# Modeling and Mapping Human Action Regulation Networks

> **NIH NIH U01** · UT SOUTHWESTERN MEDICAL CENTER · 2024 · $890,318

## Abstract

Abstract
Humans can rapidly regulate actions according to updated demands of the environment. A key component of
action regulation is action inhibition, the failure of which contributes to various neuropsychiatric diseases, such
as Parkinson’s disease (PD), obsessive compulsive disorder and Tourette syndrome. Action inhibition occurs in
at least 3 ways: (i) action selection – selecting one action requires suppressing alternative motor plans, (ii)
outright stopping – inhibiting a response when it is rendered inappropriate and (iii) action switching – change
action plans in response to environmental changes. Despite the extensive effort to understand how the brain
selects, stops and switches actions, the mechanism underlying these action regulation functions, and more
importantly, how they inter-relate remain elusive. Part of this challenge lies in the fact that studies rarely explore,
characterize, and investigate these functions together, making it difficult to develop a unified theory that explains
the computational aspects of action regulation. The current proposal aims to advance our understanding by
developing a neurocomputational model that, unlike prior models, integrates information from multiple sources
(e.g., value of targets, cost for changing an action, contextual information) and predicts both kinematics of motor
behavior and the underpinning neural mechanisms across 3 distinct types of action regulation. We will directly
evaluate model predictions with intracranial recordings in patient volunteers undergoing deep brain stimulation
implantation surgeries. These surgeries provide a unique opportunity to obtain multi-focal cortical and basal
ganglia (BG) recordings with high temporal and spectral resolution and spatial specificity across the three action
regulation tasks. The overarching goal will be achieved through three aims. In Aim 1, we will collect behavioral
data from PD patients and aged-match neurotypical participants performing tasks that involves selecting,
stopping and switching reaching actions. The motor behavior of the neurotypical group will be used to develop
a neurocomputational model that simulate the fronto-BG circuits in action regulation. Then, we will assess how
specific changes on the neural mechanisms of the model architecture predict the motor behavior of the PD
patients. In Aim 2, we will evaluate the model predictions about the mechanisms of action selection relative to
stopping by recording neural activity from PD patients who undergo surgery for DBS implantation of the
subthalamic nucleus (STN). Neural recordings will be collected without and with temporally and spatially precise
subthalamic nucleus (STN) stimulation to investigate the causal role of STN in action selection. In Aim 3, we will
evaluate the model predictions about the mechanisms for switching actions by recording neural activity from PD
patients with the STN stimulation off and on. Overall, successful completion will provide a unified theory ...

## Key facts

- **NIH application ID:** 10911212
- **Project number:** 5U01NS132788-02
- **Recipient organization:** UT SOUTHWESTERN MEDICAL CENTER
- **Principal Investigator:** Vasileios N Christopoulos
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $890,318
- **Award type:** 5
- **Project period:** 2023-08-15 → 2028-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10911212

## Citation

> US National Institutes of Health, RePORTER application 10911212, Modeling and Mapping Human Action Regulation Networks (5U01NS132788-02). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10911212. Licensed CC0.

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