Spatiotemporal dynamics of neocortical excitation and inhibition during human sleep

Abstract

Intracranial recording is an important diagnostic method routinely used in a number of neurological monitoring scenarios. In recent years, advancements in such recordings have been extended to include unit activity of an ensemble of neurons. However, a detailed functional characterization of excitatory and inhibitory cells has not been attempted in human neocortex, particularly during the sleep state. Here, we report that such feature discrimination is possible from high-density recordings in the neocortex by using 2D multielectrode arrays. Successful separation of regular-spiking neurons (or bursting cells) from fast-spiking cells resulted in well-defined clusters that each showed unique intrinsic firing properties. The high density of the array, which allowed recording from a large number of cells (up to 90), helped us to identify apparent monosynaptic connections, confirming the excitatory and inhibitory nature of regular-spiking and fast-spiking cells, thus categorized as putative pyramidal cells and interneurons, respectively. Finally, we investigated the dynamics of correlations within each class. A marked exponential decay with distance was observed in the case of excitatory but not for inhibitory cells. Although the amplitude of that decline depended on the timescale at which the correlations were computed, the spatial constant did not. Furthermore, this spatial constant is compatible with the typical size of human columnar organization. These findings provide a detailed characterization of neuronal activity, functional connectivity at the microcircuit level, and the interplay of excitation and inhibition in the human neocortex.spontaneous activity | ensemble recordings | single unit | functional dynamics F rom columnar microcircuits (1-3) to higher-order neuronal functional units, neocortical dynamics are characterized by a large range of spatial and temporal scales (4, 5). Recent technical improvements have allowed the nature of these dynamics in the human brain to be directly explored: Single-neuron activity in conjunction with local field potentials (LFPs) can be detected from the cerebral cortex and hippocampus in the course of intense monitoring of brain activity before surgical treatment of epileptic foci (6). Modern electrode systems provide the possibility of extracellular recordings of neuronal ensembles by using either microwires (7) or high-density microelectrode arrays (8,9). Prior efforts have demonstrated excellent recordings of single-neuron activity in human cerebral cortex (10-12).Separation of units between "regular-spiking" (RS) and "fastspiking" (FS) neurons, presumably excitatory (pyramidal) and inhibitory (interneuron) cells, respectively, is commonly practiced in animal experiments. In the neocortex of various mammalian species, RS and FS cells can be reliably separated based on spike waveform, duration, and firing rates (13,14). Similar criteria were also used to successfully separate units into putative pyramidal (Pyr) cells and inhibitory interneurons (Int) in human hippocampus...

Intracranial neuronal ensemble recordings and analysis in epilepsy

“…Thalamocortical unit activity underlies the generation of slow oscillation, one of the most important brain processes participating in generation of sleep (Crunelli et al, 2015). Slow wave activity in humans showed alternating neuronal excitation and inhibition patterns identified previously in animal models as upstates and downstates Csercsa et al, 2010;Nobili et al, 2012;Peyrache et al, 2012;Staba, C. L. Wilson, Bragin, Fried and Engel, 2002a). …”

Composing Music from Neuronal Activity: The Spikiss Project

“…The particularity of these recordings is double: First, they provide unit recordings of single neurons, which is a rare opportunity to access single neuron activity in humans and in different brain states. But the most original aspect of these data is the fact that we could separate excitatory from inhibitory neurons, and that this separation was confirmed by direct neuron-to-neuron interactions (Peyrache et al 2012). This type of data is very interesting and powerful to generate music, as it appears that inhibitory neurons are remarkably rhythmic and this rhythm is fundamental for this kind of musical composition.…”

“…Here, a dataset of 92 neurons was used-these neurons were simultaneously recorded in the temporal cortex of human subjects, and the main originality of these data is that excitatory and inhibitory neurons could be discriminated (Peyrache et al 2012;Dehghani et al 2016). This allowed us to create a musical sequence with two instruments, a woodblock for excitatory neurons and a xylophone for inhibitory cells.…”

“…In the present chapter, we illustrate a different approach, based on the same data (Peyrache et al 2012;Dehghani et al 2016; see Fig. 1).…”

Pairwise Ising Model Analysis of Human Cortical Neuron Recordings

“…For the latter, the pairwise model misses relevant transients of high network activity, suggesting that additional constraints are necessary to accurately model the data. Keywords: Ising model, maximum entropy principle, natural gradient, human temporal cortex, multielectrode array recording, brain states Advances in experimental techniques have recently enabled the recording of the activity of tens to hundreds of neurons simultaneously [2] and has spurred the interest in modeling their collective behavior [3,4,5,6,7,8,9]. To this purpose, the pairwise Ising model has been introduced as the maximum entropy (most generic [10]) model able to reproduce the first and second empirical moments of the recorded neurons.…”