Thermoneutral temperature exposure enhances slow-wave sleep with a correlated improvement in amyloid pathology in a triple-transgenic mouse model of Alzheimer's disease.

Accumulation of amyloid-ß (Aß) plays an important role in Alzheimer's disease (AD) pathology. There is growing evidence that disordered sleep may accelerate AD pathology by impeding the physiological clearance of Aß from the brain that occurs in normal sleep. Therapeutic strategies for improving sleep quality may therefore help slow disease progression. It is well documented that the composition and dynamics of sleep are sensitive to ambient temperature. We therefore compared Aß pathology and sleep metrics derived from polysomnography in 12-month-old female 3xTg-AD mice (n = 8) exposed to thermoneutral temperatures during the light period over 4 weeks to those of age- and sex-matched controls (n = 8) that remained at normal housing temperature (22°C) during the same period. The treated group experienced greater proportions of slow wave sleep (SWS)-i.e. epochs of elevated 0.5-2 Hz EEG slow wave activity during non-rapid eye movement (NREM) sleep-compared to controls. Assays performed on mouse brain tissue harvested at the end of the experiment showed that exposure to thermoneutral temperatures significantly reduced levels of DEA-soluble (but not RIPA- or formic acid-soluble) Aß40 and Aß42 in the hippocampus, though not in the cortex. With both groups pooled together and without regard to treatment condition, NREM sleep continuity and any measure of SWS within NREM at the end of the treatment period were inversely correlated with DEA-soluble Aß40 and Aß42 levels, again in the hippocampus but not in the cortex. These findings suggest that experimental manipulation of SWS could offer useful clues into the mechanisms and treatment of AD.

Selective REM sleep restriction in mice using a device designed for tunable somatosensory stimulation.

BACKGROUND: Sleep perturbation is widely used to investigate the physiological mechanisms that mediate sleep-wake dynamics, and to isolate the specific roles of sleep in health and disease. However, state-of-the-art methods to accomplish sleep perturbation in preclinical models are limited in their throughput, flexibility, and specificity. NEW METHOD: A system was developed to deliver vibro-tactile somatosensory stimulation aimed at controlled, selective sleep perturbation. The frequency and intensity of stimulation can be tuned to target a variety of experimental applications, from sudden arousal to sub-threshold transitions between light and deep stages of NREM sleep. This device was activated in closed-loop to selectively interrupt REM sleep in mice. RESULTS: Vibro-tactile stimulation effectively and selectively interrupted REM sleep - significantly reducing the average REM bout duration relative to matched, unstimulated baseline recordings. As REM sleep was repeatedly interrupted, homeostatic mechanisms prompted a progressively quicker return to REM sleep. These effects were dependent on the parameters of stimulation applied. COMPARISON WITH EXISTING METHODS: Existing sleep perturbation systems often require moving parts within the cage and/or restrictive housing. The system presented is unique in that it interrupts sleep without invading the animal's space. The ability to vary stimulation parameters is a great advantage over existing methods, as it allows for adaptation in response to habituation and/or circadian/homeostatic changes in arousal threshold. CONCLUSIONS: The proposed method of stimulation demonstrates feasibility in affecting mouse sleep within a standard home cage environment, thus limiting environmental stress. Furthermore, the ability to tune frequency and intensity of stimulation allows for graded control over the extent of sleep perturbation, which potentially expands the utility of this technology beyond applications related to sleep.

Hand-worn devices for assessment and rehabilitation of motor function and their potential use in BCI protocols: a review.

Introduction: Various neurological conditions can impair hand function. Affected individuals cannot fully participate in activities of daily living due to the lack of fine motor control. Neurorehabilitation emphasizes repetitive movement and subjective clinical assessments that require clinical experience to administer. Methods: Here, we perform a review of literature focused on the use of hand-worn devices for rehabilitation and assessment of hand function. We paid particular attention to protocols that involve brain-computer interfaces (BCIs) since BCIs are gaining ground as a means for detecting volitional signals as the basis for interactive motor training protocols to augment recovery. All devices reviewed either monitor, assist, stimulate, or support hand and finger movement. Results: A majority of studies reviewed here test or validate devices through clinical trials, especially for stroke. Even though sensor gloves are the most commonly employed type of device in this domain, they have certain limitations. Many such gloves use bend or inertial sensors to monitor the movement of individual digits, but few monitor both movement and applied pressure. The use of such devices in BCI protocols is also uncommon. Discussion: We conclude that hand-worn devices that monitor both flexion and grip will benefit both clinical diagnostic assessment of function during treatment and closed-loop BCI protocols aimed at rehabilitation.

Prediction of isometric handgrip force from graded event-related desynchronization of the sensorimotor rhythm.

Objective. Brain-computer interfaces (BCIs) show promise as a direct line of communication between the brain and the outside world that could benefit those with impaired motor function. But the commands available for BCI operation are often limited by the ability of the decoder to differentiate between the many distinct motor or cognitive tasks that can be visualized or attempted. Simple binary command signals (e.g. right hand at rest versus movement) are therefore used due to their ability to produce large observable differences in neural recordings. At the same time, frequent command switching can impose greater demands on the subject's focus and takes time to learn. Here, we attempt to decode the degree of effort in a specific movement task to produce a graded and more flexible command signal.Approach.Fourteen healthy human subjects (nine male, five female) responded to visual cues by squeezing a hand dynamometer to different levels of predetermined force, guided by continuous visual feedback, while the electroencephalogram (EEG) and grip force were monitored. Movement-related EEG features were extracted and modeled to predict exerted force.Main results.We found that event-related desynchronization (ERD) of the 8-30 Hz mu-beta sensorimotor rhythm of the EEG is separable for different degrees of motor effort. Upon four-fold cross-validation, linear classifiers were found to predict grip force from an ERD vector with mean accuracies across subjects of 53% and 55% for the dominant and non-dominant hand, respectively. ERD amplitude increased with target force but appeared to pass through a trough that hinted at non-monotonic behavior.Significance.Our results suggest that modeling and interactive feedback based on the intended level of motor effort is feasible. The observed ERD trends suggest that different mechanisms may govern intermediate versus low and high degrees of motor effort. This may have utility in rehabilitative protocols for motor impairments.

A real-time sleep scoring framework for closed-loop sleep manipulation in mice.

Subtle changes in sleep architecture can accompany and be symptomatic of many diseases or disorders. In order to probe and understand the complex interactions between sleep and health, the ability to model, track, and modulate sleep in preclinical animal models is vital. While various methods have been described for scoring experimental sleep recordings, few are designed to work in real time - a prerequisite for closed-loop sleep manipulation. In the present study, we have developed algorithms and software to classify sleep in real time and validated it on C57BL/6 mice (n = 8). Hidden Markov models of baseline sleep dynamics were fitted using an unsupervised algorithm to electroencephalogram (EEG) and electromyogram (EMG) data for each mouse, and were able to classify sleep in a manner highly concordant with manual scoring (Cohen's Kappa >75%) up to 3 weeks after model construction. This approach produced reasonably accurate estimates of common sleep metrics (proportion, mean duration, and number of bouts). After construction, the models were used to track sleep in real time and accomplish selective rapid eye movement (REM) sleep restriction by triggering non-invasive somatosensory stimulation. During REM restriction trials, REM bout duration was significantly reduced, and the classifier continued to perform satisfactorily despite the disrupted sleep patterns. The software can easily be tailored for use with other commercial or customised methods of sleep disruption (e.g. stir bar, optogenetic stimulation, etc.) and could serve as a robust platform to facilitate closed-loop experimentation. The source code and documentation are freely available upon request from the authors.

A comparative study of sleep and diurnal patterns in house mouse (Mus musculus) and Spiny mouse (Acomys cahirinus).

Most published sleep studies use three species: human, house mouse, or Norway rat. The degree to which data from these species captures variability in mammalian sleep remains unclear. To gain insight into mammalian sleep diversity, we examined sleep architecture in the spiny basal murid rodent Acomys cahirinus. First, we used a piezoelectric system validated for Mus musculus to monitor sleep in both species. We also included wild M. musculus to control for alterations generated by laboratory-reared conditions for M. musculus. Using this comparative framework, we found that A. cahirinus, lab M. musculus, and wild M. musculus were primarily nocturnal, but exhibited distinct behavioral patterns. Although the activity of A. cahirinus increased sharply at dark onset, it decreased sharply just two hours later under group and individual housing conditions. To further characterize sleep patterns and sleep-related variables, we set up EEG/EMG and video recordings and found that A. cahirinus sleep significantly more than M. musculus, exhibit nearly three times more REM, and sleep almost exclusively with their eyes open. The observed differences in A. cahirinus sleep architecture raise questions about the evolutionary drivers of sleep behavior.

Source localization of high-frequency activity in tripolar electroencephalography of patients with epilepsy.

OBJECTIVE: The objective of the study was to localize sources of interictal high-frequency activity (HFA), from tripolar electroencephalography (tEEG), in patient-specific, realistic head models. METHODS: Concurrent electroencephalogram (EEG) and tEEG were recorded from nine patients undergoing video-EEG, of which eight had seizures during the recordings and the other had epileptic activity. Patient-specific, realistic boundary element head models were generated from the patient's magnetic resonance images (MRIs). Forward and inverse modeling was performed to localize the HFA to cortical surfaces. RESULTS: In the present study, performed on nine patients with epilepsy, HFA observed in the tEEG was localized to the surface of subject-specific, realistic, cortical models, and found to occur almost exclusively in the seizure onset zone (SOZ)/irritative zone (IZ). SIGNIFICANCE: High-frequency oscillations (HFOs) have been studied as precise biomarkers of the SOZ in epilepsy and have resulted in good therapeutic effect in surgical candidates. Knowing where the sources of these highly focal events are located in the brain can help with diagnosis. High-frequency oscillations are not commonly observed in noninvasive EEG recordings, and invasive electrocorticography (ECoG) is usually required to detect them. However, tEEG, i.e., EEG recorded on the scalp with tripolar concentric ring electrodes (TCREs), has been found to detect narrowband HFA from high gamma (approximately 80 Hz) to almost 400 Hz that correlates with SOZ diagnosis. Thus, source localization of HFA in tEEG may help clinicians identify brain regions of the epileptic zone. At the least, the tEEG HFA localization may help determine where to perform intracranial recordings used for precise diagnosis.

Effect of Vigilance Changes on the Incidence of High Frequency Oscillations in the Epileptic Brain.

Recent studies show that the rate of cortical high frequency oscillations (HFOs) differentiates epileptogenic tissue in individuals with epilepsy. However, HFO occurrence can vary widely with vigilance state. In this study we attempt to characterize this variation, which has implications for the choice of a suitable diagnostic baseline for spatiotemporal analysis of HFO activity. We analyzed simultaneous recordings of the scalp electroencephalogram (EEG) and the electrocorticogram (ECoG) to examine the correlation of HFO activity with vigilance state. We detected HFOs (80-500 Hz) from all bipolar ECoG derivations using the well-known Staba algorithm in ten seizure-free overnight recordings from five patients being evaluated for surgery. In addition, we classified EEG features using a linkage tree into four vigilance states representing gradations in sleep depth from wakefulness to slow wave sleep. Finally, we examined the correlation between vigilance state and HFO occurrence in the five channels with the most HFOs in each recording. The proportion of 30-s epochs containing HFOs was found to increase significantly with sleep depth (p<0.01). Further analysis is necessary to examine the effects of epoch length and sample size in the choice of diagnostic baseline.

Sleep Depth Enhancement Through Ambient Temperature Manipulation in Mice.

The restorative properties of deep sleep and its central role in learning and memory are well-recognized but still in the process of being elucidated with the help of animal models. Currently available approaches for deep sleep enhancement are mainly pharmacological and may have undesirable side effects on physiology and behavior. Here, we propose a simple strategy for sleep depth enhancement that involves manipulation of ambient temperature (Ta) using a closed-loop control system. Even mild shifts in Ta are known to evoke thermoregulatory responses that alter sleep-wake dynamics. In our experiments, mice evinced greater proportions of deep NREM sleep as well as REM sleep under the dynamic sleep depth modulation protocol compared to a reference baseline in which Ta was left unchanged. The active manipulation approach taken in this study could be used as a more natural means for enhancing deep sleep in patients with disorders like epilepsy, Alzheimer's disease and Parkinson's, in which poor quality sleep is common and associated with adverse outcomes.

