Abstract #3627

Automated White Matter Lesion Quantification Correlates With Gait and Cognitive Dysfunction In Parkinson’s Disease

Eric Fang*¹, Mário João Fartaria*^2,3,4, Chu-Ning Ann⁵, Bénédicte Maréchal^2,3,4, Tobias Kober^2,3,4, Jie-Xin Lim⁶, Celeste Chen⁵, Soo-Lee Lim¹, Julian Gan⁷, Eng-King Tan^5,8, and Ling-Ling Chan^1,8

¹Singapore General Hospital, Singapore, Singapore, ²Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland, ³Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland, ⁴Signal Processing Laboratory (LTS 5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, ⁵National Neuroscience Institute, Singapore, Singapore, ⁶Nanyang Technological University, Singapore, Singapore, ⁷Siemens Healthcare, Singapore, Singapore, ⁸Duke-NUS Medical School, Singapore, Singapore

White matter lesions (WMLs) have an impact on neuronal connectivity; and consequently affect balance, mobility and cognition in both normal aging and disease states. Using a fully automated segmentation algorithm and multi-modal images, we estimated WMLs volumes to predict the clinical severity in a cohort of Parkinson’s disease (PD) patients and healthy controls (HC). Increased WMLs volume is strongly associated with both motor/gait and cognitive dysfunctions in PD. Lobar WMLs are found to have differential impact on distinctive cognitive domains. Automated volumetric quantification of WMLs load, particularly within the frontal and prefrontal regions can predict severity of symptoms in PD.

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