알림
뒤로

1. Neurocognitive Dynamics & Latent Brain State Modeling

We investigate how large-scale brain networks dynamically reconfigure across cognitive states, aging, and disease. Using state-space models, HMM/SLDS frameworks, and directed functional connectivity, we characterize latent brain state transitions and circuit-level flexibility underlying neurocognitive adaptation.

Core themes

# Dynamic reconfiguration

# Latent states

# State transitions

2. Brain Aging & Resilience Architecture

We study heterogeneity in brain aging by defining resilience and vulnerability at the circuit and systems level. Through brain-age modeling, dual-resilience frameworks, and circuit-level signatures, we identify selective and shared mechanisms that shape divergent cognitive trajectories.

Core themes

# Aging heterogeneity

# Resilience phenotypes

# Circuit signatures

3. Structural Connectomics & Network Controllability

We analyze structural brain networks derived from diffusion MRI to understand the physical backbone of large-scale brain organization. Using graph theory, network controllability, and topology-based metrics, we examine how structural architecture constrains cognitive flexibility, resilience, and functional reconfiguration.

Core themes

# Structural connectivity

# Network controllability

# Graph topology

# Structure–function constraints

4. AI-Driven Multiscale Brain Modeling

We develop computational frameworks that integrate multimodal neuroimaging, genetics, and environmental factors using deep learning and foundation models. Our goal is to bridge molecular, circuit, and systems-level representations through scalable AI-driven approaches.

Core themes

# Multimodal integration

# Foundation models

# Multiscale modeling

# Representation learning