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M168. CLINICAL-ANATOMICAL PHENOTYPES OF SCHIZOPHRENIA
BACKGROUND: Although widespread structural brain abnormalities have been consistently reported in schizophrenia, their relation to the heterogeneous clinical manifestations is not well understood. Multivariate methods are needed to uncover covariance patterns between multiple symptom dimensions and...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7234477/ http://dx.doi.org/10.1093/schbul/sbaa030.480 |
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author | Kirschner, Matthias Shafiei, Golia Markello, Ross D Markowsky, Carolina Talpalaru, Alexandra Hodzic-Santor, Benazir Devenyi, Gabriel A Lepage, Martin Chakravarty, M Mallar Dagher, Alain Misic, Bratislav |
author_facet | Kirschner, Matthias Shafiei, Golia Markello, Ross D Markowsky, Carolina Talpalaru, Alexandra Hodzic-Santor, Benazir Devenyi, Gabriel A Lepage, Martin Chakravarty, M Mallar Dagher, Alain Misic, Bratislav |
author_sort | Kirschner, Matthias |
collection | PubMed |
description | BACKGROUND: Although widespread structural brain abnormalities have been consistently reported in schizophrenia, their relation to the heterogeneous clinical manifestations is not well understood. Multivariate methods are needed to uncover covariance patterns between multiple symptom dimensions and system-wide brain imaging data. METHODS: This cross-sectional study used structural magnetic resonance imaging and neuropsychological data from 133 patients with chronic schizophrenia (48 female, 34.8±13.2 years) from the Northwestern University Schizophrenia Data and Software Tool (NUSDAST). We estimate disease-related voxel-wise tissue volume loss using deformation-based morphometry (DBM) of T1 weighted images. In patients with schizophrenia, multiple clinical dimensions including positive/negative symptoms and cognitive deficits, demographic data as well as individual tissue volume loss (DBM) were included in the multivariate model. Clinical-anatomical phenotypes were identified using partial least squares analysis. RESULTS: Multivariate analysis revealed three distinct clinical-anatomical phenotypes accounting for 27.5%, 15%, and 13% of the shared covariance between clinical-behavioural data and tissue volume loss (total of 55.5%). The first clinical-anatomical phenotype encompassed cognitive impairments, severity of negative symptoms and tissue volume loss within the default mode network and visual network. The second clinical-anatomical phenotype was associated with additional cognitive impairments and tissue volume loss within the frontoparietal and ventral attention network, while the third clinical-anatomical phenotype encompassed a mixed positive and negative symptoms phenotype and tissue volume loss within the dorsal attention network. Critically, the pattern of volume loss within the first most prevalent clinical-anatomical phenotype mediated (a*b) the effect of socioeconomic status on clinical outcome (cognitive performance and negative symptoms) (a*b=-0.033(0.008); P<1.0×〖10〗^(-4); 95% CI [-0.049, -0.018]). Finally, we partly replicated the first clinical-anatomical phenotype in an independent sample of patients with schizophrenia (n=108). DISCUSSION: The heterogeneous clinical manifestation of schizophrenia can be significantly explained by three clinical-anatomical phenotypes. Despite their distributed topography, each phenotype is centered on a specific, well-defined set of intrinsic networks. |
format | Online Article Text |
id | pubmed-7234477 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-72344772020-05-23 M168. CLINICAL-ANATOMICAL PHENOTYPES OF SCHIZOPHRENIA Kirschner, Matthias Shafiei, Golia Markello, Ross D Markowsky, Carolina Talpalaru, Alexandra Hodzic-Santor, Benazir Devenyi, Gabriel A Lepage, Martin Chakravarty, M Mallar Dagher, Alain Misic, Bratislav Schizophr Bull Poster Session II BACKGROUND: Although widespread structural brain abnormalities have been consistently reported in schizophrenia, their relation to the heterogeneous clinical manifestations is not well understood. Multivariate methods are needed to uncover covariance patterns between multiple symptom dimensions and system-wide brain imaging data. METHODS: This cross-sectional study used structural magnetic resonance imaging and neuropsychological data from 133 patients with chronic schizophrenia (48 female, 34.8±13.2 years) from the Northwestern University Schizophrenia Data and Software Tool (NUSDAST). We estimate disease-related voxel-wise tissue volume loss using deformation-based morphometry (DBM) of T1 weighted images. In patients with schizophrenia, multiple clinical dimensions including positive/negative symptoms and cognitive deficits, demographic data as well as individual tissue volume loss (DBM) were included in the multivariate model. Clinical-anatomical phenotypes were identified using partial least squares analysis. RESULTS: Multivariate analysis revealed three distinct clinical-anatomical phenotypes accounting for 27.5%, 15%, and 13% of the shared covariance between clinical-behavioural data and tissue volume loss (total of 55.5%). The first clinical-anatomical phenotype encompassed cognitive impairments, severity of negative symptoms and tissue volume loss within the default mode network and visual network. The second clinical-anatomical phenotype was associated with additional cognitive impairments and tissue volume loss within the frontoparietal and ventral attention network, while the third clinical-anatomical phenotype encompassed a mixed positive and negative symptoms phenotype and tissue volume loss within the dorsal attention network. Critically, the pattern of volume loss within the first most prevalent clinical-anatomical phenotype mediated (a*b) the effect of socioeconomic status on clinical outcome (cognitive performance and negative symptoms) (a*b=-0.033(0.008); P<1.0×〖10〗^(-4); 95% CI [-0.049, -0.018]). Finally, we partly replicated the first clinical-anatomical phenotype in an independent sample of patients with schizophrenia (n=108). DISCUSSION: The heterogeneous clinical manifestation of schizophrenia can be significantly explained by three clinical-anatomical phenotypes. Despite their distributed topography, each phenotype is centered on a specific, well-defined set of intrinsic networks. Oxford University Press 2020-05 2020-05-18 /pmc/articles/PMC7234477/ http://dx.doi.org/10.1093/schbul/sbaa030.480 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Poster Session II Kirschner, Matthias Shafiei, Golia Markello, Ross D Markowsky, Carolina Talpalaru, Alexandra Hodzic-Santor, Benazir Devenyi, Gabriel A Lepage, Martin Chakravarty, M Mallar Dagher, Alain Misic, Bratislav M168. CLINICAL-ANATOMICAL PHENOTYPES OF SCHIZOPHRENIA |
title | M168. CLINICAL-ANATOMICAL PHENOTYPES OF SCHIZOPHRENIA |
title_full | M168. CLINICAL-ANATOMICAL PHENOTYPES OF SCHIZOPHRENIA |
title_fullStr | M168. CLINICAL-ANATOMICAL PHENOTYPES OF SCHIZOPHRENIA |
title_full_unstemmed | M168. CLINICAL-ANATOMICAL PHENOTYPES OF SCHIZOPHRENIA |
title_short | M168. CLINICAL-ANATOMICAL PHENOTYPES OF SCHIZOPHRENIA |
title_sort | m168. clinical-anatomical phenotypes of schizophrenia |
topic | Poster Session II |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7234477/ http://dx.doi.org/10.1093/schbul/sbaa030.480 |
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