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Cognitive State Inference and Neuromodulation Effects on Decision-Making in Parkinson's Disease
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Cognitive State Inference and Neuromodulation Effects on Decision-Making in Parkinson's Disease/ Mohammad Reza Rezaei.
作者:
Rezaei, Mohammad Reza,
面頁冊數:
1 electronic resource (120 pages)
附註:
Source: Dissertations Abstracts International, Volume: 86-10, Section: B.
Contained By:
Dissertations Abstracts International86-10B.
標題:
Bioengineering. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31767979
ISBN:
9798310391505
Cognitive State Inference and Neuromodulation Effects on Decision-Making in Parkinson's Disease
Rezaei, Mohammad Reza,
Cognitive State Inference and Neuromodulation Effects on Decision-Making in Parkinson's Disease
[electronic resource] /Mohammad Reza Rezaei. - 1 electronic resource (120 pages)
Source: Dissertations Abstracts International, Volume: 86-10, Section: B.
Human cognition is characterized by an intricate blend of neural and behavioral signals arising from dynamic interactions within distributed brain networks. Grasping these hidden cognitive states is essential, particularly for neurological conditions like Parkinson's disease (PD), which affect decision-making when faced with conflicting sensory inputs and disrupt normal brain function. This thesis aims to create computational models to infer cognitive states linked to decision-making, especially conflict states, from intricate neural and behavioral datasets, and also to assess the effects of deep brain stimulation (DBS) on decision-making processes.Initially, we examine the limitations inherent in existing computational models when it comes to deciphering cognitive states involved in decision-making and conflict states. In response to these shortcomings, we propose an innovative framework, the High-Dimensional Generative Dynamics (HI-DGD) model. This model adeptly infers conflict states from multimodal data sources such as local field potentials (LFP) and electroencephalograms (EEG). The HI-DGD model's effectiveness has been confirmed in decision-making tasks like the verbal Stroop task, which monitors conflict states throughout conflict processing.The thesis subsequently explores the temporal effects of DBS on decision-making in PD patients under both high-conflict and no-conflict conditions. We introduce the Cognet model, which replicates the temporal influences of DBS on decision processes. The Cognet model adeptly captures the temporal effects of DBS on decision-making, providing significant insights into the timing and modulation strategies to boost cognitive performance during choices. These models provide new avenues for understanding the neural and behavioral signal mechanisms and the impact of neuromodulation on cognitive functions. This research contributes to neuroscience by advancing methods for deducing cognitive states.
English
ISBN: 9798310391505Subjects--Topical Terms:
598252
Bioengineering.
Subjects--Index Terms:
Cognitive inference
Cognitive State Inference and Neuromodulation Effects on Decision-Making in Parkinson's Disease
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Human cognition is characterized by an intricate blend of neural and behavioral signals arising from dynamic interactions within distributed brain networks. Grasping these hidden cognitive states is essential, particularly for neurological conditions like Parkinson's disease (PD), which affect decision-making when faced with conflicting sensory inputs and disrupt normal brain function. This thesis aims to create computational models to infer cognitive states linked to decision-making, especially conflict states, from intricate neural and behavioral datasets, and also to assess the effects of deep brain stimulation (DBS) on decision-making processes.Initially, we examine the limitations inherent in existing computational models when it comes to deciphering cognitive states involved in decision-making and conflict states. In response to these shortcomings, we propose an innovative framework, the High-Dimensional Generative Dynamics (HI-DGD) model. This model adeptly infers conflict states from multimodal data sources such as local field potentials (LFP) and electroencephalograms (EEG). The HI-DGD model's effectiveness has been confirmed in decision-making tasks like the verbal Stroop task, which monitors conflict states throughout conflict processing.The thesis subsequently explores the temporal effects of DBS on decision-making in PD patients under both high-conflict and no-conflict conditions. We introduce the Cognet model, which replicates the temporal influences of DBS on decision processes. The Cognet model adeptly captures the temporal effects of DBS on decision-making, providing significant insights into the timing and modulation strategies to boost cognitive performance during choices. These models provide new avenues for understanding the neural and behavioral signal mechanisms and the impact of neuromodulation on cognitive functions. This research contributes to neuroscience by advancing methods for deducing cognitive states.
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