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Related Experiment Video

Updated: Jul 22, 2025

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

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Inferring cognitive state underlying conflict choices in verbal Stroop task using heterogeneous input

Mohammad R Rezaei^1,2,3, Haseul Jeoung², Ayda Gharamani^2,4

¹Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada.

Journal of Neural Engineering

|July 20, 2023

Summary

A new model decodes cognitive states from brain activity and behavior, improving conflict processing insights in Parkinson's disease patients. This approach enhances understanding of subthalamic nucleus-medial prefrontal cortex interactions.

Keywords:

Parkinson’s disease cognitive state decoding state space model subthalamic nucleus verbal Stroop task

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Last Updated: Jul 22, 2025

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A Cognitive Paradigm to Investigate Interference in Working Memory by Distractions and Interruptions

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Published on: July 16, 2015

Area of Science:

Neuroscience
Cognitive Science
Computational Psychiatry

Background:

The subthalamic nucleus (STN) and medial prefrontal cortex (mPFC) form a crucial circuit for processing conflicting information.
Theta band activity (2-8 Hz) and phase synchronization between STN and mPFC are key neural markers of conflict processing.
A low-dimensional representation linking STN-mPFC neural activity to behavior (cognitive state) is needed.

Purpose of the Study:

To develop and validate a novel model, the heterogeneous input discriminative-generative decoder (HI-DGD), for inferring cognitive states.
To analyze decision-making processes in Parkinson's disease (PD) patients during a Stroop task using neural and behavioral data.
To identify neural features contributing to conflict and non-conflict processing in PD.

Main Methods:

Proposed the HI-DGD model to integrate neural data (STN, mPFC) and behavioral data (response time).
Applied the model to data from ten PD patients performing a Stroop task.
Validated model performance using synthetic and experimental data, comparing it against traditional methods.

Main Results:

The HI-DGD model significantly improved the estimation of cognitive states underlying conflict and non-conflict trials.
The model effectively diffused information from both neural and behavioral signals simultaneously.
Identified specific neural features contributing to conflict and non-conflict choices, aligning with existing experimental findings.

Conclusions:

The HI-DGD model provides a robust method for inferring cognitive states from neural and behavioral data.
It offers valuable insights into STN-mPFC circuitry function during conflict processing, particularly in PD.
The model's single-trial estimation capability makes it suitable for real-time applications like closed-loop neuromodulation.