Electroencephalogram Activity Research Articles

Meditation-type specific reduction in infra-slow activity of electroencephalogram

Meditation-type specific reduction in infra-slow activity of electroencephalogram

Characterizing Major Depressive Disorder (MDD) using alpha-band activity in resting-state electroencephalogram (EEG) combined with MATRICS Consensus Cognitive Battery (MCCB)

BackgroundThe diagnosis of major depressive disorder (MDD) is commonly based on the subjective evaluation by experienced psychiatrists using clinical scales. Hence, it is particularly important to find more objective biomarkers to aid in diagnosis and further treatment. Alpha-band activity (7-13 Hz) is the most prominent component in resting electroencephalogram (EEG), which is also thought to be a potential biomarker. Recent studies have shown the existence of multiple sub-oscillations within the alpha band, with distinct neural underpinnings. However, the specific contribution of these alpha sub-oscillations to the diagnosis and treatment of MDD remains unclear. MethodsIn this study, we recorded the resting-state EEG from MDD and HC populations in both open and closed-eye state conditions. We also assessed cognitive processing using the MATRICS Consensus Cognitive Battery (MCCB). ResultsWe found that the MDD group showed significantly higher power in the high alpha range (10.5–11.5 Hz) and lower power in the low alpha range (7–8.5 Hz) compared to the HC group. Notably, high alpha power in the MDD group is negatively correlated with working memory performance in MCCB, whereas no such correlation was found in the HC group. Furthermore, using five established classification algorithms, we discovered that combining alpha oscillations with MCCB scores as features yielded the highest classification accuracy compared to using EEG or MCCB scores alone. ConclusionsOur results demonstrate the potential of sub-oscillations within the alpha frequency band as a potential distinct biomarker. When combined with psychological scales, they may provide guidance relevant for the diagnosis and treatment of MDD.

Real-time field-programmable gate array-based closed-loop deep brain stimulation platform targeting cerebellar circuitry rescues motor deficits in a mouse model of cerebellar ataxia.

The open-loop nature of conventional deep brain stimulation (DBS) produces continuous and excessive stimulation to patients which contributes largely to increased prevalence of adverse side effects. Cerebellar ataxia is characterized by abnormal Purkinje cells (PCs) dendritic arborization, loss of PCs and motor coordination, and muscle weakness with no effective treatment. We aim to develop a real-time field-programmable gate array (FPGA) prototype targeting the deep cerebellar nuclei (DCN) to close the loop for ataxia using conditional double knockout mice with deletion of PC-specific LIM homeobox (Lhx)1 and Lhx5, resulting in abnormal dendritic arborization and motor deficits. We implanted multielectrode array in the DCN and muscles of ataxia mice. The beneficial effect of open-loop DCN-DBS or closed-loop DCN-DBS was compared by motor behavioral assessments, electromyography (EMG), and neural activities (neurospike and electroencephalogram) in freely moving mice. FPGA board, which performed complex real-time computation, was used for closed-loop DCN-DBS system. Closed-loop DCN-DBS was triggered only when symptomatic muscle EMG was detected in a real-time manner, which restored motor activities, electroencephalogram activities and neurospike properties completely in ataxia mice. Closed-loop DCN-DBS was more effective than an open-loop paradigm as it reduced the frequency of DBS. Our real-time FPGA-based DCN-DBS system could be a potential clinical strategy for alleviating cerebellar ataxia and other movement disorders.

Cyclic Alternating Pattern of Encephalopathy (CAPE) in CNS infection: a case report

Cyclic Alternating Pattern of Encephalopathy (CAPE) is rare EEG phenomenon first described in 1944 in a comatose patient. It is similar to sleep EEG pattern of cyclic alternating pattern (CAP) which is a periodic electroencephalogram activity of non-REM sleep. The cyclic alternating pattern (CAP) is defined by sequences of transient electrocortical events that are different from the tonic background and repeat at intervals of up to one minute. CAPE, however, is abnormal EEG pattern. In this pattern of EEG abnormality slow wave activity of 1-2Hz alternates with fast activity of 6-10 hertz. Here we present a case of patient with CNS infection on mechanical ventilation whose EEG showed the above-mentioned pattern.

