impaired neurodevelopment in schizophrenia. Pharmaco-magnetic resonance imaging is a neuroimaging method that can provide a translation of scientific knowledge about the neural networks and the disruptions in and between different brain regions, into clinically applicable and effective therapeutic results in the management of severe psychotic disorders.
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Affiliation(s)
| | - Drozdstoy Stoyanov
- Department of Psychiatry and Medical Psychology, Research Institute, Medical University Plovdiv, 4002 Plovdiv, Bulgaria;
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7
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Busatto G, Rosa PG, Serpa MH, Squarzoni P, Duran FL. Psychiatric neuroimaging research in Brazil: historical overview, current challenges, and future opportunities. REVISTA BRASILEIRA DE PSIQUIATRIA (SAO PAULO, BRAZIL : 1999) 2021; 43:83-101. [PMID: 32520165 PMCID: PMC7861184 DOI: 10.1590/1516-4446-2019-0757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 02/03/2020] [Indexed: 11/23/2022]
Abstract
The last four decades have witnessed tremendous growth in research studies applying neuroimaging methods to evaluate pathophysiological and treatment aspects of psychiatric disorders around the world. This article provides a brief history of psychiatric neuroimaging research in Brazil, including quantitative information about the growth of this field in the country over the past 20 years. Also described are the various methodologies used, the wealth of scientific questions investigated, and the strength of international collaborations established. Finally, examples of the many methodological advances that have emerged in the field of in vivo neuroimaging are provided, with discussion of the challenges faced by psychiatric research groups in Brazil, a country of limited resources, to continue incorporating such innovations to generate novel scientific data of local and global relevance.
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Affiliation(s)
- Geraldo Busatto
- Laboratório de Neuroimagem em Psiquiatria (LIM 21), Departamento e Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Pedro G. Rosa
- Laboratório de Neuroimagem em Psiquiatria (LIM 21), Departamento e Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Mauricio H. Serpa
- Laboratório de Neuroimagem em Psiquiatria (LIM 21), Departamento e Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Paula Squarzoni
- Laboratório de Neuroimagem em Psiquiatria (LIM 21), Departamento e Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Fabio L. Duran
- Laboratório de Neuroimagem em Psiquiatria (LIM 21), Departamento e Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
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8
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Todeva-Radneva A, Paunova R, Kandilarova S, St Stoyanov D. The Value of Neuroimaging Techniques in the Translation and Transdiagnostic Validation of Psychiatric Diagnoses - Selective Review. Curr Top Med Chem 2021; 20:540-553. [PMID: 32003690 DOI: 10.2174/1568026620666200131095328] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 12/09/2019] [Accepted: 12/12/2019] [Indexed: 01/05/2023]
Abstract
Psychiatric diagnosis has long been perceived as more of an art than a science since its foundations lie within the observation, and the self-report of the patients themselves and objective diagnostic biomarkers are lacking. Furthermore, the diagnostic tools in use not only stray away from the conventional medical framework but also remain invalidated with evidence-based concepts. However, neuroscience, as a source of valid objective knowledge has initiated the process of a paradigm shift underlined by the main concept of psychiatric disorders being "brain disorders". It is also a bridge closing the explanatory gap among the different fields of medicine via the translation of the knowledge within a multidisciplinary framework. The contemporary neuroimaging methods, such as fMRI provide researchers with an entirely new set of tools to reform the current status quo by creating an opportunity to define and validate objective biomarkers that can be translated into clinical practice. Combining multiple neuroimaging techniques with the knowledge of the role of genetic factors, neurochemical imbalance and neuroinflammatory processes in the etiopathophysiology of psychiatric disorders is a step towards a comprehensive biological explanation of psychiatric disorders and a final differentiation of psychiatry as a well-founded medical science. In addition, the neuroscientific knowledge gained thus far suggests a necessity for directional change to exploring multidisciplinary concepts, such as multiple causality and dimensionality of psychiatric symptoms and disorders. A concomitant viewpoint transition of the notion of validity in psychiatry with a focus on an integrative validatory approach may facilitate the building of a collaborative bridge above the wall existing between the scientific fields analyzing the mind and those studying the brain.
