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Abstract #0026

A Machine Learning approach to Predict Age-Related Motor Performance using MRI-MRS data

Akila Weerasekera1, Adrian Ion-Margineanu2, Oron Levin1, Diana Sima3, Sabine Van Huffel1, Stephan Swinnen1, and Uwe Himmelreich1

1University of Leuven, leuven, Belgium, 2Philips UK, Belfast, United Kingdom, 3icometrix, leuven, Belgium

Aging is associated with gradual alterations in structural and neurochemical characteristics of the brain, which can be assessed in vivo by MRI and MRS modalities. The process of brain aging occurs in accord with a general decline in cognitive-motor performance and increases the risk of neurodegeneration. We used MRI-MRS data from 86 individuals as inputs for machine learning models5 to predict motor performance in healthy individuals. Our analysis shows that application of machine learning algorithms on combination of age, gender and MR data can accurately predict motor performance and has potential to be used as a biomarker for neuro related diseases.

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