The temporal effect of uncertain context on the perceptual processing of painful and non-painful stimulation

Uncertainty has been demonstrated to influence the perception of noxious stimuli, but little is known about the effects of prolonged uncertain contexts on the perception of painful and non-painful stimuli. To address this knowledge gap, the present study utilized a cue-based NPU-threat task, where uncertain and certain trials were separated into distinct blocks. The objective was to investigate the impact of uncertain contexts on the temporal dynamics of electroencephalogram (EEG) activity during the processing of painful and non-painful stimuli. The results revealed that the influence of uncertain contexts on neural responses extends beyond painful trials and is also evident in non-painful trials. In uncertain contexts, it has been observed that painful stimuli elicit larger P2 amplitudes and late beta band (13–30 Hz) event-related desynchronization (ERD) around 500–700 ms. However, in certain contexts, painful stimuli evoke stronger late gamma band (50–70 Hz) event-related synchronization (ERS) around 600–700 ms. For non-painful trials, in uncertain contexts, significantly higher amplitudes of the late positive potential (LPP) component and delta-theta band (2–7 Hz) ERS were observed compared to certain non-painful stimuli. These findings demonstrate that uncertain contexts exert a significant impact on the processing of both painful and non-painful stimuli, and this influence is mediated by distinct neural mechanisms.

The Menstrual Cycle and Sleep.

Aspects of sleep change across the menstrual cycle in some women. Poorer sleep quality in the premenstrual phase and menstruation is common in women with premenstrual symptoms or painful menstrual cramps. Although objective sleep continuity remains unchanged across the regular, asymptomatic menstrual cycle, activity in the sleep electroencephalogram varies, with a prominent increase in sleep spindle activity in the postovulatory luteal phase, when progesterone is present, relative to the follicular phase. Menstrual cycle phase, reproductive stage, and menstrual-related disorders should be considered when assessing women's sleep complaints.

Effects of Planting on the Prefrontal Electroencephalogram Activity of University Students

Background and objective: This study was conducted from June 1 to August 30, 2021 with 33 students at Daegu Catholic University to investigate the effect of planting on the theta, alpha, beta, and gamma waves of the prefrontal cortex.Methods: For the experiment, the subjects wore electroencephalogram (EEG) sensors and proceeded through the following three steps: 1) preparation stage: looking at the wall (2 min), 2) work stage: planting plants (5 min), and 3) appreciation stage: looking at and appreciating the plants they planted (3 min).Results: As a result of analyzing the total relative values of the preparation, work, and appreciation stages in the raw data (excluding the delta wave, which increases during sleep), the alpha wave increased in the work stage of planting activity (Left: <i>p</i> = .000, Right: <i>p</i> = .000). Alpha waves are brain waves that are activated in states of relaxation, rest, and euphoria. In male students, alpha waves (Left: <i>p</i> = .001, Right: <i>p</i> = .001) were activated in the work stage. In female students, there was a significant difference in all EEG activity between the stages. Theta wave (Left: <i>p</i> = .046, Right: <i>p</i> = .029) was most activated in the appreciation stage, and the alpha wave (Left: <i>p</i> = .000, Right: <i>p</i> = .000) was most activated in the work stage. Beta waves (Left: <i>p</i> = .016, Right: <i>p</i> = .013) and gamma waves (Left: <i>p</i> = .015, Right: <i>p</i> = .011) were found to be inactivated in the work stage.Conclusion: The study results facilitated the investigation of planting effectiveness according to the type of activity by measuring the EEG for each activity stage.

RTMS in mental health disorders.

