Sun Jingnan, Shen Anruo, Sun Yike, Chen Xiaogang, Li Yunxia, Gao Xiaorong, Lu Bai
Department of Biomedical Engineering, Tsinghua University, 100084, Beijing, China.
IDG/McGovern Institute for Brain Research, Tsinghua University, 100084, Beijing, China.
NPJ Digit Med. 2024 Dec 23;7(1):375. doi: 10.1038/s41746-024-01384-2.
Cognitive impairment, marked by neurodegenerative damage, leads to diminished cognitive function decline. Accurate cognitive assessment is crucial for early detection and progress evaluation, yet current methods in clinical practice lack objectivity, precision, and convenience. This study included 743 participants, including healthy individuals, mild cognitive impairment (MCI), and dementia patients, with collected resting-state EEG data and cognitive scale scores. An adaptive spatiotemporal encoding framework was developed based on resting-state EEG, achieving an MAE of 3.12% (95% CI: 2.9034, 3.3975) in testing (sensitivity: 0.97, 95% CI: 0.779,1; specificity: 0.97, 95% CI: 0.779,1). The model's effectiveness was also validated on the neurofeedback (sensitivity: 0.867, 95% CI: 0.621, 0.963; specificity: 1, 95% CI: 0.439, 1.0) and TMS datasets (sensitivity: 0.833, 95% CI: 0.608, 0.942), which effectively reflect the participants' cognitive changes. The model effectively extracted repetitive spatiotemporal patterns from resting-state EEG, aiding in cognitive disease diagnosis and assessment in various scenarios.
以神经退行性损伤为特征的认知障碍会导致认知功能衰退减缓。准确的认知评估对于早期检测和进展评估至关重要，但目前临床实践中的方法缺乏客观性、精确性和便利性。本研究纳入了743名参与者，包括健康个体、轻度认知障碍（MCI）患者和痴呆患者，收集了静息态脑电图数据和认知量表评分。基于静息态脑电图开发了一种自适应时空编码框架，在测试中实现了3.12%（95%置信区间：2.9034，3.3975）的平均绝对误差（敏感性：0.97，95%置信区间：0.779，1；特异性：0.97，95%置信区间：0.779，1）。该模型的有效性也在神经反馈（敏感性：0.867，95%置信区间：0.621，0.963；特异性：1，95%置信区间：0.439，1.0）和经颅磁刺激（TMS）数据集（敏感性：0.833，95%置信区间：0.608，0.942）上得到验证，这些数据集有效地反映了参与者的认知变化。该模型有效地从静息态脑电图中提取了重复的时空模式，有助于在各种场景下进行认知疾病的诊断和评估。
