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Abstract #3254

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

Rahul Gaurav1,2,3, Romain Valabregue1,2, Nadya Pyatigorskaya1,2,3,4, Lydia Yahia-Cherif1,2, Emma Biondetti1,2,3, Graziella Mangone2,5, R. Matthew Hutchison6, Jean-Christophe Corvol2,5,7, Marie Vidailhet2,3,7, and Stephane Lehericy1,2,3,4
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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