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Liloia D, Zamfira DA, Tanaka M, Manuello J, Crocetta A, Keller R, Cozzolino M, Duca S, Cauda F, Costa T. Disentangling the role of gray matter volume and concentration in autism spectrum disorder: A meta-analytic investigation of 25 years of voxel-based morphometry research. Neurosci Biobehav Rev 2024; 164:105791. [PMID: 38960075 DOI: 10.1016/j.neubiorev.2024.105791] [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: 10/26/2023] [Revised: 05/22/2024] [Accepted: 06/27/2024] [Indexed: 07/05/2024]
Abstract
Despite over two decades of neuroimaging research, a unanimous definition of the pattern of structural variation associated with autism spectrum disorder (ASD) has yet to be found. One potential impeding issue could be the sometimes ambiguous use of measurements of variations in gray matter volume (GMV) or gray matter concentration (GMC). In fact, while both can be calculated using voxel-based morphometry analysis, these may reflect different underlying pathological mechanisms. We conducted a coordinate-based meta-analysis, keeping apart GMV and GMC studies of subjects with ASD. Results showed distinct and non-overlapping patterns for the two measures. GMV decreases were evident in the cerebellum, while GMC decreases were mainly found in the temporal and frontal regions. GMV increases were found in the parietal, temporal, and frontal brain regions, while GMC increases were observed in the anterior cingulate cortex and middle frontal gyrus. Age-stratified analyses suggested that such variations are dynamic across the ASD lifespan. The present findings emphasize the importance of considering GMV and GMC as distinct yet synergistic indices in autism research.
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Affiliation(s)
- Donato Liloia
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Denisa Adina Zamfira
- School of Psychology, Vita-Salute San Raffaele University, Milan, Italy; Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Masaru Tanaka
- HUN-REN-SZTE Neuroscience Research Group, Hungarian Research Network, University of Szeged (HUN-REN-SZTE), Danube Neuroscience Research Laboratory, Szeged, Hungary
| | - Jordi Manuello
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Annachiara Crocetta
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Roberto Keller
- Adult Autism Center, DSM Local Health Unit, ASL TO, Turin, Italy
| | - Mauro Cozzolino
- Department of Humanities, Philosophical and Educational Sciences, University of Salerno, Fisciano, Italy
| | - Sergio Duca
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Franco Cauda
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin (NIT), Turin, Italy
| | - Tommaso Costa
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin (NIT), Turin, Italy
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2
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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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3
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Samantaray T, Anand M, Saini J, Gupta CN. Introspection of UBNIN and Modified-UBNIN Algorithms for Structural MRI. Reply to Kelly et al. A Comparison of Brain-State Representations of Binary Neuroimaging Connectivity Data. Comment on "Samantaray et al. Unique Brain Network Identification Number for Parkinson's and Healthy Individuals Using Structural MRI. Brain Sci. 2023, 13, 1297". Brain Sci 2024; 14:424. [PMID: 38790403 PMCID: PMC11117835 DOI: 10.3390/brainsci14050424] [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: 02/12/2024] [Revised: 04/10/2024] [Accepted: 04/22/2024] [Indexed: 05/26/2024] Open
Abstract
The purpose of this reply is to address the comments given by Kelly et al. on our original paper "Unique Brain Network Identification Number for Parkinson's and Healthy Individuals using Structural MRI". We agree to the inadvertent rounding pitfall in our original paper due to the non-inclusion of symbolic math toolbox (MATLAB). We now provide the actual ranges (with decimal values) of the UBNIN values of healthy individuals and those with Parkinson's disease and further observations. Upon further introspection, we propose another variant, called Modified-UBNIN (UBNIN-MT,MN) which is highly weighted on the node with the highest network degree (i.e., connections). The italicized sentences within inverted commas are statements from Kelly et al.'s comment paper.
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Affiliation(s)
- Tanmayee Samantaray
- Neural Engineering Lab, Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Guwahati 781039, India; (T.S.); (M.A.)
| | - Manish Anand
- Neural Engineering Lab, Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Guwahati 781039, India; (T.S.); (M.A.)
| | - Jitender Saini
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bengaluru 560029, India;
| | - Cota Navin Gupta
- Neural Engineering Lab, Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Guwahati 781039, India; (T.S.); (M.A.)
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4
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Cao C, Li Y, Hu F, Gao X. Modeling refined differences of cortical folding patterns via spatial, morphological, and temporal fusion representations. Cereb Cortex 2024; 34:bhae146. [PMID: 38602743 DOI: 10.1093/cercor/bhae146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 03/18/2024] [Accepted: 03/20/2024] [Indexed: 04/12/2024] Open
Abstract
The gyrus, a pivotal cortical folding pattern, is essential for integrating brain structure-function. This study focuses on 2-Hinge and 3-Hinge folds, characterized by the gyral convergence from various directions. Existing voxel-level studies may not adequately capture the precise spatial relationships within cortical folding patterns, especially when relying solely on local cortical characteristics due to their variable shapes and homogeneous frequency-specific features. To overcome these challenges, we introduced a novel model that combines spatial distribution, morphological structure, and functional magnetic resonance imaging data. We utilized spatio-morphological residual representations to enhance and extract subtle variations in cortical spatial distribution and morphological structure during blood oxygenation, integrating these with functional magnetic resonance imaging embeddings using self-attention for spatio-morphological-temporal representations. Testing these representations for identifying cortical folding patterns, including sulci, gyri, 2-Hinge, and 2-Hinge folds, and evaluating the impact of phenotypic data (e.g. stimulus) on recognition, our experimental results demonstrate the model's superior performance, revealing significant differences in cortical folding patterns under various stimulus. These differences are also evident in the characteristics of sulci and gyri folds between genders, with 3-Hinge showing more variations. Our findings indicate that our representations of cortical folding patterns could serve as biomarkers for understanding brain structure-function correlations.
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Affiliation(s)
- Chunhong Cao
- The MOE Key Laboratory of Intelligent Computing and Information Processing, Xiangtan University, 411005 Xiangtan, China
| | - Yongquan Li
- The MOE Key Laboratory of Intelligent Computing and Information Processing, Xiangtan University, 411005 Xiangtan, China
| | - Fang Hu
- The Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province, Xiangnan University, 423043 Chenzhou, China
| | - Xieping Gao
- The Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, 410081 Changsha, China
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5
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Allen CH, Maurer JM, Gullapalli AR, Edwards BG, Aharoni E, Harenski CL, Anderson NE, Harenski KA, Calhoun VD, Kiehl KA. Psychopathic traits and altered resting-state functional connectivity in incarcerated adolescent girls. FRONTIERS IN NEUROIMAGING 2023; 2:1216494. [PMID: 37554634 PMCID: PMC10406221 DOI: 10.3389/fnimg.2023.1216494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 07/19/2023] [Indexed: 08/10/2023]
Abstract
Previous work in incarcerated boys and adult men and women suggest that individuals scoring high on psychopathic traits show altered resting-state limbic/paralimbic, and default mode functional network properties. However, it is unclear whether similar results extend to high-risk adolescent girls with elevated psychopathic traits. This study examined whether psychopathic traits [assessed via the Hare Psychopathy Checklist: Youth Version (PCL:YV)] were associated with altered inter-network connectivity, intra-network connectivity (i.e., functional coherence within a network), and amplitude of low-frequency fluctuations (ALFFs) across resting-state networks among high-risk incarcerated adolescent girls (n = 40). Resting-state networks were identified by applying group independent component analysis (ICA) to resting-state fMRI scans, and a priori regions of interest included limbic, paralimbic, and default mode network components. We tested the association of psychopathic traits (PCL:YV Factor 1 measuring affective/interpersonal traits and PCL:YV Factor 2 assessing antisocial/lifestyle traits) to these three resting-state measures. PCL:YV Factor 1 scores were associated with increased low-frequency and decreased high-frequency fluctuations in components corresponding to the default mode network, as well as increased intra-network FNC in components corresponding to cognitive control networks. PCL:YV Factor 2 scores were associated with increased low-frequency fluctuations in sensorimotor networks and decreased high-frequency fluctuations in default mode, sensorimotor, and visual networks. Consistent with previous analyses in incarcerated adult women, our results suggest that psychopathic traits among incarcerated adolescent girls are associated with altered intra-network ALFFs-primarily that of increased low-frequency and decreased high-frequency fluctuations-and connectivity across multiple networks including paralimbic regions. These results suggest stable neurobiological correlates of psychopathic traits among women across development.
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Affiliation(s)
- Corey H. Allen
- The Mind Research Network, Albuquerque, NM, United States
| | | | | | | | - Eyal Aharoni
- Department of Psychology, Georgia State University, Atlanta, GA, United States
| | | | | | | | - Vince D. Calhoun
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
| | - Kent A. Kiehl
- The Mind Research Network, Albuquerque, NM, United States
- Department of Psychology, University of New Mexico, Albuquerque, NM, United States
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Knolle F, Arumugham SS, Barker RA, Chee MWL, Justicia A, Kamble N, Lee J, Liu S, Lenka A, Lewis SJG, Murray GK, Pal PK, Saini J, Szeto J, Yadav R, Zhou JH, Koch K. A multicentre study on grey matter morphometric biomarkers for classifying early schizophrenia and parkinson's disease psychosis. NPJ Parkinsons Dis 2023; 9:87. [PMID: 37291143 PMCID: PMC10250419 DOI: 10.1038/s41531-023-00522-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 05/15/2023] [Indexed: 06/10/2023] Open
Abstract
Psychotic symptoms occur in a majority of schizophrenia patients and in ~50% of all Parkinson's disease (PD) patients. Altered grey matter (GM) structure within several brain areas and networks may contribute to their pathogenesis. Little is known, however, about transdiagnostic similarities when psychotic symptoms occur in different disorders, such as in schizophrenia and PD. The present study investigated a large, multicenter sample containing 722 participants: 146 patients with first episode psychosis, FEP; 106 individuals in at-risk mental state for developing psychosis, ARMS; 145 healthy controls matching FEP and ARMS, Con-Psy; 92 PD patients with psychotic symptoms, PDP; 145 PD patients without psychotic symptoms, PDN; 88 healthy controls matching PDN and PDP, Con-PD. We applied source-based morphometry in association with receiver operating curves (ROC) analyses to identify common GM structural covariance networks (SCN) and investigated their accuracy in identifying the different patient groups. We assessed group-specific homogeneity and variability across the different networks and potential associations with clinical symptoms. SCN-extracted GM values differed significantly between FEP and Con-Psy, PDP and Con-PD, PDN and Con-PD, as well as PDN and PDP, indicating significant overall grey matter reductions in PD and early schizophrenia. ROC analyses showed that SCN-based classification algorithms allow good classification (AUC ~0.80) of FEP and Con-Psy, and fair performance (AUC ~0.72) when differentiating PDP from Con-PD. Importantly, the best performance was found in partly the same networks, including the thalamus. Alterations within selected SCNs may be related to the presence of psychotic symptoms in both early schizophrenia and PD psychosis, indicating some commonality of underlying mechanisms. Furthermore, results provide evidence that GM volume within specific SCNs may serve as a biomarker for identifying FEP and PDP.
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Affiliation(s)
- Franziska Knolle
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany.
- Department of Psychiatry, University of Cambridge, Cambridge, UK.
| | - Shyam S Arumugham
- Department of Psychiatry, National Institute of Mental Health & Neurosciences (NIMHANS), Bengaluru, India
| | - Roger A Barker
- Department of Clinical Neuroscience, University of Cambridge, Cambridge, UK
| | - Michael W L Chee
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Azucena Justicia
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- IMIM (Hospital del Mar Medical Research Institute), Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Barcelona, Spain
| | - Nitish Kamble
- Department of Neurology, National Institute of Mental Health & Neurosciences (NIMHANS), Bengaluru, India
| | - Jimmy Lee
- Research Division, Institute of Mental Health, Singapore, Singapore
- Department of Psychosis, Institute of Mental Health, Singapore, Singapore
- Neuroscience and Mental Health, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Siwei Liu
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Abhishek Lenka
- Department of Neurology, National Institute of Mental Health & Neurosciences (NIMHANS), Bengaluru, India
- Department of Neurology, Medstar Georgetown University School of Medicine, Washington, DC, USA
| | - Simon J G Lewis
- ForeFront Parkinson's Disease Research Clinic, Brain and Mind Centre, School of Medical Sciences, University of Sydney, Camperdown, NSW, Australia
| | - Graham K Murray
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Pramod Kumar Pal
- Department of Neurology, National Institute of Mental Health & Neurosciences (NIMHANS), Bengaluru, India
| | - Jitender Saini
- Department of Neurology, National Institute of Mental Health & Neurosciences (NIMHANS), Bengaluru, India
| | - Jennifer Szeto
- ForeFront Parkinson's Disease Research Clinic, Brain and Mind Centre, School of Medical Sciences, University of Sydney, Camperdown, NSW, Australia
| | - Ravi Yadav
- Department of Psychiatry, National Institute of Mental Health & Neurosciences (NIMHANS), Bengaluru, India
| | - Juan H Zhou
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Kathrin Koch
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany.
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7
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Xu X, Li Q, Qian Y, Cai H, Zhang C, Zhao W, Zhu J, Yu Y. Genetic mechanisms underlying gray matter volume changes in patients with drug-naive first-episode schizophrenia. Cereb Cortex 2023; 33:2328-2341. [PMID: 35640648 DOI: 10.1093/cercor/bhac211] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 05/05/2022] [Accepted: 05/06/2022] [Indexed: 11/13/2022] Open
Abstract
Brain structural damage is a typical feature of schizophrenia. Investigating such disease phenotype in patients with drug-naive first-episode schizophrenia (DFSZ) may exclude the confounds of antipsychotics and illness chronicity. However, small sample sizes and marked clinical heterogeneity have precluded definitive identification of gray matter volume (GMV) changes in DFSZ as well as their underlying genetic mechanisms. Here, GMV changes in DFSZ were assessed using a neuroimaging meta-analysis of 19 original studies, including 605 patients and 637 controls. Gene expression data were derived from the Allen Human Brain Atlas and processed with a newly proposed standardized pipeline. Then, we used transcriptome-neuroimaging spatial correlations to identify genes associated with GMV changes in DFSZ, followed by a set of gene functional feature analyses. Meta-analysis revealed consistent GMV reduction in the right superior temporal gyrus, right insula and left inferior temporal gyrus in DFSZ. Moreover, we found that these GMV changes were spatially correlated with expression levels of 1,201 genes, which exhibited a wide range of functional features. Our findings may provide important insights into the genetic mechanisms underlying brain morphological abnormality in schizophrenia.
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Affiliation(s)
- Xiaotao Xu
- Department of Radiology, The Fourth Affiliated Hospital of Anhui Medical University, Hefei 230012, China.,Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei, 230032, China.,Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Qian Li
- Department of Radiology, Chaohu Hospital of Anhui Medical University, Hefei 238000, China.,Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei, 230032, China.,Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Yinfeng Qian
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei, 230032, China.,Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Huanhuan Cai
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei, 230032, China.,Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Cun Zhang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei, 230032, China.,Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Wenming Zhao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei, 230032, China.,Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Jiajia Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei, 230032, China.,Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei, 230032, China.,Anhui Provincial Institute of Translational Medicine, Hefei 230032, China.,Department of Radiology, Chaohu Hospital of Anhui Medical University, Hefei 238000, China.,Department of Radiology, The Fourth Affiliated Hospital of Anhui Medical University, Hefei 230012, China
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8
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Allen CH, Shold J, Michael Maurer J, Reynolds BL, Anderson NE, Harenski CL, Harenski KA, Calhoun VD, Kiehl KA. Aberrant resting-state functional connectivity associated with childhood trauma among juvenile offenders. Neuroimage Clin 2023; 37:103343. [PMID: 36764058 PMCID: PMC9929859 DOI: 10.1016/j.nicl.2023.103343] [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: 11/30/2022] [Revised: 01/20/2023] [Accepted: 02/03/2023] [Indexed: 02/10/2023]
Abstract
Individuals with history of childhood trauma are characterized by aberrant resting-state limbic and paralimbic functional network connectivity. However, it is unclear whether specific subtypes of trauma (i.e., experienced vs observed or community) showcase differential effects. This study examined whether subtypes of childhood trauma (assessed via the Trauma Checklist [TCL] 2.0) were associated with aberrant intra-network amplitude of fluctuations and connectivity (i.e., functional coherence within a network), and inter-network connectivity across resting-state networks among incarcerated juvenile males (n = 179). Subtypes of trauma were established via principal component analysis of the TCL 2.0 and resting-state networks were identified by applying group independent component analysis to resting-state fMRI scans. We tested the association of subtypes of childhood trauma (i.e., TCL Factor 1 measuring experienced trauma and TCL Factor 2 assessing community trauma), and TCL Total scores to the aforementioned functional connectivity measures. TCL Factor 2 scores were associated with increased high-frequency fluctuations and increased intra-network connectivity in cognitive control, auditory, and sensorimotor networks, occurring primarily in paralimbic regions. TCL Total scores exhibited similar neurobiological patterns to TCL Factor 2 scores (with the addition of aberrant intra-network connectivity in visual networks), and no significant associations were found for TCL Factor 1. Consistent with previous analyses of community samples, our results suggest that childhood trauma among incarcerated juvenile males is associated with aberrant intra-network amplitude of fluctuations and connectivity across multiple networks including predominately paralimbic regions. Our results highlight the importance of accounting for traumatic loss, observed trauma, and community trauma in assessing neurobiological aberrances associated with adverse experiences in childhood, as well as the value of trained-rater trauma assessments compared to self-report.
