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| Home > Publications database > Neurobiological divergence of the positive and negative schizophrenia subtypes identified upon a new factor-structure of psychopathology using non-negative factorization: An international machine-learning study |
| Home > Publications database > Neurobiological divergence of the positive and negative schizophrenia subtypes identified upon a new factor-structure of psychopathology using non-negative factorization: An international machine-learning study |
| Journal Article | FZJ-2019-04803 |
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2020
Elsevier Science
Amsterdam [u.a.]
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Please use a persistent id in citations: http://hdl.handle.net/2128/23815 doi:10.1016/j.biopsych.2019.08.031
Abstract: ObjectiveDisentangling 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 (PANSS)”, and used it to identify psychopathological subtypes as well as their neurobiological differentiations.MethodsPANSS 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 nine medical centers across Europe, USA, 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 MRI connectivity patterns.ResultsA four-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 PANSS 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 ventro-medial frontal cortex, temporoparietal junction, and precuneus.ConclusionsMachine-learning applied to multi-site 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.
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