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

Abstract

Cognitive gains following cognitive training interventions 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. We employed whole-brain multivariate pattern analysis with support vector machine (SVM) modeling to identify gray matter (GM) patterns that predicted higher vs. lower functioning after 40 h 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 to unseen SCZ patients from an independent validation sample who underwent 50 h of ABCT. 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%). In particular, greater baseline GM volumes in regions within superior temporal gyrus, thalamus, anterior cingulate, and cerebellum predicted improved functioning at the single-subject level following ABCT in SCZ participants. The present findings provide a structural MRI fingerprint associated with preserved GM volumes at a single baseline timepoint, which predicted improved functioning following an ABCT intervention, and serve as a model for how to facilitate precision clinical therapies for SCZ based on imaging data, operating at the single-subject level.

Reduced neural connectivity in the caudate anterior head predicts hallucination severity in schizophrenia

“…In this first-of-its-kind report, we now deliver robust evidence revealing the caudate anterior head division is one region that contributes to the neural pathophysiology underlying hallucinations in schizophrenia. We have previously shown that it is possible to predict treatment response at the individual level using structural MRI and resting-state fMRI ( Hinkley et al, 2022 ; Kambeitz-Ilankovic et al, 2021 ; Haas et al, 2021b ). The next step in a larger sample would be to investigate the predictive accuracy of treatment response to deep brain stimulation for improving hallucination severity at the individual level using machine learning approaches based on resting state connectivity metrics and clinical measures, such as the PANSS, Psychotic Symptoms Rating Scales (PSYRATS) ( Haddock et al, 1999 ) and Auditory Vocal Hallucination Rating Scale (AVHRS) ( Bartels-Velthuis et al, 2012 ).…”

Algorithmic fairness in precision psychiatry: analysis of prediction models in individuals at clinical high risk for psychosis

“…15 Moreover, recent studies have explored the potential of machine learning to predict further important clinical end-points for people on the psychosis spectrum, such as the development of poor psychosocial functioning. 16,17 To use such models in clinical practice, it is of utmost importance to ensure both high robustness and generalisability – a prerequisite that has only recently started to be addressed. 18,19 An equally important prerequisite for the deployment of prediction algorithms in clinical settings is their compliance with ethical principles.…”