Noninvasive seizure prediction using autonomic measurements in patients with refractory epilepsy.

There is resurgent interest in the role played by autonomic dysfunction in seizure generation. Advances in wearable sensors make it convenient to track many autonomic variables in patient populations. This study assesses peri-ictal changes in surrogate measures of autonomic activity for their predictive value in epilepsy patients. We simultaneously recorded fronto-central surface EEG and submental EMG to score vigilance state, intracranial EEG (iEEG) to compute several electrophysiological variables (EV), and measurements (heart rate, blood volume pulse, skin impedance, and skin temperature) relevant to autonomic function (AV) using a wrist-worn sensor from three patients. A naïve Bayes classifier was trained on these features and tested using five-fold cross- validation to determine whether preictal and interictal sleep (or wake) epochs could be distinguished from each other using either AV or EV features. Of 16 EV features, beta power, gamma power (30-45 Hz and 47-75 Hz), line length, and Teager energy showed significant differences for preictal versus interictal sleep (or wake) state in each patient (t test: $p<0.001$). At least one AV was significantly different in each patient for interictal and preictal sleep (or wake) segments ($p<0.001$). Using AV features, the classifier labeled preictal sleep epochs with 84% sensitivity, 79% specificity, and 64% kappa; and 78%, 80% and 55% respectively for preictal wake epochs. Using EV, the classifier labeled preictal sleep epochs with 69% sensitivity, 64% specificity, and 33% kappa; and 15%, 93% and 10% respectively for preictal wake epochs.

Cortical Statistical Correlation Tomography of EEG Resting State Networks.

Resting state networks (RSNs) have been found in human brains during awake resting states. RSNs are composed of spatially distributed regions in which spontaneous activity fluctuations are temporally and dynamically correlated. A new computational framework for reconstructing RSNs with human EEG data has been developed in the present study. The proposed framework utilizes independent component analysis (ICA) on short-time Fourier transformed inverse source maps imaged from EEG data and statistical correlation analysis to generate cortical tomography of electrophysiological RSNs. The proposed framework was evaluated on three sets of resting-state EEG data obtained in the comparison of two conditions: (1) healthy controls with eyes closed and eyes open; (2) healthy controls and individuals with a balance disorder; (3) individuals with a balance disorder before and after receiving repetitive transcranial magnetic stimulation (rTMS) treatment. In these analyses, the same group of five RSNs with similar spatial and spectral patterns were successfully reconstructed by the proposed framework from each individual EEG dataset. These EEG RSN tomographic maps showed significant similarity with RSN templates derived from functional magnetic resonance imaging (fMRI). Furthermore, significant spatial and spectral differences of RSNs among compared conditions were observed in tomographic maps as well as their spectra, which were consistent with findings reported in the literature. Beyond the success of reconstructing EEG RSNs spatially on the cortical surface as in fMRI studies, this novel approach defines RSNs further with spectra, providing a new dimension in understanding and probing basic neural mechanisms of RSNs. The findings in patients' data further demonstrate its potential in identifying biomarkers for the diagnosis and treatment evaluation of neuropsychiatric disorders.

Computation of Surface Laplacian for tri-polar ring electrodes on high-density realistic geometry head model.

Neural activity inside the human brain generate electrical signals that can be detected on the scalp. Electroencephalograph (EEG) is one of the most widely utilized techniques helping physicians and researchers to diagnose and understand various brain diseases. Due to its nature, EEG signals have very high temporal resolution but poor spatial resolution. To achieve higher spatial resolution, a novel tri-polar concentric ring electrode (TCRE) has been developed to directly measure Surface Laplacian (SL). The objective of the present study is to accurately calculate SL for TCRE based on a realistic geometry head model. A locally dense mesh was proposed to represent the head surface, where the local dense parts were to match the small structural components in TCRE. Other areas without dense mesh were used for the purpose of reducing computational load. We conducted computer simulations to evaluate the performance of the proposed mesh and evaluated possible numerical errors as compared with a low-density model. Finally, with achieved accuracy, we presented the computed forward lead field of SL for TCRE for the first time in a realistic geometry head model and demonstrated that it has better spatial resolution than computed SL from classic EEG recordings.

