Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7234477/
http://dx.doi.org/10.1093/schbul/sbaa030.480
_version_ 1783535772081586176
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
work_keys_str_mv AT kirschnermatthias m168clinicalanatomicalphenotypesofschizophrenia
AT shafieigolia m168clinicalanatomicalphenotypesofschizophrenia
AT markellorossd m168clinicalanatomicalphenotypesofschizophrenia
AT markowskycarolina m168clinicalanatomicalphenotypesofschizophrenia
AT talpalarualexandra m168clinicalanatomicalphenotypesofschizophrenia
AT hodzicsantorbenazir m168clinicalanatomicalphenotypesofschizophrenia
AT devenyigabriela m168clinicalanatomicalphenotypesofschizophrenia
AT lepagemartin m168clinicalanatomicalphenotypesofschizophrenia
AT chakravartymmallar m168clinicalanatomicalphenotypesofschizophrenia
AT dagheralain m168clinicalanatomicalphenotypesofschizophrenia
AT misicbratislav m168clinicalanatomicalphenotypesofschizophrenia