Transcranial magnetic stimulation (TMS) is an innovative and non-invasive technique used in the diagnosis and treatment of psychiatric and neurological disorders. Repetitive TMS (rTMS) can modulate neuronal activity, neuroplasticity and arousal of the waking and sleeping brain, and, more generally, overall mental health. Numerous studies have examined the predictors of the efficacy of rTMS on clinical outcome variables in various psychiatric disorders. These predictors often encompass the stimulated brain region's location, electroencephalogram (EEG) activity patterns, potential morphological and neurophysiological anomalies, and individual patient's response to treatment. Most commonly, rTMS is used in awake patients with depression, catatonia, and tinnitus. Interestingly, rTMS has also shown promise in inducing slow-wave oscillations in insomnia patients, opening avenues for future research into the potential beneficial effects of these oscillations on reports of non-restorative sleep. Furthermore, neurophysiological measures emerge as potential, disease-specific biomarkers, aiding in predicting treatment response and monitoring post-treatment changes. The study posits the convergence of neurophysiological biomarkers and individually tailored rTMS treatments as a gateway to a new era in psychiatric care. The potential of rTMS to induce slow-wave activity also surfaces as a significant contribution to personalized treatment approaches. Further investigations are called for to validate the imaging and electrophysiological biomarkers associated with rTMS. In conclusion, the potential for rTMS to significantly redefine treatment strategies through personalized approaches could enhance the outcomes in neuropsychiatric disorders.

Reduction or enhancement? Repetition effects on early brain potentials during visual word recognition are frequency dependent.

Most studies on word repetition have demonstrated that repeated stimuli yield reductions in brain activity. Despite the well-known repetition reduction effect, some literature reports repetition enhancements in electroencephalogram (EEG) activities. However, although studies of object and face recognition have consistently demonstrated both repetition reduction and enhancement effects, the results of repetition enhancement effects were not consistent in studies of visual word recognition. Therefore, the present study aimed to further investigate the repetition effect on the P200, an early event-related potential (ERP) component that indexes the coactivation of lexical candidates during visual word recognition. To achieve a high signal-to-noise ratio, EEG signals were decomposed into various modes by using the Hilbert-Huang transform. Results demonstrated a repetition enhancement effect on P200 activity in alpha-band oscillation and that lexicality and orthographic neighborhood size would influence the magnitude of the repetition enhancement effect on P200. These findings suggest that alpha activity during visual word recognition might reflect the coactivation of orthographically similar words in the early stages of lexical processing. Meantime, there were repetition reduction effects on ERP activities in theta-delta band oscillation, which might index that the lateral inhibition between lexical candidates would be omitted in repetition.

Daytime affect and sleep EEG activity: A data-driven exploration.

It has long been thought that links between affect and sleep are bidirectional. However, few studies have directly assessed the relationships between: (1) pre-sleep affect and sleep electroencephalogram (EEG) activity; and (2) sleep EEG activity and post-sleep affect. This study aims to systematically explore the correlations between pre-/post-sleep affect and EEG activity during sleep. In a community sample of adults (n = 51), we measured participants' positive and negative affect in the evening before sleep and in the next morning after sleep. Participants slept at their residence for 1 night of EEG recording. Using Fourier transforms, the EEG power at each channel was estimated during rapid eye movement sleep and non-rapid eye movement sleep for the full range of sleep EEG frequencies. We first present heatmaps of the raw correlations between pre-/post-sleep affect and EEG power during rapid eye movement and non-rapid eye movement sleep. We then thresholded the raw correlations with a medium effect size |r| ≥ 0.3. Using a cluster-based permutation test, we identified a significant cluster indicating a negative correlation between pre-sleep positive affect and EEG power in the alpha frequency range during rapid eye movement sleep. This result suggests that more positive affect during the daytime may be associated with less fragmented rapid eye movement sleep that night. Overall, our exploratory results lay the foundation for confirmatory research on the relationship between daytime affect and sleep EEG activity.

