Short Summary/Abstract:

The project investigates the neuroanatomical correlates of depressive symptoms in schizophrenia spectrum disorders (SSD) using structural MRI and machine learning. By applying support vector machine classifiers to harmonised neuroimaging datasets, it aims to identify grey-matter patterns associated with depression in psychosis and evaluate their reliability across independent cohorts. Through cross-validation and external testing using Psy-ShareD data, the work seeks to improve understanding of the biological basis of depression in psychosis and assess the generalisability of neuroimaging biomarkers.