Abstract

Bipolar disorder and schizophrenia are associated with brain morphometry alterations. This study investigates inter-individual variability in brain structural profiles, both within diagnostic groups and between patients and healthy individuals. Brain morphometric measures from three independent samples of patients with schizophrenia (n = 168), bipolar disorder (n = 122), and healthy individuals (n = 180) were modeled as single vectors to generated individualized profiles of subcortical volumes and regional cortical thickness. These profiles were then used to compute a person-based similarity index (PBSI) for subcortical volumes and for regional cortical thickness, to quantify the within-group similarity of the morphometric profile of each individual to that of the other participants in the same diagnostic group. There was no effect of diagnosis on the PBSI for subcortical volumes. In contrast, compared to healthy individuals, the PBSI for cortical thickness was lower in patients with schizophrenia (effect size = 0.4, p ≤ 0.0002), but not in patients with bipolar disorder. The results were robust and reproducible across samples. We conclude that disease mechanisms for these disorders produce modest inter-individual variations in brain morphometry that should be considered in future studies attempting to cluster patients in subgroups.

Details

Title
Personalized estimates of morphometric similarity in bipolar disorder and schizophrenia
Author
Doucet, Gaelle E 1 ; Lin, Dongdong 2 ; Du Yuhui 3 ; Fu Zening 4 ; Glahn, David C 5 ; Calhoun, Vincent D 6 ; Turner, Jessica 7 ; Frangou Sophia 8   VIAFID ORCID Logo 

1  Icahn School of Medicine at Mount Sinai, Department of Psychiatry, New York, USA (GRID:grid.59734.3c) (ISNI:0000 0001 0670 2351); Boys Town National Research Hospital, Omaha, USA (GRID:grid.414583.f) (ISNI:0000 0000 8953 4586) 
2  Georgia State University, Georgia Institute of Technology, and Emory University, Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, USA (GRID:grid.414583.f) 
3  Georgia State University, Georgia Institute of Technology, and Emory University, Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, USA (GRID:grid.414583.f); Shanxi University, School of Computer & Information Technology, Taiyuan, China (GRID:grid.163032.5) (ISNI:0000 0004 1760 2008) 
4  Georgia State University, Georgia Institute of Technology, and Emory University, Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, USA (GRID:grid.163032.5) 
5  Harvard University, Tommy Fuss Center for Neuropsychiatric Disease Research, Boston Children’s Hospital, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X) 
6  Georgia State University, Georgia Institute of Technology, and Emory University, Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, USA (GRID:grid.38142.3c) 
7  Georgia State University, Georgia Institute of Technology, and Emory University, Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, USA (GRID:grid.38142.3c); Georgia State University, Department of Psychology, Neuroscience Institute, Atlanta, USA (GRID:grid.256304.6) (ISNI:0000 0004 1936 7400) 
8  Icahn School of Medicine at Mount Sinai, Department of Psychiatry, New York, USA (GRID:grid.59734.3c) (ISNI:0000 0001 0670 2351); University of British Columbia, Centre for Brain Health, Vancouver, Canada (GRID:grid.17091.3e) (ISNI:0000 0001 2288 9830) 
Publication year
2020
Publication date
2020
Publisher
Nature Publishing Group
e-ISSN
2334-265X
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1038/s41537-020-00128-x
ProQuest document ID
2473291526
Copyright
© The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.