memory

My research goal is to build a framework that links cognitive integrity and neural dynamics to provide a coherent understanding of how cognition emerges from operations in the intact and impaired brain.  The following two areas of interest give the flavor of this approach.

Network Reorganization Following Brain Damage
Focal brain damage can be best understood in the context of neural networks; behavioral deficits following damage can reflect either the abnormal operation of a damaged network or the formation of a completely different network with a new behavioral repertoire. Ideally, to predict functional outcome after brain damage, one should take into account network reorganization. My recent work focuses on how hippocampal damage affects neural networks supporting episodic memory and other cognitive functions.  I have identified compensatory network changes in task-related signal that support good memory and verbal performance in patients with medial temporal lobe epilepsy (mTLE) and patients with amnestic mild congitive impariment.

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 mTLE.  In my current work, I am trying to use signal varibility to to identify individual differences in the capacity to benefit from treatment in psychiatric or neurologic disorders.