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Cognitive profiles across the psychosis continuum.

Tina D Kristensen1, Fabian M Mager2, Karen S Ambrosen1

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Summary

Machine learning identified six distinct cognitive profiles across psychosis spectrum disorders, predicting functional outcomes. This approach aids personalized treatment and research stratification beyond clinical diagnoses.

Keywords:
Psychosis continuumantipsychotic-naïvecognitionfirst-episodemachine learningself-organizing mapsultra-high risk

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Area of Science:

  • Neuroscience
  • Psychiatry
  • Machine Learning

Background:

  • Cognitive impairments are central to psychosis spectrum disorders, impacting functional outcomes.
  • Variability and overlap in cognitive function hinder personalized treatment and research stratification.

Purpose of the Study:

  • To apply a data-driven machine learning approach for transdiagnostic cognitive profiling.
  • To identify distinct cognitive profiles and their predictive value for functional outcomes.

Main Methods:

  • Utilized self-organizing maps (SOMs) for unsupervised clustering of cognitive data.
  • Analyzed a sample including healthy controls, individuals at ultra-high risk for psychosis, and first-episode psychosis patients.

Main Results:

  • Identified six distinct cognitive profiles using SOMs.
  • These profiles significantly predicted baseline and one-year functional levels.
  • Cognitive flexibility and executive functions were key differentiators.

Conclusions:

  • Self-organizing maps offer a promising method for individualized cognitive profiling in clinical decision-making.
  • This approach facilitates patient stratification for interventions and cross-diagnostic research.
  • The method allows for profiling across diverse data modalities, including neuroimaging and metabolic data.