High-frequency EEG Oscillations Research Articles

Progressive increase of high-frequency EEG oscillations during meditation is associated with its trait effects on heart rate and proteomics: a study on the Tibetan Buddhist.

Meditation has been a spiritual and healing practice in the East for thousands of years. However, the neurophysiologic mechanisms underlying its traditional form remain unclear. In this study, we recruited a large sample of monks (n = 73) who practice Tibetan Buddhist meditation and compared with meditation-naive local controls (n = 30). Their electroencephalography (EEG) and electrocardiogram signals were simultaneously recorded and blood samples were collected to investigate the integrative effects of Tibetan Buddhist on brain, heart, and proteomics. We found that the EEG activities in monks shifted to a higher frequency from resting to meditation. Meditation starts with decrease of the (pre)frontal delta activity and increase of the (pre)frontal high beta and gamma activity; while at the deep meditative state, the posterior high-frequency activity was also increased, and could be specified as a biomarker for the deep meditation. The state increase of posterior high-frequency EEG activity was significantly correlated with the trait effects on heart rate and nueropilin-1 in monks, with the source of brain-heart correlation mainly locating in the attention and emotion networks. Our study revealed that the effects of Tibetan Buddhist meditation on brain, heart, and proteomics were highly correlated, demonstrating meditation as an integrative body-mind training.

Long-range correlation analysis of high frequency prefrontal electroencephalogram oscillations for dynamic emotion recognition

Numerous previous studies have proved the enormous potential of high frequency EEG in emotion recognition, however, the current existing EEG analytic methods are not so effective when dealing with high frequency oscillations. Therefore, a novel refined-detrended fluctuation analysis method multi-order detrended fluctuation analysis (MODFA) was proposed. The best fitting order is selected according to the inflection point in the dependence degree curve of high frequency EEG and multi-order polynomial. MODFA measures the power-law long-range correlation of high frequency nonlinear signals. Prefrontal EEG signals were recorded during six emotion-inducing tasks (neutral, fear, sad, happy, anger, and disgust). To confirm the susceptibility and efficiency of MODFA indices, including hurst-exponent MODFA-h1 and intercept MODFA-a1, on emotion recognition, we compared MODFA with original detrended fluctuation analysis, as well as the conventionally used fuzzy entropy (FE) and power spectral density (PSD) on high frequency EEG oscillations (62.50–93.75 Hz). The results showed that MODFA achieved the best performance in binary emotion classification (positive and negative, accuracy = 96.81%), ternary classification (neutral, positive, and negative, accuracy = 76.39%), and six-classification (accuracy = 42.17%). Moreover, along with inducing time, the cumulative effects of the four negative emotions (fear, sad, anger, and disgust) were observed by MODFA-a1, FE, and PSD, which demonstrated that the accumulation of negative emotions are associated with the prefrontal lobe and could be measured via high frequency gamma rhythms. These findings indicated the nonlinear dynamics of high frequency brain activity during emotion induction, and the prefrontal EEG-based emotion recognition might have great application prospect in real-life practice.

Flash-evoked high-frequency EEG oscillations in photosensitive epilepsies

ObjectiveTo determine the feasibility of measuring scalp-recorded, flash-evoked, high-frequency EEG oscillations (F-HFOs) using a relatively simple technique. Furthermore, to assess whether F-HFOs are enhanced in photosensitive epileptic patients and if they might be proposed as a putative non-provocative biomarker of photosensitivity. MethodsWe studied 19 photosensitive patients with idiopathic generalized epilepsy, and 22 controls matched for demographic features. We extracted F-HFOs from the broadband scalp flash-visual evoked potential (b F-VEP) through appropriate filtering. We measured F-HFO amplitude, number and latency. Also, we carried out a time-frequency domain spectral F-HFO analysis. Inter-group statistics was performed. Within-groups, F-HFO features were correlated to the b F-VEP. ResultsThe N3-N3I wave of the b F-VEP was significantly (p = 0.01) larger in patients compared to controls. The same was true for the inter-group F-HFO amplitude (p = 0.01). F-HFOs showed two main spectral peaks (∼88 and ∼125 Hz), whose power was greater (p = 0.001) in patients than in controls. The ∼88 Hz peak power exceeded the upper normal range in 15/19 patients. Patients showed a significant (p = 0.04) correlation between the ∼88 Hz peak power and the size of the N3-N3I wave. SignificanceA simplified F-HFO measurement proved feasible. In patients, F-HFOs were enhanced in terms of both size and spectral power, suggesting a role in the generation of the photoparoxysmal response. Some spectral features of the F-HFOs may be proposed as a putative non-provocative marker of epileptic photosensitivity.

