Lin Qi, Rosenberg Monica D, Yoo Kwangsun, Hsu Tiffany W, O'Connell Thomas P, Chun Marvin M
Department of Psychology, Yale University, New Haven, CT, United States.
Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, United States.
Front Aging Neurosci. 2018 Apr 13;10:94. doi: 10.3389/fnagi.2018.00094. eCollection 2018.
Resting-state functional connectivity (rs-FC) is a promising neuromarker for cognitive decline in aging population, based on its ability to reveal functional differences associated with cognitive impairment across individuals, and because rs-fMRI may be less taxing for participants than task-based fMRI or neuropsychological tests. Here, we employ an approach that uses rs-FC to predict the Alzheimer's Disease Assessment Scale (11 items; ADAS11) scores, which measure overall cognitive functioning, in novel individuals. We applied this technique, connectome-based predictive modeling, to a heterogeneous sample of 59 subjects from the Alzheimer's Disease Neuroimaging Initiative, including normal aging, mild cognitive impairment, and AD subjects. First, we built linear regression models to predict ADAS11 scores from rs-FC measured with Pearson's correlation. The positive network model tested with leave-one-out cross validation (LOOCV) significantly predicted individual differences in cognitive function from rs-FC. In a second analysis, we considered other functional connectivity features, accordance and discordance, which disentangle the correlation and anticorrelation components of activity timecourses between brain areas. Using partial least square regression and LOOCV, we again built models to successfully predict ADAS11 scores in novel individuals. Our study provides promising evidence that rs-FC can reveal cognitive impairment in an aging population, although more development is needed for clinical application.
静息态功能连接（rs-FC）是一种很有前景的神经标志物，可用于评估老年人群的认知衰退，这是基于它能够揭示个体间与认知障碍相关的功能差异，并且由于与基于任务的功能磁共振成像（fMRI）或神经心理学测试相比，静息态功能磁共振成像（rs-fMRI）对参与者的负担可能更小。在此，我们采用一种方法，即使用rs-FC来预测阿尔茨海默病评估量表（11项；ADAS11）的得分，该量表用于测量新个体的整体认知功能。我们将这种技术，即基于连接组的预测建模，应用于来自阿尔茨海默病神经影像倡议组织的59名受试者的异质性样本，包括正常衰老、轻度认知障碍和阿尔茨海默病患者。首先，我们建立线性回归模型，根据用皮尔逊相关性测量的rs-FC来预测ADAS11得分。采用留一法交叉验证（LOOCV）测试的正向网络模型显著地从rs-FC中预测出认知功能的个体差异。在第二项分析中，我们考虑了其他功能连接特征，即一致性和不一致性，它们能够区分脑区之间活动时间进程的相关性和反相关性成分。使用偏最小二乘回归和LOOCV，我们再次建立模型，成功地预测了新个体的ADAS11得分。我们的研究提供了有前景的证据，表明rs-FC能够揭示老年人群的认知障碍，尽管临床应用还需要更多的发展。
