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Region Hovedstaden - en del af Københavns Universitetshospital
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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

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer 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. Theta Activity in the Left Dorsal Premotor Cortex During Action Re-Evaluation and Motor Reprogramming

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  2. 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.

OriginalsprogEngelsk
TidsskriftHuman Brain Mapping
ISSN1065-9471
DOI
StatusUdgivet - 1 sep. 2020

Bibliografisk note

© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.

ID: 60773582