Brain Rhythms Research Articles

Brain rhythms control microglial response and cytokine expression via NF-κB signaling.

Microglia transform in response to changes in sensory or neural activity, such as sensory deprivation. However, little is known about how specific frequencies of neural activity, or brain rhythms, affect microglia and cytokine signaling. Using visual noninvasive flickering sensory stimulation (flicker) to induce electrical neural activity at 40 hertz, within the gamma band, and 20 hertz, within the beta band, we found that these brain rhythms differentially affect microglial morphology and cytokine expression in healthy animals. Flicker induced expression of certain cytokines independently of microglia, including interleukin-10 and macrophage colony-stimulating factor. We hypothesized that nuclear factor κB (NF-κB) plays a causal role in frequency-specific cytokine and microglial responses because this pathway is activated by synaptic activity and regulates cytokines. After flicker, phospho-NF-κB colabeled with neurons more than microglia. Inhibition of NF-κB signaling down-regulated flicker-induced cytokine expression and attenuated flicker-induced changes in microglial morphology. These results reveal a mechanism through which brain rhythms affect brain function by altering microglial morphology and cytokines via NF-κB.

Optogenetic targeting of astrocytes restores slow brain rhythm function and slows Alzheimer’s disease pathology

Patients with Alzheimer’s disease (AD) exhibit non-rapid eye movement (NREM) sleep disturbances in addition to memory deficits. Disruption of NREM slow waves occurs early in the disease progression and is recapitulated in transgenic mouse models of beta-amyloidosis. However, the mechanisms underlying slow-wave disruptions remain unknown. Because astrocytes contribute to slow-wave activity, we used multiphoton microscopy and optogenetics to investigate whether they contribute to slow-wave disruptions in APP/PS1 mice. The power but not the frequency of astrocytic calcium transients was reduced in APP/PS1 mice compared to nontransgenic controls. Optogenetic activation of astrocytes at the endogenous frequency of slow waves restored slow-wave power, reduced amyloid deposition, prevented neuronal calcium elevations, and improved memory performance. Our findings revealed malfunction of the astrocytic network driving slow-wave disruptions. Thus, targeting astrocytes to restore circuit activity underlying sleep and memory disruptions in AD could ameliorate disease progression.

Take your time: Slow brain rhythms predict fluid intelligence

Evidence is mixed whether fluid intelligence (Gf) is associated with increased or decreased alpha and beta band activity (7–30 Hz). Moreover, the Gf relationship with the delta and theta band activity (1–7 Hz) is unknown. We recorded electroencephalographic (EEG) data in 160 healthy adults solving Raven's Advanced Progressive Matrices with a randomized item order to control for item difficulty unaffected by sequential effects. The participants studied each matrix for 30 s before the response bank onset, so we could track the time course of neural activity during problem solving. We measured Gf using six tests. Gf positively correlated with the delta band power, while there was no correlation with the theta band power. For almost all of the participants, we identified the specific slow rhythm frequency, which varied in power as a function of item difficulty. We observed that the lower this frequency, the higher Gf, but only in men. Finally, the alpha and low-beta activity correlated negatively with Gf after we had filtered out the activity during idle intervals (the latter reflecting waiting for the response bank). Overall, the brain activity in the delta, alpha, and beta bands explained 22.6% of Gf variance.

EEG-based Emotion Recognition Using Sub-Band Time-Delay Correlations.

Emotion recognition is a challenging task with many potential applications in psychology, psychiatry, and human-computer interaction (HCI). The use of time-delay information in the controlled time-delay stability (cTDS) algorithm can help to capture the temporal dynamics of EEG signals, including sub-band information and bi-directional coupling that can aid in emotion recognition and identification of specific connectivity patterns between brain rhythms. Incorporating EEG frequency bands can be used to design better emotion recognition systems. This paper evaluates the cTDS algorithm for binary classification tasks of arousal and valence using EEG sub-band signals. This method achieved a high accuracy of 91.1% for arousal and 91.7% for valence based on one electrode recording site at Fp1. The cTDS algorithm is a promising approach to analyzing brain network interactions. It can be particularly applicable to arousal and valence classification tasks, especially within a complex, multimodal feature space associated with understanding psychiatric disorders and HCI applications.

