Antipsychotic medications offer limited long-term benefit to about 30% of patients with schizophrenia. We aimed to explore the individual-specific imaging markers to predict 1-year treatment response of schizophrenia.
Structural morphology and functional topological features related to treatment response were identified using an individualized parcellation analysis in conjunction with machine learning (ML). We performed dimensionality reductions using the Pearson correlation coefficient and three feature selection analyses and classifications using 10 ML classifiers. The results were assessed through a 5-fold cross-validation (training and validation cohorts, n = 51) and validated using the external test cohort (n = 17).
ML algorithms based on individual-specific brain network proved more effective than those based on group-level brain network in predicting outcomes. The most predictive features based on individual-specific parcellation involved the GMV of the default network and the degree of the control, limbic, and default networks. The AUCs for the training, validation, and test cohorts were 0.947, 0.939, and 0.883, respectively. Additionally, the prediction performance of the models constructed by the different feature selection methods and classifiers showed no significant differences.
Our study highlighted the potential of individual-specific network parcellation in treatment resistant schizophrenia prediction and underscored the crucial role of feature attributes in predictive model accuracy.