Multivariate pattern analysis of brain structure predicts functional outcome after auditory-based cognitive training interventions

Background: Cognitive gains following cognitive training interventions (CT) are associated with improved functioning in people with schizophrenia (SCZ). However, considerable inter-individual variability is observed. Here, we evaluate the sensitivity of brain structural features to predict functional response to auditory-based cognitive training (ABCT) at a single subject level. Methods: We employed whole-brain multivariate pattern analysis (MVPA) with support vector machine (SVM) modeling to identify grey matter (GM) patterns that predicted higher vs. lower functioning after 40 hours of ABCT at the single subject level in SCZ patients. The generalization capacity of the SVM model was evaluated by applying the original model through an Out-Of-Sample Cross Validation analysis (OOCV) to unseen SCZ patients from an independent sample that underwent 50 hours of ABCT. Results: The whole-brain GM volume-based pattern classification predicted higher vs. lower functioning at follow-up with a balanced accuracy (BAC) of 69.4% (sensitivity 72.2%, specificity 66.7%) as determined by nested cross-validation. The neuroanatomical model was generalizable to an independent cohort with a BAC of 62.1% (sensitivity 90.9%, specificity 33.3%). Conclusions: In particular, greater baseline GM volume in regions within superior temporal gyrus, thalamus, anterior cingulate and cerebellum -- predicted improved functioning at the single-subject level following ABCT in SCZ participants.