(2R,6R)-hydroxynorketamine exerts mGlu2 receptor-dependent antidepressant actions

Currently approved antidepressant drugs often take months to take full effect, and ∼30% of depressed patients remain treatment resistant. In contrast, ketamine, when administered as a single subanesthetic dose, exerts rapid and sustained antidepressant actions. Preclinical studies indicate that the ketamine metabolite (2R,6R)-hydroxynorketamine [(2R,6R)-HNK] is a rapid-acting antidepressant drug candidate with limited dissociation properties and abuse potential. We assessed the role of group II metabotropic glutamate receptor subtypes 2 (mGlu2) and 3 (mGlu3) in the antidepressant-relevant actions of (2R,6R)-HNK using behavioral, genetic, and pharmacological approaches as well as cortical quantitative EEG (qEEG) measurements in mice. Both ketamine and (2R,6R)-HNK prevented mGlu2/3 receptor agonist (LY379268)-induced body temperature increases in mice lacking the Grm3, but not Grm2, gene. This action was not replicated by NMDA receptor antagonists or a chemical variant of ketamine that limits metabolism to (2R,6R)-HNK. The antidepressant-relevant behavioral effects and 30- to 80-Hz qEEG oscillation (gamma-range) increases resultant from (2R,6R)-HNK administration were prevented by pretreatment with an mGlu2/3 receptor agonist and absent in mice lacking the Grm2, but not Grm3-/-, gene. Combined subeffective doses of the mGlu2/3 receptor antagonist LY341495 and (2R,6R)-HNK exerted synergistic increases on gamma oscillations and antidepressant-relevant behavioral actions. These findings highlight that (2R,6R)-HNK exerts antidepressant-relevant actions via a mechanism converging with mGlu2 receptor signaling and suggest enhanced cortical gamma oscillations as a marker of target engagement relevant to antidepressant efficacy. Moreover, these results support the use of (2R,6R)-HNK and inhibitors of mGlu2 receptor function in clinical trials for treatment-resistant depression either alone or in combination.

Imbalance of Functional Connectivity and Temporal Entropy in Resting-State Networks in Autism Spectrum Disorder: A Machine Learning Approach.

Background: Two approaches to understanding the etiology of neurodevelopmental disorders such as Autism Spectrum Disorder (ASD) involve network level functional connectivity (FC) and the dynamics of neuronal signaling. The former approach has revealed both increased and decreased FC in individuals with ASD. The latter approach has found high frequency EEG oscillations and higher levels of epilepsy in children with ASD. Together, these findings have led to the hypothesis that atypical excitatory-inhibitory neural signaling may lead to imbalanced association pathways. However, simultaneously reconciling local temporal dynamics with network scale spatial connectivity remains a difficult task and thus empirical support for this hypothesis is lacking.Methods: We seek to fill this gap by combining two powerful resting-state functional MRI (rs-fMRI) methods—functional connectivity (FC) and wavelet-based regularity analysis. Wavelet-based regularity analysis is an entropy measure of the local rs-fMRI time series signal. We examined the relationship between the RSN entropy and integrity in individuals with ASD and controls from the Autism Brain Imaging Data Exchange (ABIDE) cohort using a putative set of 264 functional brain regions-of-interest (ROI).Results: We observed that an imbalance in intra- and inter-network FC across 11 RSNs in ASD individuals (p = 0.002) corresponds to a weakened relationship with RSN temporal entropy (p = 0.02). Further, we observed that an estimated RSN entropy model significantly distinguished ASD from controls (p = 0.01) and was associated with level of ASD symptom severity (p = 0.003).Conclusions: Imbalanced brain connectivity and dynamics at the network level coincides with their decoupling in ASD. The association with ASD symptom severity presents entropy as a potential biomarker.