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Affiliation(s)
- Anna Todeva-Radneva
- Department of Psychiatry and Medical Psychology and Scientific Research Institute, The Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Rositsa Paunova
- Department of Psychiatry and Medical Psychology and Scientific Research Institute, The Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Sevdalina Kandilarova
- Department of Psychiatry and Medical Psychology and Scientific Research Institute, The Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Drozdstoy St Stoyanov
- Department of Psychiatry and Medical Psychology and Scientific Research Institute, The Medical University of Plovdiv, Plovdiv, Bulgaria
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9
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Spreng RN, DuPre E, Ji JL, Yang G, Diehl C, Murray JD, Pearlson GD, Anticevic A. Structural Covariance Reveals Alterations in Control and Salience Network Integrity in Chronic Schizophrenia. Cereb Cortex 2020; 29:5269-5284. [PMID: 31066899 DOI: 10.1093/cercor/bhz064] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2015] [Revised: 02/07/2019] [Accepted: 02/07/2019] [Indexed: 12/20/2022] Open
Abstract
Schizophrenia (SCZ) is recognized as a disorder of distributed brain dysconnectivity. While progress has been made delineating large-scale functional networks in SCZ, little is known about alterations in grey matter integrity of these networks. We used a multivariate approach to identify the structural covariance of the salience, default, motor, visual, fronto-parietal control, and dorsal attention networks. We derived individual scores reflecting covariance in each structural image for a given network. Seed-based multivariate analyses were conducted on structural images in a discovery (n = 90) and replication (n = 74) sample of SCZ patients and healthy controls. We first validated patterns across all networks, consistent with well-established functional connectivity reports. Next, across two SCZ samples, we found reliable and robust reductions in structural integrity of the fronto-parietal control and salience networks, but not default, dorsal attention, motor and sensory networks. Well-powered exploratory analyses failed to identify relationships with symptoms. These findings provide evidence of selective structural decline in associative networks in SCZ. Such decline may be linked with recently identified functional disturbances in associative networks, providing more sensitive multi-modal network-level probes in SCZ. Absence of symptom effects suggests that identified disturbances may underlie a trait-type marker in SCZ.
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Affiliation(s)
- R Nathan Spreng
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada.,Departments of Psychiatry and Psychology, McGill University, Montreal, QC, Canada
| | - Elizabeth DuPre
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Jie Lisa Ji
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.,Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Genevieve Yang
- Department of Psychiatry, Mount Sinai School of Medicine, New York, NY
| | - Caroline Diehl
- Department of Psychology, University of California at Los Angeles, Los Angeles, CA
| | - John D Murray
- Center for Neural Science, New York University, New York, NY, USA
| | - Godfrey D Pearlson
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.,Department of Psychology, Yale University, CT, USA.,Center for Neural Science, New York University, New York, NY, USA
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.,Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA.,Department of Psychology, Yale University, CT, USA.,Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, CT, USA
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10
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Ding Y, Ou Y, Pan P, Shan X, Chen J, Liu F, Zhao J, Guo W. Brain structural abnormalities as potential markers for detecting individuals with ultra-high risk for psychosis: A systematic review and meta-analysis. Schizophr Res 2019; 209:22-31. [PMID: 31104914 DOI: 10.1016/j.schres.2019.05.015] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2018] [Revised: 02/28/2019] [Accepted: 05/06/2019] [Indexed: 02/01/2023]
Abstract
OBJECTIVE This study aims to determine whether structural alterations can be used as neuroimaging markers to detect individuals with ultra-high risk (UHR) for psychosis for the diagnosis of schizophrenia and improvement of treatment outcomes. METHODS Embase and Pubmed databases were searched for related studies in July 2018. The search was performed without restriction on time and regions or languages. A total of 188 articles on voxel-based morphometry (VBM) and 96 articles on cortical thickness were obtained, and another 6 articles were included after the reference lists were checked. Our researchers assessed and extracted the data in accordance with the PRISMA guideline. The data were processed with a seed-based mapping method. RESULTS Fourteen VBM and nine cortical thickness studies were finally included in our study. In individuals with UHR, the gray matter volumes in the bilateral median cingulate (Z = 1.034), the right fusiform gyrus (Z = 1.051), the left superior temporal gyrus (Z = 1.048), and the right thalamus (Z = 1.039) increased relative to those of healthy controls. By contrast, the gray matter volumes in the right gyrus rectus (Z = -2.109), the right superior frontal gyrus (Z = -2.321), and the left superior frontal gyrus (Z = -2.228) decreased. The robustness of these findings was verified through Jackknife sensitivity analysis, and heterogeneity across studies was low. Typically, cortical thickness alterations were not detected in individuals with UHR. CONCLUSIONS Structural abnormalities of the thalamocortical circuit may underpin the neurophysiology of psychosis and mark the vulnerability of transition to psychosis in UHR subjects.
