Using fMRI and machine learning to predict symptom improvement following cognitive behavioural therapy for psychosis.

ABSTRACT: Cognitive behavioural therapy for psychosis (CBTp) involves helping patients to understand and reframe threatening appraisals of their psychotic experiences to reduce distress and increase functioning. Whilst CBTp is effective for many, it is not effective for all patients and the factors predicting a good outcome remain poorly understood. Machine learning is a powerful approach that allows new predictors to be identified in a data-driven way, which can inform understanding of the mechanisms underlying therapeutic interventions, and ultimately make predictions about symptom improvement at the individual patient level. Thirty-eight patients with a diagnosis of schizophrenia completed a social affect task during functional MRI. Multivariate pattern analysis assessed whether treatment respons

SUBMITTER: Tolmeijer E 

PROVIDER: S-EPMC6197386 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature