Abstract #4894

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

Devin Schoen^1,2, Skyler Deutsch¹, Juhi Mehta¹, Sarah Wang³, Ian O Bledsoe³, Jill L Ostrem³, Philip A Starr⁴, Doris D Wang⁴, and Melanie A Morrison^1,2

¹Radiology and Biomedical Imaging, UCSF, San Francisco, CA, United States, ²Bioengineering, UC Berkeley, Berkeley, CA, United States, ³Neurology, UCSF, San Francisco, CA, United States, ⁴Neurological Surgery, UCSF, San Francisco, CA, United States

Synopsis

Keywords: Parkinson's Disease, Neurodegeneration

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.

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