Understanding Prefrontal and Medial Temporal Neuronal Responses to Algorithmic Cognitive Variables in Epilepsy Patients

Summary

Humans have a remarkable ability to flexibly interact with the environment. A compelling demonstration of this cognitive flexibility is human's ability to respond correctly to novel contextual situations on the first attempt, without prior rehearsal. The investigators refer to this ability as 'ad hoc self-programming': 'ad hoc' because these new behavioral repertoires are cobbled together on the fly, based on immediate demand, and then discarded when no longer necessary; 'self-programming' because the brain has to configure itself appropriately based on task demands and some combination of prior experience and/or instruction. The overall goal of our research effort is to understand the neurophysiological and computational basis for ad hoc self-programmed behavior. The previous U01 project (NS 108923) focused on how these programs of action are initially created. The results thus far have revealed tantalizing notions of how the brain represents these programs and navigates through the programs. In this proposal, therefore, the investigators focus on the question of how these mental programs are executed. Based on the preliminary findings and critical conceptual work, the investigators propose that the medial temporal lobe (MTL) and ventral prefrontal cortex (vPFC) creates representations of the critical elements of these mental programs, including concepts such as 'rules' and 'locations', to allow for effective navigation through the algorithm. These data suggest the existence of an 'algorithmic state space' represented in medial temporal and prefrontal regions. This proposal aims to understand the neurophysiological underpinnings of this algorithmic state space in humans. By studying humans, the investigators will profit from our species' powerful capacity for generalization to understand how such state spaces are constructed. The investigators therefore leverage the unique opportunities available in human neuroscience research to record from single cells and population-level signals, as well as to use intracranial stimulation for causal testing, to address this challenging problem. In Aim 1 the investigators study the basic representations of algorithmic state space using a novel behavioral task that requires the immediate formation of unique plans of action. Aim 2 directly compares representations of algorithmic state space to that of physical space by juxtaposing balanced versions of spatial and algorithmic tasks in a virtual reality (VR) environment. Finally, in Aim 3, the investigators test hypotheses regarding interactions between vPFC and MTL using intracranial stimulation.

Conditions

• Epilepsy

Interventions

DEVICE

NEUROPACE RNS SYSTEM

This device is indicated as a therapy in reducing the frequency of seizures in individuals

BEHAVIORAL

EMU

Patients are admitted to the Epilepsy Monitoring Unit for observation of seizure activity prior to further treatment

Sponsors & Collaborators

• Baylor College of Medicine

  lead OTHER

Study Design

Allocation

NON_RANDOMIZED

Purpose

HEALTH_SERVICES_RESEARCH

Masking

NONE

Model

FACTORIAL

Eligibility

Min Age

10 Years

Max Age

64 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2021-06-01

Primary Completion

2026-03-31

Completion

2026-03-31

FDA Device

Yes

Countries

• United States

Study Locations