Beyond neurons and spikes: cognon, the hierarchical dynamical unit of thought

AbstractFrom the dynamical point of view, most cognitive phenomena are hierarchical, transient and sequential. Such cognitive spatio-temporal processes can be represented by a set of sequential metastable dynamical states together with their associated transitions: The state is quasi-stationary close to one metastable state before a rapid transition to another state. Hence, we postulate that metastable states are the central players in cognitive information processing. Based on the analogy of quasiparticles as elementary units in physics, we introduce here the quantum of cognitive information dynamics, which we term “cognon”. A cognon, or dynamical unit of thought, is represented by a robust finite chain of metastable neural states. Cognons can be organized at multiple hierarchical levels and coordinate complex cognitive information representations. Since a cognon is an abstract conceptualization, we link this abstraction to brain sequential dynamics that can be measured using common modalities and argue that cognons and brain rhythms form binding spatiotemporal complexes to keep simultaneous dynamical information which relate the ‘what’, ‘where’ and ‘when’.

The measurement of mental fatigue following an overnight on-call duty among doctors using electroencephalogram.

This study aimed to measure the spectral power differences in the brain rhythms among a group of hospital doctors before and after an overnight on-call duty. Thirty-two healthy doctors who performed regular on-call duty in a tertiary hospital in Sarawak, Malaysia were voluntarily recruited into this study. All participants were interviewed to collect relevant background information, followed by a self-administered questionnaire using Chalder Fatigue Scale and electroencephalogram test before and after an overnight on-call duty. The average overnight sleep duration during the on-call period was 2.2 hours (p<0.001, significantly shorter than usual sleep duration) among the participants. The mean (SD) Chalder Fatigue Scale score of the participants were 10.8 (5.3) before on-call and 18.4 (6.6) after on-call (p-value < 0.001). The theta rhythm showed significant increase in spectral power globally after an overnight on-call duty, especially when measured at eye closure. In contrast, the alpha and beta rhythms showed reduction in spectral power, significantly at temporal region, at eye closure, following an overnight on-call duty. These effects are more statistically significant when we derived the respective relative theta, alpha, and beta values. The finding of this study could be useful for development of electroencephalogram screening tool to detect mental fatigue.

Delayed closed-loop neurostimulation for the treatment of pathological brain rhythms in mental disorders: a computational study.

Mental disorders are among the top most demanding challenges in world-wide health. A large number of mental disorders exhibit pathological rhythms, which serve as the disorders characteristic biomarkers. These rhythms are the targets for neurostimulation techniques. Open-loop neurostimulation employs stimulation protocols, which are rather independent of the patients health and brain state in the moment of treatment. Most alternative closed-loop stimulation protocols consider real-time brain activity observations but appear as adaptive open-loop protocols, where e.g., pre-defined stimulation sets in if observations fulfil pre-defined criteria. The present theoretical work proposes a fully-adaptive closed-loop neurostimulation setup, that tunes the brain activities power spectral density (PSD) according to a user-defined PSD. The utilized brain model is non-parametric and estimated from the observations via magnitude fitting in a pre-stimulus setup phase. Moreover, the algorithm takes into account possible conduction delays in the feedback connection between observation and stimulation electrode. All involved features are illustrated on pathological α- and γ-rhythms known from psychosis. To this end, we simulate numerically a linear neural population brain model and a non-linear cortico-thalamic feedback loop model recently derived to explain brain activity in psychosis.

Induced alpha and beta electroencephalographic rhythms covary with single-trial speech intelligibility in competition

Neurophysiological studies suggest that intrinsic brain oscillations influence sensory processing, especially of rhythmic stimuli like speech. Prior work suggests that brain rhythms may mediate perceptual grouping and selective attention to speech amidst competing sound, as well as more linguistic aspects of speech processing like predictive coding. However, we know of no prior studies that have directly tested, at the single-trial level, whether brain oscillations relate to speech-in-noise outcomes. Here, we combined electroencephalography while simultaneously measuring intelligibility of spoken sentences amidst two different interfering sounds: multi-talker babble or speech-shaped noise. We find that induced parieto-occipital alpha (7–15 Hz; thought to modulate attentional focus) and frontal beta (13–30 Hz; associated with maintenance of the current sensorimotor state and predictive coding) oscillations covary with trial-wise percent-correct scores; importantly, alpha and beta power provide significant independent contributions to predicting single-trial behavioral outcomes. These results can inform models of speech processing and guide noninvasive measures to index different neural processes that together support complex listening.

