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Abstract #1927

Characterizing Neuronal Loss To Differentiate Parkinsonian Subtypes Using Automated Deep Grey Nuclear Volumetry

Chu-Ning Ann*1, Bénédicte Maréchal*2,3,4, Eric Fang5, Jie-Xie Lim6, Celeste Chen1, Julian Gan7, Eng-King Tan1,8, and Ling-Ling Chan5,8

1National Neuroscience Institute, Singapore, Singapore, 2Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland, 3Department of Radiology, CHUV, Lausanne, Switzerland, 4LTS5, EPFL, Lausanne, Switzerland, 5Singapore General Hospital, Singapore, Singapore, 6Nanyang Technological University, Singapore, Singapore, 7Siemens Healthcare, Singapore, Singapore, 8Duke-NUS Medical School, Singapore, Singapore

Postural Instability Gait Disorder (PIGD), a Parkinson's Disease (PD) motor subtype, progresses rapidly with a higher prevalence of neurobehavioural changes. Using automated deep grey nuclear tissue classification combined with atlas-based segmentation, we investigated the performance of resulting estimated lesion load to aid differential diagnosis. Caudate lesion load in PIGD and idiopathic PD subtypes correlated with clinical balance and gait assessment. Combining caudate with abnormal white matter volumetric characterization further improved the discriminative power and could potentially support differential diagnosis of PD.

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