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Abstract #4539

Data Driven Subtyping Reveals Two Distinctive Patterns of Deep Gray Matter atrophy and Dopamine Availability in early Parkinson’s Disease

Yoonsang Oh1, Gilsoon Park2, and Hosung Kim2
1Neurology, College of Medicine, The Catholic University of Korea, Seoul, Korea, Republic of, 2USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States

Synopsis

Keywords: Parkinson's Disease, Parkinson's Disease

Motivation: Recent imaging evidence suggests that machine-learning technique may be useful to identify subtypes with distinct spatial patterns of Parkinson's disease.

Goal(s): We investigated possible data-driven subtypes of PD.

Approach: Using the Subtype and Stage Inference machine-learning technique, we categorized PD patients into subtypes by analyzing their progression patterns in deep gray matter atrophy and dopamine availability.

Results: Subtype 1 displayed early dopamine availability reduction, severe cardiac denervation, mild cognitive dysfunction in the early stage, and rapid decline in motor and cognitive function in the later stage, whereas subtype 2 showed early brain atrophy, mild cardiac denervation, severe cognitive dysfunction in the early stage.

Impact: Two subtypes displayed distinctive patterns in dopamine availability, deep gray matter volume, cognition, motor symptom, and cardiac denervation, supporting the body-first and brain-first concepts of PD through imaging-machine learning approach.

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