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Affiliation(s)
- Yudan Ding
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China; National Clinical Research Center on Mental Disorders, Changsha, Hunan 410011, China
| | - Yangpan Ou
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China; National Clinical Research Center on Mental Disorders, Changsha, Hunan 410011, China
| | - Pan Pan
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China; National Clinical Research Center on Mental Disorders, Changsha, Hunan 410011, China
| | - Xiaoxiao Shan
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China; National Clinical Research Center on Mental Disorders, Changsha, Hunan 410011, China
| | - Jindong Chen
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China; National Clinical Research Center on Mental Disorders, Changsha, Hunan 410011, China
| | - Feng Liu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300000, China
| | - Jingping Zhao
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China; National Clinical Research Center on Mental Disorders, Changsha, Hunan 410011, China
| | - Wenbin Guo
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China; National Clinical Research Center on Mental Disorders, Changsha, Hunan 410011, China.
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11
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Liu S, Wang H, Song M, Lv L, Cui Y, Liu Y, Fan L, Zuo N, Xu K, Du Y, Yu Q, Luo N, Qi S, Yang J, Xie S, Li J, Chen J, Chen Y, Wang H, Guo H, Wan P, Yang Y, Li P, Lu L, Yan H, Yan J, Wang H, Zhang H, Zhang D, Calhoun VD, Jiang T, Sui J. Linked 4-Way Multimodal Brain Differences in Schizophrenia in a Large Chinese Han Population. Schizophr Bull 2019; 45:436-449. [PMID: 29897555 PMCID: PMC6403093 DOI: 10.1093/schbul/sby045] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Multimodal fusion has been regarded as a promising tool to discover covarying patterns of multiple imaging types impaired in brain diseases, such as schizophrenia (SZ). In this article, we aim to investigate the covarying abnormalities underlying SZ in a large Chinese Han population (307 SZs, 298 healthy controls [HCs]). Four types of magnetic resonance imaging (MRI) features, including regional homogeneity (ReHo) from resting-state functional MRI, gray matter volume (GM) from structural MRI, fractional anisotropy (FA) from diffusion MRI, and functional network connectivity (FNC) resulted from group independent component analysis, were jointly analyzed by a data-driven multivariate fusion method. Results suggest that a widely distributed network disruption appears in SZ patients, with synchronous changes in both functional and structural regions, especially the basal ganglia network, salience network (SAN), and the frontoparietal network. Such a multimodal coalteration was also replicated in another independent Chinese sample (40 SZs, 66 HCs). Our results on auditory verbal hallucination (AVH) also provide evidence for the hypothesis that prefrontal hypoactivation and temporal hyperactivation in SZ may lead to failure of executive control and inhibition, which is relevant to AVH. In addition, impaired working memory performance was found associated with GM reduction and FA decrease in SZ in prefrontal and superior temporal area, in both discovery and replication datasets. In summary, by leveraging multiple imaging and clinical information into one framework to observe brain in multiple views, we can integrate multiple inferences about SZ from large-scale population and offer unique perspectives regarding the missing links between the brain function and structure that may not be achieved by separate unimodal analyses.
