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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.