Criticality of neuronal avalanches in human sleep and their relationship with sleep macro- and micro-architecture

Altered neural avalanche spreading in people with drug-resistant epilepsy✰

OBJECTIVE

To characterize a peculiar "EEG endophenotype" of drug-resistant epilepsy (DRE) through the graph theory characterization of avalanche spatiotemporal spreading properties.

METHODS

We performed avalanche analysis and computed avalanche transition matrices (ATMs) on 19-channel scalp EEG of 120 people with epilepsy (60 DRE and 60 non-DRE) who assumed two anti-seizure medications, comparing such results with a group of 40 healthy subjects (HS). Network topologies of ATMs were characterized through graph theory metrics. We performed an analysis of variance to compare aperiodic metrics between HS, DRE and non-DRE. Logistic regression was performed to test and compare the ability of graph theory metrics on ATM and clinical features to correctly discriminate the PwE group according to the clinical outcome (DRE or non-DRE).

RESULTS

DRE exhibited a peculiar altered avalanche spreading as proved by the higher mean betweenness centrality, the longer characteristic path length and the lower small-world index (more regular and less plastic network topology) of ATMs than non-DRE and HS (p-values from <0.001 to 0.05). Graph metrics on ATMs significantly improved the yield of detecting DRE and contributed the most to the model accuracy (0.83) than clinical features. Resting-state EEG activity of HS and PwE did not deviate from the characteristics of a system operating at criticality.

CONCLUSIONS

ATMs detect alterations of resting-state networks peculiar to the DRE condition.

SIGNIFICANCE

These findings could open new scenarios for the future identification of promising biomarkers of DRE through scalp EEG.

The futuristic manifolds of REM sleep

Since one of its first descriptions 70 years ago, rapid eye movement sleep has continually inspired and excited new generations of sleep researchers. Despite significant advancements in understanding its neurocircuitry, underlying mechanisms and microstates, many questions regarding its function, especially beyond the early neurodevelopment, remain unanswered. This opinion review delves into some of the unresolved issues in rapid eye movement sleep research, highlighting the ongoing need for comprehensive exploration in this fascinating field.

Sleep microstructure in patients with systemic sclerosis: A cyclic alternating pattern analysis study

The cyclic alternating pattern (CAP) is a cyclic variation of sleep electroencephalogram activity within non-rapid eye movement (non-REM) sleep and increased CAP is the marker of sleep instability. The present study aimed to examine the expression of cyclic alternating pattern in non-REM sleep in patients with systemic sclerosis (SSc) and to compare these findings with the control group. Thirty-one patients with SSc were compared with matched controls by age, gender, body mass index, and apnea-hypopnea index (AHI). Sleep measurements were obtained by standard polysomnography with conventional sleep scoring. A CAP analysis was performed and verified by certified somnologists. The sleep parameters of the SSc and control groups were similar for sleep efficiency, sleep stage percents N1, N2, N3, sleep latency, and minimum oxygen saturation (p = 0.627, p = 0.693, p = 0.530, p = 0.736, p = 0.772, and p = 0.693, respectively). The SSc patients presented a higher arousal and periodic limb movement index. The CAP analyses showed that SSc patients had higher CAP numbers, phase A2 index, A3 duration, and lower A1 duration (p = 0.032, p = 0.016, p = 0.016, and p = 0.016, respectively). Overall the phase A2 index, A2 total time during non-REM sleep were significantly higher and the A1 duration was significantly lower in SSc patients, although sleep efficiency and the distribution of sleep macro-structure seem to be similar to the control group. This study emphasises that microstructure analysis performed with CAP provides information that would otherwise be lost if only macrostructure analysis were considered.

Causes of Sleep Disturbance in Early ASAS Spondyloarthritis: A Retrospective Long-Term Experience

Introduction: Sleep disturbance (SD) in the second half of the night due to inflammatory pain was included in the 2009 ASAS classification criteria of Spondyloarthritis (SpA), even though its definition is uncertain. Aim: We aimed to investigate SD in early-SpA (e-SpA) patients at T1 (2010-2013), comparing them to long-term SpA (l-SpA) patients at T2 (2023-2024) after at least 10 years of follow-up. Methods: At T1, in e-SpA and l-SpA cases, SD, classified as "difficulty in initiating sleep" (DIS), "difficulty in maintaining sleep" (DMS) and "early awakening" (EA), was compared to clinical parameters (ASDAS-CRP, BASDAI, m-HAQ-S, BASMI, MASES, 68/66 joint count, tenderness of sacroiliac joints, fatigue [FACIT] and HADS for anxiety [A] and depression [D]). At T2, e-SpA patients were re-evaluated using the Pittsburgh Sleep Quality Index (PSQI). Results: At T1, 45% of 166 SpA patients had SD; in e-SpA patients (60), SD correlated with sacroiliac pain (DMS) BASDAI, FACIT and HADS-D (EA); in l-SpA patients (106), it correlated with HADS-A (DIS), BASDAI and FACIT (DMS). At T2, e-SpA patients showed a high PSQI in 51.5% of cases, correlated with T2-ASDAS-CRP and T2-BASDAI. Moreover, T1-ASDAS-CRP was predictive of T2-PSQI. Conclusions: SD is more specific for inflammatory pain in e-SpA and might be influenced by disease activity also in long-term disease.