Electrodiagnostic applications of somatosensory evoked high-frequency EEG oscillations: Technical considerations

IntroductionHigh frequency oscillations (HFOs) embedded within the somatosensory evoked potential (SEP) are not routinely recorded/measured as part of standard clinical SEPs. However, HFOs could provide important additional diagnostic/prognostic information in various patient groups in whom SEPs are tested routinely. One area is the management of patients with hypoxic ischaemic encephalopathy (HIE) in the intensive care unit (ICU). However, the sensitivity of standard clinical SEP recording techniques for detecting HFOs is unknown. MethodsSEPs were recorded using routine clinical methods in 17 healthy subjects (median nerve stimulation; 0.5 ms pulse width; 5 Hz; maximum 4000 stimuli) in an unshielded laboratory. Bipolar EEG recordings were acquired (gain 50 k; bandpass 3Hz–2 kHz; sampling rate 5 kHz; non-inverting electrode 2 cm anterior to C3/C4; inverting electrode 2 cm posterior to C3/C4). Data analysis was performed in MATLAB. ResultsSEP-HFOs were detected in 65% of controls using standard clinical recording techniques. In 3 controls without significant HFOs, experiments were repeated using a linear electrode array with higher spatial sampling frequency. SEP-HFOs were observed in all 3 subjects. ConclusionsCurrently standard clinical methods of recording SEPs are not sufficiently sensitive to permit the inclusion of SEP-HFOs in routine clinical diagnostic/prognostic assessments. Whilst an increase in the number/density of EEG electrodes should improve the sensitivity for detecting SEP-HFOs, this requires confirmation. By improving and standardising clinical SEP recording protocols to permit the acquisition/analysis of SEP-HFOs, it should be possible to gain important insights into the pathophysiology of neurological disorders and refine the management of conditions such as HIE.

S140 High frequency eeg oscillations in cognitive decline and aging

Objectives Higher cognitive functions depend critically on synchronized network activity in the gamma range (30–100 Hz), which results from activity of fast spiking (FS) parvalbumin positive (PV) interneurons. EEG oscillation in the gamma band is linked to sensory perception and integration, learning, memory and attention. Distinct alterations of these oscillatory signals have been discovered in subjects with normal healthy aging. The objective of our study was to examine whether changes in EEG gamma power accompany changes in cognitive function in otherwise healthy individuals. Methods In our presentation I would like to focus on a large Danish birth cohort of males born in 1953, which were followed longitudinally throughout their life course. Our database contains all information about cognitive development, health and environmental issues. We used visual, auditory and bi-modal steady state evoked responses to generate EEG gamma activity that was correlated to cognitive performance. Results Our data suggests that the ratio between EEG gamma and alfa power decrease with age. Subjects with reduced cognitive performance have lower EEG gamma power compared to persons with normal cognition. Furthermore we discuss how the response after visual stimulation is altered when a competing second stimulus is presented at the same time. Our data suggests that steady state responses may be predictors of intelligence, working performance and that the EEG gamma power response correlates with cognitive test results. Conclusions We conclude that steady state evoked EEG gamma responses may be useful correlates of intelligence and cognitive performance.

EPV 14. Non-invasive single-trial detection of human population spike responses in somatosensory evoked potentials in a realistic clinical setting

Introduction High-frequency EEG oscillations ( ≈ 600 Hz; HFO) evoked by median nerve stimulation and recorded above the human somatosensory cortex are non-invasive correlates of cortical population spikes. Recently, it was shown that spatiotemporal filtering and multivariate classification enables single-trial HFO detection using 29-channel low-impedance ( Ω ) low-noise EEG in an electromagnetically shielded recording chamber. It is an open question, whether this can be achieved in a realistic clinical setting. Methods With a custom-built CE-certified low-noise EEG amplifier, median nerve SEPs were recorded in 10 healthy subjects using 8 electrodes (impedances ≈ 1 k Ω ) in a standard unshielded hospital environment. After band-pass filtering (500–900 Hz), a subset of the trials (N = 2000) was used to train the two-step single-trial HFO detector, which is composed of spatiotemporal filter optimization and nonlinear classification. The performance of the algorithm was assessed using an independent set of additional trials (N = 5200). Results In the present group of 10 subjects, on average the algorithm detected evoked HFOs in 64.9% of the single trials in the correct latency window (around ≈ 20 ms) with a positive predictive value (PPV) of 61.9%. Notably, in several subjects with a higher signal-plus-noise-to-noise ratio (SNNR), detection rate (DR) and PPV were above 80% (peak values: SNNR = 2.0, DR = 95.2%, PPV = 98.5%). Conclusions A non-invasive single-trial detection of human population spike responses in somatosensory evoked potentials can be achieved also in a realistic unshielded clinical setting. The increase in sensitivity brought about by combined hardware and algorithmic improvements enables the analysis of single-trial variability and might be extended also to pathological components, such as the non-invasive detection of epileptic neocortical high-frequency oscillations.

