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Reinforcement learning deficits exhibited by postnatal PCP-treated rats enable deep neural network classification.

Michael M Tranter1,2, Samarth Aggarwal1, Jared W Young1,2

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Neuropsychopharmacology : Official Publication of the American College of Neuropsychopharmacology
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Postnatal exposure to phencyclidine (PCP) impairs flexible decision-making in rats, mimicking schizophrenia-related deficits. Deep neural networks accurately predicted treatment groups, suggesting potential for diagnostic applications.

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Area of Science:

  • Neuroscience
  • Behavioral Science
  • Computational Psychiatry

Background:

  • Flexible decision-making is crucial for cognitive function and is impaired in schizophrenia.
  • The probabilistic reversal learning (PRL) task assesses decision-making flexibility and has been adapted for rodents.
  • Disrupting glutamate neurotransmission during early development can cause schizophrenia-relevant abnormalities.

Purpose of the Study:

  • To investigate if early postnatal disruption of glutamatergic transmission using phencyclidine (PCP) impairs decision-making in rats using the PRL task.
  • To determine if computational analysis and deep neural networks (DNNs) can identify treatment-related behavioral changes.

Main Methods:

  • Rats were treated with PCP during early postnatal development to disrupt glutamatergic transmission.
  • Behavioral performance on the PRL task was assessed, measuring reversals, win-stay, and lose-shift responses.
  • Computational analysis and DNNs were employed to analyze behavioral data and predict treatment groups.

Main Results:

  • Postnatal PCP-treated rats showed impaired decision-making, completing fewer reversals and exhibiting altered reward/punishment sensitivity.
  • Computational analysis revealed a significant impairment in the learning rate in PCP-treated rats.
  • A DNN successfully predicted the treatment group based on behavioral data with high accuracy.

Conclusions:

  • Disrupting early postnatal glutamatergic neurotransmission impairs flexible decision-making, providing a relevant animal model for schizophrenia.
  • DNNs show promise as a tool for analyzing behavioral data and potentially aiding in the diagnosis of psychiatric disorders like schizophrenia.