Identifying the effects of microsaccades in tripolar EEG signals.

Microsaccades are tiny, involuntary eye movements that occur during fixation, and they are necessary to human sight to maintain a sharp image and correct the effects of other fixational movements. Researchers have theorized and studied the effects of microsaccades on electroencephalography (EEG) signals to understand and eliminate the unwanted artifacts from EEG. The tripolar concentric ring electrode (TCRE) sensors are used to acquire TCRE EEG (tEEG). The tEEG detects extremely focal signals from directly below the TCRE sensor. We have noticed a slow wave frequency found in some tEEG recordings. Therefore, we conducted the current work to determine if there was a correlation between the slow wave in the tEEG and the microsaccades. This was done by analyzing the coherence of the frequency spectrums of both tEEG and eye movement in recordings where microsaccades are present. Our preliminary findings show that there is a correlation between the two.

Unsupervised Estimation of Mouse Sleep Scores and Dynamics Using a Graphical Model of Electrophysiological Measurements.

The proportion, number of bouts, and mean bout duration of different vigilance states (Wake, NREM, REM) are useful indices of dynamics in experimental sleep research. These metrics are estimated by first scoring state, sometimes using an algorithm, based on electrophysiological measurements such as the electroencephalogram (EEG) and electromyogram (EMG), and computing their values from the score sequence. Isolated errors in the scores can lead to large discrepancies in the estimated sleep metrics. But most algorithms score sleep by classifying the state from EEG/EMG features independently in each time epoch without considering the dynamics across epochs, which could provide contextual information. The objective here is to improve estimation of sleep metrics by fitting a probabilistic dynamical model to mouse EEG/EMG data and then predicting the metrics from the model parameters. Hidden Markov models (HMMs) with multivariate Gaussian observations and Markov state transitions were fitted to unlabeled 24-h EEG/EMG feature time series from 20 mice to model transitions between the latent vigilance states; a similar model with unbiased transition probabilities served as a reference. Sleep metrics predicted from the HMM parameters did not deviate significantly from manual estimates except for rapid eye movement sleep (REM) ([Formula: see text]; Wilcoxon signed-rank test). Changes in value from Light to Dark conditions correlated well with manually estimated differences (Spearman's rho 0.43-0.84) except for REM. HMMs also scored vigilance state with over 90% accuracy. HMMs of EEG/EMG features can therefore characterize sleep dynamics from EEG/EMG measurements, a prerequisite for characterizing the effects of perturbation in sleep monitoring and control applications.

SegWay: A simple framework for unsupervised sleep segmentation in experimental EEG recordings.

Sleep analysis in animal models typically involves recording an electroencephalogram (EEG) and electromyogram (EMG) and scoring vigilance state in brief epochs of data as Wake, REM (rapid eye movement sleep) or NREM (non-REM) either manually or using a computer algorithm. Computerized methods usually estimate features from each epoch like the spectral power associated with distinctive cortical rhythms and dissect the feature space into regions associated with different states by applying thresholds, or by using supervised/unsupervised statistical classifiers; but there are some factors to consider when using them:•Most classifiers require scored sample data, elaborate heuristics or computational steps not easily reproduced by the average sleep researcher, who is the targeted end user.•Even when prediction is reasonably accurate, small errors can lead to large discrepancies in estimates of important sleep metrics such as the number of bouts or their duration.•As we show here, besides partitioning the feature space by vigilance state, modeling transitions between the states can give more accurate scores and metrics. An unsupervised sleep segmentation framework, "SegWay", is demonstrated by applying the algorithm step-by-step to unlabeled EEG recordings in mice. The accuracy of sleep scoring and estimation of sleep metrics is validated against manual scores.

Noninvasive dissection of mouse sleep using a piezoelectric motion sensor.

