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Multimodal data fusion of cortical-subcortical morphology and functional network connectivity in psychotic spectrum disorder
Multiple authors have noted overlapping symptoms and alterations across clinical, anatomical, and functional brain features in schizophrenia (SZ), schizoaffective disorder (SZA), and bipolar disorder (BPI). However, regarding brain features, few studies have approached this line of inquiry using ana...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9207350/ https://www.ncbi.nlm.nih.gov/pubmed/35709557 http://dx.doi.org/10.1016/j.nicl.2022.103056 |
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author | DeRamus, T.P. Wu, L. Qi, S. Iraji, A. Silva, R. Du, Y. Pearlson, G. Mayer, A. Bustillo, J.R. Stromberg, S.F. Calhoun, V.D. |
author_facet | DeRamus, T.P. Wu, L. Qi, S. Iraji, A. Silva, R. Du, Y. Pearlson, G. Mayer, A. Bustillo, J.R. Stromberg, S.F. Calhoun, V.D. |
author_sort | DeRamus, T.P. |
collection | PubMed |
description | Multiple authors have noted overlapping symptoms and alterations across clinical, anatomical, and functional brain features in schizophrenia (SZ), schizoaffective disorder (SZA), and bipolar disorder (BPI). However, regarding brain features, few studies have approached this line of inquiry using analytical techniques optimally designed to extract the shared features across anatomical and functional information in a simultaneous manner. Univariate studies of anatomical or functional alterations across these disorders can be limited and run the risk of omitting small but potentially crucial overlapping or joint neuroanatomical (e.g., structural images) and functional features (e.g., fMRI-based features) which may serve as informative clinical indicators of across multiple diagnostic categories. To address this limitation, we paired an unsupervised multimodal canonical correlation analysis (mCCA) together with joint independent component analysis (jICA) to identify linked spatial gray matter (GM), resting-state functional network connectivity (FNC), and white matter fractional anisotropy (FA) features across these diagnostic categories. We then calculated associations between the identified linked features and trans-diagnostic behavioral measures (MATRICs Consensus Cognitive Battery, MCCB). Component number 4 of the 13 identified displayed a statistically significant relationship with overall MCCB scores across GM, resting-state FNC, and FA. These linked modalities of component 4 consisted primarily of positive correlations within subcortical structures including the caudate and putamen in the GM maps with overall MCCB, sparse negative correlations within subcortical and cortical connection tracts (e.g., corticospinal tract, superior longitudinal fasciculus) in the FA maps with overall MCCB, and negative relationships with MCCB values and loading parameters with FNC matrices displaying increased FNC in subcortical-cortical regions with auditory, somatomotor, and visual regions. |
format | Online Article Text |
id | pubmed-9207350 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-92073502022-06-21 Multimodal data fusion of cortical-subcortical morphology and functional network connectivity in psychotic spectrum disorder DeRamus, T.P. Wu, L. Qi, S. Iraji, A. Silva, R. Du, Y. Pearlson, G. Mayer, A. Bustillo, J.R. Stromberg, S.F. Calhoun, V.D. Neuroimage Clin Regular Article Multiple authors have noted overlapping symptoms and alterations across clinical, anatomical, and functional brain features in schizophrenia (SZ), schizoaffective disorder (SZA), and bipolar disorder (BPI). However, regarding brain features, few studies have approached this line of inquiry using analytical techniques optimally designed to extract the shared features across anatomical and functional information in a simultaneous manner. Univariate studies of anatomical or functional alterations across these disorders can be limited and run the risk of omitting small but potentially crucial overlapping or joint neuroanatomical (e.g., structural images) and functional features (e.g., fMRI-based features) which may serve as informative clinical indicators of across multiple diagnostic categories. To address this limitation, we paired an unsupervised multimodal canonical correlation analysis (mCCA) together with joint independent component analysis (jICA) to identify linked spatial gray matter (GM), resting-state functional network connectivity (FNC), and white matter fractional anisotropy (FA) features across these diagnostic categories. We then calculated associations between the identified linked features and trans-diagnostic behavioral measures (MATRICs Consensus Cognitive Battery, MCCB). Component number 4 of the 13 identified displayed a statistically significant relationship with overall MCCB scores across GM, resting-state FNC, and FA. These linked modalities of component 4 consisted primarily of positive correlations within subcortical structures including the caudate and putamen in the GM maps with overall MCCB, sparse negative correlations within subcortical and cortical connection tracts (e.g., corticospinal tract, superior longitudinal fasciculus) in the FA maps with overall MCCB, and negative relationships with MCCB values and loading parameters with FNC matrices displaying increased FNC in subcortical-cortical regions with auditory, somatomotor, and visual regions. Elsevier 2022-05-23 /pmc/articles/PMC9207350/ /pubmed/35709557 http://dx.doi.org/10.1016/j.nicl.2022.103056 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Regular Article DeRamus, T.P. Wu, L. Qi, S. Iraji, A. Silva, R. Du, Y. Pearlson, G. Mayer, A. Bustillo, J.R. Stromberg, S.F. Calhoun, V.D. Multimodal data fusion of cortical-subcortical morphology and functional network connectivity in psychotic spectrum disorder |
title | Multimodal data fusion of cortical-subcortical morphology and functional network connectivity in psychotic spectrum disorder |
title_full | Multimodal data fusion of cortical-subcortical morphology and functional network connectivity in psychotic spectrum disorder |
title_fullStr | Multimodal data fusion of cortical-subcortical morphology and functional network connectivity in psychotic spectrum disorder |
title_full_unstemmed | Multimodal data fusion of cortical-subcortical morphology and functional network connectivity in psychotic spectrum disorder |
title_short | Multimodal data fusion of cortical-subcortical morphology and functional network connectivity in psychotic spectrum disorder |
title_sort | multimodal data fusion of cortical-subcortical morphology and functional network connectivity in psychotic spectrum disorder |
topic | Regular Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9207350/ https://www.ncbi.nlm.nih.gov/pubmed/35709557 http://dx.doi.org/10.1016/j.nicl.2022.103056 |
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