Sleep disrupts complex spiking dynamics in the neocortex and hippocampus

Open Access via the PLOS Agreement Acknowledgements J.G acknowledges the support of Comisi´on Acad´emica de Posgrado (CAP), CSIC Iniciaci´on and PEDECIBA. P.T also acknowledges the support of PEDECIBA. A.B.L.T acknowledges the support of CAPES and CNPq. N.R. acknowledges the CSIC group grant “CSIC2018 - FID 13 - Grupo ID 722Peer reviewedPublisher PD

Decreased electrocortical temporal complexity distinguishes sleep from wakefulness

This study was supported by the “Programa de Desarrollo de Ciencias Básicas”, PEDECIBA; Agencia Nacional de investigación e innovación (ANII), (FCE_1_2017_1_136550) and the “Comisión Sectorial de Investigación Científica” (CSIC) I + D - 2016 - 589 grant from Uruguay. N.R. acknowledges the CSIC group grant “CSIC2018 - FID 13 - Grupo ID 722”.Peer reviewedPublisher PD

Low frequency oscillations drive EEG’s complexity changes during wakefulness and sleep

ACKNOWLEDGEMENT J.G. acknowledges the support of Comisio´n Acade´mica de Posgrado (CAP), CSIC Iniciacio´n and PEDECIBA. P. T. and N.R. also acknowledges the support of PEDECIBAPeer reviewedPublisher PD

RQA differences between states correlate with the number of neurons recorded.

Absolute RQA differences between states as a function of the number of simultaneously recorded neurons. Each dot shows a recording session while the solid line the linear regression estimate with its 95% confidence interval. A shows the SWS-Wake difference, while B the SWS-REM difference. (TIF)</p

Critical branching model for neuronal activity during Wake and SWS.

A Left: Population activity (raster plot) and synthetically generated local-field potential (sLFP, as in Fig 4) of a critical branching model with 50 interacting units. The branching parameter is set at σ = 1 (critical); an excitatory Poisson noise drive each unit independently. Right: DOWN states are generated by periodically silencing (4Hz) the noisy drive of a percentage of units. B Resultant recurrence plots for the data in A. C Average (± standard deviation) results from 100 simulations using different network connectivity and initial conditions. Each simulation consisted of 106 iterations in time. Top left: RQA metrics for the original model (i.e., without silencing) as function of σ; shaded [unshaded] area shows the sub-critical [super-critical] phase. Remaining panels: differences (Δ) between RQA metrics of the original model and the model with periodical silencing as a function of the percentage of neurons having their noise drive silenced (referred to as % of neurons in DOWN state). The horizontal dashed lines show the difference between the actual SWS RQA metrics (Fig 2C) and those of the critical branching model.</p

Power spectrum slope differs among states.

A LFP [ECoG] recordings coming from the frontal cortex [M1 cortex] during the states of Wake, SWS and REM sleep. The mean and its corresponding 95% confidence intervals are shown for each plot. B Power spectrum exponents calculated through ordinary least-squares fit on a log-log scale (OLS) or through the FOOOF parametrized spectra (FOOOF) [78] which only includes the aperiodic component. (TIF)</p

Construction and analysis of synthetic local field potentials (sLFP) during Wake, SWS, and REM sleep.

A The sLFP is defined as the average of the convolutions between spike trains and a decaying exponential function. B Examples of sLFP resulting from Wake, SWS, and REM sleep population activity. C sLFP power spectra(top) and coherence between sLFP and LFP (bottom) for the different sleep-wake states (colour coded). D Boxplots of Sample Entropy (top), Permutation Entropy (middle), and Lempel-Ziv Complexity (bottom) of the sLFPs and LFPs in each state (N = 24 sessions). *p p p < 0.001.</p

Recurrence example of population activity.

A Left Example of spike trains for 3 neurons (N1-N3). The continuous line on top shows the firing counts of each spike train. Right Resultant phase-space trajectory (evolution), where the axes represent the firing counts of each neuron. For every pair of points in the trajectory, their distance (d) is computed (the dashed lines illustrate two such distances); If the distance is less than a predefined ϵ value, a recurrence between the time points is defined to occur. Two recurrent times are shown in red (ti,tj), while two non-recurrent times are shown in blue (tk,tl). B Recurrence plot for the trajectory shown in panel A. Red and blue time pairs are now depicted as coordinates in the resulting map. C Example recurrence plots from periodic, random, and chaotic trajectories. On top of the recurrence plot, we show the phase-space of the example; below we illustrate how the RQA metrics behave for each trajectory type. RR: Recurrence Rate, DET: Determinism, LAM: Laminarity, TT: Trapping Time, DIV: Divergence.</p

Persistent Homology cannot distinguish the sleep-wake states in the neocortex.

Top panels: Point clouds obtained after dimensionality reduction. A representative animal is shown during Wake, SWS and REM sleep. Bottom panels: Betti 0 (HO) and Betti 1 (H1) barcodes for the same animal shown in the top panel. The length of each bar shows the level of persistence of each Betti 0 and 1 component. (TIF)</p

UP state recurrences are similar to Wake or REM sleep.

A Recurrence plots constructed from a 10s interval of the population activity using. B 5 RQA metrics for the sleep-wake states; boxplots show results from the pool of 24 sessions across 12 animals (outliers are not shown). (TIF)</p