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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
1R01NS075425-01
Application #
8179703
Study Section
Special Emphasis Panel (ZNS1-SRB-B (26))
Program Officer
Corriveau, Roderick A
Project Start
2011-08-01
Project End
2015-07-31
Budget Start
2011-08-01
Budget End
2012-07-31
Support Year
1
Fiscal Year
2011
Total Cost
$422,500
Indirect Cost
Name
Weill Medical College of Cornell University
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
060217502
City
New York
State
NY
Country
United States
Zip Code
10065
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
Glodzik, L; Kuceyeski, A; Rusinek, H et al. (2014) Reduced glucose uptake and A? in brain regions with hyperintensities in connected white matter. Neuroimage 100:684-691
LoCastro, E; Kuceyeski, A; Raj, A (2014) Brainography: an atlas-independent surface and network rendering tool for neural connectivity visualization. Neuroinformatics 12:355-9
Kuceyeski, Amy; Kamel, Hooman; Navi, Babak B et al. (2014) Predicting future brain tissue loss from white matter connectivity disruption in ischemic stroke. Stroke 45:717-22
Abdelnour, Farras; Voss, Henning U; Raj, Ashish (2014) Network diffusion accurately models the relationship between structural and functional brain connectivity networks. Neuroimage 90:335-47
Raj, Ashish; Pandya, Sneha; Shen, Xiaobo et al. (2014) Multi-compartment T2 relaxometry using a spatially constrained multi-Gaussian model. PLoS One 9:e98391

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