# Model-Based Electroencephalography Phenotyping Uncovers Distinct Neurocomputational Mechanisms Underlying Learning Impairments Across Psychopathologies

## TL;DR

This study uses brain activity and computational models to uncover different cognitive mechanisms behind learning impairments in depression, bipolar disorder, and schizophrenia.

## Contribution

The study introduces a novel model-based EEG approach to identify distinct neurocomputational patterns in learning impairments across different psychiatric disorders.

## Key findings

- Schizophrenia is marked by reduced working memory recruitment and negative feedback integration.
- Depression shows impaired working memory management influenced by reinforcement learning.
- Bipolar disorder features deficits in both working memory and reinforcement learning recruitment, with higher working memory decay.

## Abstract

Major depressive disorder (MDD), bipolar disorder (BP), and schizophrenia (SCZ) involve learning impairments with poorly understood mechanisms. Understanding both the similarities and differences in these mechanisms is important to guide the development of new, targeted interventions.

A total of 255 participants diagnosed with MDD (n = 54), BP (n = 47), or SCZ (n = 67) or without any clinical diagnoses (control [CTRL]) (n = 87) performed an associative learning task. Computational modeling quantified the mechanistic interplay between working memory (WM) and reinforcement learning (RL). The latent RL and WM signatures in the electroencephalography (EEG) dynamics showed shared and distinct neurocognitive mechanisms underlying learning.

All clinical groups showed learning impairments at the behavioral level. Model-based EEG analyses linked these impairments to distinct patterns in the dynamic interplay between latent RL and WM mechanisms, contrasting with the typical patterns observed in the CTRL group. SCZ was characterized by reduced neural markers of WM, weakening the cooperative influence of WM onto RL (reduced WM recruitment), and reduced integration of negative feedback. Conversely, MDD was characterized by reduced reciprocal influence of RL onto WM, reducing the tendency to upregulate WM contribution with reward history (impaired WM management). Finally, BP was characterized by deficits in both WM and RL recruitment, along with higher WM decay.

Behavioral learning impairments that seem similar across clinical groups can be linked to distinct neurocognitive mechanisms via integrative neurocomputational modeling. Our approach provides insights into the interplay of underlying learning mechanisms and how they manifest differently across psychopathologies.

People with depression, bipolar disorder, and schizophrenia often show learning difficulties but the underlying causes may differ. By combining brain activity recordings with computational models, we identified distinct cognitive mechanisms driving these impairments. Our findings show how modeling and physiology can give insights into hidden decision dynamics behind learning difficulties. We also outline key steps needed to advance computational psychiatry tools toward clinical applications, including their potential use in guiding personalized treatment.

People with depression, bipolar disorder, and schizophrenia often show learning difficulties but the underlying causes may differ. By combining brain activity recordings with computational models, we identified distinct cognitive mechanisms driving these impairments. Our findings show how modeling and physiology can give insights into hidden decision dynamics behind learning difficulties. We also outline key steps needed to advance computational psychiatry tools toward clinical applications, including their potential use in guiding personalized treatment.