Identifying and Surgically Removing Brain Network Abnormalities in Focal Epilepsy
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Bio
“Identifying and Surgically Removing Brain Network Abnormalities in Focal Epilepsy”
Dr Peter Neal Taylor trained in computer science and software engineering before doing a master degree in computational molecular biology in Manchester. He completed his PhD in 2013, studying dynamical systems theory and computational modelling in epilepsy with Gerold Baier. He has since undertaken postdoctoral work at NTU Singapore, MGH Boston, and in Newcastle before starting a faculty fellowship in Newcastle in 2015. He began a lectureship (equivalent to Asst. Prof) in January 2018. Since 2021 Dr Taylor has held an open-ended faculty position and has been externally funded by a ~£1m UKRI Future Leaders Fellowship.
Dr Taylor’s research is focused on using computational methods to understand brain function and in the case of disease, brain dysfunction. He uses methods from mathematical modelling, dynamical systems and graph theory to investigate complex brain network dynamics. He uses functional data such as EEG, MEG and fMRI in addition to structural data derived from MRI and diffusion MRI. Primarily he works on epilepsy and traumatic brain injury.
He is based in the School of Computing at Newcastle University, UK. Within Computing he co-leads the Computational Neurology Neuroscience and Psychiatry Lab (www.cnnp-lab.com). Dr Taylor also holds honorary positions at University College London, and University College London Hospital NHS Foundation Trust.
To join the live event please request the link by emailing: icm@jhu.edu
RecordingAbstract
“Identifying and Surgically Removing Brain Network Abnormalities in Focal Epilepsy”
Surgery can be highly effective in achieving full seizure control in patients with medically refractory epilepsy. Successful surgery is likely predicated on accurate identification and removal of the epileptogenic tissue. This identification is typically performed using qualitative interpretations from a range of neuroimaging modalities such as EEG, MEG and MRI. In this talk I will present recent and unpublished work from my lab using advanced computational methods to quantitively identify abnormalities in patient brain networks. I will discuss how these methods could be incorporated into a clinical setting to improve outcomes for patients with epilepsy.