A predictive propensity measure to enter REM sleep

Introduction

During sleep periods, most mammals alternate multiple times between rapid-eye-movement (REM) sleep and non-REM (NREM) sleep. A common theory proposes that these transitions are governed by an "hourglass-like" homeostatic need to enter REM sleep that accumulates during the inter-REM interval and partially discharges during REM sleep. However, markers or mechanisms for REM homeostatic pressure remain undetermined. Recently, an analysis of sleep in mice demonstrated that the cumulative distribution function (CDF) of the amount of NREM sleep between REM bouts correlates with REM bout duration, suggesting that time in NREM sleep influences REM sleep need. Here, we build on those results and construct a predictive measure for the propensity to enter REM sleep as a function of time in NREM sleep since the previous REM episode.

Methods

The REM propensity measure is precisely defined as the probability to enter REM sleep before the accumulation of an additional pre-specified amount of NREM sleep.

Results

Analyzing spontaneous sleep in mice, we find that, as NREM sleep accumulates between REM bouts, the REM propensity exhibits a peak value and then decays to zero with further NREM accumulation. We show that the REM propensity at REM onset predicts features of the subsequent REM bout under certain conditions. Specifically, during the light phase and for REM propensities occurring before the peak in propensity, the REM propensity at REM onset is correlated with REM bout duration, and with the probability of the occurrence of a short REM cycle called a sequential REM cycle. Further, we also find that proportionally more REM sleep occurs during sequential REM cycles, supporting a correlation between high values of our REM propensity measure and high REM sleep need.

Discussion

These results support the theory that a homeostatic need to enter REM sleep accrues during NREM sleep, but only for a limited range of NREM sleep accumulation.

Role of the Locus Coeruleus Arousal Promoting Neurons in Maintaining Brain Criticality across the Sleep-Wake Cycle

Sleep control depends on a delicate interplay among brain regions. This generates a complex temporal architecture with numerous sleep-stage transitions and intermittent fluctuations to micro-states and brief arousals. These temporal dynamics exhibit hallmarks of criticality, suggesting that tuning to criticality is essential for spontaneous sleep-stage and arousal transitions. However, how the brain maintains criticality remains not understood. Here, we investigate θ- and δ-burst dynamics during the sleep-wake cycle of rats (Sprague-Dawley, adult male) with lesion in the wake-promoting locus coeruleus (LC). We show that, in control rats, θ- and δ-bursts exhibit power-law (θ-bursts, active phase) and exponential-like (δ-bursts, quiescent phase) duration distributions, as well as power-law long-range temporal correlations (LRTCs)-typical of non-equilibrium systems self-organizing at criticality. Furthermore, consecutive θ- and δ-bursts durations are characterized by anti-correlated coupling, indicating a new class of self-organized criticality that emerges from underlying feedback between neuronal populations and brain areas involved in generating arousals and sleep states. In contrast, we uncover that LC lesion leads to alteration of θ- and δ-burst critical features, with change in duration distributions and correlation properties, and increase in θ-δ coupling. Notably, these LC-lesion effects are opposite to those observed for lesions in the sleep-promoting ventrolateral preoptic (VLPO) nucleus. Our findings indicate that critical dynamics of θ- and δ-bursts arise from a balanced interplay of LC and VLPO, which maintains brain tuning to criticality across the sleep-wake cycle-a non-equilibrium behavior in sleep micro-architecture at short timescales that coexists with large-scale sleep-wake homeostasis.

The recovery of parabolic avalanches in spatially subsampled neuronal networks at criticality

Scaling relationships are key in characterizing complex systems at criticality. In the brain, they are evident in neuronal avalanches-scale-invariant cascades of neuronal activity quantified by power laws. Avalanches manifest at the cellular level as cascades of neuronal groups that fire action potentials simultaneously. Such spatiotemporal synchronization is vital to theories on brain function yet avalanche synchronization is often underestimated when only a fraction of neurons is observed. Here, we investigate biases from fractional sampling within a balanced network of excitatory and inhibitory neurons with all-to-all connectivity and critical branching process dynamics. We focus on how mean avalanche size scales with avalanche duration. For parabolic avalanches, this scaling is quadratic, quantified by the scaling exponent, χ = 2, reflecting rapid spatial expansion of simultaneous neuronal firing over short durations. However, in networks sampled fractionally, χ is significantly lower. We demonstrate that applying temporal coarse-graining and increasing a minimum threshold for coincident firing restores χ = 2, even when as few as 0.1% of neurons are sampled. This correction crucially depends on the network being critical and fails for near sub- and supercritical branching dynamics. Using cellular 2-photon imaging, our approach robustly identifies χ = 2 over a wide parameter regime in ongoing neuronal activity from frontal cortex of awake mice. In contrast, the common 'crackling noise' approach fails to determine χ under similar sampling conditions at criticality. Our findings overcome scaling bias from fractional sampling and demonstrate rapid, spatiotemporal synchronization of neuronal assemblies consistent with scale-invariant, parabolic avalanches at criticality.