Dapagliflozin modulates neuronal injury via instigation of LKB1/p-AMPK/GABAB R2 signaling pathway and suppression of the inflammatory cascade in an essential tremor rat model

ABSTRACT Background However, disturbances in cellular energy demarcate neuronal hyperexcitability in essential tremor (ET); nevertheless, no available data relates energy sensors and GABAergic neurotransmission in ET. Noteworthy, reports have asserted dapagliflozin’s (DAPA) role in enhancing autophagic sensors in other disorders. Herein, this study aims to investigate DAPA’s impact on the GABAB receptor subunit (GABAB R2), notwithstanding the GABA A involvement, in an ET model. Methods ET was induced by a single dose of harmaline (30 mg/kg; i.p.), while DAPA (1 mg/kg/day; p.o.) was given for 5 days before ET induction. The autophagic sensors were examined by injecting a single dose of dorsomorphin (DORSO) AMPK inhibitor (0.2 mg/kg; i.p.) on the 5th day before ET induction. Results DAPA decreased the HAR-induced tremor score and alleviated motor disabilities observed in the open field, rotarod, wire grip strength, and gait kinematics confirmed by reduced electrical activity in electroencephalogram. In the cerebella, DAPA curbed HAR-evoked inflammatory cytokines, apoptotic markers, and glutamate while restoring the disturbed GABA, BDNF, LKB1, p-AMPK, and GABAB R2 levels. DAPA’s effect was mostly obliterated by DORSO. Conclusion DAPA offers a potential neuroprotective effect in ET by augmenting the neuronal inhibitory machinery via suppressing the inflammatory and excitotoxicity systems through LKB1/p-AMPK/GABAB R2 signaling.

Identification of patients with chronic migraine by using sensory-evoked oscillations from the electroencephalogram classifier

To examine whether the modulating evoked cortical oscillations could be brain signatures among patients with chronic migraine, we investigated cortical modulation using an electroencephalogram with machine learning techniques. We directly record evoked electroencephalogram activity during nonpainful, painful, and repetitive painful electrical stimulation tasks. Cortical modulation for experimental pain and habituation processing was analyzed and used to differentiate patients with chronic migraine from healthy controls using a validated machine-learning model. This study included 80 participants: 40 healthy controls and 40 patients with chronic migraine. Evoked somatosensory oscillations were dominant in the alpha band. Longer latency (nonpainful and repetitive painful) and augmented power (nonpainful and repetitive painful) were present among patients with chronic migraine. However, for painful tasks, alpha increases were observed among healthy controls. The oscillatory activity ratios between repetitive painful and painful tasks represented the frequency modulation and power habituation among healthy controls, respectively, but not among patients with chronic migraine. The classification models with oscillatory features exhibited high performance in differentiating patients with chronic migraine from healthy controls. Altered oscillatory characteristics of sensory processing and cortical modulation reflected the neuropathology of patients with chronic migraine. These characteristics can be reliably used to identify patients with chronic migraine using a machine-learning approach.

Effect of homeostatic pressure on daytime vigilance performance: Evidence from behaviour and resting-state EEG.

Vigilance is highly sensitive to the time-of-day effect and goes through the daytime trough during the period of the post-noon dip. A midday nap could maintain individuals' vigilance at an optimal level. Thus, homeostatic sleep pressure is one of the main reasons for the post-noon dip in daytime vigilance. The current study focussed on the role of homeostatic sleep pressure in the diurnal variation of vigilance performance with normal circadian rhythms and the corresponding neural basis. With 34 healthy adults, we recorded the resting-state electroencephalogram activities and the following vigilance performance measured by psychomotor vigilance test in the morning, the no-nap mid afternoon, and the nap mid afternoon. The circadian process was controlled by measuring vigilance and resting-state electroencephalogram activities at the same time point in the nap and no-nap conditions. Homeostatic sleep pressure accumulated from morning to mid afternoon induced the declined vigilance performance and a global increase in resting-state delta, theta, alpha, and beta1 bands power, and a local increase in beta2 band power in the central region. Furthermore, the more the spontaneous beta2 power increased, the less vigilance declined from morning to mid afternoon. The current findings suggest that homeostatic sleep pressure increased cortical excitability but decreased cortical communication efficiency from morning to mid afternoon. In addition, the activity of the high beta waves probably reflected the compensatory effort to counteract the negative impact of the low arousal state on the following vigilance task by performing more action preparation in the no-nap afternoon.

