Alzheimer's disease (AD) is associated with disruptions in brain activity and networks. However, there is substantial inconsistency among studies that have investigated functional brain alterations in AD; such contradictions have hindered efforts to elucidate the core disease mechanisms. In this study, we aim to comprehensively characterize AD‐associated functional brain alterations using one of the world's largest resting‐state functional MRI (fMRI) biobank for the disorder. The biobank includes fMRI data from six neuroimaging centers, with a total of 252 AD patients, 221 mild cognitive impairment (MCI) patients and 215 healthy comparison individuals. Meta‐analytic techniques were used to unveil reliable differences in brain function among the three groups. Relative to the healthy comparison group, AD was associated with significantly reduced functional connectivity and local activity in the default‐mode network, basal ganglia and cingulate gyrus, along with increased connectivity or local activity in the prefrontal lobe and hippocampus (p  < .05, Bonferroni corrected). Moreover, these functional alterations were significantly correlated with the degree of cognitive impairment (AD and MCI groups) and amyloid‐β burden. Machine learning models were trained to recognize key fMRI features to predict individual diagnostic status and clinical score. Leave‐one‐site‐out cross‐validation established that diagnostic status (mean area under the receiver operating characteristic curve: 0.85) and clinical score (mean correlation coefficient between predicted and actual Mini‐Mental State Examination scores: 0.56, p  < .0001) could be predicted with high accuracy. Collectively, our findings highlight the potential for a reproducible and generalizable functional brain imaging biomarker to aid the early diagnosis of AD and track its progression.

中文翻译：

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Grab-AD：阿尔茨海默病大脑活动改变和诊断分类的普遍性和再现性。


阿尔茨海默病 (AD) 与大脑活动和网络的破坏有关。然而，研究 AD 中大脑功能改变的研究之间存在很大的不一致。这些矛盾阻碍了阐明核心疾病机制的努力。在这项研究中，我们的目标是利用世界上最大的 AD 静息态功能 MRI (fMRI) 生物库之一来全面描述 AD 相关的功能性大脑改变。该生物库包含来自六个神经影像中心的 fMRI 数据，共有 252 名 AD 患者、221 名轻度认知障碍 (MCI) 患者和 215 名健康对照个体。荟萃分析技术被用来揭示三组之间大脑功能的可靠差异。相对于健康对照组，AD 与默认模式网络、基底神经节和扣带回的功能连接和局部活动显着降低相关，同时前额叶和海马的连接或局部活动增加（ p < .05，邦费罗尼纠正）。此外，这些功能改变与认知障碍程度（AD 和 MCI 组）和 β 淀粉样蛋白负担显着相关。机器学习模型经过训练可以识别关键的功能磁共振成像特征，从而预测个体诊断状态和临床评分。留一站点交叉验证确定了诊断状态（接受者操作特征曲线下的平均面积：0.85）和临床评分（预测和实际简易精神状态检查分数之间的平均相关系数：0.56， p < .0001 ) 可以进行高精度预测。 总的来说，我们的研究结果强调了可重复且可推广的功能性脑成像生物标志物在帮助 AD 早期诊断并跟踪其进展方面的潜力。