Network-Based Statistic Show Aberrant Functional Connectivity in Alzheimer's Disease

• Published in: IEEE journal of selected topics in signal processing Vol. 10; no. 7; pp. 1182 - 1188
• Main Authors: Zhan, Yafeng, Yao, Hongxiang, Wang, Pan, Zhou, Bo, Zhang, Zengqiang, Guo, Yane, An, Ningyu, Ma, Jianhua, Zhang, Xi, Liu, Yong
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Alzheimer's disease; Analysis of variance; components of interest; Dementia; Hospitals; mild cognitive impairment; network-based statistic; NIST; Programmable logic arrays
• ISSN: 1932-4553; 1941-0484
• DOI: 10.1109/JSTSP.2016.2600298

Alzheimer's disease (AD) and mild cognitive impairment (MCI) have been associated with impaired communication among large-scale brain networks. Given nature that interconnected subnetworks are responsible for daily behavior than a single pair of functional connectivity, it is valid to use a network-based statistic (NBS) method to exploit the clustering structure of connectivity alterations in AD/MCI. We explored abnormal network components using NBS based on resting-state functional magnetic resonance imaging (fMRI)connectivity in a sample of patients with AD (N = 35), MCI (N = 27) and age-matched healthy subjects (N = 27). The results demonstrated that patients had reduced functional connectivity strength in several components, including the default mode network, sensorimotor network, visual-sensory network, and visual-attention network. In patients with AD, the functional connectivity of these components of interest (COIs) exhibited greater attenuation than that in MCI subjects compared with normal cognition. A greater degree of cognitive impairment was correlated with a greater decrease in functional connectivity in the identified COIs. These results indicate that the neurodegenerative disruption of fMRI connectivity is widely distributed in several networks in AD/MCI. These profiles deepen our understanding of the neural basis of AD/MCI dysfunction and indicate the potential of resting-state fMRI measures as biomarkers or predictors of AD.