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- W3082903882 abstract "The diagnostic criteria for schizophrenia comprise a diverse range of heterogeneous symptoms. As a result, individuals each present a distinct set of symptoms despite having the same overall diagnosis. Whilst previous machine learning studies have primarily focused on dichotomous patient-control classification, we predict the severity of each individual symptom on a continuum. We applied machine learning regression within a multi-modal fusion framework to fMRI and behavioural data acquired during an auditory oddball task in 80 schizophrenia patients. Brain activity was highly predictive of some, but not all symptoms, namely hallucinations, avolition, anhedonia and attention. Critically, each of these symptoms was associated with specific functional alterations across different brain regions. We also found that modelling symptoms as an ensemble of subscales was more accurate, specific and informative than models which predict compound scores directly. In principle, this approach is transferrable to any psychiatric condition or multi-dimensional diagnosis." @default.
- W3082903882 created "2020-09-08" @default.
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- W3082903882 date "2020-09-01" @default.
- W3082903882 modified "2023-09-26" @default.
- W3082903882 title "Multi‐dimensional predictions of psychotic symptoms via machine learning" @default.
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- W3082903882 doi "https://doi.org/10.1002/hbm.25181" @default.
- W3082903882 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7670649" @default.
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- W3082903882 hasPublicationYear "2020" @default.
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