Predictability of arousal in mouse slow wave sleep by accelerometer data

Sleep scoring in rodents: Criteria, automatic approaches and outstanding issues

There is nothing we spend as much time on in our lives as we do sleeping, which makes it even more surprising that we currently do not know why we need to sleep. Most of the research addressing this question is performed in rodents to allow for invasive, mechanistic approaches. However, in contrast to human sleep, we currently do not have shared and agreed upon standards on sleep states in rodents. In this article, we present an overview on sleep stages in humans and rodents and a historical perspective on the development of automatic sleep scoring systems in rodents. Further, we highlight specific issues in rodent sleep that also call into question some of the standards used in human sleep research.

Sleep and circadian rhythm disruption by NPTX2 loss of function

Sleep and circadian rhythm disruption (SCRD) is commonly observed in aging, especially in individuals who experience progressive cognitive decline to mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, precise molecular mechanisms underlying the association between SCRD and aging are not fully understood. Orexin A is a well-characterized "sleep neuropeptide" that is expressed in hypothalamic neurons and evokes wake behavior. The importance of Orexin is exemplified in narcolepsy where it is profoundly down-regulated. Interestingly, the synaptic immediate early gene NPTX2 is co-expressed in Orexin neurons and is similarly reduced in narcolepsy. NPTX2 is also down-regulated in CSF of some cognitively normal older individuals and predicts the time of transition from normal cognition to MCI. The association between Orexin and NPTX2 is further evinced here where we observe that Orexin A and NPTX2 are highly correlated in CSF of cognitively normal aged individuals and raises the question of whether SCRD that are typically attributed to Orexin A loss of function may be modified by concomitant NPTX2 down-regulation. Is NPTX2 an effector of sleep or simply a reporter of orexin-dependent SCRD? To address this question, we examined NPTX2 KO mice and found they retain Orexin expression in the brain and so provide an opportunity to examine the specific contribution of NPTX2 to SCRD. Our results reveal that NPTX2 KO mice exhibit a disrupted circadian onset time, coupled with increased activity during the sleep phase, suggesting difficulties in maintaining states. Sleep EEG indicates distinct temporal allocation shifts across vigilance states, characterized by reduced wake and increased NREM time. Evident sleep fragmentation manifests through alterations of event occurrences during Wake and NREM, notably during light transition periods, in conjunction with an increased frequency of sleep transitions in NPTX2 KO mice, particularly between Wake and NREM. EEG spectral analysis indicated significant shifts in power across various frequency bands in the wake, NREM, and REM states, suggestive of disrupted neuronal synchronicity. An intriguing observation is the diminished occurrence of sleep spindles, one of the earliest measures of human sleep disruption, in NPTX2 KO mice. These findings highlight the effector role of NPTX2 loss of function as an instigator of SCRD and a potential mediator of sleep disruption in aging.

Decreased hippocampal serotonin 5HT1A expression in mesial temporal lobe of epilepsy patients

INTRODUCTION

Mesial temporal lobe epilepsy related to hippocampal sclerosis (MTLE-HS) is a surgically remediable epilepsy with a relatively high prevalence and psychiatric comorbidities. Depressive disorders may occur in up to 25% of MTLE-HS patients suggesting a common molecular mechanism underlying both conditions.

OBJECTIVE

To compare the gene expression comprising serotonin 5HT_1A and 5HT_2A, noradrenaline (NA) ADRA1A, and ADRA2A receptors in the hippocampus of MTLE-HS patients with and without major depression.

METHODS

A cross-sectional study allocated 31 patients in three groups: MTLE-HS without psychiatric diagnosis (MTLE-HS group), MTLE-HS with major depression (MTLE-HS-D group) and a control group consisting of healthy volunteers without any neurological or psychiatric disorders. Demographic and clinical characteristics were compared among groups. Gene expression of receptors were analyzed using general linear mixed models (GLMM), with an unstructured matrix, normal link.

RESULTS

The three groups showed a similar distribution regarding age, gender (p > 0.16), history of initial precipitating injury, family history of epilepsy, monthly frequency of seizures, side of hippocampal sclerosis, interictal spike distribution and anti-seizure medications did not differ between MTLE-HS and MTLE-HS-D groups (p > 0.05). We observed a greater expression of the 5HT1A receptor in the control group when compared to the MTLE-HS (P = .004) and MTLE-HS-D (P = .007). Nevertheless, we did not observe any difference when MTLE-HS and MTLE-HS-D groups were compared to the controls for the ADRA1A (P = .931; P = .931), ADRA2A (P = .120; P = .121) and 5HT2A (P = .638; P = .318, respectively) gene expression.

