Finding synchrony in the desynchronized EEG: the history and interpretation of gamma rhythms

History and Evolution of the Electroencephalogram

This paper summarizes the history and evolution of the electroencephalogram (EEG). The EEG, used to record the electrical activity of the brain, is a pivotal tool in neuroscience and medicine. Its history and evolution reflect significant advancements in our understanding of brain function and our ability to diagnose and treat neurological conditions. This tool has revolutionized our understanding of the brain's electrical activity and is the cornerstone for the diagnosis and treatment of epilepsy and related disorders. The evolution of the EEG from early experimental observations to sophisticated modern applications highlights the profound progress in our ability to monitor and interpret brain activity. The EEG remains an invaluable tool in clinical and research settings, continually evolving with technological advancements to expand our understanding of the human brain. This review traces the journey of this iconic tool.

Local neurodynamics as a signature of cortical areas: new insights from sleep

Sleep crucial for the animal survival is accompanied by huge changes in neuronal electrical activity over time, the neurodynamics. Here, drawing on intracranial stereo-electroencephalographic (sEEG) recordings from the Montreal Neurological Institute (MNI), we analyzed local neurodynamics in the waking state at rest and during the N2, N3, and rapid eye movement (REM) sleep phases. Higuchi fractal dimension (HFD)-a measure of signal complexity-was studied as a feature of the local neurodynamics of the primary motor (M1), somatosensory (S1), and auditory (A1) cortices. The key working hypothesis, that the relationships between local neurodynamics preserve in all sleep phases despite the neurodynamics complexity reduces in sleep compared with wakefulness, was supported by the results. In fact, while HFD awake > REM > N2 > N3 (P < 0.001 consistently), HFD in M1 > S1 > A1 in awake and all sleep stages (P < 0.05 consistently). Also power spectral density was studied for consistency with previous investigations. Meaningfully, we found a local specificity of neurodynamics, well quantified by the fractal dimension, expressed in wakefulness and during sleep. We reinforce the idea that neurodynamic may become a new criterion for cortical parcellation, prospectively improving the understanding and ability of compensatory interventions for behavioral disorders.

Running speed and REM sleep control two distinct modes of rapid interhemispheric communication

Rhythmic gamma-band communication within and across cortical hemispheres is critical for optimal perception, navigation, and memory. Here, using multisite recordings in both rats and mice, we show that even faster ~140 Hz rhythms are robustly anti-phase across cortical hemispheres, visually resembling splines, the interlocking teeth on mechanical gears. Splines are strongest in superficial granular retrosplenial cortex, a region important for spatial navigation and memory. Spline-frequency interhemispheric communication becomes more coherent and more precisely anti-phase at faster running speeds. Anti-phase splines also demarcate high-activity frames during REM sleep. While splines and associated neuronal spiking are anti-phase across retrosplenial hemispheres during navigation and REM sleep, gamma-rhythmic interhemispheric communication is precisely in-phase. Gamma and splines occur at distinct points of a theta cycle and thus highlight the ability of interhemispheric cortical communication to rapidly switch between in-phase (gamma) and anti-phase (spline) modes within individual theta cycles during both navigation and REM sleep.

Gamma-rhythmic communication within and across cortical hemispheres is critical for optimal perception, navigation, and memory. Here, Ghosh et al. identify even faster ~140 Hz rhythms, named splines, that reflect anti-phase neuronal synchrony across hemispheres. The balance of anti-phase spline and in-phase gamma communication is dynamically controlled by behavior and sleep.

Neural entrainment via perceptual inferences

Entrainment depends on sequential neural phase reset by regular stimulus onset, a temporal parameter. Entraining to sequences of identical stimuli also entails stimulus feature predictability, but this component is not readily separable from temporal regularity. To test if spectral regularities concur with temporal regularities in determining the strength of auditory entrainment, we devised sound sequences that varied in conditional perceptual inferences based on deviant sound repetition probability: Strong inference (100% repetition probability: If a deviant appears, then it will repeat), weak inference (75% repetition probability), no inference (50%: A deviant may or may not repeat with equal probability). We recorded EEG data from 15 young human participants pre-attentively listening to the experimental sound sequences delivered either isochronously or anisochronously ( ± 20 % jitter), at both delta (1.67 Hz) and theta (6.67 Hz) stimulation rates. Strong perceptual inferences significantly enhanced entrainment at either stimulation rate, and determined positive correlations between precision in phase distribution at the onset of deviant trials, and entrained power. We conclude that both spectral predictability and temporal regularity govern entrainment via neural phase control.

