Automatic methods for cortex-wide layer identification of electrophysiological signals reveals a cortical motif for the expression of neuronal rhythms
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0000670.pdf
Description
Published version
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744.93 KB
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Adobe PDF
Checksum (MD5)
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Author(s)
Bastos, Andre M
Costilla-Reyes, Omar
Miller, Earl K
Date Issued
December 2019
Journal
2019 Conference on Cognitive Computational Neuroscience
Publisher
Cognitive Computational Neuroscience
Citation
Bastos, Andre M et al. "Automatic methods for cortex-wide layer identification of electrophysiological signals reveals a cortical motif for the expression of neuronal rhythms." 2019 Conference on Cognitive Computational Neuroscience, September 2019, Berlin, Germany, Cognitive Computational Neuroscience, December 2019.
Version
Final published version
Abstract
Cerebral cortex is composed of 6 anatomical layers. How these layers contribute to computations that give rise to cognition remains a challenge in neuroscience. Part of this challenge is to reliably identify laminar markers from in-vivo neurophysiological data. Classic methods for laminar identification are based on assumptions which are often violated and require expert users to identify the pattern, potentially introducing bias. We recorded local field potentials (LFP) with probes containing 16 or 32 electrodes that span all cortical layers in frontal, parietal, and visual cortex in monkeys. We describe two novel methods to identify layers in a fully automatic and quantitative way. The first method represents relative power across electrodes from as a 2-dimensional image, and maximizes image similarity across probes. The second method leverages ensemble machine learning to maximize classification accuracy of LFP data to a laminar label. Both methods detect consistent patterns, and the image similarity approach reveals a cortex-wide motif of laminar expression for delta/theta, alpha/beta and gamma rhythms. Delta/theta (1-4 Hz) and gamma (50-150 Hz) power peak in superficial layers 2/3, and alpha/beta (10-30 Hz) power peaks in deep layers 5/6.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution 3.0 unported license
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DOI of Published Version
https://doi.org/10.32470/ccn.2019.1117-0
