Direct Human Intracranial Electrophysiological Mapping of Brain Circuits that Control Sympathetic and Parasympathetic Outflow in Emotion

University Of California, San Francisco, San Francisco CA

Investigators

Abstract

PROJECT SUMMARY / ABSTRACT The central autonomic network (CAN) is a putative collection of cortical and subcortical areas in the human brain that control autonomic outflow to regulate physiology in the body. A critical gap in knowledge is our lack of clarity on the functional organization of the central autonomic network. This gap has immediate implications for epilepsy and several neurologic and psychiatric diseases with autonomic dysfunction. Although many neuroimaging studies (fMRI and PET) have identified twelve candidate nodes that could be part of the central autonomic network, current central autonomic network m odels differ drastically in which subset of nodes are included and how sympathetic and parasympathetic function is organized across these nodes. In our study, we propose a unique approach of using stereo-EEG (sEEG) implanted in epilepsy patients for clinical purposes to determine which of the twelve candidate nodes are part of the central autonomic network and how sympathetic and parasympathetic functions are organized across this network. with full candidate node coverage, this approach suggests a Based on preliminary data of nine participants medial-sympathetic, lateral-parasympathetic functional organization. We will test this hypothesis by enrolling 32 total participants and leveraging the largest emotion video clip library to date to efficiently elicit a full range of autonomic states while recording neural activity, continuous measurements of skin conductance (sympathetic), and continuous respiratory sinus arrhythmia (parasympathetic). We will model network control of autonomic outflow from these direct intracranial recordings with millisecond-level resolution and directly stimulate candidate nodes to test their autonomic function. With this unique window into human neurophysiology, we will generate new data to map the organization of the central autonomic network in the human brain.