Grab‐AD: Generalizability and reproducibility of altered brain activity and diagnostic classification in Alzheimer's Disease. Issue 12 (4th May 2020)

• Record Type: Journal Article
• Authors: Jin, Dan; Wang, Pan; Zalesky, Andrew; Liu, Bing; Song, Chengyuan; Wang, Dawei; Xu, Kaibin; Yang, Hongwei; Zhang, Zengqiang; Yao, Hongxiang; Zhou, Bo; Han, Tong; Zuo, Nianming; Han, Ying; Lu, Jie; Wang, Qing; Yu, Chunshui; Zhang, Xinqing; Zhang, Xi; Jiang, Tianzi; Zhou, Yuying; Liu, Yong
• Is Part Of: Human brain mapping. Volume 41:Issue 12(2020)
• Page Start: 3379
• Page End: 3391
• Publication Date: 2020-05-04
• Subjects: activity -- Alzheimer's disease -- functional connectivity -- multicenter -- resting‐state fMRI; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.25023
• Languages: English
• ISSNs: 1065-9471

Abstract

Abstract: 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.