Abstract #3254

Substantia Nigra Abnormalities in Early Parkinson’s Disease Patients using Convolutional Neural Networks in Neuromelanin MRI

Rahul Gaurav^1,2,3, Romain Valabregue^1,2, Nadya Pyatigorskaya^1,2,3,4, Lydia Yahia-Cherif^1,2, Emma Biondetti^1,2,3, Graziella Mangone^2,5, R. Matthew Hutchison⁶, Jean-Christophe Corvol^2,5,7, Marie Vidailhet^2,3,7, and Stephane Lehericy^1,2,3,4

¹CENIR, ICM Paris, Paris, France, ²Paris Brain Institute (ICM), Sorbonne University, UPMC Univ Paris 06, Inserm U1127, CNRS UMR 7225, Paris, France, ³ICM Team “Movement Investigations and Therapeutics” (MOV’IT), Paris, France, ⁴Department of Neuroradiology, Pitié-Salpêtrière Hospital, AP-HP, Paris, France, ⁵INSERM, Clinical Investigation Center for Neurosciences, Pitié-Salpêtrière Hospital, Paris, France, ⁶Biogen Inc., Cambridge, MA, United States, ⁷Department of Neurology, APHP, Pitié-Salpêtrière Hospital, Paris, France

There is a need of accurate imaging biomarkers of dopaminergic cell neurodegeneration to facilitate drug trials in Parkinson’s disease (PD). PD demonstrates neurodegenerative substantia nigra pars compacta (SNc) changes that can be detected efficiently using neuromelanin-sensitive MRI. Characterizing neuromelanin signal variations using manual SNc segmentation is an operator-dependent and time-consuming task. Hence, in this cross-sectional, observational, case-control study, we investigated neuromelanin SNc abnormalities in the early PD patients using convolutional neural network-based fully automatic segmentation of SNc. We found a highly significant difference in SNc volume and signal intensity between early PD and healthy volunteers.

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