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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. [ABSTRACT FROM AUTHOR]

Background: Subjective cognitive decline‐questionnaire 9 (SCD‐Q9) was developed to detect SCD complaints at risk of mild cognitive impairment (MCI). However, our previous findings indicated that its coverage might be insufficient. To test this hypothesis, we recently translated SCD‐Q21. Objective: To examine the reliability and validity of this translated SCD‐Q21 and to explore its effectiveness for discriminating MCI from controls. Methods: Item analysis was performed to understand its item discrimination and homogeneity. The Cronbach's α and Spearman‐Brown's split‐half coefficients were calculated to test its reliability. The Kaiser–Meyer–Olkin (KMO) value, Bartlett's sphericity test, and exploratory factor analysis (EFA) were used to examine its construct validity. The content validity was evaluated using five‐grade Likert scale. Finally, the SCD‐Q21 scores in MCI and controls were compared. Results: The difference of each item between the extreme groups was significant. The Cronbach's α coefficient was.913 and Spearman‐Brown's split‐half coefficient was.894. When performing holding one‐out approach, the Cronbach's α coefficient ranged from.906 to.914. The KMO value was.929 and the difference of Bartlett's Sphericity test was significant. All experts scored 5 points when assessing its content. Finally, a significant difference of score was found between MCI and NC groups. Conclusions: The reliability and validity of the SCD‐Q21 are good, which may pave a way for its application in a wider Chinese‐speaking population. [ABSTRACT FROM AUTHOR]