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

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Mohammad Reza Rezaei, Haseul Jeoung2, Ayda Gharamani, Utpal Saha, Venkat Bhat, Milos Radomir Popovic, Ali Yousefi, Robert Chen, Milad Lankarany

Published: 22 Sept 2023, Last Modified: 01 Oct 2024OpenReview Archive Direct UploadEveryoneCC BY 4.0
Abstract: IOP Science homeAccessibility Help Journals Books Publishing Support Login Journal of Neural Engineering Purpose-Led Publishing, find out more. Paper Inferring cognitive state underlying conflict choices in verbal Stroop task using heterogeneous input discriminative-generative decoder model Mohammad R Rezaei1,2,3, Haseul Jeoung2, Ayda Gharamani2,4, Utpal Saha2, Venkat Bhat1,5, Milos R Popovic1,3, Ali Yousefi4, Robert Chen2 and Milad Lankarany6,1,2,3 Published 22 September 2023 • © 2023 IOP Publishing Ltd Journal of Neural Engineering, Volume 20, Number 5 Citation Mohammad R Rezaei et al 2023 J. Neural Eng. 20 056016 DOI 10.1088/1741-2552/ace932 Publish open access in this journal at no cost See if your institution is participating in the transformative agreement with the Canadian Research Knowledge Network. References Open science Article metrics 208 Total downloads Article has an altmetric score of 1 Submit Submit to this Journal Permissions Get permission to re-use this article Share this article Article and author information 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.