Abstract #2940

Deep learning of 3D T2-weighted MRI provides support for arachnoid granulation hypertrophy in patients with Parkinson’s disease

Melanie Leguizamon¹, Colin McKnight², Jarrod Eisma¹, Alex Song¹, Jason Elenberger¹, Daniel Claassen¹, Manus Donahue¹, and Kilian Hett¹

¹Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, United States, ²Department of Radiology, Vanderbilt University Medical Center, Nashville, TN, United States

Synopsis

Keywords: Data Analysis, NeurodegenerationWe apply novel deep learning algorithms using non-contrasted T₂-weighted MRI to test hypotheses regarding arachnoid granulation (AG) hypertrophy in patients with Parkinson’s disease (PD). Using this method, we identify AGs protruding into the superior sinus lumen, which may serve as a site of CSF egress implicated in the neurofluid clearance system. Results suggest a significant increase in AG volume in the parietal and frontal lobes of PD participants compared to age-matched healthy controls, potentially indicative of reduced neurofluid clearance efficiency secondary to macromolecular aggregation along the CSF circuit.

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