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Affiliation(s)
- Corey H Allen
- The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM 87106-4188, USA.
| | - Jenna Shold
- The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM 87106-4188, USA
| | - J Michael Maurer
- The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM 87106-4188, USA
| | - Brooke L Reynolds
- The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM 87106-4188, USA; School of Graduate Psychology, Pacific University, Hillsboro, OR, USA
| | | | - Carla L Harenski
- The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM 87106-4188, USA
| | - Keith A Harenski
- The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM 87106-4188, USA
| | - Vince D Calhoun
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA; Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, 55 Park Place NE, 18th Floor, Atlanta, GA 30303, USA; Department of Computer Science, Georgia State University, Atlanta, USA
| | - Kent A Kiehl
- The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM 87106-4188, USA; Department of Psychology, University of New Mexico, Albuquerque, NM 87131, USA
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9
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Progressive brain abnormalities in schizophrenia across different illness periods: a structural and functional MRI study. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2023; 9:2. [PMID: 36604437 PMCID: PMC9816110 DOI: 10.1038/s41537-022-00328-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 11/16/2022] [Indexed: 01/07/2023]
Abstract
Schizophrenia is a chronic brain disorder, and neuroimaging abnormalities have been reported in different stages of the illness for decades. However, when and how these brain abnormalities occur and evolve remains undetermined. We hypothesized structural and functional brain abnormalities progress throughout the illness course at different rates in schizophrenia. A total of 115 patients with schizophrenia were recruited and stratified into three groups of different illness periods: 5-year group (illness duration: ≤5 years), 15-year group (illness duration: 12-18 years), and 25-year group (illness duration: ≥25 years); 230 healthy controls were matched by age and sex to the three groups, respectively. All participants underwent resting-state MRI scanning. Each group of patients with schizophrenia was compared with the corresponding controls in terms of voxel-based morphometry (VBM), fractional anisotropy (FA), global functional connectivity density (gFCD), and sample entropy (SampEn) abnormalities. In the 5-year group we observed only SampEn abnormalities in the putamen. In the 15-year group, we observed VBM abnormalities in the insula and cingulate gyrus and gFCD abnormalities in the temporal cortex. In the 25-year group, we observed FA abnormalities in nearly all white matter tracts, and additional VBM and gFCD abnormalities in the frontal cortex and cerebellum. By using two structural and two functional MRI analysis methods, we demonstrated that individual functional abnormalities occur in limited brain areas initially, functional connectivity and gray matter density abnormalities ensue later in wider brain areas, and structural connectivity abnormalities involving almost all white matter tracts emerge in the third decade of the course in schizophrenia.
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10
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Allen CH, Maurer JM, Edwards BG, Gullapalli AR, Harenski CL, Harenski KA, Calhoun VD, Kiehl KA. Aberrant resting-state functional connectivity in incarcerated women with elevated psychopathic traits. FRONTIERS IN NEUROIMAGING 2022; 1:971201. [PMID: 37555166 PMCID: PMC10406317 DOI: 10.3389/fnimg.2022.971201] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 09/20/2022] [Indexed: 08/10/2023]
Abstract
Previous work in incarcerated men suggests that individuals scoring high on psychopathy exhibit aberrant resting-state paralimbic functional network connectivity (FNC). However, it is unclear whether similar results extend to women scoring high on psychopathy. This study examined whether psychopathic traits [assessed via the Hare Psychopathy Checklist - Revised (PCL-R)] were associated with aberrant inter-network connectivity, intra-network connectivity (i.e., functional coherence within a network), and amplitude of fluctuations across limbic and surrounding paralimbic regions among incarcerated women (n = 297). Resting-state networks were identified by applying group Independent Component Analysis to resting-state fMRI scans. We tested the association of psychopathic traits (PCL-R Factor 1 measuring interpersonal/affective psychopathic traits and PCL-R Factor 2 assessing lifestyle/antisocial psychopathic traits) to the three FNC measures. PCL-R Factor 1 scores were associated with increased low-frequency fluctuations in executive control and attentional networks, decreased high-frequency fluctuations in executive control and visual networks, and decreased intra-network FNC in default mode network. PCL-R Factor 2 scores were associated with decreased high-frequency fluctuations and default mode networks, and both increased and decreased intra-network functional connectivity in visual networks. Similar to previous analyses in incarcerated men, our results suggest that psychopathic traits among incarcerated women are associated with aberrant intra-network amplitude fluctuations and connectivity across multiple networks including limbic and surrounding paralimbic regions.
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Affiliation(s)
- Corey H. Allen
- The Mind Research Network, Albuquerque, NM, United States
| | | | - Bethany G. Edwards
- The Mind Research Network, Albuquerque, NM, United States
- Department of Psychology, University of New Mexico, Albuquerque, NM, United States
| | | | | | | | - Vince D. Calhoun
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
| | - Kent A. Kiehl
- The Mind Research Network, Albuquerque, NM, United States
- Department of Psychology, University of New Mexico, Albuquerque, NM, United States
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11
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Saha DK, Calhoun VD, Du Y, Fu Z, Kwon SM, Sarwate AD, Panta SR, Plis SM. Privacy-preserving quality control of neuroimaging datasets in federated environments. Hum Brain Mapp 2022; 43:2289-2310. [PMID: 35243723 PMCID: PMC8996357 DOI: 10.1002/hbm.25788] [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: 05/05/2021] [Revised: 12/10/2021] [Accepted: 12/13/2021] [Indexed: 11/18/2022] Open
Abstract
Privacy concerns for rare disease data, institutional or IRB policies, access to local computational or storage resources or download capabilities are among the reasons that may preclude analyses that pool data to a single site. A growing number of multisite projects and consortia were formed to function in the federated environment to conduct productive research under constraints of this kind. In this scenario, a quality control tool that visualizes decentralized data in its entirety via global aggregation of local computations is especially important, as it would allow the screening of samples that cannot be jointly evaluated otherwise. To solve this issue, we present two algorithms: decentralized data stochastic neighbor embedding, dSNE, and its differentially private counterpart, DP‐dSNE. We leverage publicly available datasets to simultaneously map data samples located at different sites according to their similarities. Even though the data never leaves the individual sites, dSNE does not provide any formal privacy guarantees. To overcome that, we rely on differential privacy: a formal mathematical guarantee that protects individuals from being identified as contributors to a dataset. We implement DP‐dSNE with AdaCliP, a method recently proposed to add less noise to the gradients per iteration. We introduce metrics for measuring the embedding quality and validate our algorithms on these metrics against their centralized counterpart on two toy datasets. Our validation on six multisite neuroimaging datasets shows promising results for the quality control tasks of visualization and outlier detection, highlighting the potential of our private, decentralized visualization approach.
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Affiliation(s)
- Debbrata K Saha
- Georgia Institute of Technology, Atlanta, Georgia, USA.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Vince D Calhoun
- Georgia Institute of Technology, Atlanta, Georgia, USA.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA.,Georgia State University, Atlanta, Georgia, USA
| | - Yuhui Du
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Soo Min Kwon
- Rutgers, The State University of New Jersey, New Brunswick, New Jersey, USA
| | - Anand D Sarwate
- Rutgers, The State University of New Jersey, New Brunswick, New Jersey, USA
| | - Sandeep R Panta
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Sergey M Plis
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA.,Georgia State University, Atlanta, Georgia, USA
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12
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Rootes-Murdy K, Zendehrouh E, Calhoun VD, Turner JA. Spatially Covarying Patterns of Gray Matter Volume and Concentration Highlight Distinct Regions in Schizophrenia. Front Neurosci 2021; 15:708387. [PMID: 34720851 PMCID: PMC8551386 DOI: 10.3389/fnins.2021.708387] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 09/20/2021] [Indexed: 11/29/2022] Open
Abstract
Introduction: Individuals with schizophrenia have consistent gray matter reduction throughout the cortex when compared to healthy individuals. However, the reduction patterns vary based on the quantity (concentration or volume) utilized by study. The objective of this study was to identify commonalities between gray matter concentration and gray matter volume effects in schizophrenia. Methods: We performed both univariate and multivariate analyses of case/control effects on 145 gray matter images from 66 participants with schizophrenia and 79 healthy controls, and processed to compare the concentration and volume estimates. Results: Diagnosis effects in the univariate analysis showed similar areas of volume and concentration reductions in the insula, occipitotemporal gyrus, temporopolar area, and fusiform gyrus. In the multivariate analysis, healthy controls had greater gray matter volume and concentration additionally in the superior temporal gyrus, prefrontal cortex, cerebellum, calcarine, and thalamus. In the univariate analyses there was moderate overlap between gray matter concentration and volume across the entire cortex (r = 0.56, p = 0.02). The multivariate analyses revealed only low overlap across most brain patterns, with the largest correlation (r = 0.37) found in the cerebellum and vermis. Conclusions: Individuals with schizophrenia showed reduced gray matter volume and concentration in previously identified areas of the prefrontal cortex, cerebellum, and thalamus. However, there were only moderate correlations across the cortex when examining the different gray matter quantities. Although these two quantities are related, concentration and volume do not show identical results, and therefore, should not be used interchangeably in the literature.
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Affiliation(s)
- Kelly Rootes-Murdy
- Department of Psychology, Georgia State University, Atlanta, GA, United States.,Neuroscience Institute, Georgia State University, Atlanta, GA, United States
| | - Elaheh Zendehrouh
- Department of Computer Science, Georgia State University, Atlanta, GA, United States.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Vince D Calhoun
- Department of Psychology, Georgia State University, Atlanta, GA, United States.,Neuroscience Institute, Georgia State University, Atlanta, GA, United States.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Jessica A Turner
- Department of Psychology, Georgia State University, Atlanta, GA, United States.,Neuroscience Institute, Georgia State University, Atlanta, GA, United States.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
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13
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Matsuo K, Harada K, Fujita Y, Okamoto Y, Ota M, Narita H, Mwangi B, Gutierrez CA, Okada G, Takamura M, Yamagata H, Kusumi I, Kunugi H, Inoue T, Soares JC, Yamawaki S, Watanabe Y. Distinctive Neuroanatomical Substrates for Depression in Bipolar Disorder versus Major Depressive Disorder. Cereb Cortex 2020; 29:202-214. [PMID: 29202177 DOI: 10.1093/cercor/bhx319] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2017] [Accepted: 11/02/2017] [Indexed: 12/20/2022] Open
Abstract
No neuroanatomical substrates for distinguishing between depression of bipolar disorder (dBD) and major depressive disorder (dMDD) are currently known. The aim of the current multicenter study was to identify neuroanatomical patterns distinct to depressed patients with the two disorders. Further analysis was conducted on an independent sample to enable generalization of results. We directly compared MR images of these subjects using voxel-based morphometry (VBM) and a support vector machine (SVM) algorithm using 1531 participants. The VBM analysis showed significantly reduced gray matter volumes in the bilateral dorsolateral prefrontal (DLPFC) and anterior cingulate cortices (ACC) in patients with dBD compared with those with dMDD. Patients with the two disorders shared small gray matter volumes for the right ACC and left inferior frontal gyrus when compared with healthy subjects. Voxel signals in these regions during SVM analysis contributed to an accurate classification of the two diagnoses. The VBM and SVM results in the second cohort also supported these results. The current findings provide new evidence that gray matter volumes in the DLPFC and ACC are core regions in displaying shared and distinct neuroanatomical substrates and can shed light on elucidation of neural mechanism for depression within the bipolar/major depressive disorder continuum.
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Affiliation(s)
- Koji Matsuo
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, 1-1-1 Minamikogushi, Ube, Yamaguchi, Japan
| | - Kenichiro Harada
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, 1-1-1 Minamikogushi, Ube, Yamaguchi, Japan
| | - Yusuke Fujita
- Division of Electrical, Electronic and Information Engineering, Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Tokiwadai 2-16-1, Ube, Yamaguchi, Japan
| | - Yasumasa Okamoto
- Department of Psychiatry and Neurosciences, Institute of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Japan
| | - Miho Ota
- Department of Mental Disorder Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, 4-1-1 Ogawa-Higashi, Kodaira, Tokyo, Japan
| | - Hisashi Narita
- Department of Psychiatry, Hokkaido University Graduate School of Medicine, North 15, West 7, Kita-ku, Sapporo, Japan
| | - Benson Mwangi
- Department of Psychiatry, The University of Texas Health Science Center at Houston, TX, USA
| | - Carlos A Gutierrez
- Department of Psychiatry, The University of Texas Health Science Center at Houston, TX, USA
| | - Go Okada
- Department of Psychiatry and Neurosciences, Institute of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Japan
| | - Masahiro Takamura
- Department of Psychiatry and Neurosciences, Institute of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Japan
| | - Hirotaka Yamagata
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, 1-1-1 Minamikogushi, Ube, Yamaguchi, Japan
| | - Ichiro Kusumi
- Department of Psychiatry, Hokkaido University Graduate School of Medicine, North 15, West 7, Kita-ku, Sapporo, Japan
| | - Hiroshi Kunugi
- Department of Mental Disorder Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, 4-1-1 Ogawa-Higashi, Kodaira, Tokyo, Japan
| | - Takeshi Inoue
- Department of Psychiatry, Hokkaido University Graduate School of Medicine, North 15, West 7, Kita-ku, Sapporo, Japan.,Department of Psychiatry, Tokyo Medical University, 6-7-1, Nishishinjuku, Shinjuku-ku, Tokyo, Japan
| | - Jair C Soares
- Department of Psychiatry, The University of Texas Health Science Center at Houston, TX, USA
| | - Shigeto Yamawaki
- Department of Psychiatry and Neurosciences, Institute of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Japan
| | - Yoshifumi Watanabe
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, 1-1-1 Minamikogushi, Ube, Yamaguchi, Japan
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14
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Snodgrass P, Sandoval H, Calhoun VD, Ramos-Duran L, Song G, Sun Y, Alvarado B, Bashashati M, Sarosiek I, McCallum RW. Central Nervous System Mechanisms of Nausea in Gastroparesis: An fMRI-Based Case-Control Study. Dig Dis Sci 2020; 65:551-556. [PMID: 31494751 DOI: 10.1007/s10620-019-05766-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Accepted: 07/26/2019] [Indexed: 12/09/2022]
Abstract
BACKGROUND/AIMS Nausea is a major complaint of gastroparesis (GP), and the pathophysiology of this condition is poorly understood. Therefore, this study utilized fMRI to investigate the possible central nervous system (CNS) mechanisms of nausea in 10 GP patients versus 8 healthy controls (HCs). METHODS Nausea severity was assessed on a 0-10 scale and presented as mean ± SD. Nausea was increased from baseline utilizing up to 30 min of visual stimulation (VS). Functional network connectivity was measured with fMRI at baseline and after 30 min of VS. fMRI data were preprocessed using statistical parametric mapping software. Thirty-four independent components were identified as meaningful resting-state networks (RSNs) by group independent component analysis. The Functional Network Connectivity (FNC) among 5 RSNs considered important in CNS nausea mechanisms was calculated as the Pearson's pairwise correlation. RESULTS Baseline nausea score in GP patients was 2.7 ± 2.0 and increased to 7.0 ± 1.5 after stimulation (P < 0.01). In HCs nausea scores did not increase from baseline after stimulus (0.3 ± 0.5). When comparing GP patients to HCs after VS, a significant reduction (P < 0.001) in bilateral insula network connectivity compared to the right insula network was detected. No significant differences in connectivity were noted among the other RSNs. Additionally, the average gray matter volume was non-significantly reduced in the insula in GP patients compared to HC. CONCLUSIONS The insula connectivity network is impaired in nauseated GP patients. This phenomenon could explain the susceptibility of GP patients to nausea or may have resulted from a state of chronic nausea.
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Affiliation(s)
- Phillip Snodgrass
- Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, 5001 El Paso Dr., El Paso, TX, 79905, USA
| | - Hugo Sandoval
- Department of Radiology, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, 5001 El Paso Dr., El Paso, TX, 79905, USA
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, 55 Park Pl, 18th Floor, Atlanta, GA, 30303, USA
- The Department of Electrical and Computer Engineering, MSC01 1100, 1 University of New Mexico, Albuquerque, NM, 87131, USA
| | - Luis Ramos-Duran
- Department of Radiology, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, 5001 El Paso Dr., El Paso, TX, 79905, USA
| | - Gengqing Song
- Department of Internal Medicine, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, 4800 Alberta Ave., El Paso, TX, 79905, USA
| | - Yan Sun
- Department of Internal Medicine, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, 4800 Alberta Ave., El Paso, TX, 79905, USA
| | - Ben Alvarado
- Department of Internal Medicine, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, 4800 Alberta Ave., El Paso, TX, 79905, USA
| | - Mohammad Bashashati
- Department of Internal Medicine, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, 4800 Alberta Ave., El Paso, TX, 79905, USA
| | - Irene Sarosiek
- Department of Internal Medicine, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, 4800 Alberta Ave., El Paso, TX, 79905, USA.
| | - Richard W McCallum
- Department of Internal Medicine, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, 4800 Alberta Ave., El Paso, TX, 79905, USA
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15
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Honnorat N, Dong A, Meisenzahl-Lechner E, Koutsouleris N, Davatzikos C. Neuroanatomical heterogeneity of schizophrenia revealed by semi-supervised machine learning methods. Schizophr Res 2019; 214:43-50. [PMID: 29274735 PMCID: PMC6013334 DOI: 10.1016/j.schres.2017.12.008] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Revised: 12/09/2017] [Accepted: 12/14/2017] [Indexed: 11/19/2022]
Abstract
Schizophrenia is associated with heterogeneous clinical symptoms and neuroanatomical alterations. In this work, we aim to disentangle the patterns of neuroanatomical alterations underlying a heterogeneous population of patients using a semi-supervised clustering method. We apply this strategy to a cohort of patients with schizophrenia of varying extends of disease duration, and we describe the neuroanatomical, demographic and clinical characteristics of the subtypes discovered. METHODS We analyze the neuroanatomical heterogeneity of 157 patients diagnosed with Schizophrenia, relative to a control population of 169 subjects, using a machine learning method called CHIMERA. CHIMERA clusters the differences between patients and a demographically-matched population of healthy subjects, rather than clustering patients themselves, thereby specifically assessing disease-related neuroanatomical alterations. Voxel-Based Morphometry was conducted to visualize the neuroanatomical patterns associated with each group. The clinical presentation and the demographics of the groups were then investigated. RESULTS Three subgroups were identified. The first two differed substantially, in that one involved predominantly temporal-thalamic-peri-Sylvian regions, whereas the other involved predominantly frontal regions and the thalamus. Both subtypes included primarily male patients. The third pattern was a mix of these two and presented milder neuroanatomic alterations and comprised a comparable number of men and women. VBM and statistical analyses suggest that these groups could correspond to different neuroanatomical dimensions of schizophrenia. CONCLUSION Our analysis suggests that schizophrenia presents distinct neuroanatomical variants. This variability points to the need for a dimensional neuroanatomical approach using data-driven, mathematically principled multivariate pattern analysis methods, and should be taken into account in clinical studies.
