Electroencephalogram synchronization measure as a predictive biomarker of Vagus nerve stimulation response in refractory epilepsy: A retrospective study.

There are currently no established biomarkers for predicting the therapeutic effectiveness of Vagus Nerve Stimulation (VNS). Given that neural desynchronization is a pivotal mechanism underlying VNS action, EEG synchronization measures could potentially serve as predictive biomarkers of VNS response. Notably, an increased brain synchronization in delta band has been observed during sleep-potentially due to an activation of thalamocortical circuitry, and interictal epileptiform discharges are more frequently observed during sleep. Therefore, investigation of EEG synchronization metrics during sleep could provide a valuable insight into the excitatory-inhibitory balance in a pro-epileptogenic state, that could be pathological in patients exhibiting a poor response to VNS. A 19-channel-standard EEG system was used to collect data from 38 individuals with Drug-Resistant Epilepsy (DRE) who were candidates for VNS implantation. An EEG synchronization metric-the Weighted Phase Lag Index (wPLI)-was extracted before VNS implantation and compared between sleep and wakefulness, and between responders (R) and non-responders (NR). In the delta band, a higher wPLI was found during wakefulness compared to sleep in NR only. However, in this band, no synchronization difference in any state was found between R and NR. During sleep and within the alpha band, a negative correlation was found between wPLI and the percentage of seizure reduction after VNS implantation. Overall, our results suggest that patients exhibiting a poor VNS efficacy may present a more pathological thalamocortical circuitry before VNS implantation. EEG synchronization measures could provide interesting insights into the prerequisites for responding to VNS, in order to avoid unnecessary implantations in patients showing a poor therapeutic efficacy.

Temporal prediction changes motor-related EEG phase synchronization and network centrality in alpha and beta band.

Much neurophysiological evidence revealed motor system is involved in temporal prediction. However, It remains unknown how temporal prediction influences motor-related neural representations. Thus, more neural evidence is needed to understand better how temporal prediction influences the motor. This study designed a rhythmic finger-tap task and formed three temporal prediction conditions, i.e., 1000ms temporal prediction, 1500ms temporal prediction, and no temporal prediction. Behavioral and EEG data from 24 healthy subjects were recorded. The weighted phase lag index was calculated to measure the degree of phase synchronization. Eigenvector centrality and betweenness centrality were used to measure brain connectivity. Behavioral results showed that tap-visual asynchronies were decreased when temporal prediction existed. Phase synchronization results showed, compared to no temporal prediction, the alpha-band phase synchronization between the frontal and central area was reduced in 1000ms temporal prediction, and the beta-band phase synchronization between the frontal and parietal area was decreased in 1500ms temporal prediction. As to the brain connectivity, compared to no temporal prediction condition, the eigenvector centrality of the left frontal in 1500ms temporal prediction was decreased in the alpha band, and the betweenness centrality of the right temporal in 1000ms temporal prediction was reduced in the alpha-band. These results can provide new neural evidence for a better understanding of temporal prediction and motor interactions.

Reproducibility of Rolandic beta rhythm modulation in MEG and EEG.

The Rolandic beta rhythm, at ∼20 Hz, is generated in the somatosensory and motor cortices and is modulated by motor activity and sensory stimuli, causing a short lasting suppression that is followed by a rebound of the beta rhythm. The rebound reflects inhibitory changes in the primary sensorimotor (SMI) cortex, and thus it has been used as a biomarker to follow the recovery of patients with acute stroke. The longitudinal stability of beta rhythm modulation is a prerequisite for its use in long-term follow-ups. We quantified the reproducibility of beta rhythm modulation in healthy subjects in a 1-year-longitudinal study both for MEG and EEG at T0, 1 month (T1-month, n = 8) and 1 year (T1-year, n = 19). The beta rhythm (13-25 Hz) was modulated by fixed tactile and proprioceptive stimulations of the index fingers. The relative peak strengths of beta suppression and rebound did not differ significantly between the sessions, and intersession reproducibility was good or excellent according to intraclass correlation-coefficient values (0.70-0.96) both in MEG and EEG. Our results indicate that the beta rhythm modulation to tactile and proprioceptive stimulation is well reproducible within 1 year. These results support the use of beta modulation as a biomarker in long-term follow-up studies, e.g., to quantify the functional state of the SMI cortex during rehabilitation and drug interventions in various neurological impairments.NEW & NOTEWORTHY The present study demonstrates that beta rhythm modulation is highly reproducible in a group of healthy subjects within a year. Hence, it can be reliably used as a biomarker in longitudinal follow-up studies in different neurological patient groups to reflect changes in the functional state of the sensorimotor cortex.

