Whole-brain modeling of dynamic causal circuits in human cognition using amortized variational inference

A Bayesian deep learning framework for brain dynamic functional connectivity linked with cognition.

Transformers for fMRI analysis: from spatiotemporal modeling to brain foundation models

Functional magnetic resonance imaging (fMRI) serves as a pivotal tool for investigating the functional organization and dynamic connectivity of the human brain, indirectly reflecting neural activity through blood oxygen level-dependent (BOLD) signals. However, the high-dimensional spatiotemporal structure, prominent no...

Message in a Bottleneck: Interpretable Deep Learning for Dynamic Effective Brain Connectivity.

A novel approach for deep extraction of causally informed features via restricted architecture (DECIFRA), designed for multivariate time series modeling that captures functionally grounded brain connectivity patterns, with certain dynamics associated with age or schizophrenia classification tasks and others consistentl...

NDIB-Sim: A Multimodal Bidirectional PINN Model for Simulating Brain Dynamics.

Constructing dynamic virtual brain models is essential for understanding brain functions and pathological mechanisms, crucial in computational neuroscience. Current modeling methods can be grouped into two paradigms: deep learning models for accurate simulation, and neural dynamics models emphasizing physiological inte...

FAST-Brain: A Flow-Aligned Spatio-Temporal Surrogate Brain Model

FAST-Brain is proposed, a unified flow-aligned spatio-temporal surrogate brain model that addresses all three challenges of resting-state functional magnetic resonance imaging data, and achieves state-of-the-art performance in recovering functional connectivity, effective connectivity, and the implicit low-dimensional...

Scaffold-GAT: Grounding Brain Dynamics in Neural Anatomy