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Affiliation(s)
- Nicolas Honnorat
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA.
| | - Aoyan Dong
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Eva Meisenzahl-Lechner
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
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16
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Espinoza FA, Anderson NE, Vergara VM, Harenski CL, Decety J, Rachakonda S, Damaraju E, Koenigs M, Kosson DS, Harenski K, Calhoun VD, Kiehl KA. Resting-state fMRI dynamic functional network connectivity and associations with psychopathy traits. NEUROIMAGE-CLINICAL 2019; 24:101970. [PMID: 31473543 PMCID: PMC6728837 DOI: 10.1016/j.nicl.2019.101970] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Revised: 07/25/2019] [Accepted: 08/03/2019] [Indexed: 11/03/2022]
Abstract
Studies have used resting-state functional magnetic resonance imaging (rs-fMRI) to examine associations between psychopathy and brain connectivity in selected regions of interest as well as networks covering the whole-brain. One of the limitations of these approaches is that brain connectivity is modeled as a constant state through the scan duration. To address this limitation, we apply group independent component analysis (GICA) and dynamic functional network connectivity (dFNC) analysis to uncover whole-brain, time-varying functional network connectivity (FNC) states in a large forensic sample. We then examined relationships between psychopathic traits (PCL-R total scores, Factor 1 and Factor 2 scores) and FNC states obtained from dFNC analysis. FNC over the scan duration was better represented by five states rather than one state previously shown in static FNC analysis. Consistent with prior findings, psychopathy was associated with networks from paralimbic regions (amygdala and insula). In addition, whole-brain FNC identified 15 networks from nine functional domains (subcortical, auditory, sensorimotor, cerebellar, visual, salience, default mode network, executive control and attentional) related to psychopathy traits (Factor 1 and PCL-R scores). Results also showed that individuals with higher Factor 1 scores (affective and interpersonal traits) spend more time in a state with weaker connectivity overall, and changed states less frequently compared to those with lower Factor 1 scores. On the other hand, individuals with higher Factor 2 scores (impulsive and antisocial behaviors) showed more dynamism (changes to and from different states) than those with lower scores.
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Affiliation(s)
- Flor A Espinoza
- The Mind Research Network, Albuquerque, NM, USA; Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA.
| | | | - Victor M Vergara
- The Mind Research Network, Albuquerque, NM, USA; Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | | | - Jean Decety
- Department of Psychology, University of Chicago, Chicago, IL, USA; Departments of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, USA
| | - Srinivas Rachakonda
- The Mind Research Network, Albuquerque, NM, USA; Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - Eswar Damaraju
- The Mind Research Network, Albuquerque, NM, USA; Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - Michael Koenigs
- Department of Psychiatry, University of Wisconsin Madison, Madison, WI, USA
| | - David S Kosson
- Department of Psychology, Rosalind Franklin University of Medicine and Science, Chicago, IL, USA
| | | | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA; Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - Kent A Kiehl
- The Mind Research Network, Albuquerque, NM, USA; Department of Psychology, University of New Mexico, Albuquerque, NM, USA
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17
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Espinoza FA, Vergara VM, Damaraju E, Henke KG, Faghiri A, Turner JA, Belger AA, Ford JM, McEwen SC, Mathalon DH, Mueller BA, Potkin SG, Preda A, Vaidya JG, van Erp TGM, Calhoun VD. Characterizing Whole Brain Temporal Variation of Functional Connectivity via Zero and First Order Derivatives of Sliding Window Correlations. Front Neurosci 2019; 13:634. [PMID: 31316333 PMCID: PMC6611425 DOI: 10.3389/fnins.2019.00634] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Accepted: 06/03/2019] [Indexed: 11/13/2022] Open
Abstract
Brain functional connectivity has been shown to change over time during resting state fMRI experiments. Close examination of temporal changes have revealed a small set of whole-brain connectivity patterns called dynamic states. Dynamic functional network connectivity (dFNC) studies have demonstrated that it is possible to replicate the dynamic states across several resting state experiments. However, estimation of states and their temporal dynamicity still suffers from noisy and imperfect estimations. In regular dFNC implementations, states are estimated by comparing connectivity patterns through the data without considering time, in other words only zero order changes are examined. In this work we propose a method that includes first order variations of dFNC in the searching scheme of dynamic connectivity patterns. Our approach, referred to as temporal variation of functional network connectivity (tvFNC), estimates the derivative of dFNC, and then searches for reoccurring patterns of concurrent dFNC states and their derivatives. The tvFNC method is first validated using a simulated dataset and then applied to a resting-state fMRI sample including healthy controls (HC) and schizophrenia (SZ) patients and compared to the standard dFNC approach. Our dynamic approach reveals extra patterns in the connectivity derivatives complementing the already reported state patterns. State derivatives consist of additional information about increment and decrement of connectivity among brain networks not observed by the original dFNC method. The tvFNC shows more sensitivity than regular dFNC by uncovering additional FNC differences between the HC and SZ groups in each state. In summary, the tvFNC method provides a new and enhanced approach to examine time-varying functional connectivity.
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Affiliation(s)
- Flor A Espinoza
- Mind Research Network, Albuquerque, NM, United States.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Victor M Vergara
- Mind Research Network, Albuquerque, NM, United States.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Eswar Damaraju
- Mind Research Network, Albuquerque, NM, United States.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Kyle G Henke
- Mind Research Network, Albuquerque, NM, United States.,Department of Mathematics and Statistics, The University of New Mexico, Albuquerque, NM, United States
| | - Ashkan Faghiri
- Mind Research Network, Albuquerque, NM, United States.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States.,Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, NM, United States
| | - Jessica A Turner
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States.,Department of Psychology and Neuroscience, Georgia State University, Atlanta, GA, United States
| | - Aysenil A Belger
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Judith M Ford
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States.,San Francisco VA Medical Center, San Francisco, CA, United States
| | - Sarah C McEwen
- Pacific Neuroscience Institute, Santa Monica, CA, United States.,John Wayne Cancer Institute, Department of Translational Neurosciences and Neurotherapeutics, Santa Monica, CA, United States
| | - Daniel H Mathalon
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States.,San Francisco VA Medical Center, San Francisco, CA, United States
| | - Bryon A Mueller
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, United States
| | - Steven G Potkin
- Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, United States
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, United States
| | - Jatin G Vaidya
- Department of Psychiatry, The University of Iowa, Iowa City, IA, United States
| | - Theo G M van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, United States.,Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, CA, United States
| | - Vince D Calhoun
- Mind Research Network, Albuquerque, NM, United States.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States.,Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, NM, United States.,Department of Psychology and Neuroscience, Georgia State University, Atlanta, GA, United States
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18
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Espinoza FA, Liu J, Ciarochi J, Turner JA, Vergara VM, Caprihan A, Misiura M, Johnson HJ, Long JD, Bockholt JH, Paulsen JS, Calhoun VD. Dynamic functional network connectivity in Huntington's disease and its associations with motor and cognitive measures. Hum Brain Mapp 2019; 40:1955-1968. [PMID: 30618191 PMCID: PMC6865767 DOI: 10.1002/hbm.24504] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Revised: 12/12/2018] [Accepted: 12/14/2018] [Indexed: 02/03/2023] Open
Abstract
Dynamic functional network connectivity (dFNC) is an expansion of traditional, static FNC that measures connectivity variation among brain networks throughout scan duration. We used a large resting-state fMRI (rs-fMRI) sample from the PREDICT-HD study (N = 183 Huntington disease gene mutation carriers [HDgmc] and N = 78 healthy control [HC] participants) to examine whole-brain dFNC and its associations with CAG repeat length as well as the product of scaled CAG length and age, a variable representing disease burden. We also tested for relationships between functional connectivity and motor and cognitive measurements. Group independent component analysis was applied to rs-fMRI data to obtain whole-brain resting state networks. FNC was defined as the correlation between RSN time-courses. Dynamic FNC behavior was captured using a sliding time window approach, and FNC results from each window were assigned to four clusters representing FNC states, using a k-means clustering algorithm. HDgmc individuals spent significantly more time in State-1 (the state with the weakest FNC pattern) compared to HC. However, overall HC individuals showed more FNC dynamism than HDgmc. Significant associations between FNC states and genetic and clinical variables were also identified. In FNC State-4 (the one that most resembled static FNC), HDgmc exhibited significantly decreased connectivity between the putamen and medial prefrontal cortex compared to HC, and this was significantly associated with cognitive performance. In FNC State-1, disease burden in HDgmc participants was significantly associated with connectivity between the postcentral gyrus and posterior cingulate cortex, as well as between the inferior occipital gyrus and posterior parietal cortex.
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Affiliation(s)
- Flor A. Espinoza
- Department of Translational Neuroscience, The Mind Research NetworkAlbuquerqueNew Mexico
| | - Jingyu Liu
- Department of Translational Neuroscience, The Mind Research NetworkAlbuquerqueNew Mexico
| | - Jennifer Ciarochi
- Department of Psychology and NeuroscienceGeorgia State UniversityAtlantaGeorgia
| | - Jessica A. Turner
- Department of Psychology and NeuroscienceGeorgia State UniversityAtlantaGeorgia
| | - Victor M. Vergara
- Department of Translational Neuroscience, The Mind Research NetworkAlbuquerqueNew Mexico
| | - Arvind Caprihan
- Department of Translational Neuroscience, The Mind Research NetworkAlbuquerqueNew Mexico
| | - Maria Misiura
- Department of Psychology and NeuroscienceGeorgia State UniversityAtlantaGeorgia
| | - Hans J. Johnson
- Department of Electrical and Computer EngineeringUniversity of IowaIowa CityIowa
- Department of PsychiatryUniversity of IowaIowa CityIowa
| | - Jeffrey D. Long
- Department of PsychiatryUniversity of IowaIowa CityIowa
- Department of BiostatisticsUniversity of IowaIowa CityIowa
| | - Jeremy H. Bockholt
- Department of Translational Neuroscience, The Mind Research NetworkAlbuquerqueNew Mexico
| | | | - Vince D. Calhoun
- Department of Translational Neuroscience, The Mind Research NetworkAlbuquerqueNew Mexico
- Department of Psychology and NeuroscienceGeorgia State UniversityAtlantaGeorgia
- Department of Electrical and Computer EngineeringUniversity of New MexicoAlbuquerqueNew Mexico
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19
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Rahaman MA, Turner JA, Gupta CN, Rachakonda S, Chen J, Liu J, van Erp TGM, Potkin S, Ford J, Mathalon D, Lee HJ, Jiang W, Mueller BA, Andreassen O, Agartz I, Sponheim SR, Mayer AR, Stephen J, Jung RE, Canive J, Bustillo J, Calhoun VD. N-BiC: A Method for Multi-Component and Symptom Biclustering of Structural MRI Data: Application to Schizophrenia. IEEE Trans Biomed Eng 2019; 67:110-121. [PMID: 30946659 PMCID: PMC7906485 DOI: 10.1109/tbme.2019.2908815] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE We propose and develop a novel biclustering (N-BiC) approach for performing N-way biclustering of neuroimaging data. Our approach is applicable to an arbitrary number of features from both imaging and behavioral data (e.g., symptoms). We applied it to structural MRI data from patients with schizophrenia. METHODS It uses a source-based morphometry approach [i.e., independent component analysis of gray matter segmentation maps] to decompose the data into a set of spatial maps, each of which includes regions that covary among individuals. Then, the loading parameters for components of interest are entered to an exhaustive search, which incorporates a modified depth-first search technique to carry out the biclustering, with the goal of obtaining submatrices where the selected rows (individuals) show homogeneity in their expressions of selected columns (components) and vice versa. RESULTS Findings demonstrate that multiple biclusters have an evident association with distinct brain networks for the different types of symptoms in schizophrenia. The study identifies two components: inferior temporal gyrus (16) and brainstem (7), which are related to positive (distortion/excess of normal function) and negative (diminution/loss of normal function) symptoms in schizophrenia, respectively. CONCLUSION N-BiC is a data-driven method of biclustering MRI data that can exhaustively explore relationships/substructures from a dataset without any prior information with a higher degree of robustness than earlier biclustering applications. SIGNIFICANCE The use of such approaches is important to investigate the underlying biological substrates of mental illness by grouping patients into homogeneous subjects, as the schizophrenia diagnosis is known to be relatively nonspecific and heterogeneous.
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20
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Hua J, Blair NIS, Paez A, Choe A, Barber AD, Brandt A, Lim IAL, Xu F, Kamath V, Pekar JJ, van Zijl PCM, Ross CA, Margolis RL. Altered functional connectivity between sub-regions in the thalamus and cortex in schizophrenia patients measured by resting state BOLD fMRI at 7T. Schizophr Res 2019; 206:370-377. [PMID: 30409697 PMCID: PMC6500777 DOI: 10.1016/j.schres.2018.10.016] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Revised: 10/11/2018] [Accepted: 10/20/2018] [Indexed: 12/21/2022]
Abstract
The thalamus is a small brain structure that relays neuronal signals between subcortical and cortical regions. Abnormal thalamocortical connectivity in schizophrenia has been reported in previous studies using blood-oxygenation-level-dependent (BOLD) functional MRI (fMRI) performed at 3T. However, anatomically the thalamus is not a single entity, but is subdivided into multiple distinct nuclei with different connections to various cortical regions. We sought to determine the potential benefit of using the enhanced sensitivity of BOLD fMRI at ultra-high magnetic field (7T) in exploring thalamo-cortical connectivity in schizophrenia based on subregions in the thalamus. Seeds placed in thalamic subregions of 14 patients and 14 matched controls were used to calculate whole-brain functional connectivity. Our results demonstrate impaired thalamic connectivity to the prefrontal cortex and the cerebellum, but enhanced thalamic connectivity to the motor/sensory cortex in schizophrenia. This altered functional connectivity significantly correlated with disease duration in the patients. Remarkably, comparable effect sizes observed in previous 3T studies were detected in the current 7T study with a heterogeneous and much smaller cohort, providing evidence that ultra-high field fMRI may be a powerful tool for measuring functional connectivity abnormalities in schizophrenia. Further investigation with a larger cohort is merited to validate the current findings.
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Affiliation(s)
- Jun Hua
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of MR Research, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA.
| | - Nicholas I S Blair
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Adrian Paez
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Ann Choe
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of MR Research, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Anita D Barber
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, New York, USA; Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York, USA
| | - Allison Brandt
- Department of Psychiatry and Behavioral Sciences and Program in Cellular and Molecular Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Issel Anne L Lim
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of MR Research, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Feng Xu
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of MR Research, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Vidyulata Kamath
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - James J Pekar
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of MR Research, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Peter C M van Zijl
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of MR Research, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Christopher A Ross
- Department of Psychiatry and Behavioral Sciences and Program in Cellular and Molecular Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Neuroscience and Pharmacology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Russell L Margolis
- Department of Psychiatry and Behavioral Sciences and Program in Cellular and Molecular Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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21
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Abdolmaleky HM, Gower AC, Wong CK, Cox JW, Zhang X, Thiagalingam A, Shafa R, Sivaraman V, Zhou JR, Thiagalingam S. Aberrant transcriptomes and DNA methylomes define pathways that drive pathogenesis and loss of brain laterality/asymmetry in schizophrenia and bipolar disorder. Am J Med Genet B Neuropsychiatr Genet 2019; 180:138-149. [PMID: 30468562 PMCID: PMC6386618 DOI: 10.1002/ajmg.b.32691] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2017] [Revised: 07/23/2018] [Accepted: 09/18/2018] [Indexed: 12/15/2022]
Abstract
Although the loss of brain laterality is one of the most consistent modalities in schizophrenia (SCZ) and bipolar disorder (BD), its molecular basis remains elusive. Our limited previous studies indicated that epigenetic modifications are key to the asymmetric transcriptomes of brain hemispheres. We used whole-genome expression microarrays to profile postmortem brain samples from subjects with SCZ, psychotic BD [BD[+]] or non-psychotic BD [BD(-)], or matched controls (10/group) and performed whole-genome DNA methylation (DNAM) profiling of the same samples (3-4/group) to identify pathways associated with SCZ or BD[+] and genes/sites susceptible to epigenetic regulation. qRT-PCR and quantitative DNAM analysis were employed to validate findings in larger sample sets (35/group). Gene Set Enrichment Analysis (GSEA) demonstrated that BMP signaling and astrocyte and cerebral cortex development are significantly (FDR q < 0.25) coordinately upregulated in both SCZ and BD[+], and glutamate signaling and TGFβ signaling are significantly coordinately upregulated in SCZ. GSEA also indicated that collagens are downregulated in right versus left brain of controls, but not in SCZ or BD[+] patients. Ingenuity Pathway Analysis predicted that TGFB2 is an upstream regulator of these genes (p = .0012). While lateralized expression of TGFB2 in controls (p = .017) is associated with a corresponding change in DNAM (p ≤ .023), lateralized expression and DNAM of TGFB2 are absent in SCZ or BD. Loss of brain laterality in SCZ and BD corresponds to aberrant epigenetic regulation of TGFB2 and changes in TGFβ signaling, indicating potential avenues for disease prevention/treatment.
