The following two areas of interest exemplify this approach:

Brain Signal Variability in the Context of Brain Damage: Computational research suggests that brain signal variability is an important parameter reflecting the functional integrity of neural systems. Thus, we can think of variability as a metric of what the system is capable of doing (whereas task-related signal indicates what the system is doing at any given moment of observation). I have shown that variability tracks both tissue health and functional capacity in patients with mesial temporal lobe epilepsy (mTLE). In my current work, I am trying to use signal variability to identify individual differences in cognitive resilience during aging, and the capacity to benefit from treatment in psychiatric disorders.

Brain Network Reorganization in Major Depressive Disorder: Ideally, to predict functional changes after focal alterations in brain function, one should take into account whole-brain network reorganization. My recent work focuses on how deep brain stimulation affects mood regulation networks in treatment resistant depression. The aim is to customize neurostimulation therapy by creating personalized computer models that simulate the effects of local stimulation on large-scale brain networks using patient-specific connectivity and anatomy.