Spike-Field Granger Causality for Hybrid Neural Data Analysis.

Neurotechnological innovations allow for the simultaneous recording at various scales, ranging from spiking activity of individual neurons to large neural population's local field potentials (LFPs). This capability necessitates developing multiscale analysis of spike-field activity. A joint analysis of the hybrid neural data is crucial for bridging the scales between single neurons and local network. Granger causality is a fundamental measure to evaluate directional influences among neural signals. However, it is mainly limited to inferring causal influence between the same type of signals-either LFPs or spike trains-but not well-developed between two different signal types. Here we propose a model-free, nonparametric spike-field Granger causality measure for hybrid data analysis. Our measure is distinct from the existing methods in that we use the precise spike timing rather than the spike counts. The latter clearly distorts the information in the mixed analysis of spikes and LFP. Our measure is validated by an extensive set of simulated data. When applied to LFPs and spiking activity simultaneously recorded from visual areas V1 and V4 of monkeys performing a contour detection task, we are able to confirm computationally the long-standing experimental finding of the input-output relationship between LFPs and spikes. Importantly, we demonstrate that spike-field Granger causality can be used to reveal the modulatory effects that are inaccessible by traditional methods, such that the spike->LFP Granger causality is modulated by the behavioral task, whereas LFP->spike Granger causality is mainly related to the average synaptic input.

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