Connectome-based models of neurodegenerative disease progression: state-of-the-art and translational potential

COEX 

Neurodegenerative diseases involve accumulation of aberrant proteins in the brain, leading to brain damage and progressive cognitive and behavioral dysfunction. These diseases, including Alzheimer’s disease, Parkinson’s disease, and frontotemporal dementia spectrum disorders, represent the primary cause of dementia in older people. Many gaps exist in our understanding of how these diseases initiate and how they progress through the brain. However, evidence has accumulated supporting the hypothesis that aberrant proteins can be transported using the brain’s intrinsic network architecture — in other words, using the brain’s natural communication pathways. This theory forms the basis of connectome-based computational models, which combine real human data and theoretical disease mechanisms to simulate the progression of neurodegenerative diseases through the brain.
Connectome-based disease progression allow testing of mechanistic hypotheses of disease biology, but in the context of humans, where experimentation is often not possible. However, the potential of these models extends beyond hypothesis testing. In combination with emerging longitudinal data and advances in mesoscale molecular brain mapping, connectome-based models are poised to achieve clinically useful individual-level predictions, as well as to generate novel biological insights into disease progression. In this talk, I will highlight recent work by my lab and others that is already moving the needle toward these lofty goals.
First, I will discuss work using connectome-based models to test hypothetical modes of disease progression across several neurodegenerative diseases, including prediction of tau progression in Alzheimer’s disease. Second, I will touch on work using connectome-based models to forecast future progression of neuropathological and cognitive outcomes, highlighting their potential utility in current and upcoming clinical trials. Third, I will showcase novel efforts to model multiple disease elements simultaneously — in this case, accumulation of beta-amyloid, tau and neurodegeneration in Alzheimer’s disease. Finally, I will end by describing work combining connectome-based models with novel human brain mapping data to gain new insights into neurodegenerative disease biology. Altogether, this talk will summarize the state-of-the-art in disease progression modeling using connectome-based models, with a focus on novel efforts toward clinical utility and novel hypothesis generation.