| dc.description.abstract | We apply novel deep learning algorithms to T2-weighted MRI to test hypotheses regarding
arachnoid granulation (AG) hypertrophy in patients with Parkinson’s disease (PD). Using this method, we
identify AG protruding into the superior sagittal sinus, which may serve as a site of CSF egress. Results
from statistical analyses suggest a significant increase in total AG volume in patients with PD compared
to age-matched healthy controls, potentially indicating reduced neurofluid clearance efficiency. Further
correlational analyses revealed revealed significant relationships between total AG volume and
MiniBEST, as well as significant relationships for AG number with MiniBEST and SDMT. Actigraphy
data indicate a negative relationship between total AG volume and sleep efficiency and a positive
relationship between AG volume and number of awakenings, but no significant relationships with other
actigraphy sleep measures. Finally, sleep efficiency was strongly negatively correlated with AG number
before correcting for false discovery rate. Chronic sleep disturbance may contribute to AG hypertrophy as
a compensatory mechanism for a dysregulated glymphatic system in patients with PD to clear waste. | en_US |
