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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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Hecht EE, Zapata I, Alvarez CE, Gutman DA, Preuss TM, Kent M, Serpell JA. Neurodevelopmental scaling is a major driver of brain-behavior differences in temperament across dog breeds. Brain Struct Funct 2021; 226:2725-2739. [PMID: 34455497 DOI: 10.1007/s00429-021-02368-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 08/23/2021] [Indexed: 11/24/2022]
Abstract
Behavioral traits like aggression, anxiety, and trainability differ significantly across dog breeds and are highly heritable. However, the neural bases of these differences are unknown. Here we analyzed structural MRI scans of 62 dogs in relation to breed-average scores for the 14 major dimensions in the Canine Behavioral Assessment and Research Questionnaire, a well-validated measure of canine temperament. Several behavior categories showed significant relationships with morphologically covarying gray matter networks and regional volume changes. Networks involved in social processing and the flight-or-fight response were associated with stranger-directed fear and aggression, putatively the main behaviors under selection pressure during wolf-to-dog domestication. Trainability was significantly associated with expansion in broad regions of cortex, while fear, aggression, and other "problem" behaviors were associated with expansion in distributed subcortical regions. These results closely overlapped with regional volume changes with total brain size, in striking correspondence with models of developmental constraint on brain evolution. This suggests that the established link between dog body size and behavior is due at least in part to disproportionate enlargement of later-developing regions in larger brained dogs. We discuss how this may explain the known correlation of increasing reactivity with decreasing body size in dogs.
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Affiliation(s)
- E E Hecht
- Department of Human Evolutionary Biology, Harvard University, 11 Divinity Ave., Cambridge, MA, 02138, USA.
| | - I Zapata
- Department of Biomedical Sciences, Rocky Vista University, Parker, CO, 80134, USA
| | - C E Alvarez
- Center for Clinical and Translational Research, The Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, OH, 43205, USA.,Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, 43210, USA.,Department of Veterinary Clinical Sciences, The Ohio State University College of Veterinary Medicine, Columbus, OH, 43210, USA
| | - D A Gutman
- Department of Neurology, School of Medicine, Emory University, Atlanta, GA, 30029, USA
| | - T M Preuss
- Yerkes National Primate Research Center, Emory University, Atlanta, GA, 30029, USA
| | - M Kent
- College of Veterinary Medicine, University of Georgia at Athens, Athens, GA, 30602, USA
| | - J A Serpell
- Department of Clinical Sciences and Advanced Medicine, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
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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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Zhu Q, Huang J, Xu X. Non-negative discriminative brain functional connectivity for identifying schizophrenia on resting-state fMRI. Biomed Eng Online 2018. [PMID: 29534759 PMCID: PMC5851331 DOI: 10.1186/s12938-018-0464-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Background Schizophrenia is a clinical syndrome, and its causes have not been well determined. The objective of this study was to investigate the alteration of brain functional connectivity between schizophrenia and healthy control, and present a practical solution for accurately identifying schizophrenia at single-subject level. Methods 24 schizophrenia patients and 21 matched healthy subjects were recruited to undergo the resting-state functional magnetic resonance imaging (rs-fMRI) scanning. First, we constructed the brain network by calculating the Pearson correlation coefficient between each pair of the brain regions. Then, this study proposed a novel non-negative discriminant functional connectivity selection method, i.e. non-negative elastic-net based method (N2EN), to extract the alteration of brain functional connectivity between schizophrenia and healthy control. It ranks the significance of the connectivity with a uniform criterion by introducing the non-negative constraint. Finally, kernel discriminant analysis (KDA) is exploited to classify the subjects with the selected discriminant brain connectivity features. Results The proposed method is applied into schizophrenia classification, and achieves the sensitivity, specificity and accuracy of 100, 90.48 and 95.56%, respectively. Our findings also indicate the alteration of functional network can be used as the biomarks for guiding the schizophrenia diagnosis. The regions of cuneus, superior frontal gyrus, medial, paracentral lobule, calcarine fissure, surrounding cortex, etc. are highly relevant to schizophrenia. Conclusions This study provides a method for accurately identifying schizophrenia, which outperforms several state-of-the-art methods, including conventional brain network classification, multi-threshold brain network based classification, frequent sub-graph based brain network classification and support vector machine. Our investigation suggested that the selected discriminant resting-state functional connectivities are meaningful features for classifying schizophrenia and healthy control.
