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Predicting subclinical psychotic-like experiences on a continuum using machine learning.

Jeremy A Taylor1, Kit Melissa Larsen2, Ilvana Dzafic3

  • 1Melbourne School of Psychological Sciences, University of Melbourne, Australia; Queensland Brain Institute, University of Queensland, Australia.

Neuroimage
|July 24, 2021
PubMed
Summary
This summary is machine-generated.

Electroencephalography (EEG) data can predict psychotic-like experiences in healthy individuals. This research shows that brain activity patterns, specifically responses to predictable sounds, correlate with subclinical psychosis symptoms.

Keywords:
Mismatch negativityPsychosisRegularity learningSchizophreniaSchizotypy

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Area of Science:

  • Neuroscience
  • Psychiatry
  • Machine Learning

Background:

  • Previous machine learning studies on psychosis focused on binary classification of schizophrenia patients and healthy controls.
  • There is a need to understand psychosis on a continuum, including subclinical experiences in the general population.

Purpose of the Study:

  • To predict subclinical psychotic-like experiences in healthy individuals using electroencephalographic (EEG) data.
  • To apply pattern recognition to EEG data for predicting psychotic experiences on a continuum.

Main Methods:

  • Utilized EEG data from 73 participants during an auditory oddball regularity learning task.
  • Applied two pattern recognition approaches: feature extraction/selection and regularization of spatiotemporal maps.
  • Analyzed behavioral measures, event-related potential components (P50, N100, P200), and effective connectivity.

Main Results:

  • The regularization approach, focusing on responses to frequent sounds, achieved optimal predictive performance.
  • Event-related potential features in the P50 and P200 windows predicted lower scores on the Prodromal Questionnaire (PQ).
  • Features in the N100 window predicted higher PQ scores, indicating a link between sensory processing and psychotic experiences.

Conclusions:

  • EEG data alone can predict individual differences in psychotic-like experiences among healthy individuals.
  • Findings support the continuum model of psychosis and evidence of altered sensory processing in schizophrenia spectrum disorders.
  • This study serves as a proof-of-concept for using neurophysiological markers to identify psychosis risk in non-clinical populations.