Brain Fingerprinting and Lie Detection: A Study of Dynamic Functional Connectivity Patterns of Deception Using EEG Phase Synchrony Analysis.

This study investigated the brain functional connectivity (FC) patterns related to lie detection (LD) tasks with the purpose of analyzing the underlying cognitive processes and mechanisms in deception. Using the guilty knowledge test protocol, 30 subjects were divided randomly into guilty and innocent groups, and their electroencephalogram (EEG) signals were recorded on 32 electrodes. Phase synchrony of EEG was analyzed between different brain regions. A few-trials-based relative phase synchrony (FTRPS) measure was proposed to avoid the false synchronization that occurs due to volume conduction. FTRPS values with a significantly statistical difference between two groups were employed to construct FC patterns of deception, and the FTRPS values from the FC networks were extracted as the features for the training and testing of the support vector machine. Finally, four more intuitive brain fingerprinting graphs (BFG) on delta, theta, alpha and beta bands were respectively proposed. The experimental results reveal that deceptive responses elicited greater oscillatory synchronization than truthful responses between different brain regions, which plays an important role in executing lying tasks. The functional connectivity in the BFG is mainly implicated in the visuo-spatial imagery, bottom-top attention and memory systems, work memory and episodic encoding, and top-down attention and inhibition processing. These may, in part, underlie the mechanism of communication between different brain cortices during lying. High classification accuracy demonstrates the validation of BFG to identify deception behavior, and suggests that the proposed FTRPS could be a sensitive measure for LD in the real application.

EEG Phase Synchrony Reflects SNR Levels During Continuous Speech-in-Noise Tasks.

Comprehension of speech in noise is a challenge for hearing-impaired (HI) individuals. Electroencephalography (EEG) provides a tool to investigate the effect of different levels of signal-to-noise ratio (SNR) of the speech. Most studies with EEG have focused on spectral power in well-defined frequency bands such as alpha band. In this study, we investigate how local functional connectivity, i.e. functional connectivity within a localized region of the brain, is affected by two levels of SNR. Twenty-two HI participants performed a continuous speech in noise task at two different SNRs (+3 dB and +8 dB). The local connectivity within eight regions of interest was computed by using a multivariate phase synchrony measure on EEG data. The results showed that phase synchrony increased in the parietal and frontal area as a response to increasing SNR. We contend that local connectivity measures can be used to discriminate between speech-evoked EEG responses at different SNRs.

Spacetime in the brain: rapid brain network reorganization in visual processing and recovery.

Functional connectivity networks (FCN) are the physiological basis of brain synchronization to integrating neural activity. They are not rigid but can reorganize under pathological conditions or during mental or behavioral states. However, because mental acts can be very fast, like the blink of an eye, we now used the visual system as a model to explore rapid FCN reorganization and its functional impact in normal, abnormal and post treatment vision. EEG-recordings were time-locked to visual stimulus presentation; graph analysis of neurophysiological oscillations were used to characterize millisecond FCN dynamics in healthy subjects and in patients with optic nerve damage before and after neuromodulation with alternating currents stimulation and were correlated with visual performance. We showed that rapid and transient FCN synchronization patterns in humans can evolve and dissolve in millisecond speed during visual processing. This rapid FCN reorganization is functionally relevant because disruption and recovery after treatment in optic nerve patients correlated with impaired and recovered visual performance, respectively. Because FCN hub and node interactions can evolve and dissolve in millisecond speed to manage spatial and temporal neural synchronization during visual processing and recovery, we propose "Brain Spacetime" as a fundamental principle of the human mind not only in visual cognition but also in vision restoration.

Oscillatory activity and EEG phase synchrony of concurrent word segmentation and meaning-mapping in 9-year-old children.

When learning a new language, one must segment words from continuous speech and associate them with meanings. These complex processes can be boosted by attentional mechanisms triggered by multi-sensory information. Previous electrophysiological studies suggest that brain oscillations are sensitive to different hierarchical complexity levels of the input, making them a plausible neural substrate for speech parsing. Here, we investigated the functional role of brain oscillations during concurrent speech segmentation and meaning acquisition in sixty 9-year-old children. We collected EEG data during an audio-visual statistical learning task during which children were exposed to a learning condition with consistent word-picture associations and a random condition with inconsistent word-picture associations before being tested on their ability to recall words and word-picture associations. We capitalized on the brain dynamics to align neural activity to the same rate as an external rhythmic stimulus to explore modulations of neural synchronization and phase synchronization between electrodes during multi-sensory word learning. Results showed enhanced power at both word- and syllabic-rate and increased EEG phase synchronization between frontal and occipital regions in the learning compared to the random condition. These findings suggest that multi-sensory cueing and attentional mechanisms play an essential role in children's successful word learning.

Association between aphasia severity and brain network alterations after stroke assessed using the electroencephalographic phase synchrony index.

Electroencephalographic synchrony can help assess brain network status; however, its usefulness has not yet been fully proven. We developed a clinically feasible method that combines the phase synchrony index (PSI) with resting-state 19-channel electroencephalography (EEG) to evaluate post-stroke motor impairment. In this study, we investigated whether our method could be applied to aphasia, a common post-stroke cognitive impairment. This study included 31 patients with subacute aphasia and 24 healthy controls. We assessed the expressive function of patients and calculated the PSIs of three motor language-related regions: frontofrontal, left frontotemporal, and right frontotemporal. Then, we evaluated post-stroke network alterations by comparing PSIs of the patients and controls and by analyzing the correlations between PSIs and aphasia scores. The frontofrontal PSI (beta band) was lower in patients than in controls and positively correlated with aphasia scores, whereas the right frontotemporal PSI (delta band) was higher in patients than in controls and negatively correlated with aphasia scores. Evaluation of artifacts suggests that this association is attributed to true synchrony rather than spurious synchrony. These findings suggest that post-stroke aphasia is associated with alternations of two different networks and point to the usefulness of EEG PSI in understanding the pathophysiology of aphasia.

Gamma-band synchronisation in a frontotemporal auditory information processing network.

Neural oscillations are fundamental mechanisms of the human brain that enable coordinated activity of different brain regions during perceptual and cognitive processes. A frontotemporal network generated by means of gamma oscillations and comprising the auditory cortex (AC) and the anterior cingulate cortex (ACC) has been shown to be involved in the cognitively demanding auditory information processing. This study aims to reveal patterns of functional and effective connectivity within this network in healthy subjects by means of simultaneously recorded electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). We simultaneously recorded EEG and fMRI in 28 healthy subjects during the performance of a cognitively demanding auditory choice reaction task. Connectivity between the ACC and AC was analysed employing EEG and fMRI connectivity measures. We found a significant BOLD signal correlation between the ACC and AC, a significant task-dependant increase of fMRI connectivity (gPPI) and a significant increase in functional coupling in the gamma frequency range between these regions (LPS), which was increased in top-down direction (granger analysis). EEG and fMRI connectivity measures were positively correlated. The results of these study point to a role of a top-down influence of the ACC on the AC executed by means of gamma synchronisation. The replication of fMRI connectivity patterns in simultaneously recorded EEG data and the correlation between connectivity measures from both domains found in our study show, that brain connectivity based on the synchronisation of gamma oscillations is mirrored in fMRI connectivity patterns.

Differential classification of states of consciousness using envelope- and phase-based functional connectivity.