CONCLUSION

Mesial temporal lobe epilepsy related to hippocampal sclerosis and MTLE-HS-D patients showed a lowered expression of the 5HT1A receptors when compared with the controls adjusted for age and schooling. Data suggest that temporal lobe epilepsy plasticity may affect serotonin receptors, which may lead to more frequent cases of major depression in this population. More studies comprising wider samples are necessary to confirm these results; they also should investigate serotonin reuptake drugs as an adjuvant therapeutic option for MTLE-HS disorder.

Ephaptic entrainment in hybrid neuronal model

In recent decades, there has been a growing interest in the impact of electric fields generated in the brain. Transmembrane ionic currents originate electric fields in the extracellular space and are capable of affecting nearby neurons, a phenomenon called ephaptic neuronal communication. In the present work, the Quadratic Integrated-and-Fire model (QIF-E) underwent an adjustment/improvement to include the ephaptic entrainment behavior between neurons and electric fields. Indeed, the aim of our study is to validate the QIF-E model, which is a model to estimate the influence of electric fields on neurons. For this purpose, we evaluated whether the main properties observed in an experiment by Anastassiou et al. (Nat Neurosci 14:217-223, 2011), which analyzed the effect of an electric field on cortical pyramidal neurons, are reproduced with the QIF-E model. In this way, the analysis tools are employed according to the neuronal activity regime: (i) for the subthreshold regime, the circular statistic is used to describe the phase differences between the input stimulus signal (electrode) and the modeled membrane response; (ii) in the suprathreshold regime, the Population Vector and the Spike Field Coherence are used to estimate phase preferences and the entrainment intensity between the input stimulus and Action Potentials. The results observed are (i) in the subthreshold regime the values of the phase differences change with distinct frequencies of the input stimulus; (ii) in the supra-threshold regime the preferential phase of Action Potentials changes for different frequencies. In addition, we explore other parameters of the model, such as noise and membrane characteristic-time, in order to understand different types of neurons and extracellular environment related to ephaptic communication. Such results are consistent with results observed in empirical experiments based on ephaptic phenomenon. In addition, the QIF-E model allows further studies on the physiological importance of ephaptic communication in the brain, and its simplicity may open a door to simulate the ephaptic response in neuronal networks and assess the impact of ephaptic communication in such scenarios.

Fear conditioning potentiates the hippocampal CA1 commissural pathway in vivo and increases awake phase sleep

The hippocampus is essential for spatial learning and memory. To assess learning we used contextual fear conditioning (cFC), where animals learn to associate a place with aversive events like foot‐shocks. Candidate memory mechanisms for cFC are long‐term potentiation (LTP) and long‐term depression (LTD), but there is little direct evidence of them operating in the hippocampus in vivo following cFC. Also, little is known about the behavioral state changes induced by cFC. To address these issues, we recorded local field potentials in freely behaving mice by stimulating in the left dorsal CA1 region and recording in the right dorsal CA1 region. Synaptic strength in the commissural pathway was monitored by measuring field excitatory postsynaptic potentials (fEPSPs) before and after cFC. After cFC, the commissural pathway's synaptic strength was potentiated. Although recordings occurred during the wake phase of the light/dark cycle, the mice slept more in the post‐conditioning period than in the pre‐conditioning period. Relative to awake periods, in non‐rapid eye movement (NREM) sleep the fEPSPs were larger in both pre‐ and post‐conditioning periods. We also found a significant negative correlation between the animal's speed and fEPSP size. Therefore, to avoid confounds in the fEFSP potentiation estimates, we controlled for speed‐related and sleep‐related fEPSP changes and still found that cFC induced long‐term potentiation, but no significant long‐term depression. Synaptic strength changes were not found in the control group that simply explored the fear‐conditioning chamber, indicating that exploration of the novel place did not produce the measurable effects caused by cFC. These results show that following cFC, the CA1 commissural pathway is potentiated, likely contributing to the functional integration of the left and right hippocampi in fear memory consolidation. In addition, the cFC paradigm produces significant changes in an animal's behavioral state, which are observable as proximal changes in sleep patterns.