Resting state EEG in youth with ASD: age, sex, and relation to phenotype

BACKGROUND

Identification of ASD biomarkers is a key priority for understanding etiology, facilitating early diagnosis, monitoring developmental trajectories, and targeting treatment efforts. Efforts have included exploration of resting state encephalography (EEG), which has a variety of relevant neurodevelopmental correlates and can be collected with minimal burden. However, EEG biomarkers may not be equally valid across the autism spectrum, as ASD is strikingly heterogeneous and individual differences may moderate EEG-behavior associations. Biological sex is a particularly important potential moderator, as females with ASD appear to differ from males with ASD in important ways that may influence biomarker accuracy.

METHODS

We examined effects of biological sex, age, and ASD diagnosis on resting state EEG among a large, sex-balanced sample of youth with (N = 142, 43% female) and without (N = 138, 49% female) ASD collected across four research sites. Absolute power was extracted across five frequency bands and nine brain regions, and effects of sex, age, and diagnosis were analyzed using mixed-effects linear regression models. Exploratory partial correlations were computed to examine EEG-behavior associations in ASD, with emphasis on possible sex differences in associations.

RESULTS

Decreased EEG power across multiple frequencies was associated with female sex and older age. Youth with ASD displayed decreased alpha power relative to peers without ASD, suggesting increased neural activation during rest. Associations between EEG and behavior varied by sex. Whereas power across various frequencies correlated with social skills, nonverbal IQ, and repetitive behavior for males with ASD, no such associations were observed for females with ASD.

CONCLUSIONS

Research using EEG as a possible ASD biomarker must consider individual differences among participants, as these features influence baseline EEG measures and moderate associations between EEG and important behavioral outcomes. Failure to consider factors such as biological sex in such research risks defining biomarkers that misrepresent females with ASD, hindering understanding of the neurobiology, development, and intervention response of this important population.

Characteristic changes in EEG spectral powers of patients with opioid-use disorder as compared with those with methamphetamine- and alcohol-use disorders

Electroencephalography (EEG) likely reflects activity of cortical neurocircuits, making it an insightful estimation for mental health in patients with substance use disorder (SUD). EEG signals are recorded as sinusoidal waves, containing spectral amplitudes across several frequency bands with high spatio-temporal resolution. Prior work on EEG signal analysis has been made mainly at individual electrodes. These signals can be evaluated from advanced aspects, including sub-regional and hemispheric analyses. Due to limitation of computational techniques, few studies in earlier work could conduct data analyses from these aspects. Therefore, EEG in patients with SUD is not fully understood. In the present retrospective study, spectral powers from a data house containing opioid (OUD), methamphetamine/stimulants (MUD), and alcohol use disorder (AUD) were extracted, and then converted into five distinct topographic data (i.e., electrode-based, cortical subregion-based, left-right hemispheric, anterior-posterior based, and total cortex-based analyses). We found that data conversion and reorganization in the topographic way had an impact on EEG spectral powers in patients with OUD significantly different from those with MUD or AUD. Differential changes were observed from multiple perspectives, including individual electrodes, subregions, hemispheres, anterior-posterior cortices, and across the cortex as a whole. Understanding the differential changes in EEG signals may be useful for future work with machine learning and artificial intelligence (AI), not only for diagnostic but also for prognostic purposes in patients with SUD.

EEG correlation during the solving of simple and complex logical-mathematical problems

Solving logical-mathematical word problems is a complex task that requires numerous cognitive operations, including comprehension, reasoning, and calculation. These abilities have been associated with activation of the parietal, temporal, and prefrontal cortices. It has been suggested that the reasoning involved in solving logical-mathematical problems requires the coordinated functionality of all these cortical areas. In this study was evaluated the activation and electroencephalographic (EEG) correlation of the prefrontal, temporal, and parietal regions in young men while solving logical-mathematical word problems with two degrees of difficulty: simple and complex. During the solving of complex problems, higher absolute power and EEG correlation of the alpha and fast bands between the left frontal and parietal cortices were observed. A temporal deactivation and functional decoupling of the right parietal-temporal cortices also were obtained. Solving complex problems probably require activation of a left prefrontal-parietal circuit to maintain and manipulate multiple pieces of information. The temporal deactivation and decreased parietal-temporal correlation could be associated to text processing and suppression of the content-dependent reasoning to focus cognitive resources on the mathematical reasoning. Together, these findings support a pivotal role for the left prefrontal and parietal cortices in mathematical reasoning and of the temporal regions in text processing required to understand and solve written mathematical problems.

The Role of Quantitative EEG in the Diagnosis of Neuropsychiatric Disorders

Quantitative electroencephalography (QEEG) is a modern type of electroencephalography (EEG) analysis that involves recording digital EEG signals which are processed, transformed, and analyzed using complex mathematical algorithms. QEEG has brought new techniques of EEG signals feature extraction: analysis of specific frequency band and signal complexity, analysis of connectivity, and network analysis. The clinical application of QEEG is extensive, including neuropsychiatric disorders, epilepsy, stroke, dementia, traumatic brain injury, mental health disorders, and many others. In this review, we talk through existing evidence on the practical applications of this clinical tool. We conclude that to date, the role of QEEG is not necessarily to pinpoint an immediate diagnosis but to provide additional insight in conjunction with other diagnostic evaluations in order to objective information necessary for obtaining a precise diagnosis, correct disease severity assessment, and specific treatment response evaluation.

The neural circuitry supporting successful spatial navigation despite variable movement speeds

Ants who have successfully navigated the long distance between their foraging spot and their nest dozens of times will drastically overshoot their destination if the size of their legs is doubled by the addition of stilts. This observation reflects a navigational strategy called path integration, a strategy also utilized by mammals. Path integration necessitates that animals keep track of their movement speed and use it to precisely and instantly modify where they think they are and where they want to go. Here we review the neural circuitry that has evolved to integrate speed and space. We start with the rate and temporal codes for speed in the hippocampus and work backwards towards the motor and sensory systems. We highlight the need for experiments designed to differentiate the respective contributions of motor efference copy versus sensory inputs. In particular, we discuss the importance of high-resolution tracking of the latency of speed-encoding as a precise way to disentangle the sensory versus motor computations that enable successful spatial navigation at very different speeds.

Effects of Task Complexity on Motor Imagery-Based Brain-Computer Interface

The performance of electroencephalogram (EEG)-based brain-computer interfaces (BCIs) still needs improvements for real world applications. An improvement on BCIs could be achieved by enhancing brain signals from the source via subject intention-based modulation. In this work, we aim to investigate the effects of task complexity on performance of motor imagery (MI) based BCIs. In specific, we studied the effects of motor imagery of a complex task versus a simple task on discriminability of brain activation patterns using EEG. The results show an increase of up to 7.25% in BCI classification accuracy for motor imagery of the complex task in comparison to the simple task. Furthermore, spectral power analysis in low frequency bands, alpha and beta, shows a significant decrease in power value for the complex task. However, high frequency gamma band analysis unveils a significant increase for the complex task. These findings may lead to designing better BCIs with high performance.

Gentle abdominal stroking ('belly rubbing') of pigs by a human reduces EEG total power and increases EEG frequencies

The neurobiological response to gentle touch remains poorly understood, especially in the context of human-animal interaction. A novel approach allowed recording the pig electroencephalogram (EEG) cranially epidurally and wirelessly during positive interactions with a human. Stroking of the pig's abdomen ('belly rubbing'), applied opportunistically, elicited a distinct behavioral response characterized by lateral recumbency, limb stretching, frequent short-lasting grunts and eye closure. Pigs varied in their responsiveness to belly rubbing but all pigs showed it. Their EEG was compared to EEG during human presence and other positive interactions except belly rubbing; isolation; and in the home pen as a baseline. Total EEG power ('Ptot') was lower during belly rubbing, whereas the median frequency ('F50', 5.3 ± 0.9 Hz vs. 3.8 ± 0.9 Hz for other contexts) and the 95% spectral edge frequency ('F95', 45.2 ± 3.2 Hz vs. 40.0 ± 3.2 Hz for other contexts) were higher during belly rubbing compared to other contexts. Lower EEG total power combined with a shift in spectral power distribution toward higher frequencies were linked to behavioral changes indicative of a positive welfare state during belly rubbing. The effects of belly rubbing on animal psychobiology and well-being warrant further research as a model of positive welfare state induced by touch.

Cortical travelling waves: mechanisms and computational principles

Multichannel recording technologies have revealed travelling waves of neural activity in multiple sensory, motor and cognitive systems. These waves can be spontaneously generated by recurrent circuits or evoked by external stimuli. They travel along brain networks at multiple scales, transiently modulating spiking and excitability as they pass. Here, we review recent experimental findings that have found evidence for travelling waves at single-area (mesoscopic) and whole-brain (macroscopic) scales. We place these findings in the context of the current theoretical understanding of wave generation and propagation in recurrent networks. During the large low-frequency rhythms of sleep or the relatively desynchronized state of the awake cortex, travelling waves may serve a variety of functions, from long-term memory consolidation to processing of dynamic visual stimuli. We explore new avenues for experimental and computational understanding of the role of spatiotemporal activity patterns in the cortex.

