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Morphological Profiling of Schizophrenia: Cluster Analysis of MRI-Based Cortical Thickness Data
The diagnosis of schizophrenia is thought to embrace several distinct subgroups. The manifold entities in a single clinical patient group increase the variance of biological measures, deflate the group-level estimates of causal factors, and mask the presence of treatment effects. However, reliable n...
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/PMC7147597/ https://www.ncbi.nlm.nih.gov/pubmed/31901940 http://dx.doi.org/10.1093/schbul/sbz112 |
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author | Pan, Yunzhi Pu, Weidan Chen, Xudong Huang, Xiaojun Cai, Yan Tao, Haojuan Xue, Zhiming Mackinley, Michael Limongi, Roberto Liu, Zhening Palaniyappan, Lena |
author_facet | Pan, Yunzhi Pu, Weidan Chen, Xudong Huang, Xiaojun Cai, Yan Tao, Haojuan Xue, Zhiming Mackinley, Michael Limongi, Roberto Liu, Zhening Palaniyappan, Lena |
author_sort | Pan, Yunzhi |
collection | PubMed |
description | The diagnosis of schizophrenia is thought to embrace several distinct subgroups. The manifold entities in a single clinical patient group increase the variance of biological measures, deflate the group-level estimates of causal factors, and mask the presence of treatment effects. However, reliable neurobiological boundaries to differentiate these subgroups remain elusive. Since cortical thinning is a well-established feature in schizophrenia, we investigated if individuals (patients and healthy controls) with similar patterns of regional cortical thickness form naturally occurring morphological subtypes. K-means algorithm clustering was applied to regional cortical thickness values obtained from 256 structural MRI scans (179 patients with schizophrenia and 77 healthy controls [HCs]). GAP statistics revealed three clusters with distinct regional thickness patterns. The specific patterns of cortical thinning, clinical characteristics, and cognitive function of each clustered subgroup were assessed. The three clusters based on thickness patterns comprised of a morphologically impoverished subgroup (25% patients, 1% HCs), an intermediate subgroup (47% patients, 46% HCs), and an intact subgroup (28% patients, 53% HCs). The differences of clinical features among three clusters pertained to age-of-onset, N-back performance, duration exposure to treatment, total burden of positive symptoms, and severity of delusions. Particularly, the morphologically impoverished group had deficits in N-back performance and less severe positive symptom burden. The data-driven neuroimaging approach illustrates the occurrence of morphologically separable subgroups in schizophrenia, with distinct clinical characteristics. We infer that the anatomical heterogeneity of schizophrenia arises from both pathological deviance and physiological variance. We advocate using MRI-guided stratification for clinical trials as well as case–control investigations in schizophrenia. |
format | Online Article Text |
id | pubmed-7147597 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-71475972020-04-15 Morphological Profiling of Schizophrenia: Cluster Analysis of MRI-Based Cortical Thickness Data Pan, Yunzhi Pu, Weidan Chen, Xudong Huang, Xiaojun Cai, Yan Tao, Haojuan Xue, Zhiming Mackinley, Michael Limongi, Roberto Liu, Zhening Palaniyappan, Lena Schizophr Bull Regular Articles The diagnosis of schizophrenia is thought to embrace several distinct subgroups. The manifold entities in a single clinical patient group increase the variance of biological measures, deflate the group-level estimates of causal factors, and mask the presence of treatment effects. However, reliable neurobiological boundaries to differentiate these subgroups remain elusive. Since cortical thinning is a well-established feature in schizophrenia, we investigated if individuals (patients and healthy controls) with similar patterns of regional cortical thickness form naturally occurring morphological subtypes. K-means algorithm clustering was applied to regional cortical thickness values obtained from 256 structural MRI scans (179 patients with schizophrenia and 77 healthy controls [HCs]). GAP statistics revealed three clusters with distinct regional thickness patterns. The specific patterns of cortical thinning, clinical characteristics, and cognitive function of each clustered subgroup were assessed. The three clusters based on thickness patterns comprised of a morphologically impoverished subgroup (25% patients, 1% HCs), an intermediate subgroup (47% patients, 46% HCs), and an intact subgroup (28% patients, 53% HCs). The differences of clinical features among three clusters pertained to age-of-onset, N-back performance, duration exposure to treatment, total burden of positive symptoms, and severity of delusions. Particularly, the morphologically impoverished group had deficits in N-back performance and less severe positive symptom burden. The data-driven neuroimaging approach illustrates the occurrence of morphologically separable subgroups in schizophrenia, with distinct clinical characteristics. We infer that the anatomical heterogeneity of schizophrenia arises from both pathological deviance and physiological variance. We advocate using MRI-guided stratification for clinical trials as well as case–control investigations in schizophrenia. Oxford University Press 2020-04 2020-01-04 /pmc/articles/PMC7147597/ /pubmed/31901940 http://dx.doi.org/10.1093/schbul/sbz112 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-nd/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Regular Articles Pan, Yunzhi Pu, Weidan Chen, Xudong Huang, Xiaojun Cai, Yan Tao, Haojuan Xue, Zhiming Mackinley, Michael Limongi, Roberto Liu, Zhening Palaniyappan, Lena Morphological Profiling of Schizophrenia: Cluster Analysis of MRI-Based Cortical Thickness Data |
title | Morphological Profiling of Schizophrenia: Cluster Analysis of MRI-Based Cortical Thickness Data |
title_full | Morphological Profiling of Schizophrenia: Cluster Analysis of MRI-Based Cortical Thickness Data |
title_fullStr | Morphological Profiling of Schizophrenia: Cluster Analysis of MRI-Based Cortical Thickness Data |
title_full_unstemmed | Morphological Profiling of Schizophrenia: Cluster Analysis of MRI-Based Cortical Thickness Data |
title_short | Morphological Profiling of Schizophrenia: Cluster Analysis of MRI-Based Cortical Thickness Data |
title_sort | morphological profiling of schizophrenia: cluster analysis of mri-based cortical thickness data |
topic | Regular Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7147597/ https://www.ncbi.nlm.nih.gov/pubmed/31901940 http://dx.doi.org/10.1093/schbul/sbz112 |
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