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Multivariate regression methods for estimating velocity of ictal discharges from human microelectrode recordings

Liou, Jyun-you and Smith, Elliot H. and Bateman, Lisa M. and McKhann, Guy M., II and Goodman, Robert R. and Greger, Bradley and Davis, Tyler S. and Kellis, Spencer S. and House, Paul A. and Schevon, Catherine A. (2017) Multivariate regression methods for estimating velocity of ictal discharges from human microelectrode recordings. Journal of Neural Engineering, 14 (4). Art. No. 044001. ISSN 1741-2560. http://resolver.caltech.edu/CaltechAUTHORS:20170327-134405082

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Abstract

Objective. Epileptiform discharges, an electrophysiological hallmark of seizures, can propagate across cortical tissue in a manner similar to traveling waves. Recent work has focused attention on the origination and propagation patterns of these discharges, yielding important clues to their source location and mechanism of travel. However, systematic studies of methods for measuring propagation are lacking. Approach. We analyzed epileptiform discharges in microelectrode array recordings of human seizures. The array records multiunit activity and local field potentials at 400-micron spatial resolution, from a small cortical site free of obstructions. We evaluated several computationally efficient statistical methods for calculating traveling wave velocity, benchmarking them to analyses of associated neuronal burst firing. Main results. Over 90% of discharges met statistical criteria for propagation across the sampled cortical territory. Detection rate, direction and speed estimates derived from a multiunit estimator were compared to four field potential-based estimators: negative peak, maximum descent, high gamma power, and cross-correlation. Interestingly, the methods that were computationally simplest and most efficient (negative peak and maximal descent) offer non-inferior results in predicting neuronal traveling wave velocities compared to the other two, more complex methods. Moreover, the negative peak and maximal descent methods proved to be more robust against reduced spatial sampling challenges. Using least absolute deviation in place of least squares error minimized the impact of outliers, and reduced the discrepancies between local field potential-based and multiunit estimators. Significance. Our findings suggest that ictal epileptiform discharges typically take the form of exceptionally strong, rapidly traveling waves, with propagation detectable across millimeter distances. The sequential activation of neurons in space can be inferred from clinically-observable EEG data, with a variety of straightforward computation methods available. This opens possibilities for systematic assessments of ictal discharge propagation in clinical and research settings.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1088/1741-2552/aa68a6DOIArticle
http://iopscience.iop.org/article/10.1088/1741-2552/aa68a6PublisherArticle
Additional Information:© 2017 IOP Publishing Ltd. Received 18 July 2016; Revised 21 March 2017; Accepted 23 March 2017; Accepted Manuscript online 23 March 2017. Published 13 June 2017.
Record Number:CaltechAUTHORS:20170327-134405082
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20170327-134405082
Official Citation:Jyun-you Liou et al 2017 J. Neural Eng. 14 044001
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:75425
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:27 Mar 2017 22:31
Last Modified:13 Jun 2017 17:54

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