Investigating the Effect of Short Term Responsive VNS Therapy on Sleep Quality Using Automatic Sleep Staging

Vagus Nerve Stimulation Therapy for Drug-Resistant Epilepsy in Children-A Literature Review

Vagus nerve stimulation (VNS) is a palliative treatment for drug-resistant epilepsy (DRE) that has been in use for over two decades. VNS suppresses epileptic seizures, prevents emotional disorders, and improves cognitive function and sleep quality, a parallel effect associated with the control of epileptic seizures. The seizure suppression rate with VNS increases monthly to annually, and the incidence of side effects reduces over time. This method is effective in treating DRE in children as well as adults, such as epilepsy associated with tuberous sclerosis, Dravet syndrome, and Lennox-Gastaut syndrome. In children, it has been reported that seizures decreased by >70% approximately 8 years after initiating VNS, and the 50% responder rate was reported to be approximately 70%. VNS regulates stimulation and has multiple useful systems, including self-seizure suppression using magnets, additional stimulation using an automatic seizure detection system, different stimulation settings for day and night, and an automatic stimulation adjustment system that reduces hospital visits. VNS suppresses seizures and has beneficial behavioral effects in children with DRE. This review describes the VNS system, the mechanism of the therapeutic effect, the specific stimulation adjustment method, antiepileptic effects, and other clinical effects in patients with childhood DRE.

Effects of vagus nerve stimulation on the quality of sleep and sleep apnea in patients with drug-resistant epilepsy: A systematic review

OBJECTIVE

The objective was to systematically evaluate the current evidence surrounding the effect of vagus nerve stimulation (VNS) on quality of sleep and obstructive sleep apnea (OSA) among patients with epilepsy.

METHODS

A literature search was conducted using the Embase and MEDLINE databases. Studies were included if they involved patients with drug-resistant epilepsy treated with VNS and used validated tools to report on quality of sleep or sleep apnea. The literature search yielded 112 citations related to VNS and sleep quality, and 82 citations related to sleep apnea. Twelve articles were included in the review, of which five measured quality of sleep among patients who underwent VNS, six studies measured sleep apnea, and one study measured both outcomes.

RESULTS

Studies measuring quality of sleep used different methods, including sleep quality questionnaires and the percentage of sleep in each cycle. Studies also varied in patient populations, the use of control groups, and whether multiple measurements were taken for each patient. Some studies found improved sleep quality after VNS, whereas others found reductions in deep sleep stages. Additionally, mixed results in sleep quality were found when comparing patients with epilepsy who received VNS treatment versus patients with epilepsy who did not receive VNS treatment. Variables such as VNS intensity and age could potentially confound quality of sleep. Studies measuring sleep apnea consistently found increased proportions of patients diagnosed with OSA or increased sleep index scores after VNS implantation.

SIGNIFICANCE

Overall, the effect of VNS on quality of sleep remains unclear, as studies were very heterogeneous, although the effect on sleep apnea has consistently shown an increase in sleep apnea severity indices after VNS implantation. Future studies with consistent measures and discussions of confounding are required to determine the effect of VNS on quality of sleep, and the effect of VNS parameters should be further explored among patients who develop sleep apnea.

Functional brain connectivity indexes derived from low-density EEG of pre-implanted patients as VNS outcome predictors

Objective. In 1/3 of patients, anti-seizure medications may be insufficient, and resective surgery may be offered whenever the seizure onset is localized and situated in a non-eloquent brain region. When surgery is not feasible or fails, vagus nerve stimulation (VNS) therapy can be used as an add-on treatment to reduce seizure frequency and/or severity. However, screening tools or methods for predicting patient response to VNS and avoiding unnecessary implantation are unavailable, and confident biomarkers of clinical efficacy are unclear.Approach. To predict the response of patients to VNS, functional brain connectivity measures in combination with graph measures have been primarily used with respect to imaging techniques such as functional magnetic resonance imaging, but connectivity graph-based analysis based on electrophysiological signals such as electroencephalogram, have been barely explored. Although the study of the influence of VNS on functional connectivity is not new, this work is distinguished by using preimplantation low-density EEG data to analyze discriminative measures between responders and non-responder patients using functional connectivity and graph theory metrics.Main results. By calculating five functional brain connectivity indexes per frequency band upon partial directed coherence and direct transform function connectivity matrices in a population of 37 refractory epilepsy patients, we found significant differences (p< 0.05) between the global efficiency, average clustering coefficient, and modularity of responders and non-responders using the Mann-Whitney U test with Benjamini-Hochberg correction procedure and use of a false discovery rate of 5%.Significance. Our results indicate that these measures may potentially be used as biomarkers to predict responsiveness to VNS therapy.

SeriesSleepNet: an EEG time series model with partial data augmentation for automatic sleep stage scoring

Introduction: We propose an automatic sleep stage scoring model, referred to as SeriesSleepNet, based on convolutional neural network (CNN) and bidirectional long short-term memory (bi-LSTM) with partial data augmentation. We used single-channel raw electroencephalography signals for automatic sleep stage scoring. Methods: Our framework was focused on time series information, so we applied partial data augmentation to learn the connected time information in small series. In specific, the CNN module learns the time information of one epoch (intra-epoch) whereas the bi-LSTM trains the sequential information between the adjacent epochs (inter-epoch). Note that the input of the bi-LSTM is the augmented CNN output. Moreover, the proposed loss function was used to fine-tune the model by providing additional weights. To validate the proposed framework, we conducted two experiments using the Sleep-EDF and SHHS datasets. Results and Discussion: The results achieved an overall accuracy of 0.87 and 0.84 and overall F1-score of 0.80 and 0.78 and kappa value of 0.81 and 0.78 for five-class classification, respectively. We showed that the SeriesSleepNet was superior to the baselines based on each component in the proposed framework. Our architecture also outperformed the state-of-the-art methods with overall F1-score, accuracy, and kappa value. Our framework could provide information on sleep disorders or quality of sleep to automatically classify sleep stages with high performance.

Closed-loop modulation of local slow oscillations in human NREM sleep

Slow-wave sleep is the deep non-rapid eye-movement (NREM) sleep stage that is most relevant for the recuperative function of sleep. Its defining property is the presence of slow oscillations (<2 Hz) in the scalp electroencephalogram (EEG). Slow oscillations are generated by a synchronous back and forth between highly active UP-states and silent DOWN-states in neocortical neurons. Growing evidence suggests that closed-loop sensory stimulation targeted at UP-states of EEG-defined slow oscillations can enhance the slow oscillatory activity, increase sleep depth, and boost sleep's recuperative functions. However, several studies failed to replicate such findings. Failed replications might be due to the use of conventional closed-loop stimulation algorithms that analyze the signal from one single electrode and thereby neglect the fact that slow oscillations vary with respect to their origins, distributions, and trajectories on the scalp. In particular, conventional algorithms nonspecifically target functionally heterogeneous UP-states of distinct origins. After all, slow oscillations at distinct sites of the scalp have been associated with distinct functions. Here we present a novel EEG-based closed-loop stimulation algorithm that allows targeting UP- and DOWN-states of distinct cerebral origins based on topographic analyses of the EEG: the topographic targeting of slow oscillations (TOPOSO) algorithm. We present evidence that the TOPOSO algorithm can detect and target local slow oscillations with specific, predefined voltage maps on the scalp in real-time. When compared to a more conventional, single-channel-based approach, TOPOSO leads to fewer but locally more specific stimulations in a simulation study. In a validation study with napping participants, TOPOSO targets auditory stimulation reliably at local UP-states over frontal, sensorimotor, and centro-parietal regions. Importantly, auditory stimulation temporarily enhanced the targeted local state. However, stimulation then elicited a standard frontal slow oscillation rather than local slow oscillations. The TOPOSO algorithm is suitable for the modulation and the study of the functions of local slow oscillations.

Multiple Time Series Fusion Based on LSTM: An Application to CAP A Phase Classification Using EEG

The Cyclic Alternating Pattern (CAP) is a periodic activity detected in the electroencephalogram (EEG) signals. This pattern was identified as a marker of unstable sleep with several possible clinical applications; however, there is a need to develop automatic methodologies to facilitate real-world applications based on CAP assessment. Therefore, a deep learning-based EEG channels' feature level fusion was proposed in this work and employed for the CAP A phase classification. Two optimization algorithms optimized the channel selection, fusion, and classification procedures. The developed methodologies were evaluated by fusing the information from multiple EEG channels for patients with nocturnal frontal lobe epilepsy and patients without neurological disorders. Results showed that both optimization algorithms selected a comparable structure with similar feature level fusion, consisting of three electroencephalogram channels (Fp2-F4, C4-A1, F4-C4), which is in line with the CAP protocol to ensure multiple channels' arousals for CAP detection. Moreover, the two optimized models reached an area under the receiver operating characteristic curve of 0.82, with average accuracy ranging from 77% to 79%, a result in the upper range of the specialist agreement and best state-of-the-art works, despite a challenging dataset. The proposed methodology also has the advantage of providing a fully automatic analysis without requiring any manual procedure. Ultimately, the models were revealed to be noise-resistant and resilient to multiple channel loss, being thus suitable for real-world application.

When the Locus Coeruleus Speaks Up in Sleep: Recent Insights, Emerging Perspectives

For decades, numerous seminal studies have built our understanding of the locus coeruleus (LC), the vertebrate brain’s principal noradrenergic system. Containing a numerically small but broadly efferent cell population, the LC provides brain-wide noradrenergic modulation that optimizes network function in the context of attentive and flexible interaction with the sensory environment. This review turns attention to the LC’s roles during sleep. We show that these roles go beyond down-scaled versions of the ones in wakefulness. Novel dynamic assessments of noradrenaline signaling and LC activity uncover a rich diversity of activity patterns that establish the LC as an integral portion of sleep regulation and function. The LC could be involved in beneficial functions for the sleeping brain, and even minute alterations in its functionality may prove quintessential in sleep disorders.

Sleep and Temporal Lobe Epilepsy – Associations, Mechanisms and Treatment Implications

In this systematic review, we aim to describe the association between temporal lobe epilepsy (TLE) and sleep, with bidirectional links in mechanisms and therapeutic aspects. Sleep stages may variably impact seizure occurrence, secondary generalization and the development, frequency and distribution of interictal epileptiform discharges. Conversely, epilepsy affects sleep micro- and macroarchitecture. TLE, the most frequent form of drug resistant epilepsy (DRE), shares an enduring relationship with sleep, with some intriguing potential mechanisms specific to anatomic localization, linking the two. Sleep characteristics of TLE may also inform localizing properties in persons with DRE, since seizures arising from the temporal lobe seem to be more common during wakefulness, compared to seizures of extratemporal origin. Polysomnographic studies indicate that persons with TLE may experience excessive daytime somnolence, disrupted sleep architecture, increased wake after sleep onset, frequent shifts in sleep stages, lower sleep efficiency, decreased rapid eye movement (REM) sleep, and possibly, increased incidence of sleep apnea. Limited literature suggests that effective epilepsy surgery may remedy many of these objective and subjective sleep-related concerns, via multipronged effects, apart from reduced seizure frequency. Additionally, sleep abnormalities also seem to influence memory, language and cognitive-executive function in both medically controlled and refractory TLE. Another aspect of the relationship pertains to anti-seizure medications (ASMs), which may contribute significantly to sleep characteristics and abnormalities in persons with TLE. Literature focused on specific aspects of TLE and sleep is limited, and heterogeneous. Future investigations are essential to understand the pathogenetic mechanisms linking sleep abnormalities on epilepsy outcomes in the important sub-population of TLE.

A narrative review on invasive brain stimulation for treatment-resistant depression

While most patients with depression respond to pharmacotherapy and psychotherapy, about one-third will present treatment resistance to these interventions. For patients with treatment-resistant depression (TRD), invasive neurostimulation therapies such as vagus nerve stimulation, deep brain stimulation, and epidural cortical stimulation may be considered. We performed a narrative review of the published literature to identify papers discussing clinical studies with invasive neurostimulation therapies for TRD. After a database search and title and abstract screening, relevant English-language articles were analyzed. Vagus nerve stimulation, approved by the U.S. Food and Drug Administration as a TRD treatment, may take several months to show therapeutic benefits, and the average response rate varies from 15.2-83%. Deep brain stimulation studies have shown encouraging results, including rapid response rates (> 30%), despite conflicting findings from randomized controlled trials. Several brain regions, such as the subcallosal-cingulate gyrus, nucleus accumbens, ventral capsule/ventral striatum, anterior limb of the internal capsule, medial-forebrain bundle, lateral habenula, inferior-thalamic peduncle, and the bed-nucleus of the stria terminalis have been identified as key targets for TRD management. Epidural cortical stimulation, an invasive intervention with few reported cases, showed positive results (40-60% response), although more extensive trials are needed to confirm its potential in patients with TRD.

Fibromyalgia Detection Based on EEG Connectivity Patterns

Objective: The identification of a complementary test to confirm the diagnosis of FM. The diagnosis of fibromyalgia (FM) is based on clinical features, but there is still no consensus, so patients and clinicians might benefit from such a test. Recent findings showed that pain lies in neuronal bases (pain matrices) and, in the long term, chronic pain modifies the activity and dynamics of brain structures. Our hypothesis is that patients with FM present lower levels of brain activity and therefore less connectivity than controls. Methods: We registered the resting state EEG of 23 patients with FM and compared them with 23 control subjects’ resting state recordings from the PhysioBank database. We measured frequency, amplitude, and functional connectivity, and conducted source localization (sLORETA). ROC analysis was performed on the resulting data. Results: We found significant differences in brain bioelectrical activity at rest in all analyzed bands between patients and controls, except for Delta. Subsequent source analysis provided connectivity values that depicted a distinct profile, with high discriminative capacity (between 91.3–100%) between the two groups. Conclusions: Patients with FM show a distinct neurophysiological pattern that fits with the clinical features of the disease.

Advanced Signal Processing Methods for Characterization of Schizophrenia

OBJECTIVE

Schizophrenia is a severe mental disorder associated with nerobiological deficits. Auditory oddball P300 have been found to be one of the most consistent markers of schizophrenia. The goal of this study is to find quantitative features that can objectively distinguish patients with schizophrenia (SCZs) from healthy controls (HCs) based on their recorded auditory odd-ball P300 electroencephalogram (EEG) data.

METHODS

Using EEG dataset, we develop a machine learning (ML) algorithm to distinguish 57 SCZs from 66 HCs. The proposed ML algorithm has three steps. In the first step, a brain source localization (BSL) procedure using the linearly constrained minimum variance (LCMV) beamforming approach is employed on EEG signals to extract source waveforms from 30 specified brain regions. In the second step, a method for estimating effective connectivity, referred to as symbolic transfer entropy (STE), is applied to the source waveforms. In the third step the ML algorithm is applied to the STE connectivity matrix to determine whether a set of features can be found that successfully discriminate SCZ from HC.

RESULTS

The findings revealed that the SCZs have significantly higher effective connectivity compared to HCs and the selected STE features could achieve an accuracy of 92.68%, with a sensitivity of 92.98% and specificity of 92.42%.

CONCLUSION

The findings imply that the extracted features are from the regions that are mainly affected by SCZ and can be used to distinguish SCZs from HCs.

SIGNIFICANCE

The proposed ML algorithm may prove to be a promising tool for the clinical diagnosis of schizophrenia.

Deep Learning Classification of Neuro-Emotional Phase Domain Complexity Levels Induced by Affective Video Film Clips

In the present article, a novel emotional complexity marker is proposed for classification of discrete emotions induced by affective video film clips. Principal Component Analysis (PCA) is applied to full-band specific phase space trajectory matrix (PSTM) extracted from short emotional EEG segment of 6 s, then the first principal component is used to measure the level of local neuronal complexity. As well, Phase Locking Value (PLV) between right and left hemispheres is estimated for in order to observe the superiority of local neuronal complexity estimation to regional neuro-cortical connectivity measurements in clustering nine discrete emotions (fear, anger, happiness, sadness, amusement, surprise, excitement, calmness, disgust) by using Long-Short-Term-Memory Networks as deep learning applications. In tests, two groups (healthy females and males aged between 22 and 33 years old) are classified with the accuracy levels of [Formula: see text] and [Formula: see text] through the proposed emotional complexity markers and and connectivity levels in terms of PLV in amusement. The groups are found to be statistically different ( p << 0.5) in amusement with respect to both metrics, even if gender difference does not lead to different neuro-cortical functions in any of the other discrete emotional states. The high deep learning classification accuracy of [Formula: see text] is commonly obtained for discrimination of positive emotions from negative emotions through the proposed new complexity markers. Besides, considerable useful classification performance is obtained in discriminating mixed emotions from each other through full-band connectivity features. The results reveal that emotion formation is mostly influenced by individual experiences rather than gender. In detail, local neuronal complexity is mostly sensitive to the affective valance rating, while regional neuro-cortical connectivity levels are mostly sensitive to the affective arousal ratings.