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Affiliation(s)
- Shengfeng Liu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China,School of Automation, Harbin University of Science and Technology, Harbin, China,University of Chinese Academy of Sciences, Beijing, China,National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University, Shenzhen, China
| | - Haiying Wang
- School of Automation, Harbin University of Science and Technology, Harbin, China
| | - Ming Song
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Luxian Lv
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China,Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
| | - Yue Cui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yong Liu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Lingzhong Fan
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Nianming Zuo
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Kaibin Xu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yuhui Du
- The Mind Research Network, Albuquerque, NM,School of Computer and Information Technology, Shanxi University, Taiyuan, China
| | - Qingbao Yu
- The Mind Research Network, Albuquerque, NM
| | - Na Luo
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China,University of Chinese Academy of Sciences, Beijing, China
| | - Shile Qi
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China,University of Chinese Academy of Sciences, Beijing, China
| | - Jian Yang
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing, China
| | - Sangma Xie
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jian Li
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jun Chen
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yunchun Chen
- Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi’an, China
| | - Huaning Wang
- Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi’an, China
| | - Hua Guo
- Zhumadian Psychiatric Hospital, Zhumadian, China
| | - Ping Wan
- Zhumadian Psychiatric Hospital, Zhumadian, China
| | - Yongfeng Yang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China,Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China,Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Peng Li
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China,Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Lin Lu
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China,Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China,Queensland Brain Institute, University of Queensland, Brisbane, Australia
| | - Hao Yan
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China,Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Jun Yan
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China,Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Huiling Wang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Hongxing Zhang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China,Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China,Department of Psychology, Xinxiang Medical University, Xinxiang, China
| | - Dai Zhang
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China,Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China,Center for Life Sciences/PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM,Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, NM
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China,University of Chinese Academy of Sciences, Beijing, China,Queensland Brain Institute, University of Queensland, Brisbane, Australia,CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China,University of Chinese Academy of Sciences, Beijing, China,The Mind Research Network, Albuquerque, NM,CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China,To whom correspondence should be addressed; Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; tel: +86-10-8254-4518; fax: +86-10-8254-4777; e-mail:
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12
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Calhoun V. Data-driven approaches for identifying links between brain structure and function in health and disease. DIALOGUES IN CLINICAL NEUROSCIENCE 2018. [PMID: 30250386 PMCID: PMC6136124 DOI: 10.31887/dcns.2018.20.2/vcalhoun] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Brain imaging technology provides a powerful tool to visualize the living human brain, provide insights into disease mechanisms, and potentially provide a tool to assist clinical decision-making. The brain has a very specific structural substrate providing a foundation for functional information; however, most studies ignore the very interesting and complex relationships between brain structure and brain function. While a variety of approaches have been used to study how brain structure informs function, the study of such relationships in living humans in most cases is limited to noninvasive approaches at the macroscopic scale. The use of data-driven approaches to link structure and function provides a tool which is especially important at the macroscopic scale at which we can study the human brain. This paper reviews data-driven approaches, with a focus on independent component analysis approaches, which leverage higher order statistics to link together macroscopic structural and functional MRI data. Such approaches provide the benefit of allowing us to identify links which do not necessarily correspond spatially (eg, structural changes in one region related to functional changes in other regions). They also provide a "network level" perspective on the data, by enabling us to identify sets of brain regions that covary together. This also opens up the ability to evaluate both within and between network relationships. A variety of examples are presented, including several showing the potential of such approaches to inform us about mental illness, particularly about schizophrenia.
