Alzheimer's Disease (AD) and other dementias affect approximately 25 million people worldwide, taking a huge toll on the physical and psychological well being of elderly patients. A major reason for lack of progress in AD treatment is a fundamental lack of understanding regarding its cause and mechanism of progression. Despite impressive progress in detecting and quantifying disease progression in AD using sophisticated neuroanatomic algorithms, there are very few system-level models which can explain the observed patterns of neurodegeneration in AD. We now propose a novel model of atrophy as a diffusion process on a hypothesized brain network. The model mathematically encodes the basic understanding that propagation follows fiber pathways, going from more to less atrophied regions at an outward rate proportional to the local atrophy gradient, in a manner akin to heat transfer in a network with temperature gradients. Surprisingly, the model implies atrophy patterns in AD and other degenerative diseases. Finally, we propose to use our model to predict the future atrophic patterns for any patient using their baseline neuroimaging data.
This project has the potential to clarify and explain several hitherto unexplained features of neurodegeneration and atrophy observed in Alzheimer's and other brain disorders. It will then enable a method to accurately predict future atrophy and cognitive decline in demented patients using only their baseline MRI scans. Successful conclusion of the project would therefore change and impact several aspects of clinical care, diagnosis, monitoring and prognosis of these debilitating diseases. NOTE: The purpose of the EUREKA initiative is to foster exceptionally innovative research that, if successful, will have an unusually high impact on the areas of science that are germane to the mission of one or more of the participating NIH Institutes. EUREKA is for new projects. EUREKA is not for the continuation of existing projects. EUREKA is not for support of pilot projects (i.e., projects of limited scope that are designed primarily to generate data that will enable the PI to seek other funding opportunities). Rather, it is anticipated that EUREKA projects will begin and be completed during the funding period. Please provide an overall impact/priority score to reflect your assessment of the likelihood the project will exert a sustained, powerful influence on the research field(s) involved, Significance and Innovation should be the major determinants of your overall impact score. The approach should be evaluated for general feasibility. An application should score poorly if it is clear to the reviewers that the proposed methodology has no probability at all of being successful, either because it is inherently illogical or because the same approach has already been attempted and shown not to be feasible. Remember that unavoidable risk, which is intrinsic to novel and innovative approaches, is expected for these applications and reviewers are instructed that the presence or absence of preliminary data should not be taken into account when determining the score. Disclaimer: Please note that the following critiques were prepared by the reviewers prior to the Study Section meeting and are provided in an essentially unedited form. While there is opportunity for the reviewers to update or revise their written evaluation, based upon the group's discussion, there is no guarantee that individual critiques have been updated subsequent to the discussion at the meeting. Therefore, the critiques may not fully reflect the final opinions of the individual reviewers at the close of group discussion or the final majority opinion of the group. Thus the Resume and Summary of Discussion is the final word on what the reviewers actually considered critical at the meeting.
|Abdelnour, Farras; Dayan, Michael; Devinsky, Orrin et al. (2018) Functional brain connectivity is predictable from anatomic network's Laplacian eigen-structure. Neuroimage 172:728-739|
|Pandya, Sneha; Kuceyeski, Amy; Raj, Ashish et al. (2017) The Brain's Structural Connectome Mediates the Relationship between Regional Neuroimaging Biomarkers in Alzheimer's Disease. J Alzheimers Dis 55:1639-1657|
|Mezias, Chris; LoCastro, Eve; Xia, Chuying et al. (2017) Connectivity, not region-intrinsic properties, predicts regional vulnerability to progressive tau pathology in mouse models of disease. Acta Neuropathol Commun 5:61|
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|Kuceyeski, Amy; Navi, Babak B; Kamel, Hooman et al. (2016) Structural connectome disruption at baseline predicts 6-months post-stroke outcome. Hum Brain Mapp 37:2587-601|
|Kuceyeski, Amy; Navi, Babak B; Kamel, Hooman et al. (2015) Exploring the brain's structural connectome: A quantitative stroke lesion-dysfunction mapping study. Hum Brain Mapp 36:2147-60|
|Abdelnour, Farras; Mueller, Susanne; Raj, Ashish (2015) Relating Cortical Atrophy in Temporal Lobe Epilepsy with Graph Diffusion-Based Network Models. PLoS Comput Biol 11:e1004564|
|Raj, Ashish; LoCastro, Eve; Kuceyeski, Amy et al. (2015) Network Diffusion Model of Progression Predicts Longitudinal Patterns of Atrophy and Metabolism in Alzheimer's Disease. Cell Rep :|
|Kuceyeski, A F; Vargas, W; Dayan, M et al. (2015) Modeling the relationship among gray matter atrophy, abnormalities in connecting white matter, and cognitive performance in early multiple sclerosis. AJNR Am J Neuroradiol 36:702-9|
|LoCastro, E; Kuceyeski, A; Raj, A (2014) Brainography: an atlas-independent surface and network rendering tool for neural connectivity visualization. Neuroinformatics 12:355-9|
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