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Abstract #0029

Functional MRI-based Deep Learning Classification between Temporal Lobe Epilepsy Patients and Healthy Controls

Maribel Torres-Velázquez1, Gyujoon Hwang2, Cole John Cook2, Bruce Hermann3, Jeffery R. Binder4,5, M. Elizabeth Meyerand1,2,6, and Alan B. McMillan6

1Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States, 2Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States, 3Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States, 4Neurology, Medical College of Wisconsin, Milwaukee, WI, United States, 5Biophysics, Medical College of Wisconsin, Milwaukee, WI, United States, 6Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States

Common machine learning approaches to differentiate between Temporal Lobe Epilepsy (TLE) and healthy controls often include extensive preprocessing techniques that often entail feature extraction, resulting in a more time-intensive and variable approach. Utilizing data from both the Epilepsy Connectome Project (ECP) and Human Connectome Project (HCP), this study attempts to develop, train, and validate a deep learning classifier to automatically differentiate between TLE patients and healthy subjects using resting-state fMRI (rs-fMRI) and task fMRI (t-fMRI) data alone without advanced preprocessing steps or feature extraction.

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