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Classifying individuals at high-risk for psychosis based on functional brain activity during working memory

Kerstin Bendfeldt1, Renata Smieskova2, Nikolaos Koutsouleris3

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The at-risk mental state (ARMS) for psychosis can be identified with high accuracy in individuals at risk. This brain activity classification may help predict psychosis transition.

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ClassificationMachine learningMagnetic resonance imagingRisk factorsSchizophreniaWorking memory

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Area of Science:

  • Neuroscience
  • Psychiatry
  • Brain Imaging

Background:

  • The at-risk mental state (ARMS) for psychosis is characterized by altered brain activity during working memory tasks.
  • Distinguishing ARMS from healthy controls (HC) and first episode psychosis (FEP) is crucial for early intervention.

Purpose of the Study:

  • To classify individuals in ARMS, FEP, and HC groups using functional magnetic resonance imaging (fMRI) data.
  • To investigate the effectiveness of pattern classification in differentiating these groups based on working memory-related brain activity.

Main Methods:

  • fMRI data from 19 ARMS, 19 FEP, and 19 HC subjects were analyzed.
  • Binary automatic pattern classification with linear support vector machines and leave-one-out cross-validation was employed.
  • Classification was performed with and without a verbal working memory network mask.

Main Results:

  • Healthy controls (HC) and ARMS were classified with 76.2% accuracy using a mask (p=0.01).
  • Classification accuracy between HC and FEP (50%) or ARMS and FEP (47.4%) was not significant.
  • Without a mask, HC vs. ARMS (65.8%, p=0.03) and HC vs. FEP (65.8%, p=0.0047) showed significant classification.

Conclusions:

  • First episode psychosis (FEP) is difficult to distinguish from healthy controls (HC) or ARMS in small samples.
  • Individuals in the at-risk mental state (ARMS) can be identified with high sensitivity compared to HC.
  • This classification approach may aid in predicting psychosis transition in ARMS individuals.