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The Capital Region of Denmark - a part of Copenhagen University Hospital
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Multi-dimensional predictions of psychotic symptoms via machine learning

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DOI

  1. Generalizability of machine learning for classification of schizophrenia based on resting-state functional MRI data

    Research output: Contribution to journalJournal articleResearchpeer-review

  2. Widespread higher fractional anisotropy associates to better cognitive functions in individuals at ultra-high risk for psychosis

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  3. Classification of social anhedonia using temporal and spatial network features from a social cognition fMRI task

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  4. Transcranial direct current stimulation over the sensory-motor regions inhibits gamma synchrony

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  5. Trait Openness and serotonin 2A receptors in healthy volunteers: A positron emission tomography study

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  1. Alteration of functional brain architecture in 22q11.2 deletion syndrome - Insights into susceptibility for psychosis

    Research output: Contribution to journalReviewResearchpeer-review

  2. Theta Activity in the Left Dorsal Premotor Cortex During Action Re-Evaluation and Motor Reprogramming

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  3. 22q11.2 Deletion Syndrome Is Associated With Impaired Auditory Steady-State Gamma Response

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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.

Original languageEnglish
JournalHuman Brain Mapping
Pages (from-to)1-13
Number of pages13
ISSN1065-9471
DOIs
Publication statusPublished - 1 Sep 2020

    Research areas

  • dimensional diagnosis, ensemble, fMRI, fusion, regression, schizophrenia

ID: 60773582