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Dynamics of the Brain Connectivity During Reinforcement Learning

Mohammadzadeh, Hamid | 2025

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 58375 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Karbalaei Aghajan, Hamid
  7. Abstract:
  8. Reinforcement learning is one of the core learning mechanisms in the brain, through which an agent continuously adjusts its behavioral policies based on environmental feedback to maximize long-term rewards. This process, deeply intertwined with the brain’s reward system, plays a central role in decision-making and behavioral adaptation. Disruptions in this mechanism can contribute to psychological disorders, most notably depression. Depression, characterized by reduced motivation, diminished sensitivity to reward, and impaired feedback processing, is closely linked to dysfunctions in reinforcement learning. In this study, we investigated neurophysiological differences in reinforcement learning between healthy individuals and those with depression by recording EEG signals during a probabilistic learning task. A range of analytical techniques was employed, including event-related potential (ERP) analysis, spectral power examination in theta and beta frequency bands, and directed connectivity analysis using dPLI. The results revealed that the learning process was associated with distinct temporal-frequency dynamics, particularly in the theta band, as well as modulations in ERP components such as P50 and N100 across different learning stages. In the depression group, reduced theta power following negative feedback, increased N100 amplitude in the late learning phase, and weakened connectivity between prefrontal and parietal regions were observed, suggesting impaired cortical regulation of feedback processing. The outcome of the result analysis clearly highlighted these differences in both temporal and spatial aspects of neural activity and network organization, emphasizing the association between theta-band dynamics and behavioral performance across groups. These findings not only reinforce the critical role of cortical networks in reinforcement learning but also provide a novel perspective on depression as a dysfunction in reward and feedback processing systems
  9. Keywords:
  10. Reinforcement Learning ; Brain Reward Circuit ; Depressive Disorder ; Functional Connectivity ; Electroencphalogram Signal ; Event Related Potential (ERP)

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