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Affiliation(s)
- Hamid Mostafavi Abdolmaleky
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA,Nutrition/Metabolism Laboratory, BIDMC, Harvard Medical School, Boston, MA,Corresponding Authors: Hamid Mostafavi Abdolmaleky () and Sam Thiagalingam ()
| | - Adam Chapin Gower
- Clinical and Translational Science Institute, Boston University School of Medicine, Boston, MA
| | - Chen Khuan Wong
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA,Genetics & Genomics Graduate Program, Boston University School of Medicine, Boston, MA
| | - Jiayi Wu Cox
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA,Bioinformatics Graduate Program, Boston University, Boston, MA
| | - Xiaoling Zhang
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA
| | - Arunthathi Thiagalingam
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA
| | | | - Vadivelu Sivaraman
- Critical Care Medicine, Department of Anesthesiology, University of Maryland School of Medicine, Baltimore, MD
| | - Jin-Rong Zhou
- Nutrition/Metabolism Laboratory, BIDMC, Harvard Medical School, Boston, MA
| | - Sam Thiagalingam
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA,Genetics & Genomics Graduate Program, Boston University School of Medicine, Boston, MA,Department of Pathology & Laboratory Medicine, Boston University School of Medicine, Boston, MA,Corresponding Authors: Hamid Mostafavi Abdolmaleky () and Sam Thiagalingam ()
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22
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Anderson NE, Harenski KA, Harenski CL, Koenigs MR, Decety J, Calhoun VD, Kiehl KA. Machine learning of brain gray matter differentiates sex in a large forensic sample. Hum Brain Mapp 2018; 40:1496-1506. [PMID: 30430711 DOI: 10.1002/hbm.24462] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Revised: 09/05/2018] [Accepted: 10/27/2018] [Indexed: 12/31/2022] Open
Abstract
Differences between males and females have been extensively documented in biological, psychological, and behavioral domains. Among these, sex differences in the rate and typology of antisocial behavior remains one of the most conspicuous and enduring patterns among humans. However, the nature and extent of sexual dimorphism in the brain among antisocial populations remains mostly unexplored. Here, we seek to understand sex differences in brain structure between incarcerated males and females in a large sample (n = 1,300) using machine learning. We apply source-based morphometry, a contemporary multivariate approach for quantifying gray matter measured with magnetic resonance imaging, and carry these parcellations forward using machine learning to classify sex. Models using components of brain gray matter volume and concentration were able to differentiate between males and females with greater than 93% generalizable accuracy. Highly differentiated components include orbitofrontal and frontopolar regions, proportionally larger in females, and anterior medial temporal regions proportionally larger in males. We also provide a complimentary analysis of a nonforensic healthy control sample and replicate our 93% sex discrimination. These findings demonstrate that the brains of males and females are highly distinguishable. Understanding sex differences in the brain has implications for elucidating variability in the incidence and progression of disease, psychopathology, and differences in psychological traits and behavior. The reliability of these differences confirms the importance of sex as a moderator of individual differences in brain structure and suggests future research should consider sex specific models.
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Affiliation(s)
- Nathaniel E Anderson
- The Mind Research Network & Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico
| | - Keith A Harenski
- The Mind Research Network & Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico
| | - Carla L Harenski
- The Mind Research Network & Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico
| | | | | | - Vince D Calhoun
- The Mind Research Network & Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico.,University of New Mexico, Albuquerque, New Mexico
| | - Kent A Kiehl
- The Mind Research Network & Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico.,University of New Mexico, Albuquerque, New Mexico
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23
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Espinoza FA, Vergara VM, Reyes D, Anderson NE, Harenski CL, Decety J, Rachakonda S, Damaraju E, Rashid B, Miller RL, Koenigs M, Kosson DS, Harenski K, Kiehl KA, Calhoun VD. Aberrant functional network connectivity in psychopathy from a large (N = 985) forensic sample. Hum Brain Mapp 2018; 39:2624-2634. [PMID: 29498761 PMCID: PMC5951759 DOI: 10.1002/hbm.24028] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2017] [Revised: 02/07/2018] [Accepted: 02/20/2018] [Indexed: 01/31/2023] Open
Abstract
Psychopathy is a personality disorder characterized by antisocial behavior, lack of remorse and empathy, and impaired decision making. The disproportionate amount of crime committed by psychopaths has severe emotional and economic impacts on society. Here we examine the neural correlates associated with psychopathy to improve early assessment and perhaps inform treatments for this condition. Previous resting-state functional magnetic resonance imaging (fMRI) studies in psychopathy have primarily focused on regions of interest. This study examines whole-brain functional connectivity and its association to psychopathic traits. Psychopathy was hypothesized to be characterized by aberrant functional network connectivity (FNC) in several limbic/paralimbic networks. Group-independent component and regression analyses were applied to a data set of resting-state fMRI from 985 incarcerated adult males. We identified resting-state networks (RSNs), estimated FNC between RSNs, and tested their association to psychopathy factors and total summary scores (Factor 1, interpersonal/affective; Factor 2, lifestyle/antisocial). Factor 1 scores showed both increased and reduced functional connectivity between RSNs from seven brain domains (sensorimotor, cerebellar, visual, salience, default mode, executive control, and attentional). Consistent with hypotheses, RSNs from the paralimbic system-insula, anterior and posterior cingulate cortex, amygdala, orbital frontal cortex, and superior temporal gyrus-were related to Factor 1 scores. No significant FNC associations were found with Factor 2 and total PCL-R scores. In summary, results suggest that the affective and interpersonal symptoms of psychopathy (Factor 1) are associated with aberrant connectivity in multiple brain networks, including paralimbic regions.
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Affiliation(s)
| | | | - Daisy Reyes
- The Mind Research NetworkAlbuquerqueNew Mexico87106
- Department of Mathematics and StatisticsUniversity of New MexicoAlbuquerqueNew Mexico87131
| | | | | | - Jean Decety
- Departments of Psychology and Psychiatry and Behavioral NeuroscienceUniversity of ChicagoChicagoIllinois
| | | | | | | | | | - Michael Koenigs
- Department of PsychiatryUniversity of Wisconsin – MadisonMadisonWisconsin
| | - David S. Kosson
- Department of PsychologyRosalind Franklin UniversityNorth ChicagoIllinois
| | | | - Kent A. Kiehl
- The Mind Research NetworkAlbuquerqueNew Mexico87106
- Department of PsychologyUniversity of New MexicoAlbuquerqueNew Mexico87131
| | - Vince D. Calhoun
- The Mind Research NetworkAlbuquerqueNew Mexico87106
- Department of Electrical and Computer EngineeringUniversity of New MexicoAlbuquerqueNew Mexico87131
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24
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Espinoza FA, Turner JA, Vergara VM, Miller RL, Mennigen E, Liu J, Misiura MB, Ciarochi J, Johnson HJ, Long JD, Bockholt HJ, Magnotta VA, Paulsen JS, Calhoun VD. Whole-Brain Connectivity in a Large Study of Huntington's Disease Gene Mutation Carriers and Healthy Controls. Brain Connect 2018; 8:166-178. [PMID: 29291624 DOI: 10.1089/brain.2017.0538] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Huntington's disease (HD) is an inherited brain disorder characterized by progressive motor, cognitive, and behavioral dysfunctions. It is caused by abnormally large trinucleotide cytosine-adenine-guanine (CAG) repeat expansions on exon 1 of the Huntingtin gene. CAG repeat length (CAG-RL) inversely correlates with an earlier age of onset. Region-based studies have shown that HD gene mutation carrier (HDgmc) individuals (CAG-RL ≥36) present functional connectivity alterations in subcortical (SC) and default mode networks. In this analysis, we expand on previous HD studies by investigating associations between CAG-RL and connectivity in the whole brain, as well as between CAG-dependent connectivity and motor and cognitive performances. We used group-independent component analysis on resting-state functional magnetic resonance imaging scans of 261 individuals (183 HDgmc and 78 healthy controls) from the PREDICT-HD study, to obtain whole-brain resting state networks (RSNs). Regression analysis was applied within and between RSNs connectivity (functional network connectivity [FNC]) to identify CAG-RL associations. Connectivity within the putamen RSN is negatively correlated with CAG-RL. The FNC between putamen and insula decreases with increasing CAG-RL, and also shows significant associations with motor and cognitive measures. The FNC between calcarine and middle frontal gyri increased with CAG-RL. In contrast, FNC in other visual (VIS) networks declined with increasing CAG-RL. In addition to observed effects in SC areas known to be related to HD, our study identifies a strong presence of alterations in VIS regions less commonly observed in previous reports and provides a step forward in understanding FNC dysfunction in HDgmc.
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Affiliation(s)
- Flor A Espinoza
- 1 Department of Translational Neuroscience, The Mind Research Network , Albuquerque, New Mexico
| | - Jessica A Turner
- 2 Departments of Psychology and Neuroscience, Georgia State University , Atlanta, Georgia
| | - Victor M Vergara
- 1 Department of Translational Neuroscience, The Mind Research Network , Albuquerque, New Mexico
| | - Robyn L Miller
- 1 Department of Translational Neuroscience, The Mind Research Network , Albuquerque, New Mexico
| | - Eva Mennigen
- 1 Department of Translational Neuroscience, The Mind Research Network , Albuquerque, New Mexico
| | - Jingyu Liu
- 1 Department of Translational Neuroscience, The Mind Research Network , Albuquerque, New Mexico
| | - Maria B Misiura
- 2 Departments of Psychology and Neuroscience, Georgia State University , Atlanta, Georgia
| | - Jennifer Ciarochi
- 2 Departments of Psychology and Neuroscience, Georgia State University , Atlanta, Georgia
| | - Hans J Johnson
- 3 Department of Psychiatry, Neurology, Psychological and Brain Sciences, University of Iowa , Iowa City, Iowa
| | - Jeffrey D Long
- 3 Department of Psychiatry, Neurology, Psychological and Brain Sciences, University of Iowa , Iowa City, Iowa.,4 Department of Biostatistics, University of Iowa , Iowa City, Iowa
| | - Henry J Bockholt
- 1 Department of Translational Neuroscience, The Mind Research Network , Albuquerque, New Mexico .,3 Department of Psychiatry, Neurology, Psychological and Brain Sciences, University of Iowa , Iowa City, Iowa
| | | | - Jane S Paulsen
- 3 Department of Psychiatry, Neurology, Psychological and Brain Sciences, University of Iowa , Iowa City, Iowa
| | - Vince D Calhoun
- 1 Department of Translational Neuroscience, The Mind Research Network , Albuquerque, New Mexico .,6 Department of Electrical and Computer Engineering, University of New Mexico , Albuquerque, New Mexico
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25
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Abstract
Statistical parametric maps formed via voxel-wise mass-univariate tests, such as the general linear model, are commonly used to test hypotheses about regionally specific effects in neuroimaging cross-sectional studies where each subject is represented by a single image. Despite being informative, these techniques remain limited as they ignore multivariate relationships in the data. Most importantly, the commonly employed local Gaussian smoothing, which is important for accounting for registration errors and making the data follow Gaussian distributions, is usually chosen in an ad hoc fashion. Thus, it is often suboptimal for the task of detecting group differences and correlations with non-imaging variables. Information mapping techniques, such as searchlight, which use pattern classifiers to exploit multivariate information and obtain more powerful statistical maps, have become increasingly popular in recent years. However, existing methods may lead to important interpretation errors in practice (i.e., misidentifying a cluster as informative, or failing to detect truly informative voxels), while often being computationally expensive. To address these issues, we introduce a novel efficient multivariate statistical framework for cross-sectional studies, termed MIDAS, seeking highly sensitive and specific voxel-wise brain maps, while leveraging the power of regional discriminant analysis. In MIDAS, locally linear discriminative learning is applied to estimate the pattern that best discriminates between two groups, or predicts a variable of interest. This pattern is equivalent to local filtering by an optimal kernel whose coefficients are the weights of the linear discriminant. By composing information from all neighborhoods that contain a given voxel, MIDAS produces a statistic that collectively reflects the contribution of the voxel to the regional classifiers as well as the discriminative power of the classifiers. Critically, MIDAS efficiently assesses the statistical significance of the derived statistic by analytically approximating its null distribution without the need for computationally expensive permutation tests. The proposed framework was extensively validated using simulated atrophy in structural magnetic resonance imaging (MRI) and further tested using data from a task-based functional MRI study as well as a structural MRI study of cognitive performance. The performance of the proposed framework was evaluated against standard voxel-wise general linear models and other information mapping methods. The experimental results showed that MIDAS achieves relatively higher sensitivity and specificity in detecting group differences. Together, our results demonstrate the potential of the proposed approach to efficiently map effects of interest in both structural and functional data.
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Affiliation(s)
- Erdem Varol
- Section for Biomedical Image Analysis, Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
| | - Aristeidis Sotiras
- Section for Biomedical Image Analysis, Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Christos Davatzikos
- Section for Biomedical Image Analysis, Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, 19104, USA
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26
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Gao X, Zhang W, Yao L, Xiao Y, Liu L, Liu J, Li S, Tao B, Shah C, Gong Q, Sweeney JA, Lui S. Association between structural and functional brain alterations in drug-free patients with schizophrenia: a multimodal meta-analysis. J Psychiatry Neurosci 2018; 43:131-142. [PMID: 29481320 PMCID: PMC5837885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2016] [Revised: 08/29/2017] [Accepted: 09/09/2017] [Indexed: 03/17/2024] Open
Abstract
BACKGROUND Neuroimaging studies have shown both structural and functional abnormalities in patients with schizophrenia. Recently, studies have begun to explore the association between structural and functional grey matter abnormalities. By conducting a meta-analysis on morphometric and functional imaging studies of grey matter alterations in drug-free patients, the present study aims to examine the degree of overlap between brain regions with anatomic and functional changes in patients with schizophrenia. METHODS We performed a systematic search of PubMed, Embase, Web of Science and the Cochrane Library to identify relevant publications. A multimodal analysis was then conducted using Seed-based d Mapping software. Exploratory analyses included jackknife, subgroup and meta-regression analyses. RESULTS We included 15 structural MRI studies comprising 486 drug-free patients and 485 healthy controls, and 16 functional MRI studies comprising 403 drug-free patients and 428 controls in our meta-analysis. Drug-free patients were examined to reduce pharmacological effects on the imaging data. Multimodal analysis showed considerable overlap between anatomic and functional changes, mainly in frontotemporal regions, bilateral medial posterior cingulate/paracingulate gyrus, bilateral insula, basal ganglia and left cerebellum. There were also brain regions showing only anatomic changes in the right superior frontal gyrus, left supramarginal gyrus, right lingual gyrus and functional alternations involving the right angular gyrus. LIMITATIONS The methodological aspects, patient characteristics and clinical variables of the included studies were heterogeneous, and we cannot exclude medication effects. CONCLUSION The present study showed overlapping anatomic and functional brain abnormalities mainly in the default mode (DMN) and auditory networks (AN) in drug-free patients with schizophrenia. However, the pattern of changes differed in these networks. Decreased grey matter was associated with decreased activation within the DMN, whereas it was associated with increased activation within the AN. These discrete patterns suggest different pathophysiological changes impacting structural and functional associations within different neural networks in patients with schizophrenia.
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Affiliation(s)
- Xin Gao
- From the Department of Radiology, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China (Gao, Lui); the Department of Radiology, the Centre for Medical Imaging, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gao, Zhang, Yao, Xiao, Liu, Li, Tao, Shah, Gong, Lui); and the Department of Psychiatry, University of Texas Southwestern, Dallas, Tex, USA (Sweeney)
| | - Wenjing Zhang
- From the Department of Radiology, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China (Gao, Lui); the Department of Radiology, the Centre for Medical Imaging, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gao, Zhang, Yao, Xiao, Liu, Li, Tao, Shah, Gong, Lui); and the Department of Psychiatry, University of Texas Southwestern, Dallas, Tex, USA (Sweeney)
| | - Li Yao
- From the Department of Radiology, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China (Gao, Lui); the Department of Radiology, the Centre for Medical Imaging, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gao, Zhang, Yao, Xiao, Liu, Li, Tao, Shah, Gong, Lui); and the Department of Psychiatry, University of Texas Southwestern, Dallas, Tex, USA (Sweeney)
| | - Yuan Xiao
- From the Department of Radiology, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China (Gao, Lui); the Department of Radiology, the Centre for Medical Imaging, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gao, Zhang, Yao, Xiao, Liu, Li, Tao, Shah, Gong, Lui); and the Department of Psychiatry, University of Texas Southwestern, Dallas, Tex, USA (Sweeney)
| | - Lu Liu
- From the Department of Radiology, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China (Gao, Lui); the Department of Radiology, the Centre for Medical Imaging, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gao, Zhang, Yao, Xiao, Liu, Li, Tao, Shah, Gong, Lui); and the Department of Psychiatry, University of Texas Southwestern, Dallas, Tex, USA (Sweeney)
| | - Jieke Liu
- From the Department of Radiology, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China (Gao, Lui); the Department of Radiology, the Centre for Medical Imaging, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gao, Zhang, Yao, Xiao, Liu, Li, Tao, Shah, Gong, Lui); and the Department of Psychiatry, University of Texas Southwestern, Dallas, Tex, USA (Sweeney)
| | - Siyi Li
- From the Department of Radiology, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China (Gao, Lui); the Department of Radiology, the Centre for Medical Imaging, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gao, Zhang, Yao, Xiao, Liu, Li, Tao, Shah, Gong, Lui); and the Department of Psychiatry, University of Texas Southwestern, Dallas, Tex, USA (Sweeney)
| | - Bo Tao
- From the Department of Radiology, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China (Gao, Lui); the Department of Radiology, the Centre for Medical Imaging, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gao, Zhang, Yao, Xiao, Liu, Li, Tao, Shah, Gong, Lui); and the Department of Psychiatry, University of Texas Southwestern, Dallas, Tex, USA (Sweeney)
| | - Chandan Shah
- From the Department of Radiology, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China (Gao, Lui); the Department of Radiology, the Centre for Medical Imaging, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gao, Zhang, Yao, Xiao, Liu, Li, Tao, Shah, Gong, Lui); and the Department of Psychiatry, University of Texas Southwestern, Dallas, Tex, USA (Sweeney)
| | - Qiyong Gong
- From the Department of Radiology, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China (Gao, Lui); the Department of Radiology, the Centre for Medical Imaging, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gao, Zhang, Yao, Xiao, Liu, Li, Tao, Shah, Gong, Lui); and the Department of Psychiatry, University of Texas Southwestern, Dallas, Tex, USA (Sweeney)
| | - John A Sweeney
- From the Department of Radiology, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China (Gao, Lui); the Department of Radiology, the Centre for Medical Imaging, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gao, Zhang, Yao, Xiao, Liu, Li, Tao, Shah, Gong, Lui); and the Department of Psychiatry, University of Texas Southwestern, Dallas, Tex, USA (Sweeney)
| | - Su Lui
- From the Department of Radiology, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China (Gao, Lui); the Department of Radiology, the Centre for Medical Imaging, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gao, Zhang, Yao, Xiao, Liu, Li, Tao, Shah, Gong, Lui); and the Department of Psychiatry, University of Texas Southwestern, Dallas, Tex, USA (Sweeney)
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27
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Lottman KK, White DM, Kraguljac NV, Reid MA, Calhoun VD, Catao F, Lahti AC. Four-way multimodal fusion of 7 T imaging data using an mCCA+jICA model in first-episode schizophrenia. Hum Brain Mapp 2018; 39:1475-1488. [PMID: 29315951 DOI: 10.1002/hbm.23906] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2017] [Revised: 11/06/2017] [Accepted: 11/26/2017] [Indexed: 01/05/2023] Open
Abstract
Acquisition of multimodal brain imaging data for the same subject has become more common leading to a growing interest in determining the intermodal relationships between imaging modalities to further elucidate the pathophysiology of schizophrenia. Multimodal data have previously been individually analyzed and subsequently integrated; however, these analysis techniques lack the ability to examine true modality inter-relationships. The utilization of a multiset canonical correlation and joint independent component analysis (mCCA + jICA) model for data fusion allows shared or distinct abnormalities between modalities to be examined. In this study, first-episode schizophrenia patients (nSZ =19) and matched controls (nHC =21) completed a resting-state functional magnetic resonance imaging (fMRI) scan at 7 T. Grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), and amplitude of low frequency fluctuation (ALFF) maps were used as features in a mCCA + jICA model. Results of the mCCA + jICA model indicated three joint group-discriminating components (GM-CSF, WM-ALFF, GM-ALFF) and two modality-unique group-discriminating components (GM, WM). The joint component findings are highlighted by GM basal ganglia, somatosensory, parietal lobe, and thalamus abnormalities associated with ventricular CSF volume; WM occipital and frontal lobe abnormalities associated with temporal lobe function; and GM frontal, temporal, parietal, and occipital lobe abnormalities associated with caudate function. These results support and extend major findings throughout the literature using independent single modality analyses. The multimodal fusion of 7 T data in this study provides a more comprehensive illustration of the relationships between underlying neuronal abnormalities associated with schizophrenia than examination of imaging data independently.
