神经心理量表评估

研究内容：基于本项目所研究的新型认知评估范式，通过多模态同步任务（如EEG（脑电）+fNIRS（近红外脑功能）+眼动+平衡、fNIRS+步态双任务、fNIRS+OT 任务手功能等，其中眼动、平衡、OT 任务手功能为智能传感信息），建立行为特征与神经影像特征的耦合关系；进而优选高敏感性的EEG/fNIRS 通道，构建高准确性的行为学与低通道EEG/fNIRS 的特征组合。在上述优选的基础上，结合认知行为学测试，从感知觉（眼动平衡）检测出发、到执行控制（低通道fNIRS+步态双任务、低通道fNIRS+OT 任务）、记忆与情景（低通道干电极EEG+低通道fNIRS）开展自适应筛查检测，提高筛查的快速性与敏感性。基于大样本数据与深度学习，构建适用于社区的多模态认知障碍筛查模型。针对认知障碍患者在医院的精细分析与康复效果的即时评价的挑战，对照患者/正常人，研究静息态、任务态下的脑活动响应模式，探究认知障碍的神经/血氧响应机制，建立与认知能力相关的EEG/fNIRS 评价指标（包括时空频模式、网络连接等）及确定个体化认知训练靶点。
数据内容：神经心理量表评估（MMSE，MoCA）；数据采集地点：广东省佛山市；数据采集时间：2020.12-2021.1；设备名称：简易精神状态检查量表（MMSE），蒙特利尔认知功能评估量表（MoCA）；运行环境：无；数据类型：表格；预估数据量/记录数：103例；数据格式：Excel

Research Content: Based on the novel cognitive assessment paradigm studied in this project, we implemented multimodal synchronous tasks, including simultaneous acquisition of EEG (Electroencephalogram) + fNIRS (functional Near-Infrared Spectroscopy) + eye movement + balance data, fNIRS + gait dual-task, and fNIRS + OT (Occupational Therapy) hand function task, where eye movement, balance and OT hand function task data were collected via intelligent sensing. Firstly, we established the coupling relationship between behavioral and neuroimaging features. Subsequently, high-sensitivity EEG/fNIRS channels were selected, and feature combinations integrating high-accuracy behavioral data and low-channel EEG/fNIRS data were constructed. On this basis, combined with cognitive behavioral tests, adaptive screening detection was conducted, starting from sensory-perceptual detection (eye movement and balance), then moving to executive control tasks (low-channel fNIRS + gait dual-task, low-channel fNIRS + OT task), and finally memory and episodic tasks (low-channel dry-electrode EEG + low-channel fNIRS), so as to enhance the rapidity and sensitivity of screening. Based on large-sample data and deep learning, a community-adapted multimodal cognitive impairment screening model was developed. Aiming at the challenges of fine analysis of cognitive impairment patients in hospitals and real-time evaluation of rehabilitation effects, we investigated brain activity response patterns under resting and task states by comparing patients with normal controls, explored the neural and blood oxygen response mechanisms of cognitive impairment, established EEG/fNIRS evaluation indicators related to cognitive ability (including spatiotemporal-frequency patterns, network connections, etc.), and identified individualized cognitive training targets.
Data Content: Neuropsychological scale assessments (MMSE, MoCA);
Data Collection Location: Foshan City, Guangdong Province;
Data Collection Period: December 2020 to January 2021;
Assessment Tools: Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA);
Operating Environment: None;
Data Type: Tabular data;
Estimated Data Volume/Number of Records: 103 cases;
Data Format: Excel

搜集汇总

背景与挑战

背景概述

该数据集名为'神经心理量表评估'，包含103例来自广东省佛山市的简易精神状态检查量表（MMSE）和蒙特利尔认知功能评估量表（MoCA）评估数据，采集于2020年12月至2021年1月，以Excel表格形式存储。数据集旨在支持老年认知障碍的多模态评估研究，通过结合行为学测试和神经影像特征（如EEG/fNIRS），构建认知障碍筛查模型，并探索认知康复的神经机制，适用于康复医学、人工智能等交叉学科领域。