BACKGROUND: Changes in autonomic control cause regular breathing during NREM sleep to fluctuate during REM. Piezoelectric cage-floor sensors have been used to successfully discriminate sleep and wake states in mice based on signal features related to respiration and other movements. This study presents a classifier for noninvasively classifying REM and NREM using a piezoelectric sensor. NEW METHOD: Vigilance state was scored manually in 4-s epochs for 24-h EEG/EMG recordings in 20 mice. An unsupervised classifier clustered piezoelectric signal features quantifying movement and respiration into three states: one active; and two inactive with regular and irregular breathing, respectively. These states were hypothesized to correspond to Wake, NREM, and REM, respectively. States predicted by the classifier were compared against manual EEG/EMG scores to test this hypothesis. RESULTS: Using only piezoelectric signal features, an unsupervised classifier distinguished Wake with high (89% sensitivity, 96% specificity) and REM with moderate (73% sensitivity, 75% specificity) accuracy, but NREM with poor sensitivity (51%) and high specificity (96%). The classifier sometimes confused light NREM sleep - characterized by irregular breathing and moderate delta EEG power - with REM. A supervised classifier improved sensitivities to 90, 81, and 67% and all specificities to over 90% for Wake, NREM, and REM, respectively. COMPARISON WITH EXISTING METHODS: Unlike most actigraphic techniques, which only differentiate sleep from wake, the proposed piezoelectric method further dissects sleep based on breathing regularity into states strongly correlated with REM and NREM. CONCLUSIONS: This approach could facilitate large-sample screening for genes influencing different sleep traits, besides drug studies or other manipulations.

Closed-loop afferent electrical stimulation for recovery of hand function in individuals with motor incomplete spinal injury: early clinical results.

Afferent electrical stimulation is known to augment the effect of rehabilitative therapy through use-dependent cortical plasticity. Experiments pairing transcranial magnetic stimulation (TMS) with peripheral nerve stimulation (PNS) have shown a timing-dependent effect on motor evoked potential (MEP) amplitude suggesting that PNS applied in closed-loop (CL) mode could augment this effect through positive reinforcement. We present early results from a clinical trial in which an EEG brain-machine interface (BMI) was used to apply PNS to two subjects in response to motor intent detected from sensorimotor cortex in a cue-driven hand grip task. Both subjects had stable incomplete cervical spinal cord injury (SCI) with impaired upper limb function commensurate with the injury level. Twelve sessions of CL-PNS applied over a 4-6 week period yielded results suggesting improved hand grip strength and increased task-related modulation of the EEG in one hand of both subjects, and increased TMS-measured motor map area in one. These observations suggest that rehabilitation using such interactive therapies could benefit affected individuals.

Tunable somatosensory stimulation for selective sleep restriction studies in rodents.

Many methods for sleep restriction in rodents have emerged, but most are intrusive, lack fine control, and induce stress. Therefore, a versatile, non-intrusive means of sleep restriction that can alter sleep in a controlled manner could be of great value in sleep research. In previous work, we proposed a novel system for closed-loop somatosensory stimulation based on mechanical vibration and applied it to the task of restricting Rapid Eye Movement (REM) sleep in mice [1]. While this system was effective, it was a crude prototype and did not allow precise control over the amplitude and frequency of stimulation applied to the animal. This paper details the progression of this system from a binary, "all-or-none" version to one that allows dynamic control over perturbation to accomplish graded, state-dependent sleep restriction. Its preliminary use is described in two applications: deep sleep restriction in rats, and REM sleep restriction in mice.

Effect of temperature on sleep regulation in an animal epilepsy model.

Besides recurring seizures, disordered sleep is common in individuals with epilepsy and may present as reduced sleep depth, altered proportions of different stages of sleep, intermittent arousal, and other phenomena. Sleep loss can in turn precipitate seizures, thus sustaining a vicious cycle. It is well known that changes in ambient temperature elicit thermoregulatory responses that alter the dynamics of sleep. As a first step toward therapeutic sleep modulation for epilepsy, we assessed the effect of elevated ambient temperature on sleep dynamics and seizure yield in the chronic pilocarpine mouse model of temporal lobe epilepsy. The results in a small sample indicate that temperature does in fact significantly alter the proportions and durations of each vigilance state in this model, with possibly correlated changes in seizure incidence. Manipulation of ambient temperature therefore offers a simple and relatively unobtrusive way of titrating sleep quality and perhaps alleviating the seizure burden in epilepsy.