Sorbonne University, GRC n° 21, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Boulevard de l'hôpital, Paris, France; Brain & Spine Institute (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l'hôpital, Paris, France; Department of Neurology, Institute of Memory and Alzheimer's Disease (IM2A), Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'hôpital, Paris, France.
Sorbonne University, GRC n° 21, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Boulevard de l'hôpital, Paris, France; Department of Neurology, Institute of Memory and Alzheimer's Disease (IM2A), Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'hôpital, Paris, France.
Neurobiol Aging. 2020 Apr;88:83-90. doi: 10.1016/j.neurobiolaging.2019.12.017. Epub 2019 Dec 23.
Using a single integrated analysis, we examined the relationship between brain networks and molecular pathways in a cohort of elderly individuals at risk for Alzheimer's disease. In 205 subjective memory complainers (124 females, mean age: 75.7 ± 3.4), individual functional connectome was computed for a total of 3081 functional connections (set A) and 6 core plasma biomarkers of Alzheimer's disease (set B) were assessed. Partial least squares correlation analysis identified one dimension of population covariation between the 2 sets (p < 0.006), which we named bioneural mode. Five core plasma biomarkers and 190 functional connections presented bootstrap ratios greater than the critical value |1.96|. T-tau protein showed a trend toward significance (bootstrap resampling = 1.64). The salience, the language, the visuospatial, and the default mode networks were the strongest significant networks. We detected a strong association between network dynamics and core pathophysiological blood biomarkers. Innovative composite biomarkers, such as the bioneural mode, are promising to provide outcomes and better inform drug development and clinical practice for neurodegenerative diseases.
我们通过单一的综合分析，在一组有患阿尔茨海默病风险的老年人中研究了大脑网络与分子通路之间的关系。在 205 名主观记忆抱怨者（124 名女性，平均年龄：75.7 ± 3.4）中，我们计算了总共 3081 个功能连接的个体功能连接体（集 A），并评估了 6 种阿尔茨海默病的核心血浆生物标志物（集 B）。偏最小二乘相关分析确定了这两组之间的一个群体协变维度（p < 0.006），我们将其命名为脑神经模式。有 5 种核心血浆生物标志物和 190 个功能连接的自举比值大于临界值 |1.96|。T 蛋白tau 显示出显著的趋势（自举重采样 = 1.64）。突显网络、语言网络、视空间网络和默认模式网络是最强的显著网络。我们检测到网络动力学与核心病理生理血液生物标志物之间存在强烈关联。创新的复合生物标志物，如脑神经模式，有望为神经退行性疾病提供结果，并更好地为药物开发和临床实践提供信息。