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Affiliation(s)
- Vincent Calhoun
- Mind Research Network, University of New Mexico Albuquerque, New Mexico, USA
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13
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Conjoint and dissociated structural and functional abnormalities in first-episode drug-naive patients with major depressive disorder: a multimodal meta-analysis. Sci Rep 2017; 7:10401. [PMID: 28871117 PMCID: PMC5583354 DOI: 10.1038/s41598-017-08944-5] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2016] [Accepted: 07/21/2017] [Indexed: 02/05/2023] Open
Abstract
Published MRI evidence of structural and resting-state functional brain abnormalities in MDD has been inconsistent. To eliminate interference by repeated disease episodes and antidepressant treatment, we conducted the first multimodal voxel-wise meta-analysis of studies of voxel-based morphometry (VBM) and the amplitude of low-frequency fluctuation (ALFF) in first-episode drug-naive MDD patients, using the Seed-based d Mapping method (SDM). Fifteen VBM data sets and 11 ALFF data sets were included. SDM-based multimodal meta-analysis was used to highlight brain regions with both structural and functional abnormalities. This identified conjoint structural and functional abnormalities in left lateral orbitofrontal cortex and right supplementary motor area, and also dissociated abnormalities of structure (decreased grey matter in right dorsolateral prefrontal cortex and right inferior temporal gyrus; increased grey matter in right insula, right putamen, left temporal pole, and bilateral thalamus) and function (increased brain activity in left supplementary motor area, left parahippocampal gyrus, and hippocampus; decreased brain activity in right lateral orbitofrontal cortex). This study reveals a complex pattern of conjoint and dissociated structural and functional abnormalities, supporting the involvement of basal ganglia-thalamocortical circuits, representing emotional, cognitive and psychomotor abnormalities, in the pathophysiology of early-stage MDD. Specifically, this study adds to Psychoradiology, an emerging subspecialty of radiology, which seems primed to play a major clinical role in guiding diagnostic and treatment planning decisions in patients with mental disorder.
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14
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Research design issues for the use of magnetic resonance imaging machines in brain studies of psychological/psychiatric variables. Behav Res Methods 2010; 41:1061-72. [PMID: 19897814 DOI: 10.3758/brm.41.4.1061] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The magnetic resonance imaging (MRI) machine itself has an impact on the likelihood of obtaining successful measurements of brain size in certain groups of subjects. The differential selection and attrition in both cross-sectional and longitudinal designs, therefore, indicate that the MRI coincidentally serves as a screen for the anatomical structure of the brains that are successfully scanned. This screening effect introduces confounds in experiments whose very hypotheses are focused on comparing anatomical differences in subjects who differ, for example, in their reactions to anxiety-inducing situations. Here, behavioral interventions and possible statistical models are presented in order to reduce attrition and other effects of the confounds introduced by the MRI measurement process in research. Child and adolescent research-particularly in the attention-deficit/hyperactivity disorder research area-is used as an example to clarify and delineate the general research principles presented in the present article.
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Correa NM, Adali T, Li YO, Calhoun VD. Canonical Correlation Analysis for Data Fusion and Group Inferences: Examining applications of medical imaging data. IEEE SIGNAL PROCESSING MAGAZINE 2010; 27:39-50. [PMID: 20706554 PMCID: PMC2919827 DOI: 10.1109/msp.2010.936725] [Citation(s) in RCA: 81] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
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16
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Kaymaz N, van Os J. Heritability of Structural Brain Traits. NOVEL APPROACHES TO STUDYING BASAL GANGLIA AND RELATED NEUROPSYCHIATRIC DISORDERS 2009; 89:85-130. [DOI: 10.1016/s0074-7742(09)89005-3] [Citation(s) in RCA: 28] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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17
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Current world literature. Curr Opin Psychiatry 2008; 21:651-9. [PMID: 18852576 DOI: 10.1097/yco.0b013e3283130fb7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Imaging and imagining: current positions on the epistemic priority of theoretical concepts and data in psychiatric neuroimaging. Curr Opin Psychiatry 2008; 21:625-9. [PMID: 18852572 DOI: 10.1097/yco.0b013e328314b7a1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW To give an overview of recent developments in neuroimaging in psychiatry and on the current discussion about the relationship between theoretical concepts and data from neuroimaging studies. RECENT FINDINGS In psychiatric neuroimaging, broad concepts such as the self, well being, insight, empathy and volition form an integral part of the questions to be answered and cannot be avoided. Although, currently, the intradisciplinary discussion in neuroscience is mainly focused at the methodological and neurobiological level, psychological and philosophical theories are also needed for the interpretation of results. This raises questions regarding the epistemic priority of neuroimaging data and theories. SUMMARY In the current paper, we present the hypothesis that there is an interdependence of neuroimaging data and theoretical concepts. An approach to 'correlational neuroscience' with an awareness of this issue may help in avoiding misunderstandings and oversimplifications as well as building an interdisciplinary theoretical framework that is able to integrate findings from life sciences, mind sciences and humanities.