High-frequency EEG oscillations in hyperthermia-induced seizures of Scn1a mutant rats

We examined high-frequency oscillations (HFOs) in the ictal cortical EEGs of hyperthermia-induced seizures in a rat model of febrile seizures with an SCN1A mutation as a means of investigating the pathophysiological mechanisms underlying the generation of febrile seizures. We used 13 male homozygous Scn1a-N1417H mutant rats (F344/NSlc-Scn1a(Kyo811)) and 10 wild-type control rats. Generalized tonic-clonic seizures were induced in all mutant rats, and HFOs with frequencies ranging from 200 to 400 Hz were found to precede spikes during the clonic phases of these seizures in the ictal EEGs. The proportion of all spikes in each seizure that were associated with HFOs increased with age. In time-frequency spectra of the EEG data, the HFOs had a mean peak frequency of 301.1 ± 65.4 Hz (range: 156.3-468.8Hz) and a mean peak power of 24.6 ± 3.8 dB (range: 11.4-33.4 dB); the peak power increased with age. Regarding the wild-type rats, a brief seizure without unmistakable HFOs was exceptionally induced in only one rat. The generation mechanism of febrile seizures is still an unanswered question. The detection of HFOs from the ictal EEGs of hyperthermia-induced seizures may provide a cue to answering this open question, although in this research we were unable to provide sufficient evidence to prove that the generation of HFOs depended on the mutation.

High-Frequency EEG Oscillations: New Biomarker in Epilepsy

Researchers at University Medical Center Utrecht, The Netherlands and other centers review the pathophysiology, clinical relevance, identification, and interpretation of high-frequency oscillations (HFOs, >80Hz) in the epileptic EEG.

Interictal high frequency oscillations in an animal model of infantile spasms

While infantile spasms is the most common catastrophic epilepsy of infancy and early-childhood, very little is known about the basic mechanisms responsible for this devastating disorder. In experiments reported here, spasms were induced in rats by the chronic infusion of TTX into the neocortex beginning on postnatal days 10–12. Studies of focal epilepsy suggest that high frequency EEG oscillations (HFOs) occur interictally at sites that are most likely responsible for seizure generation. Thus, our goal was to determine if HFOs occurred and where they occurred in cortex in the TTX model. We also undertook multiunit recordings to begin to analyze the basic mechanisms responsible for HFOs. Our results show that HFOs occur most frequently during hypsarrhythmia and NREM sleep and are most prominent contralateral to the TTX infusion site in the homotopic cortex and anterior to this region in frontal cortex. While HFOs were largest and most frequent in these contralateral regions, they were also commonly recorded synchronously across multiple and widely-spaced recordings sites. The amplitude and spatial distribution of interictal HFOs were found to be very similar to the high frequency bursts seen at seizure onset. However, the latter differed from the interictal events in that the high frequency activity was more intense at seizure onset. Microwire recordings showed that neuronal unit firing increased abruptly with the generation of HFOs. A similar increase in neuronal firing occurred at the onset of the ictal events. Taken together, results suggest that neocortical networks are abnormally excitable, particularly contralateral to TTX infusion, and that these abnormalities are not restricted to small areas of cortex. Multiunit firing coincident with HFOs is fully consistent with a neocortical hyperexcitability hypothesis particularly since they both occur at seizure onset.

An Update on Promising Diagnostic and Therapeutic Interventions in Epilepsy

There have been many developments in the management of patients with epilepsy ranging from diagnostic modalities to pharmacological and non-pharmacological interventions. This review discusses several advances which the authors believe will have a significant impact on how such patients will be managed in the coming years. Electroencephalography [EEG] remains the main diagnostic tool in the evaluation of patients with seizure disorders. The novel discovery of high frequency EEG oscillations introduces new concepts of epileptogenicity. Also, techniques that couple EEG with imaging data will be discussed. The early intervention to abort seizures using a method that is easily administered by paramedic staff in the field has been shown to result in more rapid seizure cessation we elaborate on the pre-hospital management. Epilepsy surgery which has revolutionized the treatment of medically refractory patients is invasive and often resective in nature. We discuss the emerging science of neurostimulation as an alternative and potentially similarly efficacious technique.