Research

Research Themes

Our lab bridges neuroscience, computation, and clinical application to understand the brain and translate insights into mental health tools.

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Neuroimaging in Psychiatric Disorders

Investigating neural correlates of psychiatric disorders using structural and functional MRI

This project aims to understand the neural mechanisms underlying psychiatric disorders through advanced neuroimaging techniques, combining structural and functional data for comprehensive brain mapping.

Current Studies

  • β†’Cortical thickness patterns in major depressive disorder
  • β†’Default mode network connectivity in anxiety disorders
  • β†’Brain structural changes in early-stage schizophrenia

Methods

FreeSurferFSLSPMMachine Learning
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Computational Modeling of Cognition

Developing mathematical and computational models to understand cognitive processes and decision-making

This project develops computational models to understand how the brain supports cognition and how these processes are disrupted in psychiatric disorders.

Current Studies

  • β†’Computational phenotyping of depression using RL models
  • β†’Bayesian inference deficits in schizophrenia
  • β†’Cognitive control mechanisms in anxiety disorders

Methods

Hierarchical Bayesian ModelingDrift-Diffusion ModelsReinforcement LearningModel Comparison
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AI-Powered Clinical Tools

Developing artificial intelligence tools for diagnosis, prognosis, and treatment planning in psychiatry

This project develops AI-powered tools to enhance clinical decision-making and improve patient outcomes in psychiatric care, bridging the gap between neuroscience research and clinical practice.

Current Studies

  • β†’Deep learning for depression detection from speech and facial expressions
  • β†’Predictive modeling of antidepressant response
  • β†’AI-assisted risk assessment for suicide prevention

Methods

Deep Neural NetworksNatural Language ProcessingMultimodal Data FusionExplainable AI
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Naturalistic Behavior Analysis

Understanding human behavior in real-world settings using wearable sensors and video analysis

This project explores human behavior in naturalistic environments, moving beyond laboratory settings to capture real-world behavioral patterns and their relationship to mental health.

Current Studies

  • β†’Sensor-based detection of depressive episodes
  • β†’Video analysis of social withdrawal in anxiety disorders
  • β†’Digital phenotyping of cognitive decline

Methods

Time-Series AnalysisDeep Learning for VideoEcological Momentary AssessmentComputer Vision
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Integrative Approach

Our research integrates neuroimaging, computational modeling, and machine learning to build a comprehensive understanding of the brain in health and disease. By combining large-scale data with rigorous analytical methods, we aim to translate basic science into clinical tools for mental health.