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Affiliation(s)
- Kristin K Lottman
- Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, Alabama
| | - David M White
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Nina V Kraguljac
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Meredith A Reid
- Department of Electrical and Computer Engineering, MRI Research Center, Auburn University, Auburn, Alabama
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, New Mexico.,Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, New Mexico
| | - Fabio Catao
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Adrienne C Lahti
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, Alabama
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28
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Gao X, Zhang W, Yao L, Xiao Y, Liu L, Liu J, Li S, Tao B, Shah C, Gong Q, Sweeney JA, Lui S. Association between structural and functional brain alterations in drug-free patients with schizophrenia: a multimodal meta-analysis. J Psychiatry Neurosci 2017; 43:160219. [PMID: 29244020 PMCID: PMC5837885 DOI: 10.1503/jpn.160219] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2016] [Revised: 08/29/2017] [Accepted: 09/09/2017] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Neuroimaging studies have shown both structural and functional abnormalities in patients with schizophrenia. Recently, studies have begun to explore the association between structural and functional grey matter abnormalities. By conducting a meta-analysis on morphometric and functional imaging studies of grey matter alterations in drug-free patients, the present study aims to examine the degree of overlap between brain regions with anatomic and functional changes in patients with schizophrenia. METHODS We performed a systematic search of PubMed, Embase, Web of Science and the Cochrane Library to identify relevant publications. A multimodal analysis was then conducted using Seed-based d Mapping software. Exploratory analyses included jackknife, subgroup and meta-regression analyses. RESULTS We included 15 structural MRI studies comprising 486 drug-free patients and 485 healthy controls, and 16 functional MRI studies comprising 403 drug-free patients and 428 controls in our meta-analysis. Drug-free patients were examined to reduce pharmacological effects on the imaging data. Multimodal analysis showed considerable overlap between anatomic and functional changes, mainly in frontotemporal regions, bilateral medial posterior cingulate/paracingulate gyrus, bilateral insula, basal ganglia and left cerebellum. There were also brain regions showing only anatomic changes in the right superior frontal gyrus, left supramarginal gyrus, right lingual gyrus and functional alternations involving the right angular gyrus. LIMITATIONS The methodological aspects, patient characteristics and clinical variables of the included studies were heterogeneous, and we cannot exclude medication effects. CONCLUSION The present study showed overlapping anatomic and functional brain abnormalities mainly in the default mode (DMN) and auditory networks (AN) in drug-free patients with schizophrenia. However, the pattern of changes differed in these networks. Decreased grey matter was associated with decreased activation within the DMN, whereas it was associated with increased activation within the AN. These discrete patterns suggest different pathophysiological changes impacting structural and functional associations within different neural networks in patients with schizophrenia.
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Affiliation(s)
- Xin Gao
- From the Department of Radiology, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China (Gao, Lui); the Department of Radiology, the Centre for Medical Imaging, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gao, Zhang, Yao, Xiao, Liu, Li, Tao, Shah, Gong, Lui); and the Department of Psychiatry, University of Texas Southwestern, Dallas, Tex, USA (Sweeney)
| | - Wenjing Zhang
- From the Department of Radiology, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China (Gao, Lui); the Department of Radiology, the Centre for Medical Imaging, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gao, Zhang, Yao, Xiao, Liu, Li, Tao, Shah, Gong, Lui); and the Department of Psychiatry, University of Texas Southwestern, Dallas, Tex, USA (Sweeney)
| | - Li Yao
- From the Department of Radiology, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China (Gao, Lui); the Department of Radiology, the Centre for Medical Imaging, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gao, Zhang, Yao, Xiao, Liu, Li, Tao, Shah, Gong, Lui); and the Department of Psychiatry, University of Texas Southwestern, Dallas, Tex, USA (Sweeney)
| | - Yuan Xiao
- From the Department of Radiology, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China (Gao, Lui); the Department of Radiology, the Centre for Medical Imaging, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gao, Zhang, Yao, Xiao, Liu, Li, Tao, Shah, Gong, Lui); and the Department of Psychiatry, University of Texas Southwestern, Dallas, Tex, USA (Sweeney)
| | - Lu Liu
- From the Department of Radiology, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China (Gao, Lui); the Department of Radiology, the Centre for Medical Imaging, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gao, Zhang, Yao, Xiao, Liu, Li, Tao, Shah, Gong, Lui); and the Department of Psychiatry, University of Texas Southwestern, Dallas, Tex, USA (Sweeney)
| | - Jieke Liu
- From the Department of Radiology, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China (Gao, Lui); the Department of Radiology, the Centre for Medical Imaging, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gao, Zhang, Yao, Xiao, Liu, Li, Tao, Shah, Gong, Lui); and the Department of Psychiatry, University of Texas Southwestern, Dallas, Tex, USA (Sweeney)
| | - Siyi Li
- From the Department of Radiology, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China (Gao, Lui); the Department of Radiology, the Centre for Medical Imaging, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gao, Zhang, Yao, Xiao, Liu, Li, Tao, Shah, Gong, Lui); and the Department of Psychiatry, University of Texas Southwestern, Dallas, Tex, USA (Sweeney)
| | - Bo Tao
- From the Department of Radiology, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China (Gao, Lui); the Department of Radiology, the Centre for Medical Imaging, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gao, Zhang, Yao, Xiao, Liu, Li, Tao, Shah, Gong, Lui); and the Department of Psychiatry, University of Texas Southwestern, Dallas, Tex, USA (Sweeney)
| | - Chandan Shah
- From the Department of Radiology, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China (Gao, Lui); the Department of Radiology, the Centre for Medical Imaging, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gao, Zhang, Yao, Xiao, Liu, Li, Tao, Shah, Gong, Lui); and the Department of Psychiatry, University of Texas Southwestern, Dallas, Tex, USA (Sweeney)
| | - Qiyong Gong
- From the Department of Radiology, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China (Gao, Lui); the Department of Radiology, the Centre for Medical Imaging, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gao, Zhang, Yao, Xiao, Liu, Li, Tao, Shah, Gong, Lui); and the Department of Psychiatry, University of Texas Southwestern, Dallas, Tex, USA (Sweeney)
| | - John A Sweeney
- From the Department of Radiology, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China (Gao, Lui); the Department of Radiology, the Centre for Medical Imaging, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gao, Zhang, Yao, Xiao, Liu, Li, Tao, Shah, Gong, Lui); and the Department of Psychiatry, University of Texas Southwestern, Dallas, Tex, USA (Sweeney)
| | - Su Lui
- From the Department of Radiology, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China (Gao, Lui); the Department of Radiology, the Centre for Medical Imaging, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gao, Zhang, Yao, Xiao, Liu, Li, Tao, Shah, Gong, Lui); and the Department of Psychiatry, University of Texas Southwestern, Dallas, Tex, USA (Sweeney)
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29
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Nemoto K, Oka H, Fukuda H, Yamakawa Y. MRI-based Brain Healthcare Quotients: A bridge between neural and behavioral analyses for keeping the brain healthy. PLoS One 2017; 12:e0187137. [PMID: 29077756 PMCID: PMC5659647 DOI: 10.1371/journal.pone.0187137] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2017] [Accepted: 10/14/2017] [Indexed: 11/24/2022] Open
Abstract
Neurological and psychiatric disorders are a burden on social and economic resources. Therefore, maintaining brain health and preventing these disorders are important. While the physiological functions of the brain are well studied, few studies have focused on keeping the brain healthy from a neuroscientific viewpoint. We propose a magnetic resonance imaging (MRI)-based quotient for monitoring brain health, the Brain Healthcare Quotient (BHQ), which is based on the volume of gray matter (GM) and the fractional anisotropy (FA) of white matter (WM). We recruited 144 healthy adults to acquire structural neuroimaging data, including T1-weighted images and diffusion tensor images, and data associated with both physical (BMI, blood pressure, and daily time use) and social (subjective socioeconomic status, subjective well-being, post-materialism and Epicureanism) factors. We confirmed that the BHQ was sensitive to an age-related decline in GM volume and WM integrity. Further analysis revealed that the BHQ was critically affected by both physical and social factors. We believe that our BHQ is a simple yet highly sensitive, valid measure for brain health research that will bridge the needs of the scientific community and society and help us lead better lives in which we stay healthy, active, and sharp.
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Affiliation(s)
- Kiyotaka Nemoto
- Department of Neuropsychiatry, Division of Clinical Medicine, Faculty of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Hiroki Oka
- ImPACT Program of Council for Science, Technology and Innovation (Cabinet Office, Government of Japan), Chiyoda, Tokyo, Japan
| | - Hiroki Fukuda
- ImPACT Program of Council for Science, Technology and Innovation (Cabinet Office, Government of Japan), Chiyoda, Tokyo, Japan
| | - Yoshinori Yamakawa
- ImPACT Program of Council for Science, Technology and Innovation (Cabinet Office, Government of Japan), Chiyoda, Tokyo, Japan
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30
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Hua J, Brandt AS, Lee S, Blair NIS, Wu Y, Lui S, Patel J, Faria AV, Lim IAL, Unschuld PG, Pekar JJ, van Zijl PCM, Ross CA, Margolis RL. Abnormal Grey Matter Arteriolar Cerebral Blood Volume in Schizophrenia Measured With 3D Inflow-Based Vascular-Space-Occupancy MRI at 7T. Schizophr Bull 2017; 43:620-632. [PMID: 27539951 PMCID: PMC5464028 DOI: 10.1093/schbul/sbw109] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Metabolic dysfunction and microvascular abnormality may contribute to the pathogenesis of schizophrenia. Most previous studies of cerebral perfusion in schizophrenia measured total cerebral blood volume (CBV) and cerebral blood flow (CBF) in the brain, which reflect the ensemble signal from the arteriolar, capillary, and venular compartments of the microvasculature. As the arterioles are the most actively regulated blood vessels among these compartments, they may be the most sensitive component of the microvasculature to metabolic disturbances. In this study, we adopted the inflow-based vascular-space-occupancy (iVASO) MRI approach to investigate alterations in the volume of small arterial (pial) and arteriolar vessels (arteriolar cerebral blood volume [CBVa]) in the brain of schizophrenia patients. The iVASO approach was extended to 3-dimensional (3D) whole brain coverage, and CBVa was measured in the brains of 12 schizophrenia patients and 12 matched controls at ultra-high magnetic field (7T). Significant reduction in grey matter (GM) CBVa was found in multiple areas across the whole brain in patients (relative changes of 14%-51% and effect sizes of 0.7-2.3). GM CBVa values in several regions in the temporal cortex showed significant negative correlations with disease duration in patients. GM CBVa increase was also found in a few brain regions. Our results imply that microvascular abnormality may play a role in schizophrenia, and suggest GM CBVa as a potential marker for the disease. Further investigation is needed to elucidate whether such effects are due to primary vascular impairment or secondary to other causes, such as metabolic dysfunction.
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Affiliation(s)
- Jun Hua
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of MR Research, Johns Hopkins University School of Medicine, Baltimore, MD;,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD
| | - Allison S. Brandt
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD
| | - SeungWook Lee
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
| | | | - Yuankui Wu
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of MR Research, Johns Hopkins University School of Medicine, Baltimore, MD;,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD;,Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Su Lui
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China;,Department of Radiology, the Second Affiliated Hospital, Wenzhou Medical University, Wenzhou, China
| | - Jaymin Patel
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
| | - Andreia V. Faria
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of MR Research, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Issel Anne L. Lim
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of MR Research, Johns Hopkins University School of Medicine, Baltimore, MD;,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD
| | - Paul G. Unschuld
- Division of Psychiatry Research and Psychogeriatric Medicine, University of Zurich, Zurich, Switzerland
| | - James J. Pekar
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of MR Research, Johns Hopkins University School of Medicine, Baltimore, MD;,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD
| | - Peter C. M. van Zijl
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of MR Research, Johns Hopkins University School of Medicine, Baltimore, MD;,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD
| | - Christopher A. Ross
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD;,Department of Neurology and Program in Cellular and Molecular Medicine, Johns Hopkins University School of Medicine, Baltimore, MD;,Departments of Neuroscience and Pharmacology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Russell L. Margolis
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD;,Department of Neurology and Program in Cellular and Molecular Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
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31
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Cai S, Jiang Y, Wang Y, Wu X, Ren J, Lee MS, Lee S, Huang L. Modulation on brain gray matter activity and white matter integrity by APOE ε4 risk gene in cognitively intact elderly: A multimodal neuroimaging study. Behav Brain Res 2017; 322:100-109. [DOI: 10.1016/j.bbr.2017.01.027] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2016] [Revised: 01/11/2017] [Accepted: 01/13/2017] [Indexed: 12/11/2022]
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Zhou FC, Wang CY, Ungvari GS, Ng CH, Zhou Y, Zhang L, Zhou J, Shum DHK, Man D, Liu DT, Li J, Xiang YT. Longitudinal changes in prospective memory and their clinical correlates at 1-year follow-up in first-episode schizophrenia. PLoS One 2017; 12:e0172114. [PMID: 28245266 PMCID: PMC5330457 DOI: 10.1371/journal.pone.0172114] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2016] [Accepted: 01/31/2017] [Indexed: 11/25/2022] Open
Abstract
This study aimed to investigate prospective memory (PM) and the association with clinical factors at 1-year follow-up in first-episode schizophrenia (FES). Thirty-two FES patients recruited from a university-affiliated psychiatric hospital in Beijing and 17 healthy community controls (HCs) were included. Time- and event-based PM (TBPM and EBPM) performances were measured with the Chinese version of the Cambridge Prospective Memory Test (C-CAMPROMPT) at baseline and at one-year follow-up. A number of other neurocognitive tests were also administered. Remission was determined at the endpoint according to the PANSS score ≤ 3 for selected items. Repeated measures analysis of variance revealed a significant interaction between time (baseline vs. endpoint) and group (FES vs. HCs) for EBPM (F(1, 44) = 8.8, p = 0.005) and for all neurocognitive components. Paired samples t-tests showed significant improvement in EBPM in FES (13.1±3.7 vs. 10.3±4.8; t = 3.065, p = 0.004), compared to HCs (15.7±3.6 vs. 16.5±2.3; t = -1.248, p = 0.230). A remission rate of 59.4% was found in the FES group. Analysis of covariance revealed that remitters performed significantly better on EBPM (14.9±2.6 vs. 10.4±3.6; F(1, 25) = 12.2, p = 0.002) than non-remitters at study endpoint. The association between EBPM and 12-month clinical improvement in FES suggests that EBPM may be a potential neurocognitive marker for the effectiveness of standard pharmacotherapy. Furthermore, the findings also imply that PM may not be strictly a trait-related endophenotype as indicated in previous studies.