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Prasad KM, Keshavan MS. Structural cerebral variations as useful endophenotypes in schizophrenia: do they help construct "extended endophenotypes"? Schizophr Bull 2008; 34:774-90. [PMID: 18408230 PMCID: PMC2632444 DOI: 10.1093/schbul/sbn017] [Citation(s) in RCA: 76] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Endophenotypes represent intermediate phenotypes on the putative causal pathway from the genotype to the phenotype. They offer a potentially valuable strategy to examine the molecular etiopathology of complex behavioral phenotypes such as schizophrenia. Neurocognitive and neurophysiological impairments that suggest functional impairments associated with schizophrenia have been proposed as endophenotypes. However, few studies have examined the structural variations in the brain that might underlie the functional impairments as useful endophenotypes for schizophrenia. Over the past three decades, there has been an impressive body of literature supporting brain structural alterations in schizophrenia. We critically reviewed the extant literature on the neuroanatomical variations in schizophrenia in this paper to evaluate their candidacy as endophenotypes and how useful they are in furthering the understanding of etiology and pathophysiology of schizophrenia. Brain morphometric measures meet many of the criteria set by different investigators, such as being robustly associated with schizophrenia, heritable, quantifiable, and present in unaffected family members more frequently than in the general population. We conclude that the brain morphometric alterations appear largely to meet the criteria for endophenotypes in psychotic disorders. Some caveats for the utility of endophenotypes are discussed. A proposal to combine more than one endophenotype ("extended endophenotype") is suggested. Further work is needed to examine how specific genes and their interactions with the environment may produce alterations in brain structure and function that accompany psychotic disorders.
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Affiliation(s)
- Konasale M. Prasad
- Western Psychiatric Institute and Clinic, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213
| | - Matcheri S. Keshavan
- Western Psychiatric Institute and Clinic, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213
- Department of Psychiatry and Behavioral Neuroscience, Wayne State University School of Medicine, Detroit, MI 48201
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Windemuth A, Calhoun VD, Pearlson GD, Kocherla M, Jagannathan K, Ruaño G. Physiogenomic analysis of localized FMRI brain activity in schizophrenia. Ann Biomed Eng 2008; 36:877-88. [PMID: 18330705 DOI: 10.1007/s10439-008-9475-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2007] [Accepted: 02/15/2008] [Indexed: 11/29/2022]
Abstract
The search for genetic factors associated with disease is complicated by the complexity of the biological pathways linking genotype and phenotype. This analytical complexity is particularly concerning in diseases historically lacking reliable diagnostic biological markers, such as schizophrenia and other mental disorders. We investigate the use of functional magnetic resonance imaging (fMRI) as an intermediate phenotype (endophenotype) to identify physiogenomic associations to schizophrenia. We screened 99 subjects, 30 subjects diagnosed with schizophrenia, 13 unaffected relatives of schizophrenia patients, and 56 unrelated controls, for gene polymorphisms associated with fMRI activation patterns at two locations in temporal and frontal lobes previously implied in schizophrenia. A total of 22 single nucleotide polymorphisms (SNPs) in 15 genes from the dopamine and serotonin neurotransmission pathways were genotyped in all subjects. We identified three SNPs in genes that are significantly associated with fMRI activity. SNPs of the dopamine beta-hydroxylase (DBH) gene and of the dopamine receptor D4 (DRD4) were associated with activity in the temporal and frontal lobes, respectively. One SNP of serotonin-3A receptor (HTR3A) was associated with temporal lobe activity. The results of this study support the physiogenomic analysis of neuroimaging data to discover associations between genotype and disease-related phenotypes.
