衰老与神经退行性疾病中神经活动的复杂性与稳定性
Complexity and Stability of Neural Activity Across Aging and Neurodegenerative Disease
AI总结：
该研究以EEG为对象，用Wasserstein距离和内在维度分别量化神经活动的稳定性与复杂性，发现健康衰老、轻度认知障碍及阿尔茨海默病的神经表征稳定性与复杂性存在特征性变化，为相关研究提供了新框架。
AI中文摘要：
目标：即使在固定认知状态下，EEG信号也会持续波动，但一个重要问题是大脑是否仍会随时间复用相似的活动模式来表征信息。方法：为解决该问题，我们将EEG建模为滑动窗口活动模式的分布，使用Wasserstein距离量化其时间稳定性，同时用内在维度捕捉表征复杂性。结果：在多任务、全生命周期及临床EEG数据集上，我们发现神经表征呈现受约束的、与特定条件相关的稳定性，而非无约束的漂移；更高的内在维度始终与更低的稳定性相关，这表明更丰富的表征空间随时间的可重复性更差。两种指标均表现出可重复的空间组织，后脑区的维度更高、稳定性低于前脑区。健康衰老的特征是维度增加、稳定性降低，而轻度认知障碍和阿尔茨海默病则表现为两者的共同崩溃。结论：这些发现为理解认知、衰老及疾病中的神经稳定性提供了分布层面的框架。意义：该框架提供了一种量化神经表征稳定性的原则性方法，有望作为敏感生物标志物用于临床环境中追踪认知衰老与神经退行性疾病。
英文摘要：
Objective: EEG signals fluctuate continuously even within a fixed cognitive state, but an important question is whether the brain still reuses similar activity patterns to represent information over time. Methods: To address this, we model EEG as distributions of windowed activity patterns and quantify their temporal stability using Wasserstein distance, while intrinsic dimensionality captures representational complexity. Results: Across multi-task, lifespan, and clinical EEG datasets, we find that neural representations show constrained, condition-specific stability rather than unconstrained drift. Higher intrinsic dimensionality is consistently associated with lower stability, suggesting that richer representational spaces are less reproducible over time. Both measures exhibit reproducible spatial organization, with posterior regions showing higher dimensionality and lower stability than frontal regions. Healthy aging is characterized by increased dimensionality and reduced stability, whereas mild cognitive impairment and Alzheimer's disease show a joint collapse of both. Conclusions: These findings provide a distribution-level framework for understanding neural stability across cognition, aging, and disease. Significance: This framework offers a principled approach to quantifying neural representational stability, with potential utility as a sensitive biomarker for tracking cognitive aging and neurodegeneration in clinical settings.
