Date: Friday, October 28, 2022

Time: 1:00 p.m. - 2:00 p.m.

Location: Virtual (log-in instructions below)

Speaker: Leonardo Bonilha 

Speaker’s Title: Professor of Neurology and Clinician Scientist 

Speaker’s Affiliation: Emory University

Seminar Title: Deep learning for neuroradiological assessment of epilepsy

Conventional neuroradiological diagnosis of epilepsy still relies on qualitative inspection of clinical MRI. Epilepsy is associated with subtle quantitative abnormalities that have not been leveraged to improve diagnostic accuracy. In this talk, we will discuss applications of deep learning to address this scientific and clinical gap.

Biosketch: I am a Professor of Neurology and clinician scientist at Emory University. I am a clinical neurophysiologist and epileptologist, and I also have graduate and post-graduate degrees in computational neurosciences, clinical research and neuroimaging.

Overall, my research is focused on improving the understanding of the mechanisms that underlie neurological impairments, epilepsy and language processing. I am directly involved in mechanistic research projects related to epilepsy or aphasia. My research is also related to clinical trials for aphasia treatments.

In epilepsy research, I am the corresponding MPI on an R01 project to assess connectome markers of epilepsy surgical outcomes. Related to language and aphasia, I am the PI for an NIDCD supported R01 project on biomarkers of aphasia recovery using the brain connectome. I am the corresponding MPI for the phase II clinical trial speech entrainment for aphasia recovery (SpARc), and the PI of a core project related to brain health and aphasia recovery, as part of the ongoing P50 Center for the Study of Aphasia Recovery (C- STAR, PI Fridriksson).

Recommended Article:

• Bonilha, L., Rorden, C., Castellano, G., Pereira, F., Rio, P. A., Cendes, F., & Li, L. M. (2004). Voxel-based morphometry reveals gray matter network atrophy in refractory medial temporal lobe epilepsy. Archives of neurology, 61(9), 1379-1384. Link to Paper
• Gleichgerrcht, E., Munsell, B., Bhatia, S., Vandergrift III, W. A., Rorden, C., McDonald, C., ... & Bonilha, L. (2018). Deep learning applied to whole‐brain connectome to determine seizure control after epilepsy surgery. Epilepsia, 59(9), 1643-1654. Link to Paper
• Gleichgerrcht, E., Munsell, B., Keller, S. S., Drane, D. L., Jensen, J. H., Spampinato, M. V., ... & Bonilha, L. (2022). Radiological identification of temporal lobe epilepsy using artificial intelligence: a feasibility study. Brain communications, 4(2), fcab284. Link to Paper
• Gleichgerrcht, E., Munsell, B. C., Alhusaini, S., Alvim, M. K., Bargalló, N., Bender, B., ... & Wiest, R. (2021). Artificial intelligence for classification of temporal lobe epilepsy with ROI-level MRI data: A worldwide ENIGMA-Epilepsy study. NeuroImage: Clinical, 31, 102765. Link to Paper

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