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Abstract #2470

Predictive markers for Parkinson’s disease: A DTI based pattern classification study

Tejashree Suresh Takalkar1, Madhura Ingalhalikar1, Jitendra Saini2, and Pramod Pal2

1Electronics And Telecommunication, Symbiosis Institute Of Technology, Pune, India, 2Department Of Neurology, NIMHANS, India

This work presents a paradigm for predicting changes in pathology, supporting diagnosis and providing a potential biomarker for Parkinson’s disease. This is achieved by creating a high-dimensional support vector machine (SVM) based classifier that learns the underlying pattern of pathology using numerous atlas-based regional features extracted from Diffusion Tensor Imaging (DTI) data. For the dataset of 72 controls and 73 PD patients, we achieve a 10-fold cross validation accuracy of 72.8% and a testing accuracy of 78.5%. The top discriminative features included widespread patterns of mean diffusivity changes in PD.

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