Cargando…
Cognitive Implications of Correlated Structural Network Changes in Schizophrenia
BACKGROUND: Schizophrenia is a brain disorder characterized by diffuse, diverse, and wide-spread changes in gray matter volume (GM) and white matter structure (fractional anisotropy, FA), as well as cognitive impairments that greatly impact an individual’s quality of life. While the relationship of...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8811375/ https://www.ncbi.nlm.nih.gov/pubmed/35126065 http://dx.doi.org/10.3389/fnint.2021.755069 |
_version_ | 1784644421703172096 |
---|---|
author | Jensen, Dawn M. Zendrehrouh, Elaheh Calhoun, Vince Turner, Jessica A. |
author_facet | Jensen, Dawn M. Zendrehrouh, Elaheh Calhoun, Vince Turner, Jessica A. |
author_sort | Jensen, Dawn M. |
collection | PubMed |
description | BACKGROUND: Schizophrenia is a brain disorder characterized by diffuse, diverse, and wide-spread changes in gray matter volume (GM) and white matter structure (fractional anisotropy, FA), as well as cognitive impairments that greatly impact an individual’s quality of life. While the relationship of each of these image modalities and their links to schizophrenia status and cognitive impairment has been investigated separately, a multimodal fusion via parallel independent component analysis (pICA) affords the opportunity to explore the relationships between the changes in GM and FA, and the implications these network changes have on cognitive performance. METHODS: Images from 73 subjects with schizophrenia (SZ) and 82 healthy controls (HC) were drawn from an existing dataset. We investigated 12 components from each feature (FA and GM). Loading coefficients from the images were used to identify pairs of features that were significantly correlated and showed significant group differences between HC and SZ. MANCOVA analysis uncovered the relationships the identified spatial maps had with age, gender, and a global cognitive performance score. RESULTS: Three component pairs showed significant group differences (HC > SZ) in both gray and white matter measurements. Two of the component pairs identified networks of gray matter that drove significant relationships with cognition (HC > SZ) after accounting for age and gender. The gray and white matter structural networks identified in these three component pairs pull broadly from many regions, including the right and left thalamus, lateral occipital cortex, multiple regions of the middle temporal gyrus, precuneus cortex, postcentral gyrus, cingulate gyrus/cingulum, lingual gyrus, and brain stem. CONCLUSION: The results of this multimodal analysis adds to our understanding of how the relationship between GM, FA, and cognition differs between HC and SZ by highlighting the correlated intermodal covariance of these structural networks and their differential relationships with cognitive performance. Previous unimodal research has found similar areas of GM and FA differences between these groups, and the cognitive deficits associated with SZ have been well documented. This study allowed us to evaluate the intercorrelated covariance of these structural networks and how these networks are involved the differences in cognitive performance between HC and SZ. |
format | Online Article Text |
id | pubmed-8811375 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-88113752022-02-04 Cognitive Implications of Correlated Structural Network Changes in Schizophrenia Jensen, Dawn M. Zendrehrouh, Elaheh Calhoun, Vince Turner, Jessica A. Front Integr Neurosci Neuroscience BACKGROUND: Schizophrenia is a brain disorder characterized by diffuse, diverse, and wide-spread changes in gray matter volume (GM) and white matter structure (fractional anisotropy, FA), as well as cognitive impairments that greatly impact an individual’s quality of life. While the relationship of each of these image modalities and their links to schizophrenia status and cognitive impairment has been investigated separately, a multimodal fusion via parallel independent component analysis (pICA) affords the opportunity to explore the relationships between the changes in GM and FA, and the implications these network changes have on cognitive performance. METHODS: Images from 73 subjects with schizophrenia (SZ) and 82 healthy controls (HC) were drawn from an existing dataset. We investigated 12 components from each feature (FA and GM). Loading coefficients from the images were used to identify pairs of features that were significantly correlated and showed significant group differences between HC and SZ. MANCOVA analysis uncovered the relationships the identified spatial maps had with age, gender, and a global cognitive performance score. RESULTS: Three component pairs showed significant group differences (HC > SZ) in both gray and white matter measurements. Two of the component pairs identified networks of gray matter that drove significant relationships with cognition (HC > SZ) after accounting for age and gender. The gray and white matter structural networks identified in these three component pairs pull broadly from many regions, including the right and left thalamus, lateral occipital cortex, multiple regions of the middle temporal gyrus, precuneus cortex, postcentral gyrus, cingulate gyrus/cingulum, lingual gyrus, and brain stem. CONCLUSION: The results of this multimodal analysis adds to our understanding of how the relationship between GM, FA, and cognition differs between HC and SZ by highlighting the correlated intermodal covariance of these structural networks and their differential relationships with cognitive performance. Previous unimodal research has found similar areas of GM and FA differences between these groups, and the cognitive deficits associated with SZ have been well documented. This study allowed us to evaluate the intercorrelated covariance of these structural networks and how these networks are involved the differences in cognitive performance between HC and SZ. Frontiers Media S.A. 2022-01-20 /pmc/articles/PMC8811375/ /pubmed/35126065 http://dx.doi.org/10.3389/fnint.2021.755069 Text en Copyright © 2022 Jensen, Zendrehrouh, Calhoun and Turner. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Jensen, Dawn M. Zendrehrouh, Elaheh Calhoun, Vince Turner, Jessica A. Cognitive Implications of Correlated Structural Network Changes in Schizophrenia |
title | Cognitive Implications of Correlated Structural Network Changes in Schizophrenia |
title_full | Cognitive Implications of Correlated Structural Network Changes in Schizophrenia |
title_fullStr | Cognitive Implications of Correlated Structural Network Changes in Schizophrenia |
title_full_unstemmed | Cognitive Implications of Correlated Structural Network Changes in Schizophrenia |
title_short | Cognitive Implications of Correlated Structural Network Changes in Schizophrenia |
title_sort | cognitive implications of correlated structural network changes in schizophrenia |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8811375/ https://www.ncbi.nlm.nih.gov/pubmed/35126065 http://dx.doi.org/10.3389/fnint.2021.755069 |
work_keys_str_mv | AT jensendawnm cognitiveimplicationsofcorrelatedstructuralnetworkchangesinschizophrenia AT zendrehrouhelaheh cognitiveimplicationsofcorrelatedstructuralnetworkchangesinschizophrenia AT calhounvince cognitiveimplicationsofcorrelatedstructuralnetworkchangesinschizophrenia AT turnerjessicaa cognitiveimplicationsofcorrelatedstructuralnetworkchangesinschizophrenia |