Mesoscale connections and gene expression empower whole-brain modeling of a-synuclein spread, aggregation, and decay dynamics

An emerging view regarding neurodegenerative diseases is that discreet seeding of misfolded proteins leads to widespread pathology. However, the mechanisms by which misfolded proteins seed distinct brain regions and cause differential whole-brain pathology remain elusive. We used whole-brain tissue clearing and high -resolution imaging to longitudinally map pathology in an a-synuclein pre-formed fibril injection model of Par-kinson's disease. Cleared brains at different time points of disease progression were quantitatively segmented and registered to a standardized atlas, revealing distinct phases of spreading and decline. We then fit a computational model with parameters that represent a-synuclein pathology spreading, aggrega-tion, decay, and gene expression pattern to this longitudinal dataset. Remarkably, our model can generalize to predicting a-synuclein spreading patterns from several distinct brain regions and can even estimate their origins. This model empowers mechanistic understanding and accurate prediction of disease progression, paving the way for the development and testing of therapeutic interventions.

Automated summary

This study used whole-brain tissue clearing and high-resolution imaging to map the spreading of α-synuclein pathology in a Parkinson's disease model. A computational model was developed to accurately predict the spreading patterns, providing insights into disease progression and potential therapeutic interventions.