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Prasad K, Rubin J, Mitra A, Lewis M, Theis N, Muldoon B, Iyengar S, Cape J. Structural covariance networks in schizophrenia: A systematic review Part I. Schizophr Res 2022; 240:1-21. [PMID: 34906884 PMCID: PMC8917984 DOI: 10.1016/j.schres.2021.11.035] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 08/02/2021] [Accepted: 11/23/2021] [Indexed: 02/03/2023]
Abstract
BACKGROUND Schizophrenia is proposed as a disorder of dysconnectivity. However, examination of complexities of dysconnectivity has been challenging. Structural covariance networks (SCN) provide important insights into the nature of dysconnectivity. This systematic review examines the SCN studies that employed statistical approaches to elucidate covariation of regional morphometric variations. METHODS A systematic search of literature was conducted for peer-reviewed publications using different keywords and keyword combinations for schizophrenia. Fifty-two studies met the criteria. RESULTS Early SCN studies began using correlational structure of selected regions. Over the last 3 decades, methodological approaches have grown increasingly sophisticated from examining selected brain regions using correlation tests on small sample sizes to recent approaches that use advanced statistical methods to examine covariance structure of whole-brain parcellations on larger samples. Although the results are not fully consistent across all studies, a pattern of fronto-temporal, fronto-parietal and fronto-thalamic covariation is reported. Attempts to associate SCN alterations with functional connectivity, to differentiate between disease-related and neurodevelopment-related morphometric changes, and to develop "causality-based" models are being reported. Clinical correlation with outcome, psychotic symptoms, neurocognitive and social cognitive performance are also reported. CONCLUSIONS Application of advanced statistical methods are beginning to provide insights into interesting patterns of regional covariance including correlations with clinical and cognitive data. Although these findings appear similar to morphometric studies, SCNs have the advantage of highlighting topology of these regions and their relationship to the disease and associated variables. Further studies are needed to investigate neurobiological underpinnings of shared covariance, and causal links to clinical domains.
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Affiliation(s)
- Konasale Prasad
- University of Pittsburgh School of Medicine, Western Psychiatric Institute and Clinic, 3811 O'Hara St, Pittsburgh, PA 15213, United States of America; University of Pittsburgh Swanson School of Engineering, 3700 O'Hara St, Pittsburgh, PA 15213, United States of America; VA Pittsburgh Healthcare System, University Dr C, Pittsburgh, PA 15240, United States of America.
| | - Jonathan Rubin
- Department of Mathematics, University of Pittsburgh, 301 Thackeray Hall, Pittsburgh PA 15260
| | - Anirban Mitra
- Department of Statistics, University of Pittsburgh, 1826 Wesley W. Posvar Hall, Pittsburgh PA 15260
| | - Madison Lewis
- University of Pittsburgh Swanson School of Engineering, 3700 O’Hara St, Pittsburgh PA 15213
| | - Nicholas Theis
- University of Pittsburgh School of Medicine, Western Psychiatric Institute and Clinic, 3811 O’Hara St, Pittsburgh PA 15213
| | - Brendan Muldoon
- University of Pittsburgh School of Medicine, Western Psychiatric Institute and Clinic, 3811 O’Hara St, Pittsburgh PA 15213
| | - Satish Iyengar
- Department of Statistics, University of Pittsburgh, 1826 Wesley W. Posvar Hall, Pittsburgh PA 15260
| | - Joshua Cape
- Department of Statistics, University of Pittsburgh, 1826 Wesley W. Posvar Hall, Pittsburgh PA 15260
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Jiang W, Rootes-Murdy K, Chen J, Bizzozero NIP, Calhoun VD, van Erp TGM, Ehrlich S, Agartz I, Jönsson EG, Andreassen OA, Wang L, Pearlson GD, Glahn DC, Hong E, Liu J, Turner JA. Multivariate alterations in insula - Medial prefrontal cortex linked to genetics in 12q24 in schizophrenia. Psychiatry Res 2021; 306:114237. [PMID: 34655926 PMCID: PMC8643340 DOI: 10.1016/j.psychres.2021.114237] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 10/06/2021] [Accepted: 10/08/2021] [Indexed: 11/29/2022]
Abstract
The direct effect of genetic variations on clinical phenotypes within schizophrenia (SZ) remains elusive. We examined the previously identified association of reduced gray matter concentration in the insula - medial prefrontal cortex and a quantitative trait locus located in 12q24 in a SZ dataset. The main analysis was performed on 1461 SNPs and 830 participants. The highest contributing SNPs were localized in five genes including TMEM119, which encodes a microglial marker, that is associated with neuroinflammation and Alzheimer's disease. The gene set in 12q4 may partially explain brain alterations in SZ, but they may also relate to other psychiatric and developmental disorders.