The development of sophisticated computational tools to quantify changes in the brain's oscillatory dynamics across states of consciousness have included both envelope- and phase-based measures of functional connectivity (FC), but there are very few direct comparisons of these techniques using the same dataset. The goal of this study was to compare an envelope-based (i.e. Amplitude Envelope Correlation, AEC) and a phase-based (i.e. weighted Phase Lag Index, wPLI) measure of FC in their classification of states of consciousness. Nine healthy participants underwent a three-hour experimental anesthetic protocol with propofol induction and isoflurane maintenance, in which five minutes of 128-channel electroencephalography were recorded before, during, and after anesthetic-induced unconsciousness, at the following time points: Baseline; light sedation with propofol (Light Sedation); deep unconsciousness following three hours of surgical levels of anesthesia with isoflurane (Unconscious); five minutes prior to the recovery of consciousness (Pre-ROC); and three hours following the recovery of consciousness (Recovery). Support vector machine classification was applied to the source-localized EEG in the alpha (8-13 Hz) frequency band in order to investigate the ability of AEC and wPLI (separately and together) to discriminate i) the four states from Baseline; ii) Unconscious ("deep" unconsciousness) vs. Pre-ROC ("light" unconsciousness); and iii) responsiveness (Baseline, Light Sedation, Recovery) vs. unresponsiveness (Unconscious, Pre-ROC). AEC and wPLI yielded different patterns of global connectivity across states of consciousness, with AEC showing the strongest network connectivity during the Unconscious epoch, and wPLI showing the strongest connectivity during full consciousness (i.e., Baseline and Recovery). Both measures also demonstrated differential predictive contributions across participants and used different brain regions for classification. AEC showed higher classification accuracy overall, particularly for distinguishing anesthetic-induced unconsciousness from Baseline (83.7 ± 0.8%). AEC also showed stronger classification accuracy than wPLI when distinguishing Unconscious from Pre-ROC (i.e., "deep" from "light" unconsciousness) (AEC: 66.3 ± 1.2%; wPLI: 56.2 ± 1.3%), and when distinguishing between responsiveness and unresponsiveness (AEC: 76.0 ± 1.3%; wPLI: 63.6 ± 1.8%). Classification accuracy was not improved compared to AEC when both AEC and wPLI were combined. This analysis of source-localized EEG data demonstrates that envelope- and phase-based FC provide different information about states of consciousness but that, on a group level, AEC is better able to detect relative alterations in brain FC across levels of anesthetic-induced unconsciousness compared to wPLI.

Continuous sensorimotor rhythm based brain computer interface learning in a large population.

Brain computer interfaces (BCIs) are valuable tools that expand the nature of communication through bypassing traditional neuromuscular pathways. The non-invasive, intuitive, and continuous nature of sensorimotor rhythm (SMR) based BCIs enables individuals to control computers, robotic arms, wheel-chairs, and even drones by decoding motor imagination from electroencephalography (EEG). Large and uniform datasets are needed to design, evaluate, and improve the BCI algorithms. In this work, we release a large and longitudinal dataset collected during a study that examined how individuals learn to control SMR-BCIs. The dataset contains over 600 hours of EEG recordings collected during online and continuous BCI control from 62 healthy adults, (mostly) right hand dominant participants, across (up to) 11 training sessions per participant. The data record consists of 598 recording sessions, and over 250,000 trials of 4 different motor-imagery-based BCI tasks. The current dataset presents one of the largest and most complex SMR-BCI datasets publicly available to date and should be useful for the development of improved algorithms for BCI control.

EEG cross-frequency phase synchronization as an index of memory matching in visual search.

Visual perception is influenced by our expectancies about incoming sensory information. It is assumed that mental templates of expected sensory input are created and compared to actual input, which can be matching or not. When such mental templates are held in working memory, cross-frequency phase synchronization (CFS) between theta and gamma band activity has been proposed to serve matching processes between prediction and sensation. We investigated how this is affected by the number of activated templates that could be matched by comparing conditions where participants had to keep either one or multiple templates in mind for successful visual search. We found a transient CFS between EEG theta and gamma activity in an early time window around 150 ms after search display presentation, in right hemispheric parietal cortex. Our results suggest that for single template conditions, stronger transient theta-gamma CFS at posterior sites contralateral to target presentation can be observed than for multiple templates. This can be interpreted as evidence to the idea of sequential attentional templates. But mainly, it is understood in line with previous theoretical accounts strongly arguing for transient synchronization between posterior theta and gamma phase as a neural correlate of matching incoming sensory information with contents from working memory and as evidence for limitations in memory matching during multiple template search.

One Way or Another: Cortical Language Areas Flexibly Adapt Processing Strategies to Perceptual And Contextual Properties of Speech.

Cortical circuits rely on the temporal regularities of speech to optimize signal parsing for sound-to-meaning mapping. Bottom-up speech analysis is accelerated by top-down predictions about upcoming words. In everyday communications, however, listeners are regularly presented with challenging input-fluctuations of speech rate or semantic content. In this study, we asked how reducing speech temporal regularity affects its processing-parsing, phonological analysis, and ability to generate context-based predictions. To ensure that spoken sentences were natural and approximated semantic constraints of spontaneous speech we built a neural network to select stimuli from large corpora. We analyzed brain activity recorded with magnetoencephalography during sentence listening using evoked responses, speech-to-brain synchronization and representational similarity analysis. For normal speech theta band (6.5-8 Hz) speech-to-brain synchronization was increased and the left fronto-temporal areas generated stronger contextual predictions. The reverse was true for temporally irregular speech-weaker theta synchronization and reduced top-down effects. Interestingly, delta-band (0.5 Hz) speech tracking was greater when contextual/semantic predictions were lower or if speech was temporally jittered. We conclude that speech temporal regularity is relevant for (theta) syllabic tracking and robust semantic predictions while the joint support of temporal and contextual predictability reduces word and phrase-level cortical tracking (delta).

Disruptions of cortico-kinematic interactions in Parkinson's disease.

The cortical role of the motor symptoms reflected by kinematic characteristics in Parkinson's disease (PD) is poorly understood. In this study, we aim to explore how PD affects cortico-kinematic interactions. Electroencephalographic (EEG) and kinematic data were recorded from seven healthy participants and eight participants diagnosed with PD during a set of self-paced finger tapping tasks. Event-related desynchronization (ERD) was compared between groups in the α (8-14 Hz), low-ß (14-20 Hz), and high-ß (20-35 Hz) frequency bands to investigate between-group differences in the cortical activities associated with movement. Average kinematic peak amplitudes and latencies were extracted alongside Sample Entropy (SaEn), a measure of signal complexity, as variables for comparison between groups. These variables were further correlated with average EEG power in each frequency band to establish within-group interactions between cortical motor functions and kinematic motor output. High ß-band power correlated with mean kinematic peak latency and signal complexity in the healthy group, while no correlation was found in the PD group. Also, the healthy group demonstrated stronger ERD in the broad ß-band than the PD participants. Our results suggest that cortical ß-band power in healthy populations is graded to finger tapping latency and complexity of movement, but this relationship is impaired in PD. These insights could help further enhance our understanding of the role of cortical ß-band oscillations in healthy movement and the possible disruption of that relationship in PD. These outcomes can provide further directions for treatment and therapeutic applications and potentially establish cortical biomarkers of Parkinson's disease.

Beta synchrony for expressive language lateralizes to right hemisphere in development.

A left perisylvian network is known to support language in healthy adults. Low-beta (13-23 Hz) event-related desynchrony (ERD) has been observed during verb generation, at approximately 700-1200 ms post-stimulus presentation in past studies; the signal is known to reflect increased neuronal firing and metabolic demand during language production. In contrast, concurrent beta event-related synchrony (ERS) is thought to reflect neuronal inhibition but has not been well studied in the context of language. Further, while low-beta ERD for expressive language has been found to gradually shift from bilateral in childhood to left hemispheric by early adulthood, developmental lateralization of ERS has not been established. We used magnetoencephalography to study low beta ERS lateralization in a group of children and adolescents (n = 78), aged 4 to less than 19 years, who performed covert verb generation. We found that the youngest children had bilateral ERD and ERS. By adolescence, low-beta ERD was predominantly left lateralized in perisylvian cortex (i.e., Broca's and Wernicke's regions), while beta ERS was predominantly right lateralized. Increasing lateralization was significantly correlated to age for both ERD (Spearman's r = 0.45, p < 0.01) and ERS (Spearman's r = - 0.44, p < 0.01). Interestingly, while ERD lateralized in a linear manner, ERS lateralization followed a nonlinear trajectory, suggesting distinct developmental trajectories. Implications to early-age neuroplasticity and neuronal inhibition are discussed.

Synchronous spiking associated with prefrontal high γ oscillations evokes a 5-Hz rhythmic modulation of spiking in locus coeruleus.

The brainstem noradrenergic locus coeruleus (LC) is reciprocally connected with the prefrontal cortex (PFC). Coupling between LC spiking and the depolarizing phase of slow (1-2 Hz) waves in PFC field potentials during sleep and anesthesia suggests that LC drives cortical state transition. Reciprocal LC-PFC connectivity should also allow interactions in the opposing (top-down) direction, but prior work has only studied prefrontal control over LC activity using electrical or optogenetic stimulation. Here, we describe the physiological characteristics of spontaneously occurring top-down LC-PFC interactions. We recorded LC multiunit activity (MUA) simultaneously with PFC single-unit and local field potential (LFP) activity in urethane-anesthetized rats. We observed cross-regional coupling between the phase of 5-Hz oscillations in LC-MUA and the power of PFC LFP 60-200 Hz high γ (hγ). Transient increases in PFC hγ power preceded peaks in the 5-Hz LC-MUA oscillation. Analysis of cross-regional transfer entropy demonstrated that the PFC hγ transients were predictive of a transient increase in LC-MUA. An ∼29 ms delay between these signals was consistent with the conduction velocity from the PFC to the LC. Finally, we showed that PFC hγ transients are associated with synchronized spiking of a subset (27%) of PFC single units. Our data suggest that PFC hγ transients may indicate the timing of the top-down excitatory input to LC, at least under conditions when LC neuronal population activity fluctuates rhythmically at 5 Hz. Synchronized PFC neuronal spiking that occurs during hγ transients may provide a previously unknown mode of top-down control over the LC.NEW & NOTEWORTHY The prefrontal cortex (PFC) is thought to control activity in the noradrenergic locus coeruleus (LC). Prior anatomical and prefrontal stimulation studies demonstrated the potential for PFC-LC interactions; however, it is unknown what types of PFC activity affect the LC. Here, we show that transient increases in PFC high γ power and associated changes in PFC unit-pair synchrony are a potential sign of top-down control over the LC.

Configural Cues Associated with Reward Elicit Theta Oscillations of Rat Retrosplenial Cortical Neurons Phase-Locked to LFP Theta Cycles.

Previous behavioral studies implicated the retrosplenial cortex (RSC) in stimulus-stimulus associations, and also in the retrieval of remote associative memory based on EEG theta oscillations. However, neural mechanisms involved in the retrieval of stored information of such associations and memory in the RSC remain unclear. To investigate the neural mechanisms underlying these processes, RSC neurons and local field potentials (LFPs) were simultaneously recorded from well-trained rats performing a cue-reward association task. In the task, simultaneous presentation of two multimodal conditioned stimuli (configural CSs) predicted a reward outcome opposite to that associated with the individual presentation of each elemental CS. Here, we show neurophysiological evidence that the RSC is involved in stimulus-stimulus association where configural CSs are discriminated from each elementary CS that is a constituent of the configural CSs, and that memory retrieval of rewarding CSs is associated with theta oscillation of RSC neurons during CS presentation, which is phase-locked to LFP theta cycles. The results suggest that cue (elementary and configural CSs)-reinforcement associations are stored in the RSC neural circuits, and are retrieved in synchronization with LFP theta rhythm.

Eye contact during joint attention with a humanoid robot modulates oscillatory brain activity.

