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  Generalized phase locking analysis of electrophysiology data

Besserve, M., Safavi, S., Kapoor, V., Panagiotaropoulos, T., & Logothetis, N. (2019). Generalized phase locking analysis of electrophysiology data. Poster presented at Computational and Systems Neuroscience Meeting (COSYNE 2019), Lisboa, Portugal.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0003-2057-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-2059-5
Genre: Poster

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 Creators:
Besserve, M1, 2, Author              
Safavi, S, Author              
Kapoor, V1, 2, Author              
Panagiotaropoulos, TI1, 2, Author              
Logothetis, NK1, 2, Author              
Affiliations:
1Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 Abstract: Brain information processing likely relies on cooperative interactions between neural populations at multiple scales. Growing evidence suggests that network oscillations, as observed in Local Field Potentials (LFP), are instrumental to the spatiotemporal coordination of these interactions. Therefore, investigating the coupling between spatiotemporal patterns of LFP and spiking activity is instrumental to understand distributed neural information processing. Common approaches to investigate this coupling are restricted to pairwise spike-LFP interactions, which are suboptimal for modern datasets with hundreds of simultaneous recording sites. Capturing efficiently the overall spike-LFP coupling structure in this high dimensional setting is of paramount importance to exploit the full potential of modern electrophysiology recording techniques. We develop a Generalized Phase Locking Analysis (GPLA), a multivariate extension of phase locking analysis, by gathering pairwise complex phase locking information in a rectangular matrix and summarize its structure with the largest singular value and the corresponding singular vectors. Singular vectors represent the dominant LFP and spiking patterns and the singular value, called generalized Phase Locking Value (gPLV), characterizes the strength of the coupling between LFP and spike patterns. We further investigate statistical properties of the gPLV and develop a statistical testing framework. Compared to pairwise approaches, simulations with networks of Leaky Integrate and Fire (LIF) neurons show that GPLA: (1) can reliably retrieve the coupling between spikes and LFP with lesser amount of data and (2) exploits optimally the activity of multiple units to increase the statistical power while preserving individual coupling properties. Application to recordings from Utah arrays in macaque prefrontal cortex reveals a previously undetected large-scale coupling through an LFP traveling wave in the beta band synchronized with an array-wide synchronous spiking event. These results illustrate the interest of GPLA to assess global relationships between spatiotemporal patterns of spikes and network oscillations.

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 Dates: 2019-03
 Publication Status: Published online
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Title: Computational and Systems Neuroscience Meeting (COSYNE 2019)
Place of Event: Lisboa, Portugal
Start-/End Date: 2019-02-18 - 2019-03-03

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Title: Computational and Systems Neuroscience Meeting (COSYNE 2019)
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: III-3 Start / End Page: 184 - 185 Identifier: -