Characterizing Network Selectiveness to the Dynamic Spreading of Neuropathological Events in Alzheimer’s Disease

Article type: Research Article

Authors: Li, Wenchao^a | Yang, Defu^{a; b; *} | Yan, Chenggang^a | Chen, Minghan^c | Li, Quefeng^d | Zhu, Wentao^{b; *} | Wu, Guorong^{e; f} | for the Alzheimer’s Disease Neuroimaging Initiative¹

Affiliations: [a] Intelligent Information Processing Laboratory, Hangzhou Dianzi University, Hangzhou, China | [b] Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, Zhejiang, China | [c] Department of Computer Science, Wake Forest University, Winston-Salem, NC, USA | [d] Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA | [e] Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA | [f] Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Correspondence: [*] Correspondence to: Dr. Defu Yang, Intelligent Information Processing Laboratory, Hangzhou Dianzi University, Hangzhou 310018, China. E-mail: [email protected] and Wentao Zhu, Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, 311122, China. E-mail: [email protected].

Note: [1] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

Abstract: Background:Mounting evidence shows that the neuropathological burdens manifest preference in affecting brain regions during the dynamic progression of Alzheimer’s disease (AD). Since the distinct brain regions are physically wired by white matter fibers, it is reasonable to hypothesize the differential spreading pattern of neuropathological burdens may underlie the wiring topology, which can be characterized using neuroimaging and network science technologies. Objective:To study the dynamic spreading patterns of neuropathological events in AD. Methods:We first examine whether hub nodes with high connectivity in the brain network (assemble of white matter wirings) are susceptible to a higher level of pathological burdens than other regions that are less involved in the process of information exchange in the network. Moreover, we propose a novel linear mixed-effect model to characterize the multi-factorial spreading process of neuropathological burdens from hub nodes to non-hub nodes, where age, sex, and APOE4 indicators are considered as confounders. We apply our statistical model to the longitudinal neuroimaging data of amyloid-PET and tau-PET, respectively. Results:Our meta-data analysis results show that 1) AD differentially affects hub nodes with a significantly higher level of pathology, and 2) the longitudinal increase of neuropathological burdens on non-hub nodes is strongly correlated with the connectome distance to hub nodes rather than the spatial proximity. Conclusion:The spreading pathway of AD neuropathological burdens might start from hub regions and propagate through the white matter fibers in a prion-like manner.

Keywords: Alzheimer’s disease, brain networks, hub node, linear mixed-effect model, longitudinal neuroimages

DOI: 10.3233/JAD-215596

Journal: Journal of Alzheimer's Disease, vol. 86, no. 4, pp. 1805-1816, 2022