Hope vs. Hype: Closed loop technology will provide more meaningful improvement vs. directional leads in deep brain stimulation

The unmet need in current open-loop deep brain stimulation (DBS) can be appreciated by a comparison to cardiac pacemakers. Cardiac pacemakers can sense the heart's electrical rhythm. They operate in a demand fashion: they are only activated when the heart rate goes below a threshold for example for bradycardia. They can sense and learn normal from abnormal rhythms, so that they do not cause other arrhythmias. They use optimized adaptive and automated stimulation algorithms customized to the patient. Open-loop neurostimulators, which are in essence open-loop brain pacemakers, only recently have been able to sense the brain's electrical rhythms that they are modulating. They cannot sense or respond to specific symptoms such as tremor, bradykinesia, gait impairment, nor to the person's activity state, whether asleep or awake, or whether they are still or active. Open-loop DBS cannot respond to medication levels, nor their influence on brain rhythms. They are on all the time and they use a one-size-fits-all set of parameters.

Capsaicin Changes the Pattern of Brain Rhythms in Sleeping Rats.

The heat and capsaicin sensor TRPV1 ion channels were originally discovered in sensory neurons of dorsal root ganglia, and later found in many other tissues and organs. However, whether TRPV1 channels are present in brain regions other than the hypothalamus has been a subject of debate. Here, we addressed this issue with an unbiased functional test by recording electroencephalograms (EEGs) to examine whether capsaicin injection directly into the rat lateral ventricle could alter brain electrical activity. We observed that EEGs during the sleep stage could be significantly perturbed by capsaicin, whereas EEGs during the awake stage did not show a detectable change. Our results are consistent with TRPV1 expression in selective brain regions whose activities are dominative during the sleep stage.

A multi-view CNN encoding for motor imagery EEG signals

Objective:The convolution neural network (CNN) has gained lots of attentions recently in decoding electroencephalogram (EEG) signals in Motor Imagery (MI) Brain–Computer Interface (BCI) designed for improving stroke rehabilitation strategies. However, the extremely non-linear, nonstationary nature of the EEG signals and diversity among individual subjects results in the overfitting of a CNN model and limits its learning ability. In this study, a densely connected convolutional network with multi-view inputs is proposed. Methods:First, different data subsets from the original EEG signals are created as the CNN model inputs through bandpass filters applied to the EEG signals to generate multiple frequency sub-band signals based on brain rhythms. Then, temporal and spatial features are captured based on the whole frequency band and the filtered sub-band signals, respectively. Further, two dense blocks with multi-CNN layers, which connect each layer to every other layer in the feed-forward path, are used to enhance the model learning capabilities and strengthen information propagation. Finally, a concatenation fusion method is used to integrate the extracted features and a fully connected layer for finalizing the classification. Results:The proposed method achieves an average accuracy of 75.16% on the public Korea University EEG dataset which consists the EEG signals of 54 healthy subjects for the two-class motor imagery tasks, higher than other state-of-the-art deep learning methods. Conclusion:The proposed method effectively extracts much richer motor imagery information from the EEG signals in the BCI system and improves the classification accuracy.

Traveling waves induced by gamma entrainment can explain its therapeutic effects for Alzheimer’s disease

AbstractBackgroundNon‐invasive 40Hz sensory‐induced brain entrainment has shown promising results in Alzheimer’s disease (AD) therapy (Chan et al., medRxiv, 2021; Chan et al., Alzheimer’s &amp; Dementia, 2021). We previously reported on the network‐level mechanisms underlying the spatial and temporal coherence of the induced brain rhythms during gamma entrainment in dementia patients (Lahijanian et al., bioRxiv, 2021). Here, we posit that the induced gamma oscillations propagate as traveling waves circling each hemisphere of the cortex, and that the induction of such waves can explain the spatiotemporal coherence between the frontal and parietal/occipital regions reported to be involved in cognitive processes.MethodEEG data were recorded from two healthy young adults during multi‐trial visual entrainment sessions with 22Hz flickering light. Due to the harmonic property of the brain’s entrained response (Jones et al., Journal of Alzheimer’s Disease, 2019), gamma entrainment was achieved at 44Hz. Separately, EEG data were recorded from 11 dementia patients in multi‐trial auditory entrainment sessions with 40Hz chirp input. The phase of the spatiotemporal pattern at the target gamma frequency was calculated across the entire scalp as an indicator of traveling waves.ResultShortly after the stimulation onset in the young healthy adults, a circling traveling wave is observed in each hemisphere (Fig. 1). The onset and the homogeneity of the induced wave pattern is modulated by the power of the 44Hz entrained oscillations (Fig. 2). The dementia patients can be divided into two groups of entrained and non‐entrained according to the induced gamma power (see Lahijanian et al., bioRxiv, 2021), and the travelling wave effect can be occasionally observed during the stimulation in the entrained group and not in the non‐entrained group.ConclusionCortical traveling waves modulate the brain dynamics and play a critical role in functions ranging from sensory processing to memory consolidation (Muller et al., Nature Reviews Neuroscience, 2018). Gamma entrainment induces traveling waves that connect the occipital/parietal regions of the cortex to the frontal region through the temporal region. The traveling wave promotes spatiotemporal coherence across the brain and hence can serve as a network‐level mechanism for explaining the therapeutic effects of gamma entrainment.

