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Neural Basis of Effortful Decision Making

努力决策的神经基础

基本信息

批准号：
10490247
负责人：
Alexander Herman
金额：
$ 18.62万
依托单位：
UNIVERSITY OF MINNESOTA
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-30 至 2026-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10490247
关键词：
Anterior Back Behavior Behavioral Biological Assay Biological Markers Brain Code Cognitive Computer Models Cost Allocation Cost Control Cost Measures Data Decision Making Development Diffusion Electrophysiology (science)Epilepsy Etiology Foundations Frequencies Funding Goals Human Information Theory Intervention Intuition K-Series Research Career Programs Knowledge Lead Link Maps Measurement Measures Mediating Methods Modeling Monitor Neurophysiology - biologic function Operative Surgical Procedures Patients Persons Phase Physiology Play Prefrontal Cortex Process Psychiatry Randomized Recovery Research Research Personnel Resolution Resources Role Short-Term Memory Signal Transduction Time Training Translating Work addiction base career cingulate cortex cognitive control cognitive process cognitive reappraisal cost design discounting experience experimental study improved indexing innovation memory process neural correlate neural model neuroeconomics neuromechanism neurophysiology neuroregulation novel novel marker relating to nervous system skills spatiotemporal therapy development

项目摘要

PROJECT SUMMARY Current treatments for addiction remain limited by a gap in the fundamental knowledge of how different regions of prefrontal cortex interact in decision-making. Here, we examine the direct neural correlates in prefrontal cortex of the control and costs of effortful decision making in humans at high spatiotemporal resolution, laying the groundwork for the development of new treatments to improve decision making in addiction. This K23 Career Development Award aims to provide me with the necessary training to become an independent investigator translating intracranial studies of decision making into non-invasive biomarkers and targets for neuromodulation in addiction. Toward this end, I propose the following training objectives: (1) Develop advanced skills in neural decoding from intracranial electrophysiology data; (2) gain expertise in designing neuroeconomic and computational psychiatry experiments; and (3) gain expertise in utilizing brain stimulation in human behavioral experiments. The overall research objective of the proposed project is to resolve the roles played by prefrontal regions during effortful decision making by combining a neural decoding with functional connectivity analysis and with cortical stimulation to perturb network function. The central hypothesis is that anterior cingulate cortex (ACC) allocates cognitive resources for control, based on the cost of control assessed in orbitofrontal cortex (OFC), in turn determined by the efficiency evidence accumulation in dorsolateral prefrontal cortex (DLPFC). The specific aims of this research are to (1) Dissociate the roles of three prefrontal regions in the regulation of cognitive control. (2) Map the prefrontal network allocating cognitive effort, and (3) Causally dissect the cognitive effort network. Innovation: (1) Method: Use of neural decoding, information theory with intracranial electrophysiology and stimulation to characterize prefrontal networks in humans; (2) Design: Integration of experimental paradigms from neuroeconomics with computational models of neural processing and neurophysiology; (3) Concept: Measuring and modulating cognitive effort through simultaneous recording and stimulation of prefrontal regions during effortful decision-making. The proposed research is significant because it resolves a controversy over the functional roles of key regions of prefrontal cortex in effortful decision making that are putative targets for neuromodulation in addiction. The new fundamental knowledge generated by this proposal will lay the foundation for the development of novel biomarkers and targets for improving cognitive control and decision making in addiction with mechanism-based computationally guided neuromodulation.

项目总结