Criticality and partial synchronization analysis in Wilson-Cowan and Jansen-Rit neural mass models

Synchronization is a phenomenon observed in neuronal networks involved in diverse brain activities. Neural mass models such as Wilson-Cowan (WC) and Jansen-Rit (JR) manifest synchronized states. Despite extensive research on these models over the past several decades, their potential of manifesting second-order phase transitions (SOPT) and criticality has not been sufficiently acknowledged. In this study, two networks of coupled WC and JR nodes with small-world topologies were constructed and Kuramoto order parameter (KOP) was used to quantify the amount of synchronization. In addition, we investigated the presence of SOPT using the synchronization coefficient of variation. Both networks reached high synchrony by changing the coupling weight between their nodes. Moreover, they exhibited abrupt changes in the synchronization at certain values of the control parameter not necessarily related to a phase transition. While SOPT was observed only in JR model, neither WC nor JR model showed power-law behavior. Our study further investigated the global synchronization phenomenon that is known to exist in pathological brain states, such as seizure. JR model showed global synchronization, while WC model seemed to be more suitable in producing partially synchronized patterns.

The recovery of parabolic avalanches in spatially subsampled neuronal networks at criticality

Scaling relationships are key in characterizing complex systems at criticality. In the brain, they are evident in neuronal avalanches-scale-invariant cascades of neuronal activity quantified by power laws. Avalanches manifest at the cellular level as cascades of neuronal groups that fire action potentials simultaneously. Such spatiotemporal synchronization is vital to theories on brain function yet avalanche synchronization is often underestimated when only a fraction of neurons is observed. Here, we investigate biases from fractional sampling within a balanced network of excitatory and inhibitory neurons with all-to-all connectivity and critical branching process dynamics. We focus on how mean avalanche size scales with avalanche duration. For parabolic avalanches, this scaling is quadratic, quantified by the scaling exponent, χ = 2 , reflecting rapid spatial expansion of simultaneous neuronal firing over short durations. However, in networks sampled fractionally, χ is significantly lower. We demonstrate that applying temporal coarse-graining and increasing a minimum threshold for coincident firing restores χ = 2 , even when as few as 0.1% of neurons are sampled. This correction crucially depends on the network being critical and fails for near sub- and supercritical branching dynamics. Using cellular 2-photon imaging, our approach robustly identifies χ = 2 over a wide parameter regime in ongoing neuronal activity from frontal cortex of awake mice. In contrast, the common 'crackling noise' approach fails to determine χ under similar sampling conditions at criticality. Our findings overcome scaling bias from fractional sampling and demonstrate rapid, spatiotemporal synchronization of neuronal assemblies consistent with scale-invariant, parabolic avalanches at criticality.

Sleep restores an optimal computational regime in cortical networks

Sleep is assumed to subserve homeostatic processes in the brain; however, the set point around which sleep tunes circuit computations is unknown. Slow-wave activity (SWA) is commonly used to reflect the homeostatic aspect of sleep; although it can indicate sleep pressure, it does not explain why animals need sleep. This study aimed to assess whether criticality may be the computational set point of sleep. By recording cortical neuron activity continuously for 10-14 d in freely behaving rats, we show that normal waking experience progressively disrupts criticality and that sleep functions to restore critical dynamics. Criticality is perturbed in a context-dependent manner, and waking experience is causal in driving these effects. The degree of deviation from criticality predicts future sleep/wake behavior more accurately than SWA, behavioral history or other neural measures. Our results demonstrate that perturbation and recovery of criticality is a network homeostatic mechanism consistent with the core, restorative function of sleep.

Beyond pulsed inhibition: Alpha oscillations modulate attenuation and amplification of neural activity in the awake resting state

Alpha oscillations are a distinctive feature of the awake resting state of the human brain. However, their functional role in resting-state neuronal dynamics remains poorly understood. Here we show that, during resting wakefulness, alpha oscillations drive an alternation of attenuation and amplification bouts in neural activity. Our analysis indicates that inhibition is activated in pulses that last for a single alpha cycle and gradually suppress neural activity, while excitation is successively enhanced over a few alpha cycles to amplify neural activity. Furthermore, we show that long-term alpha amplitude fluctuations-the "waxing and waning" phenomenon-are an attenuation-amplification mechanism described by a power-law decay of the activity rate in the "waning" phase. Importantly, we do not observe such dynamics during non-rapid eye movement (NREM) sleep with marginal alpha oscillations. The results suggest that alpha oscillations modulate neural activity not only through pulses of inhibition (pulsed inhibition hypothesis) but also by timely enhancement of excitation (or disinhibition).