Morphological fingerprinting: Identifying patients with first-episode schizophrenia using auto-encoded morphological patterns.

ABSTRACT: Although a large number of case-control statistical and machine learning studies have been conducted to investigate structural brain changes in schizophrenia, how best to measure and characterize structural abnormalities for use in classification algorithms remains an open question. In the current study, a convolutional 3D autoencoder specifically designed for discretized volumes was constructed and trained with segmented brains from 477 healthy individuals. A cohort containing 158 first-episode schizophrenia patients and 166 matched controls was fed into the trained autoencoder to generate auto-encoded morphological patterns. A classifier discriminating schizophrenia patients from healthy controls was built using 80% of the samples in this cohort by automated machine learning and validate

SUBMITTER: Sun H 

PROVIDER: S-EPMC9842922 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature