title: Impaired long distance functional connectivity and weighted network architecture in Alzheimer's disease.
abstract: Alzheimer's disease (AD) is increasingly recognized as a disconnection syndrome, which leads to cognitive impairment due to the disruption of functional activity across large networks or systems of interconnected brain regions. We explored abnormal functional magnetic resonance imaging (fMRI) resting-state dynamics, functional connectivity, and weighted functional networks, in a sample of patients with severe AD (N = 18) and age-matched healthy volunteers (N = 21). We found that patients had reduced amplitude and regional homogeneity of low-frequency fMRI oscillations, and reduced the strength of functional connectivity, in several regions previously described as components of the default mode network, for example, medial posterior parietal cortex and dorsal medial prefrontal cortex. In patients with severe AD, functional connectivity was particularly attenuated between regions that were separated by a greater physical distance; and loss of long distance connectivity was associated with less efficient global and nodal network topology. This profile of functional abnormality in severe AD was consistent with the results of a comparable analysis of data on 2 additional groups of patients with mild AD (N = 17) and amnestic mild cognitive impairment (MCI; N = 18). A greater degree of cognitive impairment, measured by the mini-mental state examination across all patient groups, was correlated with greater attenuation of functional connectivity, particularly over long connection distances, for example, between anterior and posterior components of the default mode network, and greater reduction of global and nodal network efficiency. These results indicate that neurodegenerative disruption of fMRI oscillations and connectivity in AD affects long-distance connections to hub nodes, with the consequent loss of network efficiency. This profile was evident also to a lesser degree in the patients with less severe cognitive impairment, indicating that the potential of resting-state fMRI measures as biomarkers or predictors of disease progression in AD.
authors: LIAMA Center for Computational Medicine, National Laboratory of Pattern Recognition, Institute of Automation, The Chinese Academy of Sciences, Beijing 100190, China.
authors: Yong
authors: Y
authors: Liu
authors: Chunshui
authors: C
authors: Yu
authors: Xinqing
authors: X
authors: Zhang
authors: Jieqiong
authors: J
authors: Liu
authors: Yunyun
authors: Y
authors: Duan
authors: Aaron F
authors: AF
authors: Alexander-Bloch
authors: Bing
authors: B
authors: Liu
authors: Tianzi
authors: T
authors: Jiang
authors: Ed
authors: E
authors: Bullmore
mesh_terms: D000368:Aged; D000544:Alzheimer Disease / Q000175:diagnosis / Q000503:physiopathology* / Q000523:psychology; D001921:Brain / Q000503:physiopathology*; D001931:Brain Mapping; D060825:Cognitive Dysfunction / Q000175:diagnosis / Q000503:physiopathology / Q000523:psychology; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D009434:Neural Pathways / Q000503:physiopathology; D009483:Neuropsychological Tests; D012146:Rest / Q000502:physiology; D012815:Signal Processing, Computer-Assisted
keywords: Alzheimer's disease; disconnection; distance; functional connectivity; weighted brain networks