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Affiliation(s)
- Wenhao Jiang
- Department of Psychology, Georgia State University, United States of America; Department of Psychosomatics and Psychiatry, Zhongda Hospital, Institute of Psychosomatics, Medical School, Southeast University, Nanjing, China
| | - Kelly Rootes-Murdy
- Department of Psychology, Georgia State University, United States of America
| | - Jiayu Chen
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology and Emory University, United States of America
| | | | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology and Emory University, United States of America
| | - Theo G M van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, United States of America; Qureshey Research Laboratory, Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine, CA,United States of America
| | - Stefan Ehrlich
- Department of Psychiatry, Massachusetts General Hospital, United States of America; Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU Dresden, Germany
| | - Ingrid Agartz
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Stockholm Region, Stockholm, Sweden; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Erik G Jönsson
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Stockholm Region, Stockholm, Sweden
| | - Ole A Andreassen
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway
| | - Lei Wang
- Psychiatry and Behavioral Health, Ohio State Wexner Medical Center, United States of America
| | | | - David C Glahn
- Boston Children's Hospital and Harvard Medical School, United States of America
| | - Elliot Hong
- Maryland Psychiatric Research Center, University of Maryland School of Medicine, United States of America
| | - Jingyu Liu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology and Emory University, United States of America
| | - Jessica A Turner
- Department of Psychology, Georgia State University, United States of America; Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology and Emory University, United States of America
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Ge R, Ding S, Keeling T, Honer WG, Frangou S, Vila-Rodriguez F. SS-Detect: Development and Validation of a New Strategy for Source-Based Morphometry in Multiscanner Studies. J Neuroimaging 2020; 31:261-271. [PMID: 33270962 DOI: 10.1111/jon.12814] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 11/01/2020] [Accepted: 11/12/2020] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND AND PURPOSE Source-based morphometry(SBM) has been used in multicenter studies pooling magnetic resonance imaging data across different scanners to advance the reproducibility of neuroscience research. In the present study, we developed an analysis strategy for Scanner-Specific Detection (SS-Detect) of SBPs in multiscanner studies, and evaluated its performance relative to a conventional strategy. METHODS In the first experiment, the SimTB toolbox was used to generate simulated datasets mimicking 20 different scanners with common and scanner-specific SBPs. In the second experiment, we generated one simulated SBP from empirical gray matter volume (GMV) datasets from two different scanners. Moreover, we applied two strategies to compare SBPs between schizophrenia patients' and healthy controls' GMV from two scanners. RESULTS The outputs of the conventional strategy were limited to whole-sample-level results across all scanners; the outputs of SS-Detect included whole-sample-level and scanner-specific results. In the first simulation experiment, SS-Detect successfully estimated all simulated SBPs, including the common and scanner-specific SBPs, whereas the conventional strategy detected only some of the whole-sample SBPs. The second simulation experiment showed that both strategies could detect the simulated SBP. Quantitative evaluations of both experiments demonstrated greater accuracy of the SS-Detect in estimating spatial SBPs and subject-specific loading parameters. In the third experiment, SS-Detect detected more significant between-group SBPs, and these SBPs corresponded with the results from voxel-based morphometry analysis, suggesting that SS-Detect has higher sensitivity in detecting between-group differences. CONCLUSIONS SS-Detect outperformed the conventional strategy and can be considered advantageous when SBM is applied to a multiscanner study.
