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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 Maréchal2,3,4, Tobias Kober2,3,4, Jie-Xin Lim6, Celeste Chen5, Soo-Lee Lim1, Julian Gan7, Eng-King Tan5,8, and Ling-Ling Chan1,8

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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