Inferring cognitive state underlying conflict choices in verbal Stroop task using heterogeneous input discriminative-generative decoder model

Mohammad R Rezaei; Haseul Jeoung; Ayda Gharamani; Utpal Saha; Venkat Bhat; Milos R Popovic; Ali Yousefi; Robert Chen; Milad Lankarany

doi:10.1088/1741-2552/ace932

Inferring cognitive state underlying conflict choices in verbal Stroop task using heterogeneous input discriminative-generative decoder model

J Neural Eng. 2023 Sep 22;20(5). doi: 10.1088/1741-2552/ace932.

Authors

Mohammad R Rezaei^{1

2

3}, Haseul Jeoung², Ayda Gharamani^{2

4}, Utpal Saha², Venkat Bhat^{1

5}, Milos R Popovic^{1

3}, Ali Yousefi⁴, Robert Chen², Milad Lankarany^{1

2

3}

Affiliations

¹ Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada.
² Krembil Research Institute, University Health Network (UHN), Toronto, ON, Canada.
³ KITE Research Institute, University Health Network (UHN), Toronto, ON, Canada.
⁴ Worcester Polytechnic Institute, MA, United States of America.
⁵ Department of Psychiatry, University Health Network and University of Toronto, Toronto, ON, Canada.

PMID: 37473753
DOI: 10.1088/1741-2552/ace932

Abstract

Objective. The subthalamic nucleus (STN) of the basal ganglia interacts with the medial prefrontal cortex (mPFC) and shapes a control loop, specifically when the brain receives contradictory information from either different sensory systems or conflicting information from sensory inputs and prior knowledge that developed in the brain. Experimental studies demonstrated that significant increases in theta activities (2-8 Hz) in both the STN and mPFC as well as increased phase synchronization between mPFC and STN are prominent features of conflict processing. While these neural features reflect the importance of STN-mPFC circuitry in conflict processing, a low-dimensional representation of the mPFC-STN interaction referred to as a cognitive state, that links neural activities generated by these sub-regions to behavioral signals (e.g. the response time), remains to be identified.Approach. Here, we propose a new model, namely, the heterogeneous input discriminative-generative decoder (HI-DGD) model, to infer a cognitive state underlying decision-making based on neural activities (STN and mPFC) and behavioral signals (individuals' response time) recorded in ten Parkinson's disease (PD) patients while they performed a Stroop task. PD patients may have conflict processing which is quantitatively (may be qualitative in some) different from healthy populations.Main results. Using extensive synthetic and experimental data, we showed that the HI-DGD model can diffuse information from neural and behavioral data simultaneously and estimate cognitive states underlying conflict and non-conflict trials significantly better than traditional methods. Additionally, the HI-DGD model identified which neural features made significant contributions to conflict and non-conflict choices. Interestingly, the estimated features match well with those reported in experimental studies.Significance. Finally, we highlight the capability of the HI-DGD model in estimating a cognitive state from a single trial of observation, which makes it appropriate to be utilized in closed-loop neuromodulation systems.

Keywords: Parkinson’s disease; cognitive state decoding; state space model; subthalamic nucleus; verbal Stroop task.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Cognition
Conflict, Psychological
Deep Brain Stimulation* / methods
Humans
Parkinson Disease* / therapy
Stroop Test
Subthalamic Nucleus* / physiology