Motor hypertonia and lack of locomotor coordination in mutant mice lacking DSCAM

Down syndrome cell adherence molecule (DSCAM) contributes to the normal establishment and maintenance of neural circuits. Whereas there is abundant literature regarding the role of DSCAM in the neural patterning of the mammalian retina, less is known about motor circuits. Recently, DSCAM mutation has been shown to impair bilateral motor coordination during respiration, thus causing death at birth. DSCAM mutants that survive through adulthood display a lack of locomotor endurance and coordination in the rotarod test, thus suggesting that the DSCAM mutation impairs motor control. We investigated the motor and locomotor functions of DSCAM(2J) mutant mice through a combination of anatomical, kinematic, force, and electromyographic recordings. With respect to wild-type mice, DSCAM(2J) mice displayed a longer swing phase with a limb hyperflexion at the expense of a shorter stance phase during locomotion. Furthermore, electromyographic activity in the flexor and extensor muscles was increased and coactivated over 20% of the step cycle over a wide range of walking speeds. In contrast to wild-type mice, which used lateral walk and trot at walking speed, DSCAM(2J) mice used preferentially less coordinated gaits, such as out-of-phase walk and pace. The neuromuscular junction and the contractile properties of muscles, as well as their muscle spindles, were normal, and no signs of motor rigidity or spasticity were observed during passive limb movements. Our study demonstrates that the DSCAM mutation induces dystonic hypertonia and a disruption of locomotor gaits.

Spatial Coherence Profiles of Ictal High-Frequency Oscillations Correspond to Those of Interictal Low-Frequency Oscillations in the ECoG of Epileptic Patients

GOAL

We have previously demonstrated that the coherence of high-frequency oscillations (HFOs; 80-300 Hz) increased during extratemporal lobe seizures in a consistent and spatially focused electrode cluster. In this study, we have investigated the relationship between cohered HFO intracranial EEG (iEEG) activity with that of slower low-frequency oscillations (LFOs; <80 Hz).

METHODS

We applied wavelet phase coherence analysis to the iEEGs of patients with intractable extratemporal lobe epilepsy (ETLE).

RESULTS

It was observed that areas on the implanted patient subdural grids, which exhibited strong ictal HFO coherence were similar to tissue regions displaying strong interictal LFO coherence in the 5-12 Hz frequency range, relative to all other electrodes. A positive surgical outcome was correlated with having the clinically marked seizure onset zone(s) in close proximity to HFO/LFO coherence highlighted regions of interest (ROIs).

CONCLUSION

Recent studies have suggested that LFOs (in the 8-12 Hz frequency range) play an important role in controlling cortical excitability, by exerting an inhibitory effect on cortical processing, and that the presence of strong theta activity (4-8 Hz) in awake adults is suggestive of abnormal and/or pathological activity. We speculate that the overlapping spatial regions exhibiting increased coherence in both ictal HFOs and interictal LFOs identified local abnormalities that underlie epileptogenic networks.

SIGNIFICANCE

Whereas it is worthwhile to note that the small patient group ( n = 7) studied here, somewhat limits the clinical significance of our study, the results presented here suggest targeting HFO activity in the 80-300 Hz frequency range and/or interictal LFO activity in the 5-12 Hz frequency range, when defining seizure-related ROIs in the iEEGs of patients with ETLE.

Circuit oscillations in odor perception and memory

Olfactory system neural oscillations as seen in the local field potential have been studied for many decades. Recent research has shown that there is a functional role for the most studied gamma oscillations (40-100Hz in rats and mice, and 20Hz in insects), without which fine odor discrimination is poor. When these oscillations are increased artificially, fine discrimination is increased, and when rats learn difficult and highly overlapping odor discriminations, gamma is increased in power. Because of the depth of study on this oscillation, it is possible to point to specific changes in neural firing patterns as represented by the increase in gamma oscillation amplitude. However, we know far less about the mechanisms governing beta oscillations (15-30Hz in rats and mice), which are best associated with associative learning of responses to odor stimuli. These oscillations engage every part of the olfactory system that has so far been tested, plus the hippocampus, and the beta oscillation frequency band is the one that is most reliably coherent with other regions during odor processing. Respiratory oscillations overlapping with the theta frequency band (2-12Hz) are associated with odor sniffing and normal breathing in rats. They also show coupling in some circumstances between olfactory areas and rare coupling between the hippocampus and olfactory bulb. The latter occur in specific learning conditions in which coherence strength is negatively or positively correlated with performance, depending on the task. There is still much to learn about the role of neural oscillations in learning and memory, but techniques that have been brought to bear on gamma oscillations (current source density, computational modeling, slice physiology, behavioral studies) should deliver much needed knowledge of these events.