Yuichi Yamashita

The paper shows that a vision-language model better predicts food valuation than vision-only models by encoding higher-level food attributes.

The paper uses a hierarchical generative model to analyze brain-body interaction dynamics during interoceptive attention, finding increased latent precision in multimodal physiological signals.

The paper analyzes information dynamics in brain networks of psychiatric disorders, finding distinct patterns of synergy and redundancy reduction or increase in schizophrenia, autism, and ADHD.

The paper shows that dopamine neurons in the mesolimbic pathway encode the value of water and salt in a state-dependent manner, influencing behavior and simulated by a reinforcement learning model.

This paper introduces a Bayesian recurrent neural network model that autonomously switches between goal-directed and sensory-focused behaviors to adapt to environmental changes.

This paper compares CycleGAN and ComBat for harmonizing T1-weighted MRI images across sites, finding CycleGAN preserves image quality better with minimal preprocessing.

This study uses a homeostatic reinforcement learning model to simulate how altered interoception affects reward processing and appetite in depression subtypes.

This paper introduces a real-time ECoG simulator using a Variational Bayesian Recurrent Neural Network to predict and update brain states from primate data, enabling virtual drug administration and functional network analysis.

This paper develops a homeostatic reinforcement learning model to simulate sodium appetite behavior and explains osmoregulation as a reinforcement learning process.

This paper introduces a self-supervised deep learning method for resting-state fMRI analysis, showing improved schizophrenia diagnosis compared to conventional methods.

This paper applies a 3D convolutional autoencoder to structural brain MRI data from schizophrenia and healthy subjects to extract disease-related features without diagnostic labels.

The paper introduces CPSYMAP, a database that visualizes computational psychiatry research papers in a 2D map to show distribution across neuroscience, psychiatry, and computational dimensions.

The paper identifies a psychiatric symptom dimension linked to decision-making performance through exploration strategies in a healthy population.