Cortical Boundary Complexity on Structural MRI Distinguishes Parkinson’s Disease from Healthy Controls and Correlates with Symptom Severity

Motivation: There is a need for reliable prognostic biomarkers to guide Parkinson's disease (PD) symptom management. Goal(s): To assess whether fractal dimension (FD), as a measure of (sub)-cortical boundary complexity, can distinguish patients with PD from healthy controls and explain variance in motor symptom severity. Approach: T1-weighted MRI data from six sites, age-matched between PD and controls, were analyzed. FD was compared between the two groups, and correlations were drawn with pre-operative motor symptom scores in the OFF-medication state. Results: FD significantly distinguished PD from controls and correlated with motor symptom severity in nine distributed brain regions. Impact: This study confirms regional fractal dimension (FD) —a measure of sub-(cortical) boundary complexity —as a biomarker of Parkinson's disease diagnosis and severity. FD could be used to improve clinical decision-making including patient-specific treatment planning.