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Affiliation(s)
- Fu-Chun Zhou
- Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Chuan-Yue Wang
- Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Gabor S. Ungvari
- The University of Notre Dame Australia / Marian Centre, Perth, Australia
- School of Psychiatry & Clinical Neurosciences, University of Western Australia, Perth, Australia
| | - Chee H. Ng
- Department of Psychiatry, University of Melbourne, Melbourne, Victoria, Australia
| | - Yan Zhou
- Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Liang Zhang
- Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Jingjing Zhou
- Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - David H. K. Shum
- Menzies Health Institute Queensland and School of Applied Psychology, Griffith University, Gold Coast, Queensland, Australia
| | - David Man
- Department of Rehabilitation Sciences, Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Deng-Tang Liu
- Department of Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jun Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing, China
- Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China
| | - Yu-Tao Xiang
- Unit of Psychiatry, Faculty of Health Sciences, University of Macau, Macao SAR, China
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Gupta CN, Castro E, Rachkonda S, van Erp TGM, Potkin S, Ford JM, Mathalon D, Lee HJ, Mueller BA, Greve DN, Andreassen OA, Agartz I, Mayer AR, Stephen J, Jung RE, Bustillo J, Calhoun VD, Turner JA. Biclustered Independent Component Analysis for Complex Biomarker and Subtype Identification from Structural Magnetic Resonance Images in Schizophrenia. Front Psychiatry 2017; 8:179. [PMID: 29018368 PMCID: PMC5623192 DOI: 10.3389/fpsyt.2017.00179] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2017] [Accepted: 09/07/2017] [Indexed: 12/14/2022] Open
Abstract
Clinical and cognitive symptoms domain-based subtyping in schizophrenia (Sz) has been critiqued due to the lack of neurobiological correlates and heterogeneity in symptom scores. We, therefore, present a novel data-driven framework using biclustered independent component analysis to detect subtypes from the reliable and stable gray matter concentration (GMC) of patients with Sz. The developed methodology consists of the following steps: source-based morphometry (SBM) decomposition, selection and sorting of two component loadings, subtype component reconstruction using group information-guided ICA (GIG-ICA). This framework was applied to the top two group discriminative components namely the insula/superior temporal gyrus/inferior frontal gyrus (I-STG-IFG component) and the superior frontal gyrus/middle frontal gyrus/medial frontal gyrus (SFG-MiFG-MFG component) from our previous SBM study, which showed diagnostic group difference and had the highest effect sizes. The aggregated multisite dataset consisted of 382 patients with Sz regressed of age, gender, and site voxelwise. We observed two subtypes (i.e., two different subsets of subjects) each heavily weighted on these two components, respectively. These subsets of subjects were characterized by significant differences in positive and negative syndrome scale (PANSS) positive clinical symptoms (p = 0.005). We also observed an overlapping subtype weighing heavily on both of these components. The PANSS general clinical symptom of this subtype was trend level correlated with the loading coefficients of the SFG-MiFG-MFG component (r = 0.25; p = 0.07). The reconstructed subtype-specific component using GIG-ICA showed variations in voxel regions, when compared to the group component. We observed deviations from mean GMC along with conjunction of features from two components characterizing each deciphered subtype. These inherent variations in GMC among patients with Sz could possibly indicate the need for personalized treatment and targeted drug development.
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Affiliation(s)
- Cota Navin Gupta
- The Mind Research Network, Albuquerque, NM, United States.,Department of Biosciences and Bioengineering, Indian Institute of Technology, Guwahati, India
| | - Eduardo Castro
- The Mind Research Network, Albuquerque, NM, United States.,Computational Biology Center, IBM Thomas J. Watson Research, Yorktown Heights, NY, United States
| | | | - Theo G M van Erp
- Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, Irvine, CA, United States
| | - Steven Potkin
- Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, Irvine, CA, United States
| | - Judith M Ford
- Department of Psychiatry, School of Medicine, University of California, San Francisco, San Francisco, CA, United States
| | - Daniel Mathalon
- Department of Psychiatry, School of Medicine, University of California, San Francisco, San Francisco, CA, United States
| | - Hyo Jong Lee
- Divisions of Electronics and Information Engineering, Chonbuk National University, Jeonju, South Korea
| | - Bryon A Mueller
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, United States
| | - Douglas N Greve
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
| | - Ole A Andreassen
- NORMENT, KG Jebsen Center for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ingrid Agartz
- NORMENT, KG Jebsen Center for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden.,Department of Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Andrew R Mayer
- The Mind Research Network, Albuquerque, NM, United States
| | - Julia Stephen
- The Mind Research Network, Albuquerque, NM, United States
| | - Rex E Jung
- Department of Neurosurgery, University of New Mexico Health Sciences Center, Albuquerque, NM, United States
| | - Juan Bustillo
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, United States
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM, United States.,Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, United States
| | - Jessica A Turner
- The Mind Research Network, Albuquerque, NM, United States.,Department of Psychology, Neuroscience Institute, Georgia State University, Atlanta, GA, United States
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Castro E, Hjelm RD, Plis SM, Dinh L, Turner JA, Calhoun VD. Deep Independence Network Analysis of Structural Brain Imaging: Application to Schizophrenia. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:1729-1740. [PMID: 26891483 PMCID: PMC4965265 DOI: 10.1109/tmi.2016.2527717] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Linear independent component analysis (ICA) is a standard signal processing technique that has been extensively used on neuroimaging data to detect brain networks with coherent brain activity (functional MRI) or covarying structural patterns (structural MRI). However, its formulation assumes that the measured brain signals are generated by a linear mixture of the underlying brain networks and this assumption limits its ability to detect the inherent nonlinear nature of brain interactions. In this paper, we introduce nonlinear independent component estimation (NICE) to structural MRI data to detect abnormal patterns of gray matter concentration in schizophrenia patients. For this biomedical application, we further addressed the issue of model regularization of nonlinear ICA by performing dimensionality reduction prior to NICE, together with an appropriate control of the complexity of the model and the usage of a proper approximation of the probability distribution functions of the estimated components. We show that our results are consistent with previous findings in the literature, but we also demonstrate that the incorporation of nonlinear associations in the data enables the detection of spatial patterns that are not identified by linear ICA. Specifically, we show networks including basal ganglia, cerebellum and thalamus that show significant differences in patients versus controls, some of which show distinct nonlinear patterns.
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Torres US, Duran FLS, Schaufelberger MS, Crippa JAS, Louzã MR, Sallet PC, Kanegusuku CYO, Elkis H, Gattaz WF, Bassitt DP, Zuardi AW, Hallak JEC, Leite CC, Castro CC, Santos AC, Murray RM, Busatto GF. Patterns of regional gray matter loss at different stages of schizophrenia: A multisite, cross-sectional VBM study in first-episode and chronic illness. NEUROIMAGE-CLINICAL 2016; 12:1-15. [PMID: 27354958 PMCID: PMC4910144 DOI: 10.1016/j.nicl.2016.06.002] [Citation(s) in RCA: 90] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/04/2016] [Revised: 05/27/2016] [Accepted: 06/02/2016] [Indexed: 12/17/2022]
Abstract
Background: Structural brain abnormalities in schizophrenia have been repeatedly demonstrated in magnetic resonance imaging (MRI) studies, but it remains unclear whether these are static or progressive in nature. While longitudinal MRI studies have been traditionally used to assess the issue of progression of brain abnormalities in schizophrenia, information from cross-sectional neuroimaging studies directly comparing first-episode and chronic schizophrenia patients to healthy controls may also be useful to further clarify this issue. With the recent interest in multisite mega-analyses combining structural MRI data from multiple centers aiming at increased statistical power, the present multisite voxel-based morphometry (VBM) study was carried out to examine patterns of brain structural changes according to the different stages of illness and to ascertain which (if any) of such structural abnormalities would be specifically correlated to potential clinical moderators, including cumulative exposure to antipsychotics, age of onset, illness duration and overall illness severity. Methods: We gathered a large sample of schizophrenia patients (161, being 99 chronic and 62 first-episode) and controls (151) from four previous morphometric MRI studies (1.5 T) carried out in the same geographical region of Brazil. Image processing and analyses were conducted using Statistical Parametric Mapping (SPM8) software with the diffeomorphic anatomical registration through exponentiated Lie algebra (DARTEL) algorithm. Group effects on regional gray matter (GM) volumes were investigated through whole-brain voxel-wise comparisons using General Linear Model Analysis of Co-variance (ANCOVA), always including total GM volume, scan protocol, age and gender as nuisance variables. Finally, correlation analyses were performed between the aforementioned clinical moderators and regional and global brain volumes. Results: First-episode schizophrenia subjects displayed subtle volumetric deficits relative to controls in a circumscribed brain regional network identified only in small volume-corrected (SVC) analyses (p < 0.05, FWE-corrected), including the insula, temporolimbic structures and striatum. Chronic schizophrenia patients, on the other hand, demonstrated an extensive pattern of regional GM volume decreases relative to controls, involving bilateral superior, inferior and orbital frontal cortices, right middle frontal cortex, bilateral anterior cingulate cortices, bilateral insulae and right superior and middle temporal cortices (p < 0.05, FWE-corrected over the whole brain). GM volumes in several of those brain regions were directly correlated with age of disease onset on SVC analyses for conjoined (first-episode and chronic) schizophrenia groups. There were also widespread foci of significant negative correlation between duration of illness and relative GM volumes, but such findings remained significant only for the right dorsolateral prefrontal cortex after accounting for the influence of age of disease onset. Finally, significant negative correlations were detected between life-time cumulative exposure to antipsychotics and total GM and white matter volumes in schizophrenia patients, but no significant relationship was found between indices of antipsychotic usage and relative GM volume in any specific brain region. Conclusion: The above data indicate that brain changes associated with the diagnosis of schizophrenia are more widespread in chronic schizophrenia compared to first-episode patients. Our findings also suggest that relative GM volume deficits may be greater in (presumably more severe) cases with earlier age of onset, as well as varying as a function of illness duration in specific frontal brain regions. Finally, our results highlight the potentially complex effects of the continued use of antipsychotic drugs on structural brain abnormalities in schizophrenia, as we found that cumulative doses of antipsychotics affected brain volumes globally rather than selectively on frontal-temporal regions. Structural brain changes are more widespread in chronic than first-episode schizophrenia. Regional GM deficits may be greater in cases with earlier age of onset. Illness duration seems to impact in some specific frontal structural brain changes. Antipsychotics seem to affect brain volumes globally rather than regionally.
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Affiliation(s)
- Ulysses S Torres
- Post-Graduation Program in Radiology, Institute of Radiology (INRAD), Faculty of Medicine, University of São Paulo, Brazil; Laboratory of Psychiatric Neuroimaging (LIM-21), Department and Institute of Psychiatry, Faculty of Medicine, University of São Paulo, Brazil; Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), University of São Paulo, Brazil
| | - Fabio L S Duran
- Laboratory of Psychiatric Neuroimaging (LIM-21), Department and Institute of Psychiatry, Faculty of Medicine, University of São Paulo, Brazil; Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), University of São Paulo, Brazil
| | - Maristela S Schaufelberger
- Laboratory of Psychiatric Neuroimaging (LIM-21), Department and Institute of Psychiatry, Faculty of Medicine, University of São Paulo, Brazil; Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), University of São Paulo, Brazil; Department of Neuroscience and Behaviour, School of Medicine of Ribeirão Preto, University of São Paulo, Ribeirão Preto, Brazil
| | - José A S Crippa
- Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), University of São Paulo, Brazil; Department of Neuroscience and Behaviour, School of Medicine of Ribeirão Preto, University of São Paulo, Ribeirão Preto, Brazil
| | - Mario R Louzã
- Department and Institute of Psychiatry, University of Sao Paulo Medical School, Brazil
| | - Paulo C Sallet
- Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), University of São Paulo, Brazil; Department and Institute of Psychiatry, University of Sao Paulo Medical School, Brazil
| | | | - Helio Elkis
- Department and Institute of Psychiatry, University of Sao Paulo Medical School, Brazil
| | - Wagner F Gattaz
- Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), University of São Paulo, Brazil; Department and Institute of Psychiatry, University of Sao Paulo Medical School, Brazil; Laboratory of Neuroscience (LIM 27), Department and Institute of Psychiatry, Faculty of Medicine, University of São Paulo, Brazil
| | - Débora P Bassitt
- Department and Institute of Psychiatry, University of Sao Paulo Medical School, Brazil
| | - Antonio W Zuardi
- Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), University of São Paulo, Brazil; Department of Neuroscience and Behaviour, School of Medicine of Ribeirão Preto, University of São Paulo, Ribeirão Preto, Brazil
| | - Jaime Eduardo C Hallak
- Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), University of São Paulo, Brazil; Department of Neuroscience and Behaviour, School of Medicine of Ribeirão Preto, University of São Paulo, Ribeirão Preto, Brazil
| | - Claudia C Leite
- Post-Graduation Program in Radiology, Institute of Radiology (INRAD), Faculty of Medicine, University of São Paulo, Brazil; Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), University of São Paulo, Brazil
| | - Claudio C Castro
- Post-Graduation Program in Radiology, Institute of Radiology (INRAD), Faculty of Medicine, University of São Paulo, Brazil; Department of Diagnostic Imaging, Heart Institute (InCor), Faculty of Medicine, University of São Paulo, Brazil
| | - Antonio Carlos Santos
- Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), University of São Paulo, Brazil; Department of Internal Medicine - Radiology Division, School of Medicine of Ribeirão Preto, University of São Paulo, Ribeirão Preto, Brazil
| | - Robin M Murray
- Department of Psychosis Studies, Institute of Psychiatry, King's College London, UK
| | - Geraldo F Busatto
- Post-Graduation Program in Radiology, Institute of Radiology (INRAD), Faculty of Medicine, University of São Paulo, Brazil; Laboratory of Psychiatric Neuroimaging (LIM-21), Department and Institute of Psychiatry, Faculty of Medicine, University of São Paulo, Brazil; Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), University of São Paulo, Brazil; Department and Institute of Psychiatry, University of Sao Paulo Medical School, Brazil
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36
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Calhoun VD, Sui J. Multimodal fusion of brain imaging data: A key to finding the missing link(s) in complex mental illness. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2016; 1:230-244. [PMID: 27347565 PMCID: PMC4917230 DOI: 10.1016/j.bpsc.2015.12.005] [Citation(s) in RCA: 165] [Impact Index Per Article: 20.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
It is becoming increasingly clear that combining multi-modal brain imaging data is able to provide more information for individual subjects by exploiting the rich multimodal information that exists. However, the number of studies that do true multimodal fusion (i.e. capitalizing on joint information among modalities) is still remarkably small given the known benefits. In part, this is because multi-modal studies require broader expertise in collecting, analyzing, and interpreting the results than do unimodal studies. In this paper, we start by introducing the basic reasons why multimodal data fusion is important and what it can do, and importantly how it can help us avoid wrong conclusions and help compensate for imperfect brain imaging studies. We also discuss the challenges that need to be confronted for such approaches to be more widely applied by the community. We then provide a review of the diverse studies that have used multimodal data fusion (primarily focused on psychosis) as well as provide an introduction to some of the existing analytic approaches. Finally, we discuss some up-and-coming approaches to multi-modal fusion including deep learning and multimodal classification which show considerable promise. Our conclusion is that multimodal data fusion is rapidly growing, but it is still underutilized. The complexity of the human brain coupled with the incomplete measurement provided by existing imaging technology makes multimodal fusion essential in order to mitigate against misdirection and hopefully provide a key to finding the missing link(s) in complex mental illness.
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Affiliation(s)
- Vince D Calhoun
- The Mind Research Network & LBERI, Albuquerque, New Mexico.; Dept. of ECE, University of New Mexico, Albuquerque, New Mexico
| | - Jing Sui
- The Mind Research Network & LBERI, Albuquerque, New Mexico.; Brainnetome Center and National Laboratory of Pattern Recognition, Beijing, China; CAS Center for Excellence in Brain Science, Institute of Automation, Chinese Academy of Sciences, Beijing, China
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37
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Panta SR, Wang R, Fries J, Kalyanam R, Speer N, Banich M, Kiehl K, King M, Milham M, Wager TD, Turner JA, Plis SM, Calhoun VD. A Tool for Interactive Data Visualization: Application to Over 10,000 Brain Imaging and Phantom MRI Data Sets. Front Neuroinform 2016; 10:9. [PMID: 27014049 PMCID: PMC4791544 DOI: 10.3389/fninf.2016.00009] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2015] [Accepted: 02/22/2016] [Indexed: 11/21/2022] Open
Abstract
In this paper we propose a web-based approach for quick visualization of big data from brain magnetic resonance imaging (MRI) scans using a combination of an automated image capture and processing system, nonlinear embedding, and interactive data visualization tools. We draw upon thousands of MRI scans captured via the COllaborative Imaging and Neuroinformatics Suite (COINS). We then interface the output of several analysis pipelines based on structural and functional data to a t-distributed stochastic neighbor embedding (t-SNE) algorithm which reduces the number of dimensions for each scan in the input data set to two dimensions while preserving the local structure of data sets. Finally, we interactively display the output of this approach via a web-page, based on data driven documents (D3) JavaScript library. Two distinct approaches were used to visualize the data. In the first approach, we computed multiple quality control (QC) values from pre-processed data, which were used as inputs to the t-SNE algorithm. This approach helps in assessing the quality of each data set relative to others. In the second case, computed variables of interest (e.g., brain volume or voxel values from segmented gray matter images) were used as inputs to the t-SNE algorithm. This approach helps in identifying interesting patterns in the data sets. We demonstrate these approaches using multiple examples from over 10,000 data sets including (1) quality control measures calculated from phantom data over time, (2) quality control data from human functional MRI data across various studies, scanners, sites, (3) volumetric and density measures from human structural MRI data across various studies, scanners and sites. Results from (1) and (2) show the potential of our approach to combine t-SNE data reduction with interactive color coding of variables of interest to quickly identify visually unique clusters of data (i.e., data sets with poor QC, clustering of data by site) quickly. Results from (3) demonstrate interesting patterns of gray matter and volume, and evaluate how they map onto variables including scanners, age, and gender. In sum, the proposed approach allows researchers to rapidly identify and extract meaningful information from big data sets. Such tools are becoming increasingly important as datasets grow larger.