An attention-based multi-resolution deep learning model for automatic A-phase detection of cyclic alternating pattern in sleep using single-channel EEG

Sleep is a crucial part of human well-being. Many people suffer from various sleep disorders and insufficient sleep. The detection of the cyclic alternating pattern (CAP) of electroencephalogram (EEG) activity during sleep is essential for identifying and monitoring these problems. In this paper, we present a multi-resolution deep neural network model with temporal and channel attention for detecting A-phase and its subtypes. A multi-branch one-dimensional convolutional neural network (1D-CNN) is employed where each branch has different kernel sizes to extract features of different frequency resolutions automatically. An attention-based transformer network exploits the dynamic and temporal relationship between CAP event features extracted from the single-channel EEG data. Our model achieves 90.31% accuracy, 95.30% specificity, and 65.73% F1-Score in A-phase detection and 86.72% accuracy, 89.53% specificity, and 59.59% F1-Score in the detection of its subtypes, superior performance as compared to those of the recent approaches.

Source-Space Brain Functional Connectivity Features in Electroencephalogram-Based Driver Fatigue Classification

This study examined the brain source space functional connectivity from the electroencephalogram (EEG) activity of 48 participants during a driving simulation experiment where they drove until fatigue developed. Source-space functional connectivity (FC) analysis is a state-of-the-art method for understanding connections between brain regions that may indicate psychological differences. Multi-band FC in the brain source space was constructed using the phased lag index (PLI) method and used as features to train an SVM classification model to classify driver fatigue and alert conditions. With a subset of critical connections in the beta band, a classification accuracy of 93% was achieved. Additionally, the source-space FC feature extractor demonstrated superiority over other methods, such as PSD and sensor-space FC, in classifying fatigue. The results suggested that source-space FC is a discriminative biomarker for detecting driving fatigue.

Desflurane is risk factor for postoperative delirium in older patients' independent from intraoperative burst suppression duration.

Postoperative Delirium (POD) is the most frequent neurocognitive complication after general anesthesia in older patients. The development of POD is associated with prolonged periods of burst suppression activity in the intraoperative electroencephalogram (EEG). The risk to present burst suppression activity depends not only on the age of the patient but is also more frequent during propofol anesthesia as compared to inhalative anesthesia. The aim of our study is to determine, if the risk to develop POD differs depending on the anesthetic agent given and if this correlates with a longer duration of intraoperative burst suppression. In this secondary analysis of the SuDoCo trail [ISRCTN 36437985] 1277 patients, older than 60 years undergoing general anesthesia were included. We preprocessed and analyzed the raw EEG files from each patient and evaluated the intraoperative burst suppression duration. In a logistic regression analysis, we assessed the impact of burst suppression duration and anesthetic agent used for maintenance on the risk to develop POD. 18.7% of patients developed POD. Burst suppression duration was prolonged in POD patients (POD 27.5 min ± 21.3 min vs. NoPOD 21.4 ± 16.2 min, p < 0.001), for each minute of prolonged intraoperative burst suppression activity the risk to develop POD increased by 1.1% (OR 1.011, CI 95% 1.000-1.022, p = 0.046). Burst suppression duration was prolonged under propofol anesthesia as compared to sevoflurane and desflurane anesthesia (propofol 32.5 ± 20.3 min, sevoflurane 17.1 ± 12.6 min and desflurane 20.1 ± 16.0 min, p < 0.001). However, patients receiving desflurane anesthesia had a 1.8fold higher risk to develop POD, as compared to propofol anesthesia (OR 1.766, CI 95% 1.049-2.974, p = 0.032). We found a significantly increased risk to develop POD after desflurane anesthesia in older patients, even though burst suppression duration was shorter under desflurane anesthesia as compared to propofol anesthesia. Our finding might help to explain some discrepancies in studies analyzing the impact of burst suppression duration and EEG-guided anesthesia on the risk to develop POD.

