Department of Radiology, University of North Carolina, Chapel Hill, North Carolina, USA.
Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, North Carolina, USA.
Hum Brain Mapp. 2023 Jun 1;44(8):2993-3006. doi: 10.1002/hbm.26257. Epub 2023 Mar 10.
Brain wiring redundancy counteracts aging-related cognitive decline by reserving additional communication channels as a neuroprotective mechanism. Such a mechanism plays a potentially important role in maintaining cognitive function during the early stages of neurodegenerative disorders such as Alzheimer's disease (AD). AD is characterized by severe cognitive decline and involves a long prodromal stage of mild cognitive impairment (MCI). Since MCI subjects are at high risk of converting to AD, identifying MCI individuals is essential for early intervention. To delineate the redundancy profile during AD progression and enable better MCI diagnosis, we define a metric that reflects redundant disjoint connections between brain regions and extract redundancy features in three high-order brain networks-medial frontal, frontoparietal, and default mode networks-based on dynamic functional connectivity (dFC) captured by resting-state functional magnetic resonance imaging (rs-fMRI). We show that redundancy increases significantly from normal control (NC) to MCI individuals and decreases slightly from MCI to AD individuals. We further demonstrate that statistical features of redundancy are highly discriminative and yield state-of-the-art accuracy of up to 96.8 ± 1.0% in support vector machine (SVM) classification between NC and MCI individuals. This study provides evidence supporting the notion that redundancy serves as a crucial neuroprotective mechanism in MCI.
大脑布线冗余通过保留额外的通信通道作为神经保护机制，抵消与年龄相关的认知能力下降。这种机制在维持神经退行性疾病（如阿尔茨海默病（AD））早期的认知功能方面发挥着潜在的重要作用。AD 的特征是严重的认知能力下降，涉及轻度认知障碍（MCI）的漫长前驱阶段。由于 MCI 患者有向 AD 转化的高风险，因此识别 MCI 个体对于早期干预至关重要。为了描绘 AD 进展过程中的冗余情况，并实现更好的 MCI 诊断，我们定义了一个反映大脑区域之间冗余不相交连接的度量标准，并基于静息态功能磁共振成像（rs-fMRI）捕获的动态功能连接（dFC），提取了三个高阶大脑网络（内侧额、额顶和默认模式网络）的冗余特征。我们发现，冗余从正常对照组（NC）到 MCI 个体显著增加，而从 MCI 到 AD 个体略有减少。我们进一步证明，冗余的统计特征具有高度的可区分性，在支持向量机（SVM）分类中，NC 和 MCI 个体之间的准确率高达 96.8±1.0%。这项研究提供了证据支持冗余作为 MCI 中重要的神经保护机制的观点。
