Human Sensorimotor Beta Event Characteristics and Aperiodic Signal Are Highly Heritable.

Individuals' phenotypes, including the brain's structure and function, are largely determined by genes and their interplay. The resting brain generates salient rhythmic patterns that can be characterized noninvasively using functional neuroimaging such as magnetoencephalography (MEG). One of these rhythms, the somatomotor (rolandic) beta rhythm, shows intermittent high amplitude "events" that predict behavior across tasks and species. Beta rhythm is altered in neurological disease. The aperiodic (1/f) signal present in electrophysiological recordings is also modulated by some neurological conditions and aging. Both sensorimotor beta and aperiodic signal could thus serve as biomarkers of sensorimotor function. Knowledge about the extent to which these brain functional measures are heritable could shed light on the mechanisms underlying their generation. We investigated the heritability and variability of human spontaneous sensorimotor beta rhythm events and aperiodic activity in 210 healthy male and female adult siblings' spontaneous MEG activity. The most heritable trait was the aperiodic 1/f signal, with a heritability of 0.87 in the right hemisphere. Time-resolved beta event amplitude parameters were also highly heritable, whereas the heritabilities for overall beta power, peak frequency, and measures of event duration remained nonsignificant. Human sensorimotor neural activity can thus be dissected into different components with variable heritability. We postulate that these differences partially reflect different underlying signal-generating mechanisms. The 1/f signal and beta event amplitude measures may depend more on fixed, anatomical parameters, whereas beta event duration and its modulation reflect dynamic characteristics, guiding their use as potential disease biomarkers.

Age differentially modulates the cortical tracking of the lower and higher level linguistic structures during speech comprehension.

Speech comprehension requires listeners to rapidly parse continuous speech into hierarchically-organized linguistic structures (i.e. syllable, word, phrase, and sentence) and entrain the neural activities to the rhythm of different linguistic levels. Aging is accompanied by changes in speech processing, but it remains unclear how aging affects different levels of linguistic representation. Here, we recorded magnetoencephalography signals in older and younger groups when subjects actively and passively listened to the continuous speech in which hierarchical linguistic structures of word, phrase, and sentence were tagged at 4, 2, and 1 Hz, respectively. A newly-developed parameterization algorithm was applied to separate the periodically linguistic tracking from the aperiodic component. We found enhanced lower-level (word-level) tracking, reduced higher-level (phrasal- and sentential-level) tracking, and reduced aperiodic offset in older compared with younger adults. Furthermore, we observed the attentional modulation on the sentential-level tracking being larger for younger than for older ones. Notably, the neuro-behavior analyses showed that subjects' behavioral accuracy was positively correlated with the higher-level linguistic tracking, reversely correlated with the lower-level linguistic tracking. Overall, these results suggest that the enhanced lower-level linguistic tracking, reduced higher-level linguistic tracking and less flexibility of attentional modulation may underpin aging-related decline in speech comprehension.

Spectral Distribution Dynamics across Different Attentional Priority States.

Anticipatory covert spatial attention improves performance on tests of visual detection and discrimination, and shifts are accompanied by decreases and increases of α band power at electroencephalography (EEG) electrodes corresponding to the attended and unattended location, respectively. Although the increase at the unattended location is often interpreted as an active mechanism (e.g., inhibiting processing at the unattended location), most experiments cannot rule out the alternative possibility that it is a secondary consequence of selection elsewhere. To adjudicate between these accounts, we designed a Posner-style visual cueing task in which male and female human participants made orientation judgments of targets appearing at one of four locations: up, down, right, or left. Critically, trials were blocked such that within a block the locations along one meridian alternated in status between attended and unattended, and targets never appeared at the other two, making them irrelevant. Analyses of the concurrently measured EEG signal were conducted on "traditional" narrowband α (8-14 Hz), as well as on two components resulting from the decomposition of this signal: "periodic" α; and the slope of the aperiodic 1/f-like component. Although data from right-left blocks replicated the familiar pattern of lateralized asymmetry in narrowband α power, with neither α signal could we find evidence for any difference in the time course at unattended versus irrelevant locations, an outcome consistent with the secondary-consequence interpretation of attention-related dynamics in the α band. Additionally, 1/f slope was shallower at attended and unattended locations, relative to irrelevant, suggesting a tonic adjustment of physiological state.SIGNIFICANCE STATEMENT Visual spatial attention, the prioritization of one location in the visual field, is critical for guiding behavior in cluttered environments. Although influential theories posit an important role for α band oscillations in the inhibition of processing at unattended locations, we used a novel procedure to find evidence for an alternative interpretation: selection of one location may simply result in a return to physiological baseline at all others. In addition to determining one way that attention does not work (important for future progress in this field), we also discovered novel evidence for one way that it does work: by modifying the tonic physiological state (indexed by an aperiodic component of the electroencephalography (EEG)] at locations where spatial selection is likely to occur.

Aperiodic brain activity and response to anesthesia vary in disorders of consciousness.

In the human electroencephalogram (EEG), oscillatory power co-exist with non-oscillatory, aperiodic activity. Although EEG analysis has traditionally focused exclusively on oscillatory power, recent investigations have shown that the aperiodic EEG component can distinguish conscious wakefulness from sleep and anesthetic-induced unconsciousness. This study investigates the aperiodic EEG component of individuals in a disorder of consciousness (DOC); how it changes in response to exposure to anesthesia; and how it relates to the brain's information richness and criticality. High-density EEG was recorded from 43 individuals in a DOC, with 16 of these individuals undergoing a protocol of propofol anesthesia. The aperiodic component was defined by the spectral slope of the power spectral density. Our results demonstrate that the EEG aperiodic component is more informative about the participants' level of consciousness than the oscillatory component, especially for patients that suffered from a stroke. Importantly, the pharmacologically induced change in the spectral slope from 30 to 45 Hz positively correlated with individual's pre-anesthetic level of consciousness. The pharmacologically induced loss of information-richness and criticality was associated with individual's pre-anesthetic aperiodic component. During exposure to anesthesia, the aperiodic component distinguished individuals with DOC, according to their 3-month recovery status. The aperiodic EEG component has been historically neglected; this research highlights the necessity of considering this measure for the assessment of individuals in DOC and future research that seeks to understand the neurophysiological underpinnings of consciousness.

Characterization of the planarian surface electroencephalogram.

BACKGROUND: Despite large morphological differences between the nervous systems of lower animals and humans, striking functional similarities have been reported. However, little is known about how these functional similarities translate to cognitive similarities. As a first step towards studying the cognitive abilities of simple nervous systems, we here characterize the ongoing electrophysiological activity of the planarian Schmidtea mediterranea. One previous report using invasive microelectrodes describes that the ongoing neural activity is characterized by a 1/fx power spectrum with the exponent 'x' of the power spectrum close to 1. To extend these findings, we aimed to establish a recording protocol to measure ongoing neural activity safely and securely from alive and healthy planarians under different lighting conditions using non-invasive surface electrodes. RESULTS: As a replication and extension of the previous results, we show that the ongoing neural activity is characterized by a 1/fx power spectrum, that the exponent 'x' in living planarians is close to 1, and that changes in lighting induce changes in neural activity likely due to the planarian photophobia. CONCLUSIONS: We confirm the existence of continuous EEG activity in planarians and show that it is possible to noninvasively record this activity with surface wire electrodes. This opens up broad possibilities for continuous recordings across longer intervals, and repeated recordings from the same animals to study cognitive processes.

Chronic intracranial recordings after resection for epilepsy reveal a "running down" of epileptiform activity.

We describe an electrical "running down" phenomenon and also a consistent spectral change (in the aperiodic component of the power spectrum) derived from chronic interictal electrocorticography (ECoG) after surgery in a patient with drug-resistant epilepsy. These data were recorded using a closed-loop neurostimulation system that was implanted after resection. The patient has been seizure-free for 2.5 years since resection without requiring the neurostimulator to be turned on to stimulate. Concurrently, there was an exponential decrease in the number of epileptiform electrographic detections recorded by the device, particularly over the first 26 weeks, indicative of an electrical running down phenomenon as the brain adapted to an extended period of seizure freedom. We also find that the aperiodic exponent of the power spectrum gradually decreases over time. The aperiodic component of intracranial ECoG may represent a novel marker of epileptogenicity, independent of seizures.

Dopamine depletion can be predicted by the aperiodic component of subthalamic local field potentials.

Electrophysiological biomarkers reflecting the pathological activities in the basal ganglia are essential to gain an etiological understanding of Parkinson's disease (PD) and develop a method of diagnosing and treating the disease. Previous studies that explored electrophysiological biomarkers in PD have focused mainly on oscillatory or periodic activities such as beta and gamma oscillations. Emerging evidence has suggested that the nonoscillatory, aperiodic component reflects the firing rate and synaptic current changes corresponding to cognitive and pathological states. Nevertheless, it has never been thoroughly examined whether the aperiodic component can be used as a biomarker that reflects pathological activities in the basal ganglia in PD. In this study, we examined the parameters of the aperiodic component in hemiparkinsonian rats and tested its practicality as an electrophysiological biomarker of pathological activity. We found that a set of aperiodic parameters, aperiodic offset and exponent, were significantly decreased by the nigrostriatal lesion. To further prove the usefulness of the parameters as biomarkers, acute levodopa treatment reverted the aperiodic offset. We then compared the aperiodic parameters with a previously established periodic biomarker of PD, beta frequency oscillation. We found a significantly low negative correlation with beta power. We showed that the performance of the machine learning-based prediction of pathological activities in the basal ganglia can be improved by using both beta power and the aperiodic component, which showed a low correlation with each other. We suggest that the aperiodic component will provide a more sensitive measurement to early diagnosis PD and have the potential to use as the feedback parameter for the adaptive deep brain stimulation.

Sleep-related hypermotor epilepsy and non-rapid eye movement parasomnias: Differences in the periodic and aperiodic component of the electroencephalographic power spectra.

Over the last two decades, our understanding of clinical and pathophysiological aspects of sleep-related epileptic and non-epileptic paroxysmal behaviours has improved considerably, although it is far from complete. Indeed, even if many core characteristics of sleep-related hypermotor epilepsy and non-rapid eye movement parasomnias have been clarified, some crucial points remain controversial, and the overlap of the behavioural patterns between these disorders represents a diagnostic challenge. In this work, we focused on segments of multichannel sleep electroencephalogram free from clinical episodes, from two groups of subjects affected by sleep-related hypermotor epilepsy (N = 15) and non-rapid eye movement parasomnias (N = 16), respectively. We examined sleep stages N2 and N3 of the first part of the night (cycles 1 and 2), and assessed the existence of differences in the periodic and aperiodic components of the electroencephalogram power spectra between the two groups, using the Fitting Oscillations & One Over f (FOOOF) toolbox. A significant difference in the gamma frequency band was found, with an increased relative power in sleep-related hypermotor epilepsy subjects, during both N2 (p < .001) and N3 (p < .001), and a significant higher slope of the aperiodic component in non-rapid eye movement parasomnias, compared with sleep-related hypermotor epilepsy, during N3 (p = .012). We suggest that the relative power of the gamma band and the slope extracted from the aperiodic component of the electroencephalogram signal may be helpful to characterize differences between subjects affected by non-rapid eye movement parasomnias and those affected by sleep-related hypermotor epilepsy.

A Novel Time-Frequency Parameterization Method for Oscillations in Specific Frequency Bands and Its Application on OPM-MEG.

Time-frequency parameterization for oscillations in specific frequency bands reflects the dynamic changes in the brain. It is related to cognitive behavior and diseases and has received significant attention in neuroscience. However, many studies do not consider the impact of the aperiodic noise and neural activity, including their time-varying fluctuations. Some studies are limited by the low resolution of the time-frequency spectrum and parameter-solved operation. Therefore, this paper proposes super-resolution time-frequency periodic parameterization of (transient) oscillation (STPPTO). STPPTO obtains a super-resolution time-frequency spectrum with Superlet transform. Then, the time-frequency representation of oscillations is obtained by removing the aperiodic component fitted in a time-resolved way. Finally, the definition of transient events is used to parameterize oscillations. The performance of this method is validated on simulated data and its reliability is demonstrated on magnetoencephalography. We show how it can be used to explore and analyze oscillatory activity under rhythmic stimulation.

Long-term effects of vagus nerve stimulation on EEG aperiodic components in patients with drug-resistant epilepsy.

Background: Drug-resistant epilepsy (DRE) affects approximately one-third of epilepsy patients who do not achieve adequate seizure control with medication. Vagus nerve stimulation (VNS) is an adjunctive therapy for DRE, but its long-term effects on cortical excitability remain unclear. Objectives: This study aims to elucidate the long-term effects of VNS on electroencephalography (EEG) aperiodic components in patients with DRE. Our objective is to identify biomarkers that can serve as indicators of therapeutic efficacy and provide mechanistic insights into the underlying neural processes. Design: This longitudinal observational study focused on patients with DRE undergoing VNS therapy at Sanbo Brain Hospital. The reduction in seizure frequency rates was quantified over short-term (⩽1 year), medium-term (1-3 years), and long-term (⩾3 years) intervals to assess the therapeutic efficacy of VNS. Both the periodic and aperiodic components of EEG data were analyzed. Methods: Advanced signal processing techniques were utilized to parameterize the periodic and aperiodic components of EEG data, focusing particularly on "offset" and "exponent." These measures were compared before and after VNS therapy. Correlation analyses were conducted to explore the relationship between these EEG parameters and clinical outcomes. Results: In all, 18 patients with DRE participated in this study. During the long-term follow-up period, the responder rate was 55.56%. Significant decreases were observed in aperiodic offset (p = 0.022) and exponent (p = 0.039) among responders. The impact of age on these results was not significant. Correlation analyses revealed a negative association between therapeutic efficacy and a decrease in offset (R = -0.546, p = 0.019) and exponent (R = -0.636, p = 0.019). Conclusion: EEG aperiodic parameters, including offset and exponent, have the potential to serve as promising biomarkers for evaluating the efficacy of VNS. An understanding of the regulatory influence of VNS on cortical excitability through these aperiodic parameters could provide a basis for the development of more effective stimulation parameters and therapeutic strategies.

Identification of atypical sleep microarchitecture biomarkers in children with autism spectrum disorder.

Importance: Sleep disorders are one of the most frequent comorbidities in children with autism spectrum disorder (ASD). However, the link between neurodevelopmental effects in ASD children with their underlying sleep microarchitecture is not well understood. An improved understanding of etiology of sleep difficulties and identification of sleep-associated biomarkers for children with ASD can improve the accuracy of clinical diagnosis. Objectives: To investigate whether machine learning models can identify biomarkers for children with ASD based on sleep EEG recordings. Design setting and participants: Sleep polysomnogram data were obtained from the Nationwide Children' Health (NCH) Sleep DataBank. Children (ages: 8-16 yrs) with 149 autism and 197 age-matched controls without neurodevelopmental diagnosis were selected for analysis. An additional independent age-matched control group (n = 79) selected from the Childhood Adenotonsillectomy Trial (CHAT) was also used to validate the models. Furthermore, an independent smaller NCH cohort of younger infants and toddlers (age: 0.5-3 yr.; 38 autism and 75 controls) was used for additional validation. Main outcomes and measures: We computed periodic and non-periodic characteristics from sleep EEG recordings: sleep stages, spectral power, sleep spindle characteristics, and aperiodic signals. Machine learning models including the Logistic Regression (LR) classifier, Support Vector Machine (SVM), and Random Forest (RF) model were trained using these features. We determined the autism class based on the prediction score of the classifier. The area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, and specificity were used to evaluate the model performance. Results: In the NCH study, RF outperformed two other models with a 10-fold cross-validated median AUC of 0.95 (interquartile range [IQR], [0.93, 0.98]). The LR and SVM models performed comparably across multiple metrics, with median AUC 0.80 [0.78, 0.85] and 0.83 [0.79, 0.87], respectively. In the CHAT study, three tested models have comparable AUC results: LR: 0.83 [0.76, 0.92], SVM: 0.87 [0.75, 1.00], and RF: 0.85 [0.75, 1.00]. Sleep spindle density, amplitude, spindle-slow oscillation (SSO) coupling, aperiodic signal's spectral slope and intercept, as well as the percentage of REM sleep were found to be key discriminative features in the predictive models. Conclusion and relevance: Our results suggest that integration of EEG feature engineering and machine learning can identify sleep-based biomarkers for ASD children and produce good generalization in independent validation datasets. Microstructural EEG alterations may help reveal underlying pathophysiological mechanisms of autism that alter sleep quality and behaviors. Machine learning analysis may reveal new insight into the etiology and treatment of sleep difficulties in autism.

Preterm birth accelerates the maturation of spontaneous and resting activity in the visual cortex.

Prematurity is among the leading risks for poor neurocognitive outcomes. The brains of preterm infants show alterations in structure and electrical activity, but the underlying circuit mechanisms are unclear. To address this, we performed a cross-species study of the electrophysiological activity in the visual cortices of prematurely born infants and mice. Using electroencephalography (EEG) in a sample of healthy preterm (N = 29) and term (N = 28) infants, we found that the maturation of the aperiodic EEG component was accelerated in the preterm cohort, with a significantly flatter 1/f slope when compared to the term infants. The flatter slope was a result of decreased spectral power in the theta and alpha bands and was correlated with the degree of prematurity. To determine the circuit and cellular changes that potentially mediate the changes in 1/f slope after preterm birth, we used in vivo electrophysiology in preterm mice and found that, similar to infants, preterm birth results in a flattened 1/f slope. We analyzed neuronal activity in the visual cortex of preterm (N = 6) and term (N = 9) mice and found suppressed spontaneous firing of neurons. Using immunohistochemistry, we further found an accelerated maturation of inhibitory circuits. In both preterm mice and infants, the functional maturation of the cortex was accelerated, underscoring birth as a critical checkpoint in cortical maturation. Our study points to a potential mechanism of preterm birth-related changes in resting neural activity, highlighting the utility of a cross-species approach in studying the neural circuit mechanisms of preterm birth-related neurodevelopmental conditions.

Clinical applications of EEG power spectra aperiodic component analysis: A mini-review.

OBJECTIVE: The present mini-review summarizes recent clinical findings related to the analysis of the aperiodic component of EEG (electroencephalographic) power spectra, making them quickly accessible to medical specialists and health researchers, with the aim of boosting related research. METHODS: Based on our experience about clinicians' literature-searching, we queried the PubMed database with terms related to EEG power spectra aperiodic component analysis and selected clinical studies that referenced such terms in the title/abstract, and were published in the last five years. RESULTS: A total of 11 journal articles, dealing with 9 different neurologic and psychiatric conditions published between 1st January 2016 - April 1st 2021, were surveyed. CONCLUSIONS: All the reviewed studies focused on exploring the pathophysiological significance of the aperiodic component and its correlation with disease presence, stage, and severity. Despite the heterogeneity of pathologies, it was possible to cluster most of them according to the mechanism underlying slope alterations, namely hypo-/hyper-excitability. It was also possible to identify some counterintuitive findings, probably related to compensation mechanisms of disease-specific neurophysiological alterations. SIGNIFICANCE: All the findings seem to support the role of the aperiodic activity as index of excitation/inhibition balance, with promising clinical applications that might challenge the traditional approach to pathologies diagnosis/treatment/follow-up.

Estimated EEG functional connectivity and aperiodic component induced by vagal nerve stimulation in patients with drug-resistant epilepsy.

Background: Vagal nerve stimulation (VNS) improves seizure frequency and quality of life in patients with drug-resistant epilepsy (DRE), although the exact mechanism is not fully understood. Previous studies have evaluated the effect of VNS on functional connectivity using the phase lag index (PLI), but none has analyzed its effect on EEG aperiodic parameters (offset and exponent), which are highly conserved and related to physiological functions. Objective: This study aimed to evaluate the effect of VNS on PLI and aperiodic parameters and infer whether these changes correlate with clinical responses in subjects with DRE. Materials and methods: PLI, exponent, and offset were derived for each epoch (and each frequency band for PLI), on scalp-derived 64-channel EEG traces of 10 subjects with DRE, recorded before and 1 year after VNS. PLI, exponent, and offset were compared before and after VNS for each patient on a global basis, individual scalp regions, and channels and separately in responders and non-responders. A correlation analysis was performed between global changes in PLI and aperiodic parameters and clinical response. Results: PLI (global and regional) decreased after VNS for gamma and delta bands and increased for an alpha band in responders, but it was not modified in non-responders. Aperiodic parameters after VNS showed an opposite trend in responders vs. non-responders: both were reduced in responders after VNS, but they were increased in non-responders. Changes in aperiodic parameters correlated with the clinical response. Conclusion: This study explored the action of VNS therapy from a new perspective and identified EEG aperiodic parameters as a new and promising method to analyze the efficacy of neuromodulation.

EEG fingerprinting: Subject-specific signature based on the aperiodic component of power spectrum.

During the last few years, there has been growing interest in the effects induced by individual variability on activation patterns and brain connectivity. The practical implications of individual variability are of basic relevance for both group level and subject level studies. The Electroencephalogram (EEG), still represents one of the most used recording techniques to investigate a wide range of brain-related features. In this work, we aim to estimate the effect of individual variability on a set of very simple and easily interpretable features extracted from the EEG power spectra. In particular, in an identification scenario, we investigated how the aperiodic (1/f background) component of the EEG power spectra can accurately identify subjects from a large EEG dataset. The results of this study show that the aperiodic component of the EEG signal is characterized by strong subject-specific properties, that this feature is consistent across different experimental conditions (eyes-open and eyes-closed) and outperforms the canonically-defined frequency bands. These findings suggest that the simple features (slope and offset) extracted from the aperiodic component of the EEG signal are sensitive to individual traits and may help to characterize and make inferences at single subject-level.