Frontal-to-visual information flow explains predictive motion tracking

Predictive tracking demonstrates our ability to maintain a line of vision on moving objects even when they temporarily disappear. Models of smooth pursuit eye movements posit that our brain achieves this ability by directly streamlining motor programming from continuously updated sensory motion information. To test this hypothesis, we obtained sensory motion representation from multivariate electroencephalogram activity while human participants covertly tracked a temporarily occluded moving stimulus with their eyes remaining stationary at the fixation point. The sensory motion representation of the occluded target evolves to its maximum strength at the expected timing of reappearance, suggesting a timely modulation of the internal model of the visual target. We further characterize the spatiotemporal dynamics of the task-relevant motion information by computing the phase gradients of slow oscillations. We discovered a predominant posterior-to-anterior phase gradient immediately after stimulus occlusion; however, at the expected timing of reappearance, the axis reverses the gradient, becoming anterior-to-posterior. The behavioral bias of smooth pursuit eye movements, which is a signature of the predictive process of the pursuit, was correlated with the posterior division of the gradient. These results suggest that the sensory motion area modulated by the prediction signal is involved in updating motor programming.

Electroencephalogram activity underlying virtual reality-based sublime experiences in major depressive disorder

Electroencephalogram activity underlying virtual reality-based sublime experiences in major depressive disorder

Aperiodic electroencephalogram activity in major depressive disorder and healthy controls and the relation to symptoms, childhood trauma and familial dispositions

Aperiodic electroencephalogram activity in major depressive disorder and healthy controls and the relation to symptoms, childhood trauma and familial dispositions

FPT, a 2-Aminotetralin, Is a Potent Serotonin 5-HT1A, 5-HT1B, and 5-HT1D Receptor Agonist That Modulates Cortical Electroencephalogram Activity in Adult Fmr1 Knockout Mice.

There are no approved medicines for fragile X syndrome (FXS), a monogenic, neurodevelopmental disorder. Electroencephalogram (EEG) studies show alterations in resting-state cortical EEG spectra, such as increased gamma-band power, in patients with FXS that are also observed in Fmr1 knockout models of FXS, offering putative biomarkers for drug discovery. Genes encoding serotonin receptors (5-HTRs), including 5-HT1A, 5-HT1B, and 5-HT1DRs, are differentially expressed in FXS, providing a rationale for investigating them as pharmacotherapeutic targets. Previously we reported pharmacological activity and preclinical neurotherapeutic effects in Fmr1 knockout mice of an orally active 2-aminotetralin, (S)-5-(2'-fluorophenyl)-N,N-dimethyl-1,2,3,4-tetrahydronaphthalen-2-amine (FPT). FPT is a potent (low nM), high-efficacy partial agonist at 5-HT1ARs and a potent, low-efficacy partial agonist at 5-HT7Rs. Here we report new observations that FPT also has potent and efficacious agonist activity at human 5-HT1B and 5-HT1DRs. FPT's Ki values at 5-HT1B and 5-HT1DRs were <5 nM, but it had nil activity (>10 μM Ki) at 5-HT1FRs. We tested the effects of FPT (5.6 mg/kg, subcutaneous) on EEG recorded above the somatosensory and auditory cortices in freely moving, adult Fmr1 knockout and control mice. Consistent with previous reports, we observed significantly increased relative gamma power in untreated or vehicle-treated male and female Fmr1 knockout mice from recordings above the left somatosensory cortex (LSSC). In addition, we observed sex effects on EEG power. FPT did not eliminate the genotype difference in relative gamma power from the LSSC. FPT, however, robustly decreased relative alpha power in the LSSC and auditory cortex, with more pronounced effects in Fmr1 KO mice. Similarly, FPT decreased relative alpha power in the right SSC but only in Fmr1 knockout mice. FPT also increased relative delta power, with more pronounced effects in Fmr1 KO mice and caused small but significant increases in relative beta power. Distinct impacts of FPT on cortical EEG were like effects caused by certain FDA-approved psychotropic medications (including baclofen, allopregnanolone, and clozapine). These results advance the understanding of FPT's pharmacological and neurophysiological effects.