Arterial spin labeling-based machine learning for idiopathic rapid eye movement sleep behavior disorder and Parkinson's disease

Motivation: Investigating cerebral blood flow (CBF) alterations between idiopathic REM sleep behavior disorder (iRBD) and Parkinson's disease (PD) using arterial spin labeling (ASL) can provide crucial insights into the shared neurobiological underpinnings of these conditions, facilitating effective disease management and treatment. Goal(s): Analyzing the cerebral blood flow (CBF) variations and CBF-connectivity and evaluating their diagnostic utility. Approach: By employing ASL, we conducted a detailed analysis of CBF variations and constructed a CBF-connectivity network. Results: Observed increased CBF in PD vs iRBD in specific regions, as well as elevated connectivity. Impact: The present study provides objective biomarkers for the progression of iRBD and PD through the study of cerebral perfusion. It also provides direction for adjunctive treatment of microcirculatory abnormalities to further inhibit the progression of associated dysfunction.