Abstract
Pinpointing the sources of dementia is crucial to the effective treatment of neurodegenerative diseases. In this paper, we model the dementia progression by a diffusive model over the brain network with sparse impulsive stimulations. By solving inverse problems, we localize the possible origins of Alzheimer’s disease based on a large set of repeated magnetic resonance imaging (MRI) scans in ADNI. The distribution of the sources averaged over the sample population is evaluated. We find that the dementia sources have different concentrations in the brain lobes for Alzheimer’s disease (AD) patients and mild cognitive impairment (MCI) subjects, indicating possible switch of the dementia driving mechanism. Our model provides a quantitative way to perform explanatory analysis of the dynamics of dementia.
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References
Goedert, M., Clavaguera, F., Tolnay, M.: The propagation of prion-like protein inclusions in neurodegenerative diseases. Trends in Neurosciences 33(7), 317–325 (2010)
Wenk, G.L.: Neuropathologic changes in Alzheimers disease. J. Clin. Psychiatry 64(suppl. 9), 7–10 (2003)
Raj, A., Kuceyeski, A., Weiner, M.: A network diffusion model of disease progression in dementia. Neuron 73(6), 1204–1215 (2012)
Bassett, D.S., Bullmore, E.T.: Human brain networks in health and disease. Current Opinion in Neurology 22(4), 340 (2009)
Abdelnour, F., Voss, H.U., Raj, A.: Network diffusion accurately models the relationship between structural and functional brain connectivity networks. Neuroimage (in press)
Jucker, M., Walker, L.C.: Self-propagation of pathogenic protein aggregates in neurodegenerative diseases. Nature 501(7465), 45–51 (2013)
Thompson, P.M., Hayashi, K.M., Zubicaray, G.D., Janke, A.L., Rose, S.E., Semple, J.: Dynamics of gray matter loss in Alzheimer’s disease. The Journal of Neuroscience 23(3), 994–1005 (2003)
Sabuncu, M.R., Desikan, R.S., Sepulcre, J., Yeo, B.T.T., Liu, H., Schmansky, N.J.: The dynamics of cortical and hippocampal atrophy in Alzheimer disease. Archives of Neurology 68(8), 1040–1048 (2011)
Schmidt, M., Fung, G., Rosales, R.: Fast optimization methods for L1 regularization: A comparative study and two new approaches. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 286–297. Springer, Heidelberg (2007)
Hua, X., Lee, S., Yanovsky, I., Leow, A., Chou, Y., Ho, A.: Optimizing power to track brain degeneration in Alzheimer’s disease and mild cognitive impairment with tensor-based morphometry: an ADNI study of 515 subjects. NeuroImage 48, 668–681 (2009)
Alexander-Bloch, A.F., Vértes, P.E., Stidd, R., Lalonde, F., Clasen, L., Rapoport, J.: The anatomical distance of functional connections predicts brain network topology in health and schizophrenia. Cereb. Cortex 23(1), 127–138 (2013)
Xia, M., Wang, J., He, Y.: BrainNet Viewer: a network visualization tool for human brain connectomics. PLoS One 8(7), e68910 (2013)
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Hu, C., Hua, X., Thompson, P.M., El Fakhri, G., Li, Q. (2014). Inferring Sources of Dementia Progression with Network Diffusion Model. In: Wu, G., Zhang, D., Zhou, L. (eds) Machine Learning in Medical Imaging. MLMI 2014. Lecture Notes in Computer Science, vol 8679. Springer, Cham. https://doi.org/10.1007/978-3-319-10581-9_6
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DOI: https://doi.org/10.1007/978-3-319-10581-9_6
Publisher Name: Springer, Cham
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