β Band Rhythms Influence Reaction Times.

Despite their involvement in many cognitive functions, β oscillations are among the least understood brain rhythms. Reports on whether the functional role of β is primarily inhibitory or excitatory have been contradictory. Our framework attempts to reconcile these findings and proposes that several β rhythms co-exist at different frequencies. β Frequency shifts and their potential influence on behavior have thus far received little attention. In this human magnetoencephalography (MEG) experiment, we asked whether changes in β power or frequency in auditory cortex and motor cortex influence behavior (reaction times) during an auditory sweep discrimination task. We found that in motor cortex, increased β power slowed down responses, while in auditory cortex, increased β frequency slowed down responses. We further characterized β as transient burst events with distinct spectro-temporal profiles influencing reaction times. Finally, we found that increased motor-to-auditory β connectivity also slowed down responses. In sum, β power, frequency, bursting properties, cortical focus, and connectivity profile all influenced behavioral outcomes. Our results imply that the study of β oscillations requires caution as β dynamics are multifaceted phenomena, and that several dynamics must be taken into account to reconcile mixed findings in the literature.

Magnetoencephalography(MEG) spectral signature of cognitive impairment ‐ A perspective on pathophysiological changes with blood pressure

AbstractBackgroundSpectral ratios (e.g., resting‐state EEG alpha/theta power ratio), which index rhythmic brain activity, have been shown to distinguish early‐onset AD from healthy controls. Magnetoencephalographic (MEG) brain imaging offers advantages over EEG through its unique combination of excellent temporal as well as spatial precision for detecting brain activity. The purpose of this study is to compare MEG spectral activity in cognitively impaired and healthy controls with changes in systolic blood pressure (sbp), specifically focused in the frontal lobe as this region is known to be susceptible to hypertension related white matter degradation.MethodsSixteen cognitively impaired, including 13 possible and 3 probable mild cognitive impairment (MCI), (mean age 65.6 years, males/females: 5/11, Whites/Blacks: 11/5) and sixteen age, sex, and race matched healthy controls (mean age 62.4 years, males/females: 4/12, Whites/Blacks: 11/5) were recruited from an ongoing multiethnic community‐based study to complete six minutes of resting‐state MEG along with a T1w brain MRI. All MEG data underwent standard preprocessing. Resultant whole‐brain maps of spectral activity were filtered into six canonical frequency bands, including delta (2‐4 Hz), theta (5‐7 Hz), alpha (8‐12 Hz), beta (15‐29 Hz), gamma (30‐59 Hz) and high gamma (60‐90 Hz). Relative mean power of each frequency band, and spectral ratios of alpha/theta, beta/delta as well as a spectral slowing index [(alpha+beta+gamma)/(delta+theta)] were computed.ResultMean sbp was 135 mm Hg for both groups. Generalized linear regression models showed cognitive impairment was negatively associated with spectral ratios beta/delta and spectral slowing index. We also found a significant positive association between spectral ratios (beta/delta β = ‐0.0115, p = 0.0015; spectral slowing index β = ‐0.0125, p = 0.0144) and interaction of systolic blood pressure with cognitive impairment. Results are summarized in tables 1‐2.ConclusionLower spectral ratios have previously been demonstrated in individuals with AD. Somewhat surprisingly, our findings indicate that lower blood pressure is associated with lower values on these MEG metrics in cognitively impaired individuals. Interestingly, overtreatment of hypertension in elderly with MCI has been associated with greater cognitive decline. Thus, these results will be examined in a larger cohort and the analysis will be expanded to include anti‐hypertensive medication data to potentially explain the above findings.

