Yi Luopeiwen, Lutz Michael William, Wu Yutong, Li Yang, Songdechakraiwut Tananun
Social Science Research Institute (SSRI), Duke University, Durham, North Carolina, USA.
Departments of Neurology and Pathology, Duke University School of Medicine, Durham, North Carolina, USA.
Alzheimers Dement. 2025 Aug;21(8):e70545. doi: 10.1002/alz.70545.
This study explores magnetic resonance imaging (MRI) as a promising non-invasive approach to monitor Alzheimer's disease (AD) and related dementias. We investigate whether dynamic functional connectivity (dFC), which captures time-varying neural interactions, can reveal sex-specific brain network disruptions in AD that conventional static connectivity analyses may miss.
We analyzed dFC in the Open Access Series of Imaging Studies (OASIS-3) dataset across three diagnostic groups (normal cognition, mild cognitive impairment, dementia), stratified by sex, and regressed out age. We evaluated group differences using multiple distance metrics sensitive to various aspects of network structure, with statistical significance assessed via permutation testing.
Distinct sex-specific patterns emerged across diagnostic groups, with each metric sensitive to different aspects of network disruption. Peak connectivity states, rather than mean levels, more effectively reflected brain network dynamics.
By emphasizing network dynamics, our findings highlight promising signatures for early detection and longitudinal biomarkers. Future work will explore metrics tailored to specific demographic or clinical subpopulations.
Dynamic connectivity reveals sex-specific brain disruptions in Alzheimer's disease (AD). Peak-based analysis improves sensitivity over mean-based connectivity measures. Topological and geometric metrics capture distinct network disruptions by sex. Mild cognitive impairment shows less consistent connectivity changes due to diagnostic instability. Findings support dynamic magnetic resonance imaging (MRI) metrics as early AD biomarkers in future studies.
本研究探索磁共振成像（MRI）作为一种有前景的非侵入性方法来监测阿尔茨海默病（AD）及相关痴呆症。我们调查动态功能连接性（dFC），即捕捉随时间变化的神经交互作用，是否能揭示AD中传统静态连接性分析可能遗漏的性别特异性脑网络破坏。
我们分析了开放获取影像研究系列（OASIS - 3）数据集中三个诊断组（正常认知、轻度认知障碍、痴呆）的dFC，按性别分层，并对年龄进行了回归分析。我们使用对网络结构各方面敏感的多种距离度量来评估组间差异，通过置换检验评估统计学显著性。
不同诊断组出现了明显的性别特异性模式，每个度量对网络破坏的不同方面敏感。峰值连接状态而非平均水平更有效地反映了脑网络动态。
通过强调网络动态，我们的发现突出了早期检测和纵向生物标志物的有前景的特征。未来的工作将探索针对特定人口统计学或临床亚群的度量。
动态连接性揭示了阿尔茨海默病（AD）中性别特异性的脑破坏。基于峰值的分析比基于平均的连接性测量提高了敏感性。拓扑和几何度量捕捉了按性别区分的不同网络破坏。由于诊断的不稳定性，轻度认知障碍显示出不太一致的连接性变化。研究结果支持动态磁共振成像（MRI）度量在未来研究中作为早期AD生物标志物。
