Synchrony surfacing: epicortical recording of correlated action potentials

Abstract

Synchronous spiking of multiple neurons is a key phenomenon in normal brain function and pathologies. Recently, approaches to record spikes from the intact cortical surface using small high-density arrays of microelectrodes have been reported. It remained unaddressed how epicortical spiking relates to intracortical unit activity. We introduce a mesoscale approach using an array of 64 electrodes with intermediate diameter (250 {micro}m) and combined large-coverage epicortical recordings in ferrets with intracortical recordings via laminar probes. Empirical data and modeling strongly suggest that our epicortical electrodes selectively captured synchronized spiking of neurons in the subjacent cortex. As a result, responses to sensory stimulation were more robust and less noisy as compared to intracortical activity, and receptive field properties were well preserved in epicortical recordings. This should promote insights into assembly-coding beyond the informative value of subdural EEG or single-unit spiking, and be advantageous to real-time applications in brain-machine interfacing.\n\nSignificance statementElectrocorticography allows chronic, low-noise recordings from the intact cortical surface - a prerequisite for investigations into brain network dynamics and brain-machine interfaces. Novel electrodes can capture spiking activity at the surface, which should boost precision in the spatial - and time domain, compared to conventional EEG-like measurements. To clarify how surface spiking relates to intracortically fired action potentials, we recorded both types of signal simultaneously from sensory cortices in anesthetized ferrets. Results suggest that mesoscale (250 {micro}m) surface electrodes can selectively capture synchronized spiking from nearby cortical columns, which reduces contamination by non-representative, jittering spikes. Given the high relevance of neural synchrony for sensorimotor and cognitive processing, the novel methodology may improve signal decoding in brain-machine interface approaches.\n\nAuthor contributionsE.G.L., T.B. and A.K.E. conceptualized the research; E.G.L. and F.P. performed experiments; T.B. and E.G.L. wrote Matlab routines for data analysis; T.B. and E.G.L. analyzed the data; T.S. provided technical resources; T.B., E.G.L. and A.K.E. wrote the manuscript; G.E. administrated the project; A.K.E. acquired funding.