RT Journal Article
SR Electronic
T1 Multi-dimensional predictions of psychotic symptoms via machine learning
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.03.02.974246
DO 10.1101/2020.03.02.974246
A1 Jeremy A Taylor
A1 Kit Melissa Larsen
A1 Marta I Garrido
YR 2020
UL http://biorxiv.org/content/early/2020/05/08/2020.03.02.974246.abstract
AB 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.Competing Interest StatementThe authors have declared no competing interest.