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Diagnosis of Parkinson's disease by eliciting trait-specific eye movements in multi-visual tasks.

J Transl Med

January 2025

School of Information and Communication Engineering, Dalian University of Technology, No. 2 Linggong Road, 116024, Dalian, China.

Background: Parkinson's Disease (PD) is a neurodegenerative disorder, and eye movement abnormalities are a significant symptom of its diagnosis. In this paper, we developed a multi-task driven by eye movement in a virtual reality (VR) environment to elicit PD-specific eye movement abnormalities. The abnormal features were subsequently modeled by using the proposed deep learning algorithm to achieve an auxiliary diagnosis of PD.

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We examined the intricate mechanisms underlying visual processing of complex motion stimuli by measuring the detection sensitivity to contraction and expansion patterns and the discrimination sensitivity to the location of the center of motion (CoM) in various real and unreal optic flow stimuli. We conducted two experiments (N = 20 each) and compared responses to both "real" optic flow stimuli containing information about self-movement in a three-dimensional scene and "unreal" optic flow stimuli lacking such information. We found that detection sensitivity to contraction surpassed that to expansion patterns for unreal optic flow stimuli, whereas this trend was reversed for real optic flow stimuli.

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Multi-Scale Pyramid Squeeze Attention Similarity Optimization Classification Neural Network for ERP Detection.

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Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China; Center of Intelligent Computing, School of Mathematics, East China University of Science and Technology, Shanghai 200237, China. Electronic address:

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