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Article type: Research Article
Authors: Powell, Fona | Tosun, Duygub | Sadeghi, Roksanab | Weiner, Michaelb | Raj, Ashisha; * | for the Alzheimer’s Disease Neuroimaging Initiative1
Affiliations: [a] Department of Radiology, Weill Cornell Medicine, New York, NY, USA | [b] Department of Radiology, University of California San Francisco, San Francisco, CA, USA
Correspondence: [*] Correspondence to: Ashish Raj, PhD, Weill Cornell Medicine, 407 East 61st Street, RR-114, New York, NY 10065, USA. Tel.: +1 415 353 3442; 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: Models of Alzheimer’s disease (AD) hypothesize stereotyped progression via white matter (WM) fiber connections, most likely via trans-synaptic transmission of toxic proteins along neuronal pathways. An important question in the field is whether and how organization of fiber pathways is affected by disease. It remains unknown whether fibers act as conduits of degenerative pathologies, or if they also degenerate with the gray matter network. This work uses graph theoretic modeling in a longitudinal design to investigate the impact of WM network organization on AD pathology spread. We hypothesize if altered WM network organization mediates disease progression, then a previously published network diffusion model will yield higher prediction accuracy using subject-specific connectomes in place of a healthy template connectome. Neuroimaging data in 124 subjects from ADNI were assessed. Graph topology metrics show preserved network organization in patients compared to controls. Using a published diffusion model, we further probe the effect of network alterations on degeneration spread in AD. We show that choice of connectome does not significantly impact the model’s predictive ability. These results suggest that, despite measurable changes in integrity of specific fiber tracts, WM network organization in AD is preserved. Further, there is no difference in the mediation of putative pathology spread between healthy and AD-impaired networks. This conclusion is somewhat at variance with previous results, which report global topological disturbances in AD. Our data indicates the combined effect of edge thresholding, binarization, and inclusion of subcortical regions to network graphs may be responsible for previously reported effects.
Keywords: Alzheimer’s disease, atrophy, biomarkers, diffusion tensor imaging, longitudinal, magnetic resonance imaging, neural networks, structural connectivity
DOI: 10.3233/JAD-170798
Journal: Journal of Alzheimer's Disease, vol. 65, no. 3, pp. 747-764, 2018
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