Revealing distinct neuroanatomical subtypes of Major depression disorder and Schizophrenia
Poster No:
1403
Submission Type:
Abstract Submission
Authors:
Yinghan Zhu1, Norihide Maikusa1, Kiyoto Kasai1, Shinsuke Koike1
Institutions:
1The University of Tokyo, Tokyo, Tokyo
First Author:
Co-Author(s):
Introduction:
Prior neuroimaging studies primarily focused on investigating neuroanatomical abnormalities in mental disorders such as major depression disorder (MDD) or schizophrenia (SCZ) use binary case-control approach.Brain structural abnormalities such as cortical thinning and subcortical volume increasing were reported in MDD or SCZ compare to heathy controls (HCs). We investigated neuroanatomical subtypes in multi-disorder, multi-site multi-protocol , using semi-supervised machine learning methods heterogeneity through discriminative analysis (Varol et al., 2017) to discover variations of anatomical alterations within disorders.
Methods:
T1-weighted structural brain MRI scans from patients with MDD (n=544), SCZ (n= 176), and 1,819 HCs, were obtained from 9 sites. Regional cortical thickness (CT), surface area (SA) and subcortical volumes (SV) served as features in building the classifier differentiating disorder subtypes from HCs. We used traveling subjects dataset to harmonize measures of CT, SA and SV data. Next, we fitted general addictive models to only the HC data (n=1148) to estimate non-linear effects of age and sex for every structural feature; then we applied the fitted GAMs to obtain non-linear age- and sex-corrected features. Individuals those who were younger than 65 years old (MDD, n=445; SCZ, n=158) and HC (n=599) data served as training, test, and external validation datasets.
Results:
Two distinct neuroanatomical subtypes were found for MDD and SCZ(Figure 1). In differentiating Subtype 1 or subtype 2 from HCs, the accuracy of each classifier was higher than those built for disorder without subtypes (MDD subtype1 vs HCs 79%; MDD subtype2 vs HCs 76%; MDD vs HCs 67%; SCZ subtype1 vs HCs 83%; SCZ subtype2 vs HCs 86%; SCZ vs HCs 75%). Indicating that by identifying subtypes of MDD and SCZ improved the performance in differentiating those from HCs.
·Figure 1 Patterns of cortical and subcortical features identifying the two subtypes of MDD and SCZ.
Conclusions:
Discovering of neuroanatomical subtypes of MDD and SCZ may be helpful to identify their prognosis and underlying neuropathological processes. Future prospective studies are required about whether the classifier could be actually helpful in the clinical settings.
Lifespan Development:
Aging 2
Lifespan Development Other
Modeling and Analysis Methods:
Classification and Predictive Modeling 1
Keywords:
Aging
Cortex
Machine Learning
MRI
Psychiatric Disorders
Schizophrenia
STRUCTURAL MRI
1|2Indicates the priority used for review