Decoding task engagement from distributed network electrophysiology in humans

Objective. Here, our objective was to develop a binary decoder to detect task engagement in humans during two distinct, conflict-based behavioral tasks. Effortful, goal-directed decision-making requires the coordinated action of multiple cognitive processes, including attention, working memory and a...

Full description

Saved in:
Bibliographic Details
Published in:Journal of neural engineering 2019-08, Vol.16 (5), p.056015-056015
Main Authors: Provenza, Nicole R, Paulk, Angelique C, Peled, Noam, Restrepo, Maria I, Cash, Sydney S, Dougherty, Darin D, Eskandar, Emad N, Borton, David A, Widge, Alik S
Format: Article
Language:English
Subjects:
Brain - physiology
Conflict, Psychological
Decision Making - physiology
Drug Resistant Epilepsy - diagnosis
Drug Resistant Epilepsy - physiopathology
Emotions - physiology
Humans
Nerve Net - physiology
network connectivity
neural decoding
Photic Stimulation - methods
Psychomotor Performance - physiology
task engagement
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Objective. Here, our objective was to develop a binary decoder to detect task engagement in humans during two distinct, conflict-based behavioral tasks. Effortful, goal-directed decision-making requires the coordinated action of multiple cognitive processes, including attention, working memory and action selection. That type of mental effort is often dysfunctional in mental disorders, e.g. when a patient attempts to overcome a depression or anxiety-driven habit but feels unable. If the onset of engagement in this type of focused mental activity could be reliably detected, decisional function might be augmented, e.g. through neurostimulation. However, there are no known algorithms for detecting task engagement with rapid time resolution. Approach. We defined a new network measure, fixed canonical correlation (FCCA), specifically suited for neural decoding applications. We extracted FCCA features from local field potential recordings in human volunteers to give a temporally continuous estimate of mental effort, defined by engagement in experimental conflict tasks. Main results. Using a small number of features per participant, we accurately decoded and distinguished task engagement from other mental activities. Further, the decoder distinguished between engagement in two different conflict-based tasks within seconds of their onset. Significance. These results demonstrate that network-level brain activity can detect specific types of mental efforts. This could form the basis of a responsive intervention strategy for decision-making deficits.
ISSN:1741-2560
1741-2552
1741-2552
DOI:10.1088/1741-2552/ab2c58