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. Binder^4,5, M. Elizabeth Meyerand^1,2,6, 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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