Exploring the Mysteries of
Lifelong Brain Resilience

Aging is often assumed to follow a common, universal trajectory.
However, a closer look at real-world data reveals a very different picture. The course of aging is far from uniform.

Some individuals maintain remarkable cognitive function despite substantial pathological burden.
Others, under seemingly similar biological conditions, experience rapid cognitive decline.

Our laboratory begins with a fundamental question:

   Why, under comparable aging conditions, do some individuals exhibit high levels of neural robustness 
   and resilience, while others show pronounced vulnerability?

   What structural and dynamical properties of brain networks give rise to these divergent trajectories?

   How do genetic, cellular, molecular, and environmental factors interact with brain network
   organization to shape these differences?

We approach these questions through computational modeling and multimodal&multiscale brain network analysis.

Structural Determinants of Brain Resilience

The white matter connectome is the physical backbone that defines a brain’s capacity to withstand aging and pathology. We identify the structural architectures—such as topological redundancy and hierarchical organization—that maintain network integrity despite localized neurodegeneration. By analyzing these physical constraints, we uncover how specific wiring patterns allow for the preservation of global communication pathways, defining the structural blueprint of a resilient brain.

Dynamical Determinants of Cognitive Resilience

Resilience is manifested in the brain's ability to actively reconfigure its functional states to meet cognitive demands. We investigate latent brain state dynamics to characterize how large-scale networks transition during task performance and rest. This research identifies the dynamical signatures of neural efficiency—understanding how a resilient brain maintains a flexible repertoire of functional states and transition rules to compensate for underlying biological decline.

Multiscale Drivers of Individual Resilience

To explain why aging trajectories diverge, we bridge the gap between microscopic biological drivers and macroscopic system outcomes. We develop AI-driven frameworks that integrate multimodal neuroimaging, genetics, and environmental factors to model the multiscale origins of resilience. This pillar translates high-dimensional biological data into predictive models, uncovering the causal interactions that shape a resilient brain across the lifespan.