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Deterioration from healthy to mild cognitive impairment and Alzheimer’s disease mirrored in corresponding loss of centrality in directed brain networks

  • Zhao, Sinan1
  • Rangaprakash, D.1, 2
  • Liang, Peipeng3
  • Deshpande, Gopikrishna1, 4, 5, 4, 4, 6
  • 1 Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL, 36849, USA , Auburn (United States)
  • 2 Northwestern University, Chicago, IL, USA , Chicago (United States)
  • 3 Capital Normal University, Beijing, China , Beijing (China)
  • 4 Auburn University, Auburn, AL, USA , Auburn (United States)
  • 5 Alabama Advanced Imaging Consortium, Auburn, AL, USA , Auburn (United States)
  • 6 National Institute of Mental Health and Neurosciences, Bangalore, India , Bangalore (India)
Published Article
Brain Informatics
Springer Berlin Heidelberg
Publication Date
Dec 02, 2019
DOI: 10.1186/s40708-019-0101-x
Springer Nature


ObjectiveIt is important to identify brain-based biomarkers that progressively deteriorate from healthy to mild cognitive impairment (MCI) to Alzheimer’s disease (AD). Cortical thickness, amyloid-ß deposition, and graph measures derived from functional connectivity (FC) networks obtained using functional MRI (fMRI) have been previously identified as potential biomarkers. Specifically, in the latter case, betweenness centrality (BC), a nodal graph measure quantifying information flow, is reduced in both AD and MCI. However, all such reports have utilized BC calculated from undirected networks that characterize synchronization rather than information flow, which is better characterized using directed networks.MethodsTherefore, we estimated BC from directed networks using Granger causality (GC) on resting-state fMRI data (N = 132) to compare the following populations (p < 0.05, FDR corrected for multiple comparisons): normal control (NC), early MCI (EMCI), late MCI (LMCI) and AD. We used an additional metric called middleman power (MP), which not only characterizes nodal information flow as in BC, but also measures nodal power critical for information flow in the entire network.ResultsMP detected more brain regions than BC that progressively deteriorated from NC to EMCI to LMCI to AD, as well as exhibited significant associations with behavioral measures. Additionally, graph measures obtained from conventional FC networks could not identify a single node, underscoring the relevance of GC.ConclusionOur findings demonstrate the superiority of MP over BC as well as GC over FC in our case. MP obtained from GC networks could serve as a potential biomarker for progressive deterioration of MCI and AD.

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