Sloan-Swartz Meeting 2008
Princeton, New Jersey
Scott Makeig , Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA
Multiscale electrophysiology: Modeling large-scale and neural-scale dynamics in multi-resolution human data.
Brain dynamics are inherently multiscale, their operant structures at different spatial scales (e.g., cortical regions, columns, neurons, synapses, molecules) having different dynamics and connectivity structure. Currently, the most pressing questions in brain dynamics concern coordination of these dynamics at multiple space and time scales, a subject long neglected during the experimental era dominated by single microelectrode data recording and analysis. Paradoxically or not, imaging modalities that view brain activity from the greatest distance (e.g. from the scalp) are also the most suited to studying distributed dynamics. However, scalp EEG (or MEG) can only see the far-field projections of locally synchronized field activity across (largely) cortical domains of currently unknown size and spatiotemporal dynamics. Ideal measures of cortical field dynamics, therefore, need to be multi-resolution.
A unique window of opportunity into human brain dynamics is afforded by the current clinical practice of invasive monitoring of cortical (and/or sub-cortical) activities in subjects with complex cases of intractable epilepsy for the purpose of planning remediative brain surgery. In collaboration with Dr. Greg Worrell (MD, PhD) at the Mayo Clinic (Rochester, MN), I and colleagues at the Swartz Center have been analyzing first multi-resolution data from Dr. Worrell's practice. These consist of up to 30 scalp EEG electrodes recorded synchronously with electrocorticographic (ECOG) grids or strips of 1-cm spaced subdural electrodes. Recently, Dr. Worrell has begun to intersperse among these additional 40-um wire tip electrodes, and has used the resultant field recordings to uncover a new human epileptic phenomenon -- microseizures.
Adequate joint analysis of simultaneous recordings at these three and optimally still more intervening spatial scales is not simple, however. I and Greg Worrell, Zeynep Akalin Acar (UCSD), Maxim Bazhenov (UC Riverside), and Tanya Baker (Salk) are beginning an ambitious multiscale modeling project to address more comprehensively the problem of multiscale brain dynamic modeling . I argue this must involve no less than a seven-layer data collection and analysis model, which I will describe. Then Zeynep and Maxim will discuss the modeling problem from the 'field-data-down' and 'neural-model-up' perspectives.
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