Elucidating salient site-specific functional connectivity features and site-invariant biomarkers in schizophrenia via deep neural networks.

ABSTRACT: Schizophrenia is a highly heterogeneous disorder and salient functional connectivity (FC) features have been observed to vary across study sites, warranting the need for methods that can differentiate between site-invariant FC biomarkers and site-specific salient FC features. We propose a technique named Semi-supervised learning with data HaRmonisation via Encoder-Decoder-classifier (SHRED) to examine these features from resting state functional magnetic resonance imaging scans gathered from four sites. Our approach involves an encoder-decoder-classifier architecture that simultaneously performs data harmonisation and semi-supervised learning (SSL) to deal with site differences and labelling inconsistencies across sites respectively. The minimisation of reconstruction loss from SSL was sho

SUBMITTER: Chan YH 

PROVIDER: S-EPMC10687079 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature