Low-frequency Alpha Research Articles

How predictability and individual alpha frequency shape memory: Insights from an event-related potential investigation

Prediction and memory are strongly intertwined, with predictions relying on memory retrieval, whilst also influencing memory encoding. However, it is unclear how predictability influences explicit memory performance, and how individual neural factors may modulate this relationship. The current study sought to investigate the effect of predictability on memory processing with an analysis of the N400 event-related potential in a context extending beyond language. Participants (N = 48, females = 33) completed a study-test paradigm where they first viewed predictable and unpredictable four-item ‘ABCD’ sequences of outdoor scene images, whilst their brain activity was recorded using electroencephalography. Subsequently, their memory for the images was tested, and N400 patterns during learning were compared with memory outcomes. Behavioural results revealed better memory for images in predictable sequences in contrast to unpredictable sequences. Memory was also strongest for predictable images in the ‘B’ position, suggesting that when processing longer sequences, the brain may prioritise the data deemed most informative. Strikingly, greater N400 amplitudes during learning were associated with enhanced memory at test for individuals with low versus high individual alpha frequencies. In light of the relationship between the N400 and stimulus predictability, this finding may imply that predictive processing differs between individuals to influence the extent of memory encoding. Finally, exploratory analyses provided evidence for a later positivity that was predictive of subsequent memory performance. Ultimately, the results highlight the complex and interconnected relationship between predictive processing and memory, whilst shedding light on the accumulation of predictions across longer sequences.

Oscillatory brain activity in the canonical alpha-band conceals distinct mechanisms in attention.

Brain oscillations in the alpha-band (8-14Hz) over posterior areas have been linked to specific processes in attention and perception. In particular, decreases in alpha-amplitude are thought to reflect activation of perceptually relevant brain areas for target engagement, while increases in alpha-amplitude have been associated with inhibition for distractor suppression. Traditionally, these alpha-changes have been viewed as two facets of the same process. However, more recent evidence calls for revisiting this interpretation. Here, we concurrently recorded MEG/EEG in 32 participants (19 females) during covert visuo-spatial attention shifts (spatial cues), and two control conditions (neutral cue, no-attention cue), while tracking fixational eye-movements. In disagreement with a single, perceptually relevant alpha-process, we found the typical alpha-modulations contra- and ipsilateral to the attention focus to be triple dissociated in their timing, topography, and spectral features: Ipsilateral alpha-increases occurred early, over occipital sensors, at a high alpha-frequency (10-14Hz) and were expressed during spatial attention (alpha spatial cue > neutral cue). By contrast, contralateral alpha-decreases occurred later, over parietal sensors, at a lower alpha-frequency (7-10Hz) and were associated with attention deployment in general (alpha spatial&neutral cue < no-attention cue). Additionally, the lateralized early alpha-increases but not -decreases during spatial attention coincided in time with directionally biased microsaccades. Overall, these findings suggest that the attention-related early alpha-increases and late -decreases reflect distinct, likely reflexive versus endogenously controlled attention mechanisms. We conclude that there is more than one perceptually relevant posterior alpha-oscillation, which need to be dissociated for a detailed account of their roles in perception and attention.Significance statement This study provides novel insights into perceptually relevant brain oscillations in the "canonical" alpha-band, and the neural correlates of covert and overt attention processes. This by simultaneously recording MEG and EEG, and isolating by design visuospatial and temporal expectations respectively, while tracking fixational eye-movements concurrently to the nominally covert attention shifts. The data reveal the presence of two, spatio-temporally and spectrally dissociated patterns of posterior alpha-changes that are distinctively associated with deployment of spatial and temporal anticipation, and eye-movement activity. This refines our understanding of the role of brain oscillations in perception and attention, the neural underpinnings of attention deployment in space and time, and provides methodological pointers for the study of perceptually relevant oscillatory activity through MEG/EEG.

Peak alpha frequency differs between chronic back pain and chronic widespread pain.

Low peak alpha frequency (PAF) is an electroencephalography (EEG) outcome associated reliably with high acute pain sensitivity. However, existing research suggests that the relationship between PAF and chronic pain is more variable. This variability could be attributable to chronic pain groups typically being examined as homogenous populations, without consideration being given to potential diagnosis-specific differences. Indeed, while emerging work has compared individuals with chronic pain to healthy controls, no previous studies have examined differences in PAF between diagnoses or across chronic pain subtypes. To address this gap, we reanalysed a dataset of resting state EEG previously used to demonstrate a lack of difference in PAF between individuals with chronic pain and healthy controls. In this new analysis, we separated patients by diagnosis before comparing PAF across three subgroups: chronic widespread pain (n = 30), chronic back pain (n = 38), and healthy controls (n = 87). We replicate the original finding of no significant difference between chronic pain groups and controls, but also find that individuals with widespread pain had significantly higher global average PAF values than those of people with chronic back pain [p = 0.028, β = 0.25 Hz] after controlling for age, sex, and depression. These novel findings reveal PAF values in individuals with chronic pain may be diagnosis-specific and not uniformly shifted from the values of healthy controls. Future studies should account for diagnosis and be cautious with exploring homogenous 'chronic pain' classifications during investigations of PAF. Our work suggests that, contrary to previous hypotheses, inter-individual differences in PAF reflect diagnosis-specific mechanisms rather than the general presence of chronic pain, and therefore may have important implications for future work regarding individually-tailored pain management strategies.

Frontal alpha asymmetries and behavioral immune system: moderating role of behavioral inhibition system

Background and purpose: Behavioral immune system is a cluster of psychological mechanisms enabling detection and avoidance of pathogens in one’s immediate environment. Its presumed activation has been implicated in myriads of psychological phenomena, stemming from pathogen related disgust to more complex behaviors, such as mate choice and xenophobic cognitions. However, little is known about its biological underpinnings. The aim of this preliminary study was twofold: 1) to explore the role of another neuropsychological system governing avoidant motivations, the behavioral inhibition system, in pathogen-induced disgust and 2) to determine if frontal hemispheric asymmetries (a neural correlate of avoidant motivations) might serve as indicators of behavioral immune system activation. Materials and methods: 62 participants completed the Behavioral inhibition scale. Based on their z-scores, two extreme groups were formed: high (n=9) and low behavioral inhibition (n=9) group. After the baseline EEG recordings, participants were exposed to a set of neutral stimuli, followed by a set of pathogen disgust inducing stimuli. The frontal asymmetry (FAA) indexes (lnR-LnL) were calculated within both low (8-10 Hz) and high (11-13 Hz) alpha frequency bands on analogue pairs of frontal electrodes. Results: There were no baseline FAA differences between groups. However, compared to low behavioral inhibition group, high behavioral inhibition group showed larger shifts in FAA on frontopolar locations while watching the pathogen related disgust-inducing stimuli, as compared to neutral photographs. Conclusions: This pattern of FAA shifts suggests that high behavioral inhibition individuals attend to pathogen threat related cues more readily, i.e., have a more reactive behavioral immune system. With this preliminary study we are proposing a new line of research in order to determine if there is evidence of a calibrated response in terms of interplay between one’s immune status and pathogen treat related neural reactivity.

Electroencephalography (EEG) Evidence for the Psychological Processes of Humor Generation: A Comparison Perspective on Humor and Creativity.

(1) Background: Humor stands out as the most dynamic and innovative aspect of human intelligence. Drawing on the cognitive parallels between humor and creativity, this study explored the EEG alpha frequency band activity patterns during humor generation by comparing the process of generating humorous and creative ideas. (2) Methods: Thirty-six participants were randomly assigned to either the humor generation group or the creative generation group, and the dependent variable was the neural oscillation in both low-frequency and high-frequency alpha during the early, middle, and late stages of both humor and creative generation. (3) Results: In the early stages, both humor and creative generation exhibited significantly higher power in low-frequency alpha and high-frequency alpha in the temporal region compared to the middle and late stages. In the middle and late stages, the low-frequency alpha oscillation in the frontal region for humor generation was significantly higher than that for creative generation. (4) Conclusions: Humor and creative generation share similar neural activation patterns in the early stages, involving the activation and retrieval of long-term memory information based on contextual cues. The differences between the two primarily manifest in the middle and late stages, where the selection of humorous ideas requires inhibiting not only irrelevant or ordinary ideas, akin to creative generation but also novel yet non-humorous ideas. This study sheds light on the neurocognitive mechanisms of humor generation and provides insights into the cognitive parallels and distinctions between humor generation and creative generation.

Neurodevelopment of children exposed to prolonged anesthesia in infancy: GABA study interim analysis of resting-state brain networks at 2, 4, and 10-months old

BackgroundPrevious studies have raised concerns regarding neurodevelopmental impacts of early exposures to general anesthesia and surgery. Electroencephalography (EEG) can be used to study ontogeny of brain networks during infancy. As a substudy of an ongoing study, we examined measures of functional connectivity in awake infants with prior early and prolonged anesthetic exposures and in control infants. MethodsEEG functional connectivity was assessed using debiased weighted phase lag index at source and sensor levels and graph theoretical measures for resting state activity in awake infants in the early anesthesia (n = 26 at 10 month visit, median duration of anesthesia = 4 [2, 7 h]) and control (n = 38 at 10 month visit) groups at ages approximately 2, 4 and 10 months. Theta and low alpha frequency bands were of primary interest. Linear mixed models incorporated impact of age and cumulative hours of general anesthesia exposure. ResultsModels showed no significant impact of cumulative hours of general anesthesia exposure on debiased weighted phase lag index, characteristic path length, clustering coefficient or small-worldness (conditional R2 0.05–0.34). An effect of age was apparent in many of these measures. ConclusionsWe could not demonstrate significant impact of general anesthesia in the first months of life on early development of resting state brain networks over the first postnatal year. Future studies will explore these networks as these infants grow older.

Demonstration of colored noise on brainwave activity during reading tasks: An electroencephalogram study

This study investigates the effects of different types of colored noise on brainwave activity during reading task, with a unique intent to use this research as a practical demonstration for the participant to learn about acoustics. Electroencephalogram (EEG) data from 29 participants were obtained and analyzed to assess brainwave activity across all channels when exposed to these colored noises. After brainwave recording process, participants were invited to view their recorded individual brainwave activities and topo mapping, so that they could learn the different colored noises might affecting their brainwave responses. The study found that most of the participants are interested to learn more about how the colored noise affects their daily activity. Besides demonstration to get participants interested in learning noise effects on brainwave activity, our brainwave study revealed that pink noise significantly influenced theta, low alpha, high alpha, low beta, and high beta frequencies during the reading tasks. In comparison, white noise had a notable impact on low and high alpha frequencies, while brown noise yielded significant changes in theta brainwave activity. This research points out that colored noise, could alter brainwave activity. It is potentially affecting cognitive engagement such as alertness and focus. By involving different major students as participants, we demonstrated the tangible effects of acoustics on cognitive functions, fostering a more profound understanding of the subject matter.

The Importance of Low-frequency Alpha (8-10 Hz) Waves and Default Mode Network in Behavioral Inhibition.

: Alpha wave of electroencephalography (EEG) is known to be related to behavioral inhibition. Both the alpha wave and default mode network (DMN) are predominantly activated during resting-state. To study the mechanisms of the trait inhibition, this research investigating the relations among alpha wave, DMN and behavioral inhibition in resting-state. : We explored the relationship among behavioral inhibition, resting-state alpha power, and DMN. Resting-state EEG, behavioral inhibition/behavioral activation scale (BIS/BAS), Barratt impulsivity scale, and no-go accuracy were assessed in 104 healthy individuals. Three groups (i.e., participants with low/middle/high band power) were formed based on the relative power of each total-alpha, low-alpha (LA), and high-alpha band. Source-reconstructed EEG and functional network measures of 25 DMN regions were calculated. : Significant differences and correlations were found based on LA band power alone. The high LA group had significantly greater BIS, clustering coefficient, efficiency, and strength, and significantly lower path length than low/middle LA group. BIS score showed a significant correlation with functional network measures of DMN. : Our study revealed that LA power is related to behavioral inhibition and functional network measures of DMN of LA band appear to represent significant inhibitory function.

Impact of Macrocephaly, as an Isolated Trait, on EEG Signal as Measured by Spectral Power and Multiscale Entropy during the First Year of Life

Macrocephaly has been associated with neurodevelopmental disorders; however, it has been mainly studied in the context of pathological or high-risk populations and little is known about its impact, as an isolated trait, on brain development in general population. Electroencephalographic (EEG) power spectral density (PSD) and signal complexity have shown to be sensitive to neurodevelopment and its alterations. We aimed to investigate the impact of macrocephaly, as an isolated trait, on EEG signal as measured by PSD and multiscale entropy during the first year of life. We recorded high-density EEG resting-state activity of 74 healthy full-term infants, 50 control (26 girls), and 24 macrocephalic (12 girls) aged between 3 and 11 months. We used linear regression models to assess group and age effects on EEG PSD and signal complexity. Sex and brain volume measures, obtained via a 3D transfontanellar ultrasound, were also included into the models to evaluate their contribution. Our results showed lower PSD of the low alpha (8–10 Hz) frequency band and lower complexity in the macrocephalic group compared to the control group. In addition, we found an increase in low alpha (8.5–10 Hz) PSD and in the complexity index with age. These findings suggest that macrocephaly as an isolated trait has a significant impact on brain activity during the first year of life.

Observation of Boredom during Repetitive Listening of the Same Music in Various Time Spans

Streaming services repetitively provide similar kinds of music, and listeners might get tired of them quickly. This is one of the serious problems for streaming services today. Boredom is developed with repetition, and it depends on personal preference. Therefore, boredom development depends on the time spans of the repetition and personal preferences when people listen to the same music over days. This study conducts an experiment that the participants repetitively listen to one music piece 294 times for seven days in a row to observe boredom development. We measured the boredom level as the reactional factor of the failure to keep the concentration and motivation of listening. As a result, the concentration decreased in the short-term, whereas the motivation decreased in the long-term. Therefore, we could distinguish the state of boredom by the length of the time span. In addition, compared to repetitive listening with a day as the short-term, repetitive listening every day in a week as the long-term had an equal or slightly higher power to generate boredom, but there is a weak correlation between boredom and preference for a music piece. In conclusion, the results showed that the amount of repetition had a stronger effect than personal preference for music, and people may get bored even with their favorite music when they listen too many times. Moreover, the behavior of the low-frequency alpha wave in the brain had only a weak relationship to the amount of listening within a day, not in the week.

What a single electroencephalographic (EEG) channel can tell us about patients with dementia due to Alzheimer's disease

Abnormalities in cortical sources of resting-state eyes-closed electroencephalographic (rsEEG) rhythms recorded by hospital settings (10–20 electrode montage) with 19 scalp electrodes provide useful markers of neurophysiological dysfunctions in the vigilance regulation in patients with Alzheimer's disease dementia (ADD). Here we tested whether these markers may be effective from a few scalp electrodes towards the use of low-cost recording devices.Clinical and rsEEG data acquired in hospital settings (10–20 electrode montage) from 88 ADD participants and 68 age-, education-, and sex-matched normal elderly controls (Nold) were available in an international Eurasian database. Standard spectral FFT analysis of rsEEG data for individual delta, theta, and alpha frequency bands was from C3-P3, C4-P4, P3-O1, and P4-O2 bipolar channels.As compared to the Nold group, the ADD group showed increased delta, theta, low-frequency alpha power density and decreased high-frequency alpha power density at all those bipolar channels. The highest classification accuracy between the ADD and Nold individuals reached 90 % (area under the receiver operating characteristic curve) using Alpha2/Theta power density computed at the C3-P3 bipolar channel.Standard rsEEG power density computed from a few posterior bipolar channels successfully classified Nold and ADD individuals, thus encouraging a massive prescreening of neurophysiological mechanisms underpinning the vigilance dysregulation in underserved old seniors.

Rapid adaptation of predictive models during language comprehension: Aperiodic EEG slope, individual alpha frequency and idea density modulate individual differences in real-time model updating.

Predictive coding provides a compelling, unified theory of neural information processing, including for language. However, there is insufficient understanding of how predictive models adapt to changing contextual and environmental demands and the extent to which such adaptive processes differ between individuals. Here, we used electroencephalography (EEG) to track prediction error responses during a naturalistic language processing paradigm. In Experiment 1, 45 native speakers of English listened to a series of short passages. Via a speaker manipulation, we introduced changing intra-experimental adjective order probabilities for two-adjective noun phrases embedded within the passages and investigated whether prediction error responses adapt to reflect these intra-experimental predictive contingencies. To this end, we calculated a novel measure of speaker-based, intra-experimental surprisal (“speaker-based surprisal”) as defined on a trial-by-trial basis and by clustering together adjectives with a similar meaning. N400 amplitude at the position of the critical second adjective was used as an outcome measure of prediction error. Results showed that N400 responses attuned to speaker-based surprisal over the course of the experiment, thus indicating that listeners rapidly adapt their predictive models to reflect local environmental contingencies (here: the probability of one type of adjective following another when uttered by a particular speaker). Strikingly, this occurs in spite of the wealth of prior linguistic experience that participants bring to the laboratory. Model adaptation effects were strongest for participants with a steep aperiodic (1/f) slope in resting EEG and low individual alpha frequency (IAF), with idea density (ID) showing a more complex pattern. These results were replicated in a separate sample of 40 participants in Experiment 2, which employed a highly similar design to Experiment 1. Overall, our results suggest that individuals with a steep aperiodic slope adapt their predictive models most strongly to context-specific probabilistic information. Steep aperiodic slope is thought to reflect low neural noise, which in turn may be associated with higher neural gain control and better cognitive control. Individuals with a steep aperiodic slope may thus be able to more effectively and dynamically reconfigure their prediction-related neural networks to meet current task demands. We conclude that predictive mechanisms in language are highly malleable and dynamic, reflecting both the affordances of the present environment as well as intrinsic information processing capabilities of the individual.

Heterogeneity of resting-state EEG features in juvenile myoclonic epilepsy and controls.

Abnormal EEG features are a hallmark of epilepsy, and abnormal frequency and network features are apparent in EEGs from people with idiopathic generalized epilepsy in both ictal and interictal states. Here, we characterize differences in the resting-state EEG of individuals with juvenile myoclonic epilepsy and assess factors influencing the heterogeneity of EEG features. We collected EEG data from 147 participants with juvenile myoclonic epilepsy through the Biology of Juvenile Myoclonic Epilepsy study. Ninety-five control EEGs were acquired from two independent studies [Chowdhury et al. (2014) and EU-AIMS Longitudinal European Autism Project]. We extracted frequency and functional network-based features from 10 to 20 s epochs of resting-state EEG, including relative power spectral density, peak alpha frequency, network topology measures and brain network ictogenicity: a computational measure of the propensity of networks to generate seizure dynamics. We tested for differences between epilepsy and control EEGs using univariate, multivariable and receiver operating curve analysis. In addition, we explored the heterogeneity of EEG features within and between cohorts by testing for associations with potentially influential factors such as age, sex, epoch length and time, as well as testing for associations with clinical phenotypes including anti-seizure medication, and seizure characteristics in the epilepsy cohort. P-values were corrected for multiple comparisons. Univariate analysis showed significant differences in power spectral density in delta (2–5 Hz) (P = 0.0007, hedges’ g = 0.55) and low-alpha (6–9 Hz) (P = 2.9 × 10−8, g = 0.80) frequency bands, peak alpha frequency (P = 0.000007, g = 0.66), functional network mean degree (P = 0.0006, g = 0.48) and brain network ictogenicity (P = 0.00006, g = 0.56) between epilepsy and controls. Since age (P = 0.009) and epoch length (P = 1.7 × 10−8) differed between the two groups and were potential confounders, we controlled for these covariates in multivariable analysis where disparities in EEG features between epilepsy and controls remained. Receiver operating curve analysis showed low-alpha power spectral density was optimal at distinguishing epilepsy from controls, with an area under the curve of 0.72. Lower average normalized clustering coefficient and shorter average normalized path length were associated with poorer seizure control in epilepsy patients. To conclude, individuals with juvenile myoclonic epilepsy have increased power of neural oscillatory activity at low-alpha frequencies, and increased brain network ictogenicity compared with controls, supporting evidence from studies in other epilepsies with considerable external validity. In addition, the impact of confounders on different frequency-based and network-based EEG features observed in this study highlights the need for careful consideration and control of these factors in future EEG research in idiopathic generalized epilepsy particularly for their use as biomarkers.

Electroencephalography Correlates of Well-Being Using a Low-Cost Wearable System.

Electroencephalography (EEG) alpha asymmetry is thought to reflect crucial brain processes underlying executive control, motivation, and affect. It has been widely used in psychopathology and, more recently, in novel neuromodulation studies. However, inconsistencies remain in the field due to the lack of consensus in methodological approaches employed and the recurrent use of small samples. Wearable technologies ease the collection of large and diversified EEG datasets that better reflect the general population, allow longitudinal monitoring of individuals, and facilitate real-world experience sampling. We tested the feasibility of using a low-cost wearable headset to collect a relatively large EEG database (N = 230, 22–80 years old, 64.3% female), and an open-source automatic method to preprocess it. We then examined associations between well-being levels and the alpha center of gravity (CoG) as well as trait EEG asymmetries, in the frontal and temporoparietal (TP) areas. Robust linear regression models did not reveal an association between well-being and alpha (8–13 Hz) asymmetry in the frontal regions, nor with the CoG. However, well-being was associated with alpha asymmetry in the TP areas (i.e., corresponding to relatively less left than right TP cortical activity as well-being levels increased). This effect was driven by oscillatory activity in lower alpha frequencies (8–10.5 Hz), reinforcing the importance of dissociating sub-components of the alpha band when investigating alpha asymmetries. Age was correlated with both well-being and alpha asymmetry scores, but gender was not. Finally, EEG asymmetries in the other frequency bands were not associated with well-being, supporting the specific role of alpha asymmetries with the brain mechanisms underlying well-being levels. Interpretations, limitations, and recommendations for future studies are discussed. This paper presents novel methodological, experimental, and theoretical findings that help advance human neurophysiological monitoring techniques using wearable neurotechnologies and increase the feasibility of their implementation into real-world applications.

Deep learning reveals personalized spatial spectral abnormalities of high delta and low alpha bands in EEG of patients with early Parkinson’s disease

Objective. Parkinson’s disease (PD) is one of the most common neurodegenerative diseases, and early diagnosis is crucial to delay disease progression. The diagnosis of early PD has always been a difficult clinical problem due to the lack of reliable biomarkers. Electroencephalogram (EEG) is the most common clinical detection method, and studies have attempted to discover the EEG spectrum characteristics of early PD, but the reported conclusions are not uniform due to the heterogeneity of early PD patients. There is an urgent need for a more advanced algorithm to extract spectrum characteristics from EEG to satisfy the personalized requirements. Approach. The structured power spectral density with spatial distribution was used as the input of convolutional neural network (CNN). A visualization technique called gradient-weighted class activation mapping was used to extract the optimal frequency bands for identifying early PD. Based on the model visualization, we proposed a novel quantitative index of spectral characteristics, spatial-mapping relative power (SRP), to detect personalized abnormalities in the spatial spectral characteristics of EEG in early PD. Main results. We demonstrated the feasibility of applying CNN to identify the patients with early PD with an accuracy of 99.87% ± 0.03%. The models indicated the characteristic frequency bands (high-delta (3.5–4.5 Hz) and low-alpha (7.5–11 Hz) frequency bands) that are used to identify the early PD. The SRP of these two characteristic bands in early PD patients was significantly higher than that in the control group, and the abnormalities were consistent at the group and individual levels. Significance. This study provides a novel personalized detection algorithm based on deep learning to reveal the optimal frequency bands for identifying early PD and obtain the spatial frequency characteristics of early PD. The findings of this study will provide an effective reference for the auxiliary diagnosis of early PD in clinical practice.

Mu rhythm dynamics suggest automatic activation of motor and premotor brain regions during speech processing

Results are mixed regarding the question of whether listening to speech activates automatic speech specific neural processes, or whether such activity is due to general cognitive mechanisms such as attention and memory. We suggest that analyzing the dynamics of mu rhythms over the course of a trial using event-related spectral perturbations (ERSPs) is useful for disentangling the competing hypotheses. Our findings suggest that there is automatic activation of motor and premotor areas of the brain that occurs while passively listening to speech, and that this activation is not present while listening to noise. Moreover, this activation is more strongly present in the low alpha frequencies, which are associated with attention, when participants engage in active discrimination of stimuli, and late in the trial prior to a button press.

A multi scale time–frequency analysis on Electroencephalogram signals

It is well known, the Electroencephalogram (EEG) signals are non-stationary signals which helps us in understanding the complex brain dynamics, cognitive processes, etc. In this paper, we have analysed the healthy and Epilepsy seizure subjects through continuous wavelet transform using Morlet wavelet function. We identify the key differences between healthy and epilepsy seizure EEG signals’ low frequency periodic modulations and transient time varying patterns. Persistent intermixing of alpha and beta waves is found to be a key characteristic feature of the patients. The frequency intermixing is completely absent in signals from the hippocampal formation of the opposite hemisphere of the brain for the patients without seizure, akin to the healthy subjects. Our study further reveals dominance of frequency broadened gamma waves for seizure patients as compared to the low frequency regular alpha and beta waves for the healthy subjects. The time–frequency localization of wavelet transform clearly shows transfer of power to high frequency beta waves from the low frequency alpha waves in the signals of the epileptogenic zone of the patients. The observed frequency intermixing, reported here, is analogous to the bi-stability behaviour of dynamical systems.

Delineating between-subject heterogeneity in alpha networks with Spatio-Spectral Eigenmodes

Between subject variability in the spatial and spectral structure of oscillatory networks can be highly informative but poses a considerable analytic challenge. Here, we describe a data-driven modal decomposition of a multivariate autoregressive model that simultaneously identifies oscillations by their peak frequency, damping time and network structure. We use this decomposition to define a set of Spatio-Spectral Eigenmodes (SSEs) providing a parsimonious description of oscillatory networks. We show that the multivariate system transfer function can be rewritten in these modal coordinates, and that the full transfer function is a linear superposition of all modes in the decomposition. The modal transfer function is a linear summation and therefore allows for single oscillatory signals to be isolated and analysed in terms of their spectral content, spatial distribution and network structure. We validate the method on simulated data and explore the structure of whole brain oscillatory networks in eyes-open resting state MEG data from the Human Connectome Project. We are able to show a wide between participant variability in peak frequency and network structure of alpha oscillations and show a distinction between occipital ’high-frequency alpha’ and parietal ’low-frequency alpha’. The frequency difference between occipital and parietal alpha components is present within individual participants but is partially masked by larger between subject variability; a 10Hz oscillation may represent the high-frequency occipital component in one participant and the low-frequency parietal component in another. This rich characterisation of individual neural phenotypes has the potential to enhance analyses into the relationship between neural dynamics and a person’s behavioural, cognitive or clinical state.

Identifying Emergent Mesoscopic-Macroscopic Functional Brain Network Dynamics in Infants at Term-Equivalent Age with Electric Source Neuroimaging.

Aim: To identify and characterize the functional brain networks at the time when the brain is yet to develop higher order functions in term-born and preterm infants at term-equivalent age. Introduction: Although functional magnetic resonance imaging (fMRI) data have revealed the existence of spatially structured resting-state brain activity in infants, the temporal information of fMRI data limits the characterization of fast timescale brain oscillations. In this study, we use infants' high-density electroencephalography (EEG) to characterize spatiotemporal and spectral functional organizations of brain network dynamics. Methods: We used source-reconstructed EEG and graph theoretical analyses in 100 infants (84 preterm, 16 term born) to identify the rich-club topological organization, temporal dynamics, and spectral fingerprints of dynamic functional brain networks. Results: Five dynamic functional brain networks are identified, which have rich-club topological organizations, distinctive spectral fingerprints (in the delta and low-alpha frequency), and scale-invariant temporal dynamics (<0.1 Hz): The default mode, primary sensory-limbic system, thalamo-frontal, thalamo-sensorimotor, and visual-limbic system. The temporal dynamics of these networks are correlated in a hierarchically leading-following organization, showing that infant brain networks arise from long-range synchronization of band-limited cortical oscillation based on interacting fast- and slow-coherent cortical oscillations. Conclusion: Dynamic functional brain networks do not solely depend on the maturation of cognitive networks; instead, the brain network dynamics exist in infants at term age well before the childhood and adulthood, and hence, it offers a quantitative measurement of neurotypical development in infants. Clinical Trial Registration Number: ACTRN12615000591550. Impact statement Our work offers novel functional insights into the brain network characterization in infants, providing a new functional basis for future deployable prognostication approaches.

Interictal electroencephalographic functional network topology in drug-resistant and well-controlled idiopathic generalized epilepsy.

The study aim was to compare interictal encephalographic (EEG) functional network topology between people with well-controlled idiopathic generalized epilepsy (WC-IGE) and drug-resistant IGE (DR-IGE). Nineteen participants with WC-IGE, 18 with DR-IGE, and 20 controls underwent a resting state, 64-channel EEG. An artifact-free epoch was bandpass filtered into the frequency range of high and low extended alpha. Weighted functional connectivity matrices were calculated. Mean degree, degree distribution variance, characteristic path length (L), clustering coefficient, small world index (SWI), and betweenness centrality were measured. A Kruskal-Wallis H-test assessed effects across groups. Where significant differences were found, Bonferroni-corrected Mann-Whitney pairwise comparisons were calculated. In the low alpha band (6-9Hz), there was a significant difference in L at the three-group level (p<.0001). This was lower in controls than both WC-IGE and DR-IGE (p<.0001 for both), with no difference in L between WC-IGE and DR-IGE. Mean degree (p=.031), degree distribution variance (p=.032), and SWI (p=.023) differed across the three groups in the high alpha band (10-12Hz). Mean degree and degree distribution variance were lower in WC-IGE than controls (p=.029 for both), and SWI was higher in WC-IGE compared with controls (p=.038), with no differences in other pairwise comparisons. IGE network topology is more regular in the low alpha frequency band, potentially reflecting a more vulnerable structure. WC-IGE network topology is different from controls in the high alpha band. This may reflect drug-induced network changes that have stabilized the WC-IGE network by rendering it less likely to synchronize. These results are of potential importance in advancing the understanding of mechanisms of epilepsy drug resistance and as a possible basis for a biomarker of DR-IGE.