Connection between sleeping patterns and cognitive deterioration in women with Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) causes symptoms such as dementia, memory loss, disorientation, and even aggressiveness, and is more common in women than in men. AD may also manifest itself in changes in sleep patterns. However, the relationship between AD (in all stages) and bedtime behavior has not been thoroughly investigated.

METHODS

In a prospective, cross-sectional survey, we evaluated 74 women categorized in two different stages of cognitive decline associated with AD (mild and severe) along with 37 women with no cognitive decline who served as controls. We obtained demographic and medical information such as age, health status, and medication, as well as psychiatrically confirmed staging of AD. We also collected actigraphy data for several nights in a row with a medical grade wristband using a 3-axis accelerometer and solid-state on-board memory. These data served as parameters for a clustering machine learning (ML) algorithm.

RESULTS

The ML process was able to unsupervisedly identify 85% of the participants according to their pre-assigned degree of dementia. When the clustering was carried out in a binary fashion (i.e., only taking into account healthy members vs. severely affected AD patients), it was possible to correctly classify 91% of the cases.

CONCLUSIONS

This study revealed a strong connection between the severity of the intellectual decline and the features distilled from actigraphically derived sleep parameters.

Short-term sleep deprivation immediately after contextual conditioning inhibits BDNF signaling and disrupts memory consolidation in predator odor trauma mice model of PTSD

Post-traumatic stress disorder (PTSD) is a debilitating neuropsychiatric illness affecting > 7 million people every year in the US. Recently, we have shown that the mouse model of predator odor trauma (POT) displayed contextual conditioning and core features of PTSD including sleep disturbances (hyperarousal) and retrieval of traumatic memories following exposure to objective reminders (re-experiencing). PTSD is a disorder of memory function. Since memory consolidation requires the expression of BDNF along with an activation of MAPK/pERK signaling pathway in limbic brain structures (hippocampus and amygdala) and sleep favors memory consolidation, we hypothesized that short-term sleep deprivation (SD, 3 h), immediately after contextual conditioning will attenuate molecular correlates of memory consolidation, sleep disturbances, and memory consolidation. We performed two experiments in adult male C57BL/6J mice to test our hypothesis. Experiment 1 determined the effects of SD on contextual conditioning and changes in sleep wakefulness. Experiment 2 determined the effects of SD on contextual conditioning-induced changes in the expression of BDNF and pERK in hippocampus and amygdala. SD immediately after contextual conditioning (POT + SD group) significantly attenuated sleep disturbances, memory retrieval, and expression of pERK and BDNF in the hippocampus and amygdala as compared to POT-SD group (no SD after contextual conditioning). No significant differences were observed between POT + SD, NOC-SD (no contextual conditioning + no SD), and NOC + SD (no contextual conditioning + SD) groups. Memory consolidation requires sleep and the expression of pERK and BDNF in hippocampus and amygdala immediately after contextual conditioning in POT model of PTSD in mice.

Maximum entropy principle in recurrence plot analysis on stochastic and chaotic systems

The recurrence analysis of dynamic systems has been studied since Poincaré's seminal work. Since then, several approaches have been developed to study recurrence properties in nonlinear dynamical systems. In this work, we study the recently developed entropy of recurrence microstates. We propose a new quantifier, the maximum entropy (S_max). The new concept uses the diversity of microstates of the recurrence plot and is able to set automatically the optimum recurrence neighborhood (ϵ-vicinity), turning the analysis free of the vicinity parameter. In addition, ϵ turns out to be a novel quantifier of dynamical properties itself. We apply S_max and the optimum ϵ to deterministic and stochastic systems. The S_max quantifier has a higher correlation with the Lyapunov exponent and, since it is a parameter-free measure, a more useful recurrence quantifier of time series.

Motor execution reduces EEG signals complexity: Recurrence quantification analysis study

The development of new approaches to detect motor-related brain activity is key in many aspects of science, especially in brain-computer interface applications. Even though some well-known features of motor-related electroencephalograms have been revealed using traditionally applied methods, they still lack a robust classification of motor-related patterns. Here, we introduce new features of motor-related brain activity and uncover hidden mechanisms of the underlying neuronal dynamics by considering event-related desynchronization (ERD) of μ-rhythm in the sensorimotor cortex, i.e., tracking the decrease of the power spectral density in the corresponding frequency band. We hypothesize that motor-related ERD is associated with the suppression of random fluctuations of μ-band neuronal activity. This is due to the lowering of the number of active neuronal populations involved in the corresponding oscillation mode. In this case, we expect more regular dynamics and a decrease in complexity of the EEG signal recorded over the sensorimotor cortex. In order to support this, we apply measures of signal complexity by means of recurrence quantification analysis (RQA). In particular, we demonstrate that certain RQA quantifiers are very useful to detect the moment of movement onset and, therefore, are able to classify the laterality of executed movements.

Hippocampal and cortical communication around micro-arousals in slow-wave sleep

Sleep plays a crucial role in the regulation of body homeostasis and rhythmicity in mammals. Recently, a specific component of the sleep structure has been proposed as part of its homeostatic mechanism, named micro-arousal. Here, we studied the unique progression of the dynamic behavior of cortical and hippocampal local field potentials (LFPs) during slow-wave sleep-related to motor-bursts (micro-arousals) in mice. Our main results comprised: (i) an abrupt drop in hippocampal LFP amplitude preceding micro-arousals which persisted until the end of motor-bursts (we defined as t interval, around 4s) and a similar, but delayed amplitude reduction in cortical (S1/M1) LFP activity occurring at micro-arousal onset; (ii) two abrupt frequency jumps in hippocampal LFP activity: from Theta (6-12 Hz) to Delta (2-4 Hz), also t seconds before the micro-arousal onset, and followed by another frequency jump from Delta to Theta range (5-7 Hz), now occurring at micro-arousal onset; (iii) a pattern of cortico-hippocampal frequency communication precedes micro-arousals: the analysis between hippocampal and cortical LFP fluctuations reveal high coherence during τ interval in a broader frequency band (2-12 Hz), while at a lower frequency band (0.5-2 Hz) the coherence reaches its maximum after the onset of micro-arousals. In conclusion, these novel findings indicate that oscillatory dynamics pattern of cortical and hippocampal LFPs preceding micro-arousals could be part of the regulatory processes in sleep architecture.

Phase synchronization and intermittent behavior in healthy and Alzheimer-affected human-brain-based neural network

We study the dynamical proprieties of phase synchronization and intermittent behavior of neural systems using a network of networks structure based on an experimentally obtained human connectome for healthy and Alzheimer-affected brains. We consider a network composed of 78 neural subareas (subnetworks) coupled with a mean-field potential scheme. Each subnetwork is characterized by a small-world topology, composed of 250 bursting neurons simulated through a Rulkov model. Using the Kuramoto order parameter we demonstrate that healthy and Alzheimer-affected brains display distinct phase synchronization and intermittence properties as a function of internal and external coupling strengths. In general, for the healthy case, each subnetwork develops a substantial level of internal synchronization before a global stable phase-synchronization state has been established. For the unhealthy case, despite the similar internal subnetwork synchronization levels, we identify higher levels of global phase synchronization occurring even for relatively small internal and external coupling. Using recurrence quantification analysis, namely the determinism of the mean-field potential, we identify regions where the healthy and unhealthy networks depict nonstationary behavior, but the results denounce the presence of a larger region or intermittent dynamics for the case of Alzheimer-affected networks. A possible theoretical explanation based on two locally stable but globally unstable states is discussed.

Optimizing the detection of nonstationary signals by using recurrence analysis

Recurrence analysis and its quantifiers are strongly dependent on the evaluation of the vicinity threshold parameter, i.e., the threshold to regard two points close enough in phase space to be considered as just one. We develop a new way to optimize the evaluation of the vicinity threshold in order to assure a higher level of sensitivity to recurrence quantifiers to allow the detection of even small changes in the dynamics. It is used to promote recurrence analysis as a tool to detect nonstationary behavior of time signals or space profiles. We show that the ability to detect small changes provides information about the present status of the physical process responsible to generate the signal and offers mechanisms to predict future states. Here, a higher sensitive recurrence analysis is proposed as a precursor, a tool to predict near future states of a particular system, based on just (experimentally) obtained signals of some available variables of the system. Comparisons with traditional methods of recurrence analysis show that the optimization method developed here is more sensitive to small variations occurring in a signal. The method is applied to numerically generated time series as well as experimental data from physiology.