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

IF 3.7 3区 医学 Q2 ENGINEERING, BIOMEDICAL Journal of neural engineering Pub Date : 2023-09-22 DOI:10.1088/1741-2552/ace932
Mohammad Reza Rezaei, Haseul Jeoung, Ayda Ghahramani, Uptal Saha, Venkat Bhat, Milos R Popovic, Ali Yousefi, Robert E W Chen, Milad Lankarany
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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.

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使用异质输入判别生成解码器模型推断言语Stroop任务中冲突选择的认知状态。
客观的基底神经节的丘脑底核(STN)与内侧前额叶皮层(mPFC)相互作用,形成控制回路,特别是当大脑从不同的感觉系统接收到矛盾的信息,或从大脑中形成的感觉输入和先验知识接收到矛盾信息时。实验研究表明θ活性显著增加(2-8 Hz)以及mPFC和STN之间增加的相位同步是冲突处理的显著特征。虽然这些神经特征反映了STN-mPFC电路在冲突处理中的重要性,但被称为认知状态的mPFC-STN相互作用的低维表示仍有待确定,该低维表示将这些子区域产生的神经活动与行为信号(例如响应时间)联系起来。方法在这里,我们提出了一个新的模型,即异质输入判别生成解码器(HI-DGD)模型,以基于10名帕金森病(PD)患者在执行Stroop任务时记录的神经活动(STN和mPFC)和行为信号(个体的反应时间)来推断决策的认知状态。帕金森病患者可能具有与健康人群在数量上(在某些情况下可能是定性的)不同的冲突处理。主要结果。使用大量的合成和实验数据,我们表明HI-DGD模型可以同时扩散来自神经和行为数据的信息,并比传统方法更好地估计冲突和非冲突试验的认知状态。此外,HI-DGD模型确定了哪些神经特征对冲突和非冲突选择做出了重大贡献。有趣的是,估计的特征与实验研究中报道的特征非常吻合。意义最后,我们强调了HI-DGD-模型从一次观察试验中估计认知状态的能力,这使其适合用于闭环神经调控系统。
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来源期刊
Journal of neural engineering
Journal of neural engineering 工程技术-工程:生物医学
CiteScore
7.80
自引率
12.50%
发文量
319
审稿时长
4.2 months
期刊介绍: The goal of Journal of Neural Engineering (JNE) is to act as a forum for the interdisciplinary field of neural engineering where neuroscientists, neurobiologists and engineers can publish their work in one periodical that bridges the gap between neuroscience and engineering. The journal publishes articles in the field of neural engineering at the molecular, cellular and systems levels. The scope of the journal encompasses experimental, computational, theoretical, clinical and applied aspects of: Innovative neurotechnology; Brain-machine (computer) interface; Neural interfacing; Bioelectronic medicines; Neuromodulation; Neural prostheses; Neural control; Neuro-rehabilitation; Neurorobotics; Optical neural engineering; Neural circuits: artificial & biological; Neuromorphic engineering; Neural tissue regeneration; Neural signal processing; Theoretical and computational neuroscience; Systems neuroscience; Translational neuroscience; Neuroimaging.
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