Speakers: Dora Hermes & Kai Miller
Title: Basis profile curve identification to understand the effects of electrical stimulation on human brain networks.
Bios: Dr. Hermes did her PhD at the UMC Utrecht in the Netherlands where she worked on integrating functional MRI with intracranial EEG measurements. During her postdoc, she worked with Jonathan Winawer (NYU) and Brian Wandell (Stanford) and received a prestigious Dutch Veni-fellowship to study the relation between visual input and neural oscillations in health and disease. She is now an assistant professor at the Department of Physiology and Biomedical Engineering at the Mayo Clinic in Rochester where she leads the Multimodal Neuroimaging Laboratory to study human brain signals in order to identify biomarkers of neurological and neuropsychiatric diseases and develop neuroprosthetics to interface with the brain.
Dr. Miller attended the University of Washington for graduate school, obtaining a PhD in Physics, an MD, and a second PhD in Neuroscience. After completing his neurosurgery residency at Stanford University, he completed fellowships in epilepsy, deep-brain stimulation, and tumor resection in children and adults. Dr. Miller joined the neurosurgery staff at Mayo Clinic in Rochester in 2019 where he practices pediatric and functional neurosurgery. Dr. Miller studies basic human neurophysiology and clinical translation for cybernetics, epilepsy and functional neurosurgery. His group, the Cybernetics and Motor Physiology Laboratory, is focused on the creation of new devices to 1) control cybernetic prostheses, 2) induce brain plasticity after injury, and 3) intervene with distributed circuits in neuropsychiatric disease and movement dysfunction.
Many different inputs converge in single brain regions. To understand and distinguish different cortico-cortical inputs, we share human intracranial EEG (iEEG) data while many different pairs of electrodes were stimulated with single brief electrical pulses. We developed a convergent paradigm to study the brain dynamics elicited by electrical stimulation, focusing on a single brain site to observe the effect of stimulating each of many other brain sites. We will demonstrate how these iEEG data are organized according to the Brain Imaging Data Structure (BIDS) and when viewing these data in the convergent paradigm, visually apparent motifs in the temporal response shape emerge from groups of stimulation sites. To extract these response shapes, we present a new machine learning approach to determine the spatiotemporal structure of the CCEPs, summarized as a set of unique “basis profile curves” (BPCs). Each BPC may be mapped back to underlying anatomy in a natural way, quantifying projection strength from each stimulation site using simple metrics. This interactive implementation of this method allows us to discover the shape of the responses in time from the data, revealing new interactions beyond those previously described in the human connectome.