EEG Analysis for Images Encode and Retrieval under White Noise Stimulation

Utilization of an EEG machine aids researchers in detecting the brain responses and states during provided stimulation. The EEG comes with multiple electrode channels for recording the electrical activity of the brain. This work aims to discover the brain responses under stimulation of white noise during images encoding and retrieving using the EEG machine. The motivation of this research is because limited previous works determine the association between memory encoding and retrieving process based on EEG analysis. Twenty college students participated in this experimental work. They were required to remember the images in two different conditions: control/silent and exposure to white noise with two task difficulties: easy and difficult phases. The EEG was recorded during the remembering and recalling period for data acquisition. The obtained raw EEG dataset was imported to .txt format for further processing. The Butterworth bandpass filter with a frequency range of 4 to 45 Hz was applied to filter the low and high noise components in the EEG signal. Next, the mean of de-noise EEG voltage was obtained to select the most affected channels. It was found that the Fp1, Fp2, F7, and F3 channels achieved the highest mean voltage that higher than 5 μV. Therefore, these channels were selected for extraction of relative rhythm power to investigate the effect of white noise on memory performance. The findings showed that the alpha, gamma, beta, and theta activities were highly found during the encoding and retrieving process when participants were exposed to white noise. These brain rhythms were related to participants' attentional level, information processing and calm state during the assessment. The behavioral data showed that the participants successfully remembered the images more when exposed to white noise compared to the control condition with percentage differences of 21% and 33% for easy and difficult phases due to the stochastic resonance effect and activation of brain rhythms.

Using guided breathing exercises to prevent mental health complications in the post-COVID period

The novel coronavirus infection has been one of the major public health issues of the past few years. Most COVID-19 patients report loss of energy, difficulty concentrating, memory loss and difficulty remembering, decreased motivation and mood. The purpose of our work was to determine the differences between general performance and brain rhythms in COVID-19 patients and those who had not been infected, and to assess the effects of breathing exercises (BE) on changes in these parameters in order to recommend the proposed therapy. The parameters of bioelectrical activity of the brain in patients after the novel coronavirus infection and those who had not been infected were studied by means of hardware-software complex «Omega-M». Statistical processing was performed using Microsoft Office.During the study, the following significant changes were observed: decreased efficiency in COVID-19 sufferers, reduced alpha and beta rhythms and negligible changes after BE compared to those who had not had COVID-19, and higher delta and theta waves and lack of their normalization after BE in COVID-19 sufferers. Based on the results obtained, it can be concluded that the use of guided breathing exercises can be recommended after COVID-19.

On temporal scale-free non-periodic stimulation and its mechanisms as an infinite improbability drive of the brain's functional connectogram.

Rationalized development of electrical stimulation (ES) therapy is of paramount importance. Not only it will foster new techniques and technologies with increased levels of safety, efficacy, and efficiency, but it will also facilitate the translation from basic research to clinical practice. For such endeavor, design of new technologies must dialogue with state-of-the-art neuroscientific knowledge. By its turn, neuroscience is transitioning-a movement started a couple of decades earlier-into adopting a new conceptual framework for brain architecture, in which time and thus temporal patterns plays a central role in the neuronal representation of sampled data from the world. This article discusses how neuroscience has evolved to understand the importance of brain rhythms in the overall functional architecture of the nervous system and, consequently, that neuromodulation research should embrace this new conceptual framework. Based on such support, we revisit the literature on standard (fixed-frequency pulsatile stimuli) and mostly non-standard patterns of ES to put forward our own rationale on how temporally complex stimulation schemes may impact neuromodulation strategies. We then proceed to present a low frequency, on average (thus low energy), scale-free temporally randomized ES pattern for the treatment of experimental epilepsy, devised by our group and termed NPS (Non-periodic Stimulation). The approach has been shown to have robust anticonvulsant effects in different animal models of acute and chronic seizures (displaying dysfunctional hyperexcitable tissue), while also preserving neural function. In our understanding, accumulated mechanistic evidence suggests such a beneficial mechanism of action may be due to the natural-like characteristic of a scale-free temporal pattern that may robustly compete with aberrant epileptiform activity for the recruitment of neural circuits. Delivering temporally patterned or random stimuli within specific phases of the underlying oscillations (i.e., those involved in the communication within and across brain regions) could both potentiate and disrupt the formation of neuronal assemblies with random probability. The usage of infinite improbability drive here is obviously a reference to the "The Hitchhiker's Guide to the Galaxy" comedy science fiction classic, written by Douglas Adams. The parallel is that dynamically driving brain functional connectogram, through neuromodulation, in a manner that would not favor any specific neuronal assembly and/or circuit, could re-stabilize a system that is transitioning to fall under the control of a single attractor. We conclude by discussing future avenues of investigation and their potentially disruptive impact on neurotechnology, with a particular interest in NPS implications in neural plasticity, motor rehabilitation, and its potential for clinical translation.