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Affiliation(s)
- Sandeep R Panta
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute Albuquerque, NM, USA
| | - Runtang Wang
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute Albuquerque, NM, USA
| | - Jill Fries
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute Albuquerque, NM, USA
| | - Ravi Kalyanam
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute Albuquerque, NM, USA
| | - Nicole Speer
- Intermountain Neuroimaging Consortium, University of Boulder Colorado Boulder, CO, USA
| | - Marie Banich
- Intermountain Neuroimaging Consortium, University of Boulder Colorado Boulder, CO, USA
| | - Kent Kiehl
- The Mind Research Network and Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA; Department of Psychology, University of New MexicoAlbuquerque, NM, USA
| | - Margaret King
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute Albuquerque, NM, USA
| | - Michael Milham
- The Child Mind Institute and The Nathan Kline Institute New York, NY, USA
| | - Tor D Wager
- Intermountain Neuroimaging Consortium, University of Boulder Colorado Boulder, CO, USA
| | - Jessica A Turner
- Department of Psychology, Georgia Tech University Atlanta, GA, USA
| | - Sergey M Plis
- The Mind Research Network and Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA; Department of Electrical & Computer Engineering, University of New MexicoAlbuquerque, NM, USA
| | - Vince D Calhoun
- The Mind Research Network and Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA; Department of Electrical & Computer Engineering, University of New MexicoAlbuquerque, NM, USA
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HYDRA: Revealing heterogeneity of imaging and genetic patterns through a multiple max-margin discriminative analysis framework. Neuroimage 2016; 145:346-364. [PMID: 26923371 DOI: 10.1016/j.neuroimage.2016.02.041] [Citation(s) in RCA: 102] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2015] [Revised: 02/11/2016] [Accepted: 02/12/2016] [Indexed: 11/23/2022] Open
Abstract
Multivariate pattern analysis techniques have been increasingly used over the past decade to derive highly sensitive and specific biomarkers of diseases on an individual basis. The driving assumption behind the vast majority of the existing methodologies is that a single imaging pattern can distinguish between healthy and diseased populations, or between two subgroups of patients (e.g., progressors vs. non-progressors). This assumption effectively ignores the ample evidence for the heterogeneous nature of brain diseases. Neurodegenerative, neuropsychiatric and neurodevelopmental disorders are largely characterized by high clinical heterogeneity, which likely stems in part from underlying neuroanatomical heterogeneity of various pathologies. Detecting and characterizing heterogeneity may deepen our understanding of disease mechanisms and lead to patient-specific treatments. However, few approaches tackle disease subtype discovery in a principled machine learning framework. To address this challenge, we present a novel non-linear learning algorithm for simultaneous binary classification and subtype identification, termed HYDRA (Heterogeneity through Discriminative Analysis). Neuroanatomical subtypes are effectively captured by multiple linear hyperplanes, which form a convex polytope that separates two groups (e.g., healthy controls from pathologic samples); each face of this polytope effectively defines a disease subtype. We validated HYDRA on simulated and clinical data. In the latter case, we applied the proposed method independently to the imaging and genetic datasets of the Alzheimer's Disease Neuroimaging Initiative (ADNI 1) study. The imaging dataset consisted of T1-weighted volumetric magnetic resonance images of 123 AD patients and 177 controls. The genetic dataset consisted of single nucleotide polymorphism information of 103 AD patients and 139 controls. We identified 3 reproducible subtypes of atrophy in AD relative to controls: (1) diffuse and extensive atrophy, (2) precuneus and extensive temporal lobe atrophy, as well some prefrontal atrophy, (3) atrophy pattern very much confined to the hippocampus and the medial temporal lobe. The genetics dataset yielded two subtypes of AD characterized mainly by the presence/absence of the apolipoprotein E (APOE) ε4 genotype, but also involving differential presence of risk alleles of CD2AP, SPON1 and LOC39095 SNPs that were associated with differences in the respective patterns of brain atrophy, especially in the precuneus. The results demonstrate the potential of the proposed approach to map disease heterogeneity in neuroimaging and genetic studies.
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Gupta CN, Calhoun VD, Rachakonda S, Chen J, Patel V, Liu J, Segall J, Franke B, Zwiers MP, Arias-Vasquez A, Buitelaar J, Fisher SE, Fernandez G, van Erp TGM, Potkin S, Ford J, Mathalon D, McEwen S, Lee HJ, Mueller BA, Greve DN, Andreassen O, Agartz I, Gollub RL, Sponheim SR, Ehrlich S, Wang L, Pearlson G, Glahn DC, Sprooten E, Mayer AR, Stephen J, Jung RE, Canive J, Bustillo J, Turner JA. Patterns of Gray Matter Abnormalities in Schizophrenia Based on an International Mega-analysis. Schizophr Bull 2015; 41:1133-42. [PMID: 25548384 PMCID: PMC4535628 DOI: 10.1093/schbul/sbu177] [Citation(s) in RCA: 165] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Analyses of gray matter concentration (GMC) deficits in patients with schizophrenia (Sz) have identified robust changes throughout the cortex. We assessed the relationships between diagnosis, overall symptom severity, and patterns of gray matter in the largest aggregated structural imaging dataset to date. We performed both source-based morphometry (SBM) and voxel-based morphometry (VBM) analyses on GMC images from 784 Sz and 936 controls (Ct) across 23 scanning sites in Europe and the United States. After correcting for age, gender, site, and diagnosis by site interactions, SBM analyses showed 9 patterns of diagnostic differences. They comprised separate cortical, subcortical, and cerebellar regions. Seven patterns showed greater GMC in Ct than Sz, while 2 (brainstem and cerebellum) showed greater GMC for Sz. The greatest GMC deficit was in a single pattern comprising regions in the superior temporal gyrus, inferior frontal gyrus, and medial frontal cortex, which replicated over analyses of data subsets. VBM analyses identified overall cortical GMC loss and one small cluster of increased GMC in Sz, which overlapped with the SBM brainstem component. We found no significant association between the component loadings and symptom severity in either analysis. This mega-analysis confirms that the commonly found GMC loss in Sz in the anterior temporal lobe, insula, and medial frontal lobe form a single, consistent spatial pattern even in such a diverse dataset. The separation of GMC loss into robust, repeatable spatial patterns across multiple datasets paves the way for the application of these methods to identify subtle genetic and clinical cohort effects.
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Affiliation(s)
| | | | | | - Jiayu Chen
- The Mind Research Network, Albuquerque, NM
| | | | - Jingyu Liu
- The Mind Research Network, Albuquerque, NM;,Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM
| | | | - Barbara Franke
- Department of Psychiatry and Human Genetics, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands;,Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands
| | - Marcel P. Zwiers
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands
| | - Alejandro Arias-Vasquez
- Department of Psychiatry and Human Genetics, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
| | - Jan Buitelaar
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands
| | - Simon E. Fisher
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands;,Department of Language and Genetics, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Guillen Fernandez
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands
| | - Theo G. M. van Erp
- Department of Psychiatry & Human Behavior, School of Medicine, University of California, Irvine, CA
| | - Steven Potkin
- Department of Psychiatry & Human Behavior, School of Medicine, University of California, Irvine, CA
| | - Judith Ford
- Department of Psychiatry, School of Medicine, University of California, San Francisco, CA
| | - Daniel Mathalon
- Department of Psychiatry, School of Medicine, University of California, San Francisco, CA
| | - Sarah McEwen
- Department of Psychiatry & Biobehavioral Sciences, University of California, Los Angeles, CA
| | - Hyo Jong Lee
- Division of Electronics and Information Engineering, Chonbuk National University, Jeonju, Korea
| | - Bryon A. Mueller
- Department of Psychiatry, University of Minnesota, Minneapolis, MN
| | - Douglas N. Greve
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA
| | - Ole Andreassen
- NORMENT, KG Jebsen Center for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway;,Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ingrid Agartz
- NORMENT, KG Jebsen Center for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway;,Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden;,Department of Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Randy L. Gollub
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA;,Department of Psychiatry, Massachusetts General Hospital, HMS, Boston, MA
| | - Scott R. Sponheim
- Department of Psychiatry, University of Minnesota, Minneapolis, MN;,Minneapolis VA Healthcare System, Minneapolis, MN
| | - Stefan Ehrlich
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA;,Department of Child and Adolescent Psychiatry, University Hospital Carl Gustav Carus, Dresden University of Technology, Dresden, Germany
| | - Lei Wang
- Department of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL;,Department of Radiology, Northwestern University, Chicago, IL
| | - Godfrey Pearlson
- Department of Psychiatry, School of Medicine, Yale University, New Haven, CT;,Institute of Living, Hartford Healthcare Corporation, Hartford, CT;,Department of Neurobiology, School of Medicine, Yale University, New Haven, CT
| | - David C. Glahn
- Department of Psychiatry, School of Medicine, Yale University, New Haven, CT;,Institute of Living, Hartford Healthcare Corporation, Hartford, CT
| | - Emma Sprooten
- Department of Psychiatry, School of Medicine, Yale University, New Haven, CT;,Institute of Living, Hartford Healthcare Corporation, Hartford, CT
| | | | | | - Rex E. Jung
- Department of Neurosurgery, University of New Mexico Health Sciences Center, Albuquerque, NM
| | - Jose Canive
- University of New Mexico Health Sciences Center, Albuquerque, NM;,Department of Psychiatry, University of New Mexico, Albuquerque, NM;,Raymond G. Murphy VA Medical Center, Albuquerque, NM
| | - Juan Bustillo
- University of New Mexico Health Sciences Center, Albuquerque, NM;,Department of Psychiatry, University of New Mexico, Albuquerque, NM
| | - Jessica A. Turner
- The Mind Research Network, Albuquerque, NM;,Department of Psychology and Neuroscience Institute, Georgia State University, Atlanta, GA,To whom correspondence should be addressed; Department of Psychology, Georgia State University, PO Box 5010, Atlanta, GA 30302-5010, US; tel: 404-413-6211, fax: 404-413-6207, e-mail:
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40
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Palaniyappan L, Maayan N, Bergman H, Davenport C, Adams CE, Soares‐Weiser K. Voxel-based morphometry for separation of schizophrenia from other types of psychosis in first episode psychosis. Cochrane Database Syst Rev 2015; 2015:CD011021. [PMID: 26252640 PMCID: PMC7104330 DOI: 10.1002/14651858.cd011021.pub2] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
BACKGROUND Schizophrenia is a psychiatric disorder which involves distortions in thought and perception, blunted affect, and behavioural disturbances. The longer psychosis goes unnoticed and untreated, the more severe the repercussions for relapse and recovery. There is some evidence that early intervention services can help, and diagnostic techniques that could contribute to early intervention may offer clinical utility in these situations. The index test being evaluated in this review is the structural magnetic resonance imaging (MRI) analysis technique known as voxel-based morphometry (VBM) that estimates the distribution of grey matter tissue volume across several brain regions. This review is an exploratory examination of the diagnostic 'potential' of VBM for use as an additional tool in the clinical examination of patients with first episode psychosis to establish whether an individual will progress on to developing schizophrenia as opposed to other types of psychosis. OBJECTIVES To determine whether VBM applied to the brain can be used to differentiate schizophrenia from other types of psychosis in participants who have received a clinical diagnosis of first episode psychosis. SEARCH METHODS In December 2013, we updated a previous search (May 2012) of MEDLINE, EMBASE, and PsycInfo using OvidSP. SELECTION CRITERIA We included retrospective and prospective studies that consecutively or randomly selected adolescent and adult participants (< 45 years) with a first episode of psychosis; and that evaluated the diagnostic accuracy of VBM for differentiating schizophrenia from other psychoses compared with a clinical diagnosis made by a qualified mental health professional, with or without the use of standard operational criteria or symptom checklists. We excluded studies in children, and in adult participants with organic brain disorders or who were at high risk for schizophrenia, such as people with a genetic predisposition. DATA COLLECTION AND ANALYSIS Two review authors screened all references for inclusion. We assessed the quality of studies using the QUADAS-2 instrument. Due to a lack of data, we were not able to extract 2 x 2 data tables for each study nor undertake any meta-analysis. MAIN RESULTS We included four studies with a total of 275 participants with first episode psychosis. VBM was not used to diagnose schizophrenia in any of the studies, instead VBM was used to quantify the magnitude of differences in grey matter volume. Therefore, none of the included studies reported data that could be used in the analysis, and we summarised the findings narratively for each study. AUTHORS' CONCLUSIONS There is no evidence to currently support diagnosing schizophrenia (as opposed to other psychotic disorders) using the pattern of brain changes seen in VBM studies in patients with first episode psychosis. VBM has the potential to discriminate between diagnostic categories but the methods to do this reliably are currently in evolution. In addition, the lack of applicability of the use of VBM to clinical practice in the studies to date limits the usefulness of VBM as a diagnostic aid to differentiate schizophrenia from other types of psychotic presentations in people with first episode of psychosis.
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Affiliation(s)
- Lena Palaniyappan
- The University of NottinghamDivison of Psychiatry, Institute of Mental HealthRoom 09, C FloorInnovation Park, Triumph RoadNottinghamUKNG7 2TU
| | - Nicola Maayan
- Enhance Reviews LtdCentral Office, Cobweb BuildingsThe Lane, LyfordWantageUKOX12 0EE
| | - Hanna Bergman
- Enhance Reviews LtdCentral Office, Cobweb BuildingsThe Lane, LyfordWantageUKOX12 0EE
| | - Clare Davenport
- University of BirminghamPublic Health, Epidemiology and BiostatisticsBirminghamUKB15 2TT
| | - Clive E Adams
- The University of NottinghamCochrane Schizophrenia GroupInstitute of Mental HealthInnovation Park, Triumph Road,NottinghamUKNG7 2TU
| | - Karla Soares‐Weiser
- Enhance Reviews LtdCentral Office, Cobweb BuildingsThe Lane, LyfordWantageUKOX12 0EE
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Egashira K, Matsuo K, Mihara T, Nakano M, Nakashima M, Watanuki T, Matsubara T, Watanabe Y. Different and shared brain volume abnormalities in late- and early-onset schizophrenia. Neuropsychobiology 2015; 70:142-51. [PMID: 25358262 DOI: 10.1159/000364827] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2013] [Accepted: 05/24/2014] [Indexed: 11/19/2022]
Abstract
The differences in clinical characteristics between late- (LOS) and early-onset schizophrenia (EOS) are well documented. However, very little is known about the neural mechanisms underlying these differences. Here, we compared morphometric abnormalities between patients with EOS and those with LOS. A total of 22 patients with LOS, 24 patients with EOS and 41 healthy control subjects were included in this magnetic resonance imaging study. Brain images were analyzed using DARTEL preprocessing for voxel-based morphometry in SPM8. We tested a main effect of diagnosis in the whole-brain analysis and compared the results among the three groups. We also carried out correlation analyses between regional volumes and clinical variables. Patients with LOS showed larger gray matter (GM) volume of the left precuneus compared with healthy subjects and patients with EOS. Patients with LOS and EOS showed decreased GM volumes in the right insula, left superior temporal gyrus and left orbitofrontal gyrus compared with healthy subjects. A longer duration of illness was associated with reduced GM volume in the temporal pole in patients with EOS. Our findings may help improve our understanding of schizophrenia pathophysiology and shed light on the different and shared neurobiological underpinnings of LOS and EOS.
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Affiliation(s)
- Kazuteru Egashira
- Department of Psychiatry, University of Occupational and Environmental Health, Kitakyusyu, Japan
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42
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Nan J, Liu J, Mu J, Zhang Y, Zhang M, Tian J, Liang F, Zeng F. Anatomically related gray and white matter alterations in the brains of functional dyspepsia patients. Neurogastroenterol Motil 2015; 27:856-64. [PMID: 25825020 DOI: 10.1111/nmo.12560] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2014] [Accepted: 03/03/2015] [Indexed: 12/24/2022]
Abstract
BACKGROUND Previous studies summarized altered brain functional patterns in functional dyspepsia (FD) patients, but how the brain structural patterns are related to FD remains largely unclear. The objective of this study was to determine the brain structural characteristics in FD patients. METHODS Optimized voxel-based morphometry and tract-based spatial statistics were employed to investigate the changes in gray matter (GM) and white matter (WM) respectively in 34 FD patients with postprandial distress syndrome and 33 healthy controls based on T1-weighted and diffusion-weighted imaging. The Pearson's correlation evaluated the link among GM alterations, WM abnormalities, and clinical variables in FD patients. The optimal brain structural parameters for identifying FD were explored using the receiver operating characteristic curve. KEY RESULTS Compared to controls, FD patients exhibited a decrease in GM density (GMD) in the right posterior insula/temporal superior cortex (marked as pINS), right inferior frontal cortex (IFC), and left middle cingulate cortex, and an increase in fractional anisotropy (FA) in the posterior limb of the internal capsule, posterior thalamic radiation, and external capsule (EC). Interestingly, the GMD in the pINS was significantly associated with GMD in the IFC and FA in the EC. Moreover, the EC adjacent to the pINS provided the best performance for distinguishing FD patients from controls. CONCLUSIONS & INFERENCES Our results showed pINS-related structural abnormalities in FD patients, indicating that GM and WM parameters were not affected independently. These findings would lay the foundation for probing an efficient target in the brain for treating FD.
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Affiliation(s)
- J Nan
- School of Life Science and Technology, Xidian University, Xi'an, China
| | - J Liu
- School of Life Science and Technology, Xidian University, Xi'an, China
| | - J Mu
- School of Life Science and Technology, Xidian University, Xi'an, China
| | - Y Zhang
- School of Life Science and Technology, Xidian University, Xi'an, China
| | - M Zhang
- Department of Medical Imaging, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - J Tian
- School of Life Science and Technology, Xidian University, Xi'an, China
| | - F Liang
- The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - F Zeng
- The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, China
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43
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Zhang T, Koutsouleris N, Meisenzahl E, Davatzikos C. Heterogeneity of structural brain changes in subtypes of schizophrenia revealed using magnetic resonance imaging pattern analysis. Schizophr Bull 2015; 41:74-84. [PMID: 25261565 PMCID: PMC4266302 DOI: 10.1093/schbul/sbu136] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
BACKGROUND Schizophrenia is a multifaceted mental disorder characterized by cognitive, perceptual, and affective symptom dimensions. This heterogeneity at the phenomenological level may be subserved by complex and heterogeneous patterns of structural abnormalities. Thus, delineating such patterns may improve the insight into the variability of disease and facilitate future magnetic resonance imaging-based diagnosis. METHODS We aimed to identify structurally complex signatures that directly differentiate patients with predominantly negative (pNEG), positive (pPOS), and disorganized (pDIS) symptoms using Optimally-Discriminative Voxel-Based Analysis (ODVBA). ODVBA is a new analytical framework for group analysis, which showed to have superior sensitivity and specificity over conventional voxel-based morphometric approaches, thus facilitating the identification of subtle neuroanatomical signatures delineating different subgroups. RESULTS pPOS were characterized by pronounced gray matter (GM) volume reductions in the ventromedial prefrontal cortex (vmPFC), which herein is defined to include the orbitofrontal cortex, and in occipitotemporal GM and parts of the lingual gyrus. pNEG was found to have vmPFC reduction but to a lesser degree than pPOS and with a relative sparing of the more medial vmPFC regions, compared to pDIS; it also had significantly less cerebellar GM. pDIS showed relatively highest GM volume preservation among three subtypes. CONCLUSIONS Although a common prefronto-perisylvian GM reduction pattern was present at the whole-group level, marked morphometric differences emerged between the three subgroups, including reduced cerebellar GM in pNEG and reduced vmPFC and occipitotemporal GM in pPOS. Besides deepening our insight into the neurobiological underpinnings of clinical heterogeneity, these results also identify important imaging biomarkers that may aid patient stratification.
