In Biological psychiatry ; h5-index 105.0
BACKGROUND : Disentangling psychopathological heterogeneity in schizophrenia is challenging, and previous results remain inconclusive. We employed advanced machine learning to identify a stable and generalizable factorization of the Positive and Negative Syndrome Scale and used it to identify psychopathological subtypes as well as their neurobiological differentiations.
METHODS : Positive and Negative Syndrome Scale data from the Pharmacotherapy Monitoring and Outcome Survey cohort (1545 patients; 586 followed up after 1.35 ± 0.70 years) were used for learning the factor structure by an orthonormal projective non-negative factorization. An international sample, pooled from 9 medical centers across Europe, the United States, and Asia (490 patients), was used for validation. Patients were clustered into psychopathological subtypes based on the identified factor structure, and the neurobiological divergence between the subtypes was assessed by classification analysis on functional magnetic resonance imaging connectivity patterns.
RESULTS : A 4-factor structure representing negative, positive, affective, and cognitive symptoms was identified as the most stable and generalizable representation of psychopathology. It showed higher internal consistency than the original Positive and Negative Syndrome Scale subscales and previously proposed factor models. Based on this representation, the positive-negative dichotomy was confirmed as the (only) robust psychopathological subtypes, and these subtypes were longitudinally stable in about 80% of the repeatedly assessed patients. Finally, the individual subtype could be predicted with good accuracy from functional connectivity profiles of the ventromedial frontal cortex, temporoparietal junction, and precuneus.
CONCLUSIONS : Machine learning applied to multisite data with cross-validation yielded a factorization generalizable across populations and medical systems. Together with subtyping and the demonstrated ability to predict subtype membership from neuroimaging data, this work further disentangles the heterogeneity in schizophrenia.
Chen Ji, Patil Kaustubh R, Weis Susanne, Sim Kang, Nickl-Jockschat Thomas, Zhou Juan, Aleman André, Sommer Iris E, Liemburg Edith J, Hoffstaedter Felix, Habel Ute, Derntl Birgit, Liu Xiaojin, Fischer Jona M, Kogler Lydia, Regenbogen Christina, Diwadkar Vaibhav A, Stanley Jeffrey A, Riedl Valentin, Jardri Renaud, Gruber Oliver, Sotiras Aristeidis, Davatzikos Christos, Eickhoff Simon B
Brain imaging, Machine learning, Multivariate classification, Non-negative factorization, Schizophrenia, Subtyping