Human cognition arises from the interaction of multiple brain areas and neurotransmitter systems. In decision-making behavior, a major focus of our research, these include prefrontal cortical areas, dopaminergic and serotonergic systems. We study these directly in the human brain by leveraging invasive neurosurgical approaches to carry out intracranial electrophysiological and electrochemical recordings – a unique and powerful way of studying human brain activity. We seek a more complete characterization of the neurobiological basis of human decision-making behavior that will allow development of novel neurotherapeutical approaches for psychiatric conditions (i.e. depression).
Funding & Awards
K01 – Electrocorticography of human prefrontal cortex during value-based decision-making (IS)
R01 – Invasive decoding and stimulation of altered reward computations in depression (pending)
iEEG and decision-making under uncertainty
We are working to characterize the neural basis of human decision-making under uncertainty using distributed intracranial EEG (iEEG) recordings in epilepsy patients. Specifically, we examine the relationship between neural activity across frequency bands and brain regions (orbitofrontal, lateral prefrontal, cingulate cortices, etc.) and overt choice behavior using a combination of iEEG recordings and neuroeconomic probes of decision-making.
Value-based decision-making behavior depends on the coordinated activation of a variety of brain regions, both prefrontal (orbitofrontal, lateral prefrontal, cingulate cortices) and subcortical (striatum, hippocampus, amygdala). In this project we seek to apply machine learning methods to decode overt subject behavior (i.e. choices) from preceding, distributed brain activity in reward-related brain regions.
Stimulation of altered reward computations in depression
Depression is highly prevalent in intractable epilepsy patients undergoing invasive electrophysiological monitoring, which provides a unique opportunity to study multi-areal brain activity across depression states. In this project, we combine distributed iEEG recordings and reinforcement learning models of decision-making to study reward and mood processing across multiple brain areas in epilepsy patients with and without comorbid depression. Combined with invasive neurostimulation approaches, we hope to use this knowledge to develop new patient-tailored therapeutical strategies.