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Affiliation(s)
- Qi Zhu
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China. .,Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, 210093, China.
| | - Jiashuang Huang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China
| | - Xijia Xu
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, China
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Dynamical changes in neurological diseases and anesthesia. Curr Opin Neurobiol 2012; 22:693-703. [PMID: 22446010 DOI: 10.1016/j.conb.2012.02.009] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2012] [Revised: 02/11/2012] [Accepted: 02/19/2012] [Indexed: 12/22/2022]
Abstract
Dynamics of neuronal networks can be altered in at least two ways: by changes in connectivity, that is, the physical architecture of the network, or changes in the amplitudes and kinetics of the intrinsic and synaptic currents within and between the elements making up a network. We argue that the latter changes are often overlooked as sources of alterations in network behavior when there are also structural (connectivity) abnormalities present; indeed, they may even give rise to the structural changes observed in these states. Here we look at two clinically relevant states (Parkinson's disease and schizophrenia) and argue that non-structural changes are important in the development of abnormal dynamics within the networks known to be relevant to each disorder. We also discuss anesthesia, since it is entirely acute, thus illustrating the potent effects of changes in synaptic and intrinsic membrane currents in the absence of structural alteration. In each of these, we focus on the role of changes in GABAergic function within microcircuits, stressing literature within the last few years.
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Sui J, Yu Q, He H, Pearlson GD, Calhoun VD. A selective review of multimodal fusion methods in schizophrenia. Front Hum Neurosci 2012; 6:27. [PMID: 22375114 PMCID: PMC3285795 DOI: 10.3389/fnhum.2012.00027] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2011] [Accepted: 02/08/2012] [Indexed: 12/29/2022] Open
Abstract
Schizophrenia (SZ) is one of the most cryptic and costly mental disorders in terms of human suffering and societal expenditure (van Os and Kapur, 2009). Though strong evidence for functional, structural, and genetic abnormalities associated with this disease exists, there is yet no replicable finding which has proven accurate enough to be useful in clinical decision making (Fornito et al., 2009), and its diagnosis relies primarily upon symptom assessment (Williams et al., 2010a). It is likely in part that the lack of consistent neuroimaging findings is because most models favor only one data type or do not combine data from different imaging modalities effectively, thus missing potentially important differences which are only partially detected by each modality (Calhoun et al., 2006a). It is becoming increasingly clear that multimodal fusion, a technique which takes advantage of the fact that each modality provides a limited view of the brain/gene and may uncover hidden relationships, is an important tool to help unravel the black box of schizophrenia. In this review paper, we survey a number of multimodal fusion applications which enable us to study the schizophrenia macro-connectome, including brain functional, structural, and genetic aspects and may help us understand the disorder in a more comprehensive and integrated manner. We also provide a table that characterizes these applications by the methods used and compare these methods in detail, especially for multivariate models, which may serve as a valuable reference that helps readers select an appropriate method based on a given research question.
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Affiliation(s)
- Jing Sui
- The Mind Research NetworkAlbuquerque, NM, USA
| | - Qingbao Yu
- The Mind Research NetworkAlbuquerque, NM, USA
| | - Hao He
- The Mind Research NetworkAlbuquerque, NM, USA
- Department of Electrical and Computer Engineering, University of New MexicoAlbuquerque, NM, USA
| | - Godfrey D. Pearlson
- Olin Neuropsychiatry Research CenterHartford, CT, USA
- Department of Psychiatry, Yale UniversityNew Haven, CT, USA
- Department of Neurobiology, Yale UniversityNew Haven, CT, USA
| | - Vince D. Calhoun
- The Mind Research NetworkAlbuquerque, NM, USA
- Department of Electrical and Computer Engineering, University of New MexicoAlbuquerque, NM, USA
- Olin Neuropsychiatry Research CenterHartford, CT, USA
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