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Lenroot RK, Giedd JN. The changing impact of genes and environment on brain development during childhood and adolescence: initial findings from a neuroimaging study of pediatric twins. Dev Psychopathol 2008; 20:1161-75. [PMID: 18838036 PMCID: PMC2892674 DOI: 10.1017/s0954579408000552] [Citation(s) in RCA: 87] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Human brain development is created through continuing complex interactions of genetic and environmental influences. The challenge of linking specific genetic or environmental risk factors to typical or atypical behaviors has led to interest in using brain structural features as an intermediate phenotype. Twin studies in adults have found that many aspects of brain anatomy are highly heritable, demonstrating that genetic factors provide a significant contribution to variation in brain structures. Less is known about the relative impact of genes and environment while the brain is actively developing. We summarize results from the ongoing National Institute of Mental Health child and adolescent twin study that suggest that heritability of different brain areas changes over the course of development in a regionally specific fashion. Areas associated with more complex reasoning abilities become increasingly heritable with maturation. The potential mechanisms by which gene-environment interactions may affect heritability values during development is discussed.
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Abstract
Human anxiety disorders represent one of the most common mental illnesses. They are complex diseases with both genetic and environmental factors affecting their predisposition. Since the basic neuronal mechanisms are shared across mammalian species, the same set of genes may regulate critical aspects of anxiety in humans and in lower species. In this review, we first summarize findings from human molecular genetic approaches to anxiety disorders or anxiety-related personality traits: genome-wide scans and candidate gene studies in large families or case-control cohorts. We then discuss recent studies that have used genome-wide methods in mouse strains to identify genes that regulate anxiety-like behavior. Although it has been difficult to pinpoint specific susceptibility genes for anxiety disorders, ongoing efforts to collect larger study cohorts and to develop new genetic tools should help in this task. Studies in animals have shown that novel quantitative trait locus (QTL) and functional genomics approaches might lead to the identification of regulators of anxiety in mice, and that these genes can be tested for their involvement in human anxiety disorders. Finally, breakthroughs are expected in the fine-mapping of human and mouse genetic linkage regions and in the identification of novel candidate genes using genome-wide methods in mouse models of anxiety.
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Affiliation(s)
- Iiris Hovatta
- Research Program of Molecular Neurology, Biomedicum, University of Helsinki, Finland.
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Keshavan MS, Prasad KM, Pearlson G. Are brain structural abnormalities useful as endophenotypes in schizophrenia? Int Rev Psychiatry 2007; 19:397-406. [PMID: 17671872 DOI: 10.1080/09540260701486233] [Citation(s) in RCA: 39] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Endophenotypes, which represent intermediate phenotypes on the causal pathway from the genotype to the phenotype, can help unravel the molecular etiopathology of complex psychiatric disorders such as schizophrenia. Several candidate endophenotypic markers have been proposed in schizophrenia, including neurocognitive and neurophysiological impairments. Over the past three decades, there has been an impressive body of literature in support of brain structural alterations in schizophrenia, but few studies have critically examined whether these abnormalities can be considered useful endophenotypic markers. We critically reviewed the extant literature on the neuroanatomy of schizophrenia in this paper to evaluate their candidacy as endophenotypes. Structural brain changes are robustly associated with schizophrenia, are state independent and may cut across the diagnostic boundaries of major psychotic illnesses. Brain morphometric measures are heritable, co-segregate with the broadly defined neurocognitive and behavioural phenotypes within the first degree relatives of schizophrenia patients and are present in unaffected family members more frequently than in the general population. Taken together, brain morphometric alterations appear largely to meet the criteria for endophenotypes in psychotic disorders. Further work is needed to examine how specific genes and their interactions with the environment may produce alterations in brain structure and function that accompany psychotic disorders.
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Affiliation(s)
- Matcheri S Keshavan
- Department of Psychiatry, Wayne State University School of Medicine, 4201 St. Antoine Boulevard, Detroit, MA 48201, USA.
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Zipursky RB. Imaging mental disorders in the 21st century. CANADIAN JOURNAL OF PSYCHIATRY. REVUE CANADIENNE DE PSYCHIATRIE 2007; 52:133-4. [PMID: 17479520 DOI: 10.1177/070674370705200301] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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