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Affiliation(s)
- Tianhao Zhang
- Center for Biomedical Image Computing and Analytics, and Department of Radiology, University of Pennsylvania, Philadelphia, PA; These authors contributed equally to the article;
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University, Munich, Germany;,These authors contributed equally to the article
| | - Eva Meisenzahl
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University, Munich, Germany;,These authors shared the senior coauthorship
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, and Department of Radiology, University of Pennsylvania, Philadelphia, PA;,These authors shared the senior coauthorship
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44
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Zhou FC, Hou WM, Wang CY, Ungvari GS, Chiu HFK, Correll CU, Shum DHK, Man D, Liu DT, Xiang YT. Prospective memory performance in non-psychotic first-degree relatives of patients with schizophrenia: a controlled study. PLoS One 2014; 9:e111562. [PMID: 25365028 PMCID: PMC4218767 DOI: 10.1371/journal.pone.0111562] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2014] [Accepted: 10/03/2014] [Indexed: 11/24/2022] Open
Abstract
Objective We aimed at investigating prospective memory and its socio-demographic and neurocognitive correlates in non-psychotic, first-degree relatives (FDRs) of patients with schizophrenia compared to patients with first episode schizophrenia (FES), and healthy controls (HCs). Methods Forty-seven FES patients, 50 non-psychotic FDRs (23 offspring and 27 siblings) of patients with chronic schizophrenia (unrelated to the FES group) and 51 HCs were studied. The Chinese version of the Cambridge Prospective Memory Test (C-CAMPROMPT) was used to measure time-based prospective memory (TBPM) and event-based prospective memory (EBPM) performance. Other cognitive functions (involving respective memory and executive functions) were evaluated with standardized tests. Results After controlling for basic demographic characteristics including age, gender and educational level, there was a significant difference between FDRs, FES and HCs with respect to both TBPM (F(2,142) = 10.4, p<0.001) and EBPM (F(2,142) = 10.8, p<0.001). Multiple linear regression analyses revealed that lower scores of the Hopkins Verbal Learning Test-Revised (HVLT-R) and the STROOP Word-Color Test (SWCT) contributed to TBPM impairment, while lower educational level and higher scores of the Color Trails Test-2 (CTT-2) contributed to EBPM deficit in FDRs. Conclusions FDRs share similar but attenuated prospective memory impairments with schizophrenia patients, suggesting that prospective memory deficits may represent an endophenotype of schizophrenia.
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Affiliation(s)
- Fu-Chun Zhou
- Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Wei-Min Hou
- Beijing Daxing Mental Health Center, Beijing, China
| | - Chuan-Yue Wang
- Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- * E-mail: (C-YW); (Y-TX)
| | - Gabor S. Ungvari
- School of Psychiatry & Clinical Neurosciences, University of Western Australia, Perth, Australia
| | - Helen F. K. Chiu
- Department of Psychiatry, Chinese University of Hong Kong, Hong Kong SAR, China
| | - Christoph U. Correll
- Division of Psychiatry Research, The Zucker Hillside Hospital, North Shore-Long Island Jewish Health System, Glen Oaks, New York, United States of America
| | - David H. K. Shum
- School of Psychology and Griffith Health Institute, Griffith University, Brisbane, Queensland, Australia
| | - David Man
- Department of Rehabilitation Sciences, Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Deng-Tang Liu
- Department of Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yu-Tao Xiang
- Faculty of Health Sciences, University of Macau, Macao SAR, China
- * E-mail: (C-YW); (Y-TX)
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45
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Plis SM, Hjelm DR, Salakhutdinov R, Allen EA, Bockholt HJ, Long JD, Johnson HJ, Paulsen JS, Turner JA, Calhoun VD. Deep learning for neuroimaging: a validation study. Front Neurosci 2014; 8:229. [PMID: 25191215 PMCID: PMC4138493 DOI: 10.3389/fnins.2014.00229] [Citation(s) in RCA: 261] [Impact Index Per Article: 26.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2014] [Accepted: 07/11/2014] [Indexed: 11/13/2022] Open
Abstract
Deep learning methods have recently made notable advances in the tasks of classification and representation learning. These tasks are important for brain imaging and neuroscience discovery, making the methods attractive for porting to a neuroimager's toolbox. Success of these methods is, in part, explained by the flexibility of deep learning models. However, this flexibility makes the process of porting to new areas a difficult parameter optimization problem. In this work we demonstrate our results (and feasible parameter ranges) in application of deep learning methods to structural and functional brain imaging data. These methods include deep belief networks and their building block the restricted Boltzmann machine. We also describe a novel constraint-based approach to visualizing high dimensional data. We use it to analyze the effect of parameter choices on data transformations. Our results show that deep learning methods are able to learn physiologically important representations and detect latent relations in neuroimaging data.
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Affiliation(s)
| | - Devon R Hjelm
- Department of Computer Science, University of New Mexico Albuquerque, NM, USA
| | | | - Elena A Allen
- The Mind Research Network Albuquerque, NM, USA ; Department of Biological and Medical Psychology, University of Bergen Bergen, Norway
| | - Henry J Bockholt
- Advanced Biomedical Informatics Group, LLC, University of Iowa Iowa City, IA, USA
| | - Jeffrey D Long
- Department of Psychiatry, Carver College of Medicine, University of Iowa Iowa City, IA, USA ; Department of Biostatistics, College of Public Health, University of Iowa Iowa City, IA, USA
| | - Hans J Johnson
- Department of Psychiatry, Carver College of Medicine, University of Iowa Iowa City, IA, USA ; Department of Biomedical Engineering, College of Engineering, University of Iowa Iowa City, IA, USA
| | - Jane S Paulsen
- Department of Psychiatry, Carver College of Medicine, University of Iowa Iowa City, IA, USA ; Department of Psychology, Neuroscience Institute, University of Iowa Iowa City, IA, USA ; Department of Neurology, Carver College of Medicine, University of Iowa Iowa City, IA, USA
| | - Jessica A Turner
- Department of Psychology, Neuroscience Institute, Georgia State University Atlanta, GA, USA
| | - Vince D Calhoun
- The Mind Research Network Albuquerque, NM, USA ; Department of Computer Science, University of New Mexico Albuquerque, NM, USA ; Department of Electrical and Computer Engineering, University of New Mexico Albuquerque, NM, USA
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Sarwate AD, Plis SM, Turner JA, Arbabshirani MR, Calhoun VD. Sharing privacy-sensitive access to neuroimaging and genetics data: a review and preliminary validation. Front Neuroinform 2014; 8:35. [PMID: 24778614 PMCID: PMC3985022 DOI: 10.3389/fninf.2014.00035] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2013] [Accepted: 03/19/2014] [Indexed: 11/16/2022] Open
Abstract
The growth of data sharing initiatives for neuroimaging and genomics represents an exciting opportunity to confront the “small N” problem that plagues contemporary neuroimaging studies while further understanding the role genetic markers play in the function of the brain. When it is possible, open data sharing provides the most benefits. However, some data cannot be shared at all due to privacy concerns and/or risk of re-identification. Sharing other data sets is hampered by the proliferation of complex data use agreements (DUAs) which preclude truly automated data mining. These DUAs arise because of concerns about the privacy and confidentiality for subjects; though many do permit direct access to data, they often require a cumbersome approval process that can take months. An alternative approach is to only share data derivatives such as statistical summaries—the challenges here are to reformulate computational methods to quantify the privacy risks associated with sharing the results of those computations. For example, a derived map of gray matter is often as identifiable as a fingerprint. Thus alternative approaches to accessing data are needed. This paper reviews the relevant literature on differential privacy, a framework for measuring and tracking privacy loss in these settings, and demonstrates the feasibility of using this framework to calculate statistics on data distributed at many sites while still providing privacy.
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Affiliation(s)
- Anand D Sarwate
- Department of Electrical and Computer Engineering, Rutgers, The State University of New Jersey Piscataway, NJ, USA
| | | | - Jessica A Turner
- Mind Research Network Albuquerque, NM, USA ; Department of Psychology and Neuroscience Institute, Georgia State University Atlanta, GA, USA
| | - Mohammad R Arbabshirani
- Mind Research Network Albuquerque, NM, USA ; Department of Electrical and Computer Engineering, University of New Mexico Albuquerque, NM, USA
| | - Vince D Calhoun
- Mind Research Network Albuquerque, NM, USA ; Department of Electrical and Computer Engineering, University of New Mexico Albuquerque, NM, USA
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47
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Roman-Urrestarazu A, Murray GK, Barnes A, Miettunen J, Jääskeläinen E, Mäki P, Nikkinen J, Remes J, Mukkala S, Koivukangas J, Heinimaa M, Moilanen I, Suckling J, Kiviniemi V, Jones PB, Veijola J. Brain structure in different psychosis risk groups in the Northern Finland 1986 birth cohort. Schizophr Res 2014; 153:143-9. [PMID: 24462264 DOI: 10.1016/j.schres.2013.12.019] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2013] [Revised: 12/01/2013] [Accepted: 12/27/2013] [Indexed: 10/25/2022]
Abstract
We tested the hypothesis that family risk for psychosis (FR) and clinical risk for psychosis (CR) are associated with structural brain abnormalities, with increased deficits in those at both family risk and clinical risk for psychosis (FRCR). The study setting was the Oulu Brain and Mind Study, with subjects drawn from the Northern Finland 1986 Birth Cohort (n=9479) using register and questionnaire based screening, and interviews using the Structured Interview for Prodromal Symptoms. After this procedure, 172 subjects were included in the study, classified as controls (n=73) and three risk groups: FR excluding CR (FR, n=60), CR without FR (CR, n=26), and individuals at both FR and CR (FRCR, n=13). T1-weighted brain scans were acquired and processed in a voxel-based analysis using permutation-based statistics. In the comparison between FRCR versus controls, we found lower grey matter volume (GMV) in a cluster (1689 voxels at -4.00, -72.00, -18.00mm) covering both cerebellar hemispheres and the vermis. This cluster was subsequently used as a mask to extract mean GMV in all four groups: FR had a volume intermediate between controls and FRCR. Within FRCR there was an association between cerebellar cluster brain volume and motor function. These findings are consistent with an evolving pattern of cerebellar deficits in psychosis risk with the most pronounced deficits in those at highest risk of psychosis.
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Affiliation(s)
| | - Graham K Murray
- Department of Psychiatry, University of Cambridge, Cambridge, UK; Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK.
| | - Anna Barnes
- Department of Nuclear Medicine, University College London Hospitals NHS Foundation Trust, London, UK
| | - Jouko Miettunen
- Institute of Clinical Medicine, Department of Psychiatry, University of Oulu and Oulu University Hospital, Oulu, Finland; Institute of Health Sciences, Department of Public Health Sciences and General Practice, University of Oulu, Oulu, Finland
| | - Erika Jääskeläinen
- Institute of Clinical Medicine, Department of Psychiatry, University of Oulu and Oulu University Hospital, Oulu, Finland
| | - Pirjo Mäki
- Institute of Clinical Medicine, Department of Psychiatry, University of Oulu and Oulu University Hospital, Oulu, Finland
| | - Juha Nikkinen
- Institute of Diagnostics, Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
| | - Jukka Remes
- Institute of Diagnostics, Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
| | - Sari Mukkala
- Institute of Clinical Medicine, Department of Psychiatry, University of Oulu and Oulu University Hospital, Oulu, Finland
| | - Jenni Koivukangas
- Institute of Clinical Medicine, Department of Psychiatry, University of Oulu and Oulu University Hospital, Oulu, Finland
| | - Markus Heinimaa
- Department of Psychiatry, University of Turku, Turku, Finland
| | - Irma Moilanen
- Institute of Clinical Medicine, Department of Psychiatry, University of Oulu and Oulu University Hospital, Oulu, Finland; Institute of Clinical Medicine, Clinic of Child Psychiatry, University of Oulu, Oulu, Finland
| | - John Suckling
- Department of Psychiatry, University of Cambridge, Cambridge, UK; Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK; Cambridgeshire and Peterborough NHS Foundation Trust, UK
| | - Vesa Kiviniemi
- Institute of Diagnostics, Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
| | - Peter B Jones
- Department of Psychiatry, University of Cambridge, Cambridge, UK; Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
| | - Juha Veijola
- Institute of Clinical Medicine, Department of Psychiatry, University of Oulu and Oulu University Hospital, Oulu, Finland
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48
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Koelkebeck K, Hirao K, Miyata J, Kawada R, Saze T, Dannlowski U, Ubukata S, Ohrmann P, Bauer J, Pedersen A, Fukuyama H, Sawamoto N, Takahashi H, Murai T. Impact of gray matter reductions on theory of mind abilities in patients with schizophrenia. Soc Neurosci 2013; 8:631-9. [PMID: 24047258 DOI: 10.1080/17470919.2013.837094] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
To identify the brain regions involved in the interpretation of intentional movement by patients with schizophrenia, we investigated the association between cerebral gray matter (GM) volumes and performance on a theory of mind (ToM) task using voxel-based morphometry. Eighteen patients with schizophrenia and thirty healthy controls participated in the study. Participants were given a moving shapes task that employs the interpretation of intentional movement. Verbal descriptions were rated according to intentionality. ToM performance deficits in patients were found to be positively correlated with GM volume reductions in the superior temporal sulcus and medial prefrontal cortex. Our findings confirm that divergent brain regions contribute to mentalizing abilities and that GM volume reductions impact behavioral deficits in patients with schizophrenia.
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Affiliation(s)
- Katja Koelkebeck
- a Department of Psychiatry and Psychotherapy, School of Medicine , University of Muenster , Muenster , Germany
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49
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Effects of study design in multi-scanner voxel-based morphometry studies. Neuroimage 2013; 84:133-40. [PMID: 23994315 DOI: 10.1016/j.neuroimage.2013.08.046] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2013] [Revised: 08/15/2013] [Accepted: 08/19/2013] [Indexed: 01/04/2023] Open
Abstract
Interest has recently grown in multi-center studies, which have more power than smaller studies in conducting sophisticated evaluations of basic neuroanatomy and neurodegenerative disorders. The large number of subjects that result from pooling multi-center datasets increases sensitivity, but also introduces a between-center variance component. Taking sex differences as an example, we examined the effects of different ratios of cases to controls (males to females) between scanners in multi-scanner morphometric studies, using voxel-based morphometry and data obtained on two scanners of the exact same model. Each subject was scanned twice with both scanners. Using the image obtained on either of the two scanners for each subject, voxel-based analyses were repeated with different ratios of males to females for each scanner. As the ratio of males to females became more imbalanced between the scanners, the differences between the two scanners more strongly affected the results of analyses of sex differences. When the ratio of males to females was balanced, the inclusion of scanner as a covariate in the statistical analysis had almost no influence on the results of analyses of sex differences. When the ratio of males to females was ill-balanced, the inclusion of scanner as a covariate suppressed scanner effects on the results, but made sex differences less likely to become significant. The present results suggest that as long as the ratio of cases to controls is well-balanced across different scanners, it is not always necessary to include scanner as a covariate in the statistical analysis, and that when the ratio of cases to controls is ill-balanced across scanners, the inclusion of scanner as a covariate in the statistical analysis can suppress scanner effects, but may make differences less likely to be detected.
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50
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Jamadar SD, Pearlson GD, O’Neil KM, Assaf M. Semantic association fMRI impairments represent a potential schizophrenia biomarker. Schizophr Res 2013; 145:20-6. [PMID: 23403412 PMCID: PMC3732787 DOI: 10.1016/j.schres.2012.12.029] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2012] [Revised: 12/18/2012] [Accepted: 12/20/2012] [Indexed: 11/27/2022]
Abstract
Semantic association retrieval task (SORT) requires participants to indicate whether word pairs recall a third object, e.g. 'honey' and 'stings' activates 'bees'. We have previously shown that individuals with schizophrenia with more severe positive symptoms tend to report associations between unrelated word pairs than healthy controls; schizophrenia individuals with more severe negative symptoms tend to fail to report associations between related word pairs. This over-retrieval and under-retrieval on SORT correlates with functional magnetic resonance imaging (fMRI) activity in inferior parietal lobule (IPL). To examine the suitability of SORT as an endophenotype for schizophrenia, we examined SORT performance and activity across multiple stages of the illness: chronic, relapse, and first episode. We also examine SORT performance and activity in unaffected relatives. SORT performance and fMRI activity in schizophrenia-first episode, schizophrenia-chronic and schizophrenia-relapse were significantly impaired relative to healthy controls and unaffected relatives. Schizophrenia-chronic and schizophrenia-relapse participants showing more severe PANSS-positive and -general symptoms showed larger SORT impairments. For schizophrenia-first episode more severe negative symptoms were related to lower IPL activation, consistent with previous results showing that negative symptoms are among the first to emerge in the schizophrenia prodrome and that more severe symptoms in the first episode predict worse future outcomes. Unaffected relatives showed no impairments on SORT performance or fMRI activity relative to healthy controls, which is incompatible with the concept of SORT as an endophenotype for schizophrenia, but is consistent with the concept of SORT as a potential schizophrenia biomarker.
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Affiliation(s)
- Sharna D Jamadar
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT 06106, USA.
| | - Godfrey D Pearlson
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford CT USA,Department of Psychiatry, Yale University, New Haven CT USA,Department of Neurobiology, Yale University, New Haven CT USA
| | - Kasey M O’Neil
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford CT USA
| | - Michal Assaf
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford CT USA,Department of Psychiatry, Yale University, New Haven CT USA
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