Prediction of neurodegeneration based on network diffusion model

Pinpointing the sources of dementia is crucial to the effective treatment of neurodegenerative diseases. We propose a diffusion model with impulsive sources over the brain connectivity network to model the progression of brain atrophy. To reliably estimate the atrophy sources, we impose sparse regularization on the source distribution and solve the inverse problem with an efficient gradient descent method. We localize the possible origins of Alzheimer’s disease (AD) based on a large set of repeated magnetic resonance imaging (MRI) scans in Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. 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 AD patients and mild cognitive impairment (MCI) subjects, indicating possible switch of the dementia driving mechanism. Moreover, we demonstrate that we can effectively predict changes of brain atrophy patterns with the proposed model. Our work could help understand the dynamics and origin of dementia, as well as monitor the progression of the diseases in an early stage.

Hu, Chenhui, et al. "Localizing Sources of Brain Disease Progression with Network Diffusion Model." IEEE Journal of Selected Topics in Signal Processing 10.7 (2016): 1214-1225.