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Affiliation(s)
- Ruiyang Ge
- Non-Invasive Neurostimulation Therapies (NINET) Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Shiqing Ding
- Non-Invasive Neurostimulation Therapies (NINET) Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Tyler Keeling
- Non-Invasive Neurostimulation Therapies (NINET) Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
| | - William G Honer
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Sophia Frangou
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada.,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, New York, US
| | - Fidel Vila-Rodriguez
- Non-Invasive Neurostimulation Therapies (NINET) Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
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Cupertino RB, Soheili-Nezhad S, Grevet EH, Bandeira CE, Picon FA, Tavares MEDA, Naaijen J, van Rooij D, Akkermans S, Vitola ES, Zwiers MP, Rovaris DL, Hoekstra PJ, Breda V, Oosterlaan J, Hartman CA, Beckmann CF, Buitelaar JK, Franke B, Bau CHD, Sprooten E. Reduced fronto-striatal volume in attention-deficit/hyperactivity disorder in two cohorts across the lifespan. NEUROIMAGE-CLINICAL 2020; 28:102403. [PMID: 32949876 PMCID: PMC7502360 DOI: 10.1016/j.nicl.2020.102403] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 08/05/2020] [Accepted: 08/25/2020] [Indexed: 12/19/2022]
Abstract
Attention-Deficit/Hyperactivity Disorder (ADHD) has been associated with altered brain anatomy in neuroimaging studies. However, small and heterogeneous study samples, and the use of region-of-interest and tissue-specific analyses have limited the consistency and replicability of these effects. We used a data-driven multivariate approach to investigate neuroanatomical features associated with ADHD in two independent cohorts: the Dutch NeuroIMAGE cohort (n = 890, 17.2 years) and the Brazilian IMpACT cohort (n = 180, 44.2 years). Using independent component analysis of whole-brain morphometry images, 375 neuroanatomical components were assessed for association with ADHD. In both discovery (corrected-p = 0.0085) and replication (p = 0.032) cohorts, ADHD was associated with reduced volume in frontal lobes, striatum, and their interconnecting white-matter. Current results provide further evidence for the role of the fronto-striatal circuit in ADHD in children, and for the first time show its relevance to ADHD in adults. The fact that the cohorts are from different continents and comprise different age ranges highlights the robustness of the findings.
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Affiliation(s)
| | - Sourena Soheili-Nezhad
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Radboud University Medical Center, Nijmegen, The Netherlands; Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Eugenio Horacio Grevet
- Department of Psychiatry, Faculdade de Medicina, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; Adulthood ADHD Outpatient Program (ProDAH), Clinical Research Center, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil; Developmental Psychiatry Program, Experimental Research Center, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | - Cibele Edom Bandeira
- Adulthood ADHD Outpatient Program (ProDAH), Clinical Research Center, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil; Developmental Psychiatry Program, Experimental Research Center, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil; Department of Genetics, Instituto de Biociências, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Felipe Almeida Picon
- Adulthood ADHD Outpatient Program (ProDAH), Clinical Research Center, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil; Developmental Psychiatry Program, Experimental Research Center, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | - Maria Eduarda de Araujo Tavares
- Adulthood ADHD Outpatient Program (ProDAH), Clinical Research Center, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil; Developmental Psychiatry Program, Experimental Research Center, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil; Department of Genetics, Instituto de Biociências, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Jilly Naaijen
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Radboud University Medical Center, Nijmegen, The Netherlands; Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Daan van Rooij
- Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Sophie Akkermans
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Radboud University Medical Center, Nijmegen, The Netherlands; Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Eduardo Schneider Vitola
- Adulthood ADHD Outpatient Program (ProDAH), Clinical Research Center, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil; Developmental Psychiatry Program, Experimental Research Center, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | - Marcel P Zwiers
- Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Diego Luiz Rovaris
- Universidade de Sao Paulo Instituto de Ciencias Biomedicas Departamento de Fisiologia e Biofisica, São Paulo, Brazil
| | - Pieter J Hoekstra
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Groningen, The Netherlands
| | - Vitor Breda
- Adulthood ADHD Outpatient Program (ProDAH), Clinical Research Center, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil; Developmental Psychiatry Program, Experimental Research Center, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | - Jaap Oosterlaan
- Emma Children's Hospital, Amsterdam UMC, University of Amsterdam, Emma Neuroscience Group, Department of Pediatrics, Amsterdam Reproduction & Development, Amsterdam, The Netherlands; Vrije Universiteit, Clinical Neuropsychology Section, Van der Boechortstraat 7, 1081 BT Amsterdam, The Netherlands
| | - Catharina A Hartman
- Emma Children's Hospital, Amsterdam UMC, University of Amsterdam, Emma Neuroscience Group, Department of Pediatrics, Amsterdam Reproduction & Development, Amsterdam, The Netherlands; Vrije Universiteit, Clinical Neuropsychology Section, Van der Boechortstraat 7, 1081 BT Amsterdam, The Netherlands
| | - Christian F Beckmann
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Radboud University Medical Center, Nijmegen, The Netherlands; Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jan K Buitelaar
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Radboud University Medical Center, Nijmegen, The Netherlands; Karakter Child and Adolescent Psychiatry University Centre, Nijmegen, The Netherlands
| | - Barbara Franke
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands; Department of Psychiatry, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Claiton Henrique Dotto Bau
- Adulthood ADHD Outpatient Program (ProDAH), Clinical Research Center, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil; Developmental Psychiatry Program, Experimental Research Center, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil; Department of Genetics, Instituto de Biociências, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
| | - Emma Sprooten
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Radboud University Medical Center, Nijmegen, The Netherlands; Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Radboud University Medical Center, Nijmegen, The Netherlands.