Fast and Slow Rhythms of Naturalistic Reading Revealed by Combined Eye-Tracking and Electroencephalography.

Neural oscillations are thought to support speech and language processing. They may not only inherit acoustic rhythms, but might also impose endogenous rhythms onto processing. In support of this, we here report that human (both, male and female) eye movements during naturalistic reading exhibit rhythmic patterns that show frequency-selective coherence with the electroencephalogram (EEG)-in the absence of any stimulation rhythm. Periodicity was observed in two distinct frequency bands: First, word-locked saccades at 4-5 Hertz display coherence with whole-head theta-band activity. Second, fixation durations fluctuate rhythmically at ∼1 Hertz, in coherence with occipital delta-band activity. This latter effect was additionally phase-locked to sentence endings, suggesting a relationship with the formation of multi-word chunks. Taken together, eye movements during reading contain rhythmic patterns that occur in synchrony with oscillatory brain activity. This suggests that linguistic processing imposes preferred processing time scales onto reading, largely independent of actual physical rhythms in the stimulus.SIGNIFICANCE STATEMENT:The sampling, grouping, and transmission of information is supported by rhythmic brain activity, so-called neural oscillations. In addition to sampling external stimuli, such rhythms may also be endogenous, affecting processing from the inside out. In particular, endogenous rhythms may impose their pace onto language processing. Studying this is challenging because speech contains physical rhythms that mask endogenous activity. To overcome this challenge, we turned to naturalistic reading, where text does not require the reader to sample in a specific rhythm. We observed rhythmic patterns of eye movements that are synchronized to brain activity as recorded with EEG. This rhythmicity is not imposed by the external stimulus, which indicates that rhythmic brain activity may serve as a pacemaker for language processing.

Dynamic models for musical rhythm perception and coordination.

Rhythmicity permeates large parts of human experience. Humans generate various motor and brain rhythms spanning a range of frequencies. We also experience and synchronize to externally imposed rhythmicity, for example from music and song or from the 24-h light-dark cycles of the sun. In the context of music, humans have the ability to perceive, generate, and anticipate rhythmic structures, for example, "the beat." Experimental and behavioral studies offer clues about the biophysical and neural mechanisms that underlie our rhythmic abilities, and about different brain areas that are involved but many open questions remain. In this paper, we review several theoretical and computational approaches, each centered at different levels of description, that address specific aspects of musical rhythmic generation, perception, attention, perception-action coordination, and learning. We survey methods and results from applications of dynamical systems theory, neuro-mechanistic modeling, and Bayesian inference. Some frameworks rely on synchronization of intrinsic brain rhythms that span the relevant frequency range; some formulations involve real-time adaptation schemes for error-correction to align the phase and frequency of a dedicated circuit; others involve learning and dynamically adjusting expectations to make rhythm tracking predictions. Each of the approaches, while initially designed to answer specific questions, offers the possibility of being integrated into a larger framework that provides insights into our ability to perceive and generate rhythmic patterns.

Phase-Amplitude Coupling Localizes Pathologic Brain with Aid of Behavioral Staging in Sleep.

Low frequency brain rhythms facilitate communication across large spatial regions in the brain and high frequency rhythms are thought to signify local processing among nearby assemblies. A heavily investigated mode by which these low frequency and high frequency phenomenon interact is phase-amplitude coupling (PAC). This phenomenon has recently shown promise as a novel electrophysiologic biomarker, in a number of neurologic diseases including human epilepsy. In 17 medically refractory epilepsy patients undergoing phase-2 monitoring for the evaluation of surgical resection and in whom temporal depth electrodes were implanted, we investigated the electrophysiologic relationships of PAC in epileptogenic (seizure onset zone or SOZ) and non-epileptogenic tissue (non-SOZ). That this biomarker can differentiate seizure onset zone from non-seizure onset zone has been established with ictal and pre-ictal data, but less so with interictal data. Here we show that this biomarker can differentiate SOZ from non-SOZ interictally and is also a function of interictal epileptiform discharges. We also show a differential level of PAC in slow-wave-sleep relative to NREM1-2 and awake states. Lastly, we show AUROC evaluation of the localization of SOZ is optimal when utilizing beta or alpha phase onto high-gamma or ripple band. The results suggest an elevated PAC may reflect an electrophysiology-based biomarker for abnormal/epileptogenic brain regions.