Eye contact established by a human partner has been shown to affect various cognitive processes of the receiver. However, little is known about humans' responses to eye contact established by a humanoid robot. Here, we aimed at examining humans' oscillatory brain response to eye contact with a humanoid robot. Eye contact (or lack thereof) was embedded in a gaze-cueing task and preceded the phase of gaze-related attentional orienting. In addition to examining the effect of eye contact on the recipient, we also tested its impact on gaze-cueing effects (GCEs). Results showed that participants rated eye contact as more engaging and responded with higher desynchronization of alpha-band activity in left fronto-central and central electrode clusters when the robot established eye contact with them, compared to no eye contact condition. However, eye contact did not modulate GCEs. The results are interpreted in terms of the functional roles involved in alpha central rhythms (potentially interpretable also as mu rhythm), including joint attention and engagement in social interaction.

Noise-induced neurophysiological alterations in the rat medial geniculate body and thalamocortical desynchronization by deep brain stimulation.

The thalamic medial geniculate body (MGB) is uniquely positioned within the neural tinnitus networks. Deep brain stimulation (DBS) of the MGB has been proposed as a possible novel treatment for tinnitus, yet mechanisms remain elusive. The aim of this study was to characterize neurophysiologic hallmarks in the MGB after noise exposure and to assess the neurophysiological effects of electrical stimulation of the MGB. Fourteen male Sprague-Dawley rats were included. Nine subjects were unilaterally exposed to a 16-kHz octave-band noise at 115 dB for 90 min, five received sham exposure. Single units were recorded from the contralateral MGB where spontaneous firing, coefficient of variation, response type, rate-level functions, and thresholds were determined. Local field potentials and electroencephalographical (EEG) recordings were performed before and after high-frequency DBS of the MGB. Thalamocortical synchronization and power were analyzed. In total, 214 single units were identified (n = 145 in noise-exposed group, n = 69 in control group). After noise exposure, fast-responding neurons become less responsive or nonresponsive without change to their spontaneous rate, whereas sustained- and suppressed-type neurons exhibit enhanced spontaneous activity without change to their stimulus-driven activity. MGB DBS suppressed thalamocortical synchronization in the ß and γ bands, supporting suppression of thalamocortical synchronization as an underlying mechanism of tinnitus suppression by high frequency DBS. These findings contribute to our understanding of the neurophysiologic consequences of noise exposure and the mechanism of potential DBS therapy for tinnitus.NEW & NOTEWORTHY Separate functional classes of MGB neurons might have distinct roles in tinnitus pathophysiology. After noise exposure, fast-responding neurons become less responsive or nonresponsive without change to their spontaneous firing, whereas sustained and suppressed neurons exhibit enhanced spontaneous activity without change to their stimulus-driven activity. Furthermore, results suggest desynchronization of thalamocortical ß and γ oscillations as a mechanism of tinnitus suppression by MGB DBS.

Melatonin effects on EEG activity during non-rapid eye movement sleep in mild-to-moderate Alzheimer´s disease: a pilot study.

INTRODUCTION: There is evidence to suggest that melatonin diminishes non-rapid eye movement sleep (NREMS) latency in patients with Alzheimer´s disease (AD). However, melatonin's effects on cortical activity during NREMS in AD have not been studied. The objective of this research was to analyze the effects of melatonin on cortical activity during the stages of NREMS in 8 mild-to-moderate AD patients that received 5-mg of fast-release melatonin. METHODS: During a single-blind, placebo-controlled crossover study, polysomnographic recordings were obtained from C3-A1, C4-A2, F7-T3, F8-T4, F3-F4 and O1-O2. Also, the relative power (RP) and EEG coherences of the delta, theta, alpha1, alpha2, beta1, beta2 and gamma bands were calculated during NREMS-1, NREMS-2 and NREMS-3. These sleep latencies and all EEG data were then compared between the placebo and melatonin conditions. RESULTS: During NREMS-2, a significant RP increase was observed in the theta band of the left-central hemisphere. During NREMS-3, significant RP decreases in the beta bands were recorded in the right-central hemisphere, compared to the placebo group. After melatonin administration, significant decreases of EEG coherences in the beta2, beta1 and gamma bands were observed in the right hemisphere during NREMS-3. DISCUSSION: We conclude that short NREMS onset related to melatonin intake in AD patients is associated with a significant RP increase in the theta band and a decrease in RP and EEG coherences in the beta and gamma bands during NREMS-3. These results suggest that the GABAergic pathways are preserved in mild-to-moderate AD.