Abstract #3268

Resting state fMRI “Epilepsy networks”.

Rose Dawn Bharath¹, Rajanikant Panda², Jeetu Raj³, Sanjib Sinha⁴, Kenchaiah Raghavendra⁴, Ravindranadh Chowdary Mundlamuri⁴, Ganne Chaitanya⁵, Anita Mahadevan⁶, Arivazhagan Arimappamagan⁷, Malla Bhaskara Rao⁷, Kandavel Thennarasu⁸, Kaushik Majumdar⁹, Parthasarathy Satishchandra⁴, and Tapan Gandhi³

¹Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bengaluru, India, ²Coma Science Group, Universitè de Liège, Liège, Belgium, ³Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, India, ⁴Department of Neurology, National Institute of Mental Health and Neurosciences, Bengaluru, India, ⁵Department of Neurology, Thomas Jefferson University, Philadelphia, PA, United States, ⁶Department of Neuropathology, National Institute of Mental Health and Neurosciences, Bengaluru, India, ⁷Department of Neurosurgery, National Institute of Mental Health and Neurosciences, Bengaluru, India, ⁸Department of Biostatistics, National Institute of Mental Health and Neurosciences, Bengaluru, India, ⁹Systems Science and Informatics Unit, Indian Statistical Institute, Bengaluru, India

Resting state functional MRI (rsfMRI) research typically focuses on few well identified networks though many more networks (15-80) are often visualized, in the course of investigating functional networks. It is customary to discard these networks as they are presumed to have no functional relevance. We used machine learning methods to identify “epilepsy networks” in 45 individuals with TLE using FSL derived 88 independent components. In line with evidence from experimental models, the current results indicates that TLE is associated with disease specific “rsfMRI epilepsy networks” which can be visualised in-vivo at individual subject level.

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