Konasale Prasad1, Jonathan Rubin2, Anirban Mitra3, Madison Lewis4, Nicholas Theis5, Brendan Muldoon5, Satish Iyengar3, Joshua Cape3. 1. 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. Electronic address: kmp8@pitt.edu. 2. Department of Mathematics, University of Pittsburgh, 917 Cathedral of Learning, Pittsburgh, PA 15260, United States of America. 3. Department of Statistics, University of Pittsburgh, 230 South Bouquet Street, Pittsburgh, PA 15260, United States of America. 4. University of Pittsburgh Swanson School of Engineering, 3700 O'Hara St, Pittsburgh, PA 15213, United States of America. 5. University of Pittsburgh School of Medicine, Western Psychiatric Institute and Clinic, 3811 O'Hara St, Pittsburgh, PA 15213, United States of America.
Abstract
BACKGROUND: Examination of structural covariance network (SCN) is gaining prominence among the strategies to delineate dysconnectivity that case-control morphometric comparisons cannot address. Part II of this review extends on the part I of the review that included SCN studies using statistical approaches by examining SCN studies applying graph theoretic approaches to elucidate network properties in schizophrenia. This review also includes SCN studies using graph theoretic or statistical approaches on persons at-risk for schizophrenia. METHODS: A systematic literature search was conducted for peer-reviewed publications using different keywords and keyword combinations for schizophrenia and risk for schizophrenia. Thirteen studies on schizophrenia and five on persons at risk for schizophrenia met the criteria. RESULTS: A variety of findings from over the last 1½ decades showing qualitative and quantitative differences in the global and local structural connectome in schizophrenia are described. These observations include altered hub patterns, disrupted network topology and hierarchical organization of the brain, and impaired connections that may be localized to default mode, executive control, and dorsal attention networks. Some of these connectomic alterations were observed in persons at-risk for schizophrenia before the onset of the illness. CONCLUSIONS: Observed disruptions may reduce network efficiency and capacity to integrate information. Further, global connectomic changes were not schizophrenia-specific but local network changes were. Existing studies have used different atlases for brain parcellation, examined different morphometric features, and patients at different stages of illness making it difficult to conduct meta-analysis. Future studies should harmonize such methodological differences to facilitate meta-analysis and also elucidate causal underpinnings of dysconnectivity.
BACKGROUND: Examination of structural covariance network (SCN) is gaining prominence among the strategies to delineate dysconnectivity that case-control morphometric comparisons cannot address. Part II of this review extends on the part I of the review that included SCN studies using statistical approaches by examining SCN studies applying graph theoretic approaches to elucidate network properties in schizophrenia. This review also includes SCN studies using graph theoretic or statistical approaches on persons at-risk for schizophrenia. METHODS: A systematic literature search was conducted for peer-reviewed publications using different keywords and keyword combinations for schizophrenia and risk for schizophrenia. Thirteen studies on schizophrenia and five on persons at risk for schizophrenia met the criteria. RESULTS: A variety of findings from over the last 1½ decades showing qualitative and quantitative differences in the global and local structural connectome in schizophrenia are described. These observations include altered hub patterns, disrupted network topology and hierarchical organization of the brain, and impaired connections that may be localized to default mode, executive control, and dorsal attention networks. Some of these connectomic alterations were observed in persons at-risk for schizophrenia before the onset of the illness. CONCLUSIONS: Observed disruptions may reduce network efficiency and capacity to integrate information. Further, global connectomic changes were not schizophrenia-specific but local network changes were. Existing studies have used different atlases for brain parcellation, examined different morphometric features, and patients at different stages of illness making it difficult to conduct meta-analysis. Future studies should harmonize such methodological differences to facilitate meta-analysis and also elucidate causal underpinnings of dysconnectivity.
Authors: C J Honey; O Sporns; L Cammoun; X Gigandet; J P Thiran; R Meuli; P Hagmann Journal: Proc Natl Acad Sci U S A Date: 2009-02-02 Impact factor: 11.205
Authors: Yohan Yee; Darren J Fernandes; Leon French; Jacob Ellegood; Lindsay S Cahill; Dulcie A Vousden; Leigh Spencer Noakes; Jan Scholz; Matthijs C van Eede; Brian J Nieman; John G Sled; Jason P Lerch Journal: Neuroimage Date: 2018-05-18 Impact factor: 6.556
Authors: Armin Raznahan; Jason P Lerch; Nancy Lee; Dede Greenstein; Gregory L Wallace; Michael Stockman; Liv Clasen; Phillip W Shaw; Jay N Giedd Journal: Neuron Date: 2011-12-08 Impact factor: 17.173
Authors: Kareen Heinze; Renate L E P Reniers; Barnaby Nelson; Alison R Yung; Ashleigh Lin; Ben J Harrison; Christos Pantelis; Dennis Velakoulis; Patrick D McGorry; Stephen J Wood Journal: Biol Psychiatry Date: 2014-11-11 Impact factor: 13.382