Large-scale dynamic modeling of task-fMRI signals via subspace system identification.

Objective: We analyze task-based fMRI time series to produce large-scale dynamical models that are capable of approximating the observed signal with good accuracy.

Approach: We extend subspace system identification methods for deterministic and stochastic state-space models with external inputs. The dynamic behavior of the generated models is characterized using control-theoretic analysis tools.

Spectral mapping of brain functional connectivity from diffusion imaging.

Understanding the relationship between the dynamics of neural processes and the anatomical substrate of the brain is a central question in neuroscience. On the one hand, modern neuroimaging technologies, such as diffusion tensor imaging, can be used to construct structural graphs representing the architecture of white matter streamlines linking cortical and subcortical structures. On the other hand, temporal patterns of neural activity can be used to construct functional graphs representing temporal correlations between brain regions.