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Gupta CN, Turner JA, Calhoun VD. Source-based morphometry: a decade of covarying structural brain patterns. Brain Struct Funct 2019; 224:3031-3044. [PMID: 31701266 DOI: 10.1007/s00429-019-01969-8] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2018] [Accepted: 10/16/2019] [Indexed: 12/24/2022]
Abstract
In this paper, we review and discuss brain imaging studies which have used the source-based morphometry (SBM) approach over the past decade. SBM is a data-driven linear multivariate approach for decomposing structural brain imaging data into commonly covarying imaging components and subject-specific loading parameters. It is a well-established technique which has predominantly been used to study neuroanatomic differences between healthy controls and patients with neuropsychiatric diseases. We start by discussing the advantages of this technique over univariate analysis for imaging studies, followed by a discussion of results from recent studies which have successfully applied this methodology. We also present recent extensions of this framework including nonlinear SBM, biclustered independent component analysis (B-ICA) and conclude with the possible directions of work for future.
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Affiliation(s)
- Cota Navin Gupta
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, US.
- Neural Engineering Lab, Department of Biosciences and Bioengineering (BSBE), Indian Institute of Technology Guwahati, Guwahati, India.
| | - 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, Georgia, US
- Department of Psychology and Neuroscience Institute, Georgia State University, Atlanta, GA, USA
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, US
- Department of Psychology and Neuroscience Institute, Georgia State University, Atlanta, GA, USA
- Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
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Gupta CN, Turner JA, Calhoun VD. Source-Based Morphometry: Data-Driven Multivariate Analysis of Structural Brain Imaging Data. NEUROMETHODS 2018. [DOI: 10.1007/978-1-4939-7647-8_7] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Ciarochi JA, Calhoun VD, Lourens S, Long JD, Johnson HJ, Bockholt HJ, Liu J, Plis SM, Paulsen JS, Turner JA. Patterns of Co-Occurring Gray Matter Concentration Loss across the Huntington Disease Prodrome. Front Neurol 2016; 7:147. [PMID: 27708610 PMCID: PMC5030293 DOI: 10.3389/fneur.2016.00147] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2016] [Accepted: 08/29/2016] [Indexed: 12/25/2022] Open
Abstract
Huntington disease (HD) is caused by an abnormally expanded cytosine-adenine-guanine (CAG) trinucleotide repeat in the HTT gene. Age and CAG-expansion number are related to age at diagnosis and can be used to index disease progression. However, observed onset-age variability suggests that other factors also modulate progression. Indexing prodromal (pre-diagnosis) progression may highlight therapeutic targets by isolating the earliest-affected factors. We present the largest prodromal HD application of the univariate method voxel-based morphometry (VBM) and the first application of the multivariate method source-based morphometry (SBM) to, respectively, compare gray matter concentration (GMC) and capture co-occurring GMC patterns in control and prodromal participants. Using structural MRI data from 1050 (831 prodromal, 219 control) participants, we characterize control-prodromal, whole-brain GMC differences at various prodromal stages. Our results provide evidence for (1) regional co-occurrence and differential patterns of decline across the prodrome, with parietal and occipital differences commonly co-occurring, and frontal and temporal differences being relatively independent from one another, (2) fronto-striatal circuits being among the earliest and most consistently affected in the prodrome, (3) delayed degradation in some movement-related regions, with increasing subcortical and occipital differences with later progression, (4) an overall superior-to-inferior gradient of GMC reduction in frontal, parietal, and temporal lobes, and (5) the appropriateness of SBM for studying the prodromal HD population and its enhanced sensitivity to early prodromal and regionally concurrent differences.
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Affiliation(s)
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
| | - Spencer Lourens
- Department of Psychiatry, University of Iowa , Iowa City, IA , USA
| | - Jeffrey D Long
- Department of Psychiatry, 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, University of Iowa, Iowa City, IA, USA; Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, USA
| | | | - Jingyu Liu
- The Mind Research Network , Albuquerque, NM , USA
| | | | - Jane S Paulsen
- Department of Psychiatry, University of Iowa, Iowa City, IA, USA; Department of Neurology, University of Iowa, Iowa City, IA, USA; Department of Psychology, University of Iowa, Iowa City, IA, USA
| | - Jessica A Turner
- Neuroscience Institute, Georgia State University, Atlanta, GA, USA; The Mind Research Network, Albuquerque, NM, USA
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