Decomposing the role of alpha oscillations during brain maturation

Test-Retest Reliability of EEG Aperiodic Components in Resting and Mental Task States

Aperiodic activity is derived from the electroencephalography (EEG) power spectrum and reflects changes in the slope and shifts of the broadband spectrum. Studies have shown inconsistent test-retest reliability of the aperiodic components. This study systematically measured how the test-retest reliability of the aperiodic components was affected by data duration (1, 2, 3, 4, and 5 min), states (resting with eyes closed, resting with eyes open, performing mental arithmetic, recalling the events of the day, and mentally singing songs), and methods (the Fitting Oscillations and One-Over-F (FOOOF) and Linear Mixed-Effects Regression (LMER)) at both short (90-min) and long (one-month) intervals. The results showed that aperiodic components had fair, good, or excellent test-retest reliability (ranging from 0.53 to 0.91) at both short and long intervals. It is recommended that better reliability of the aperiodic components be obtained using data durations longer than 3 min, the resting state with eyes closed, the mental arithmetic task state, and the LMER method.

Developmental trajectories of EEG aperiodic and periodic components in children 2-44 months of age

The development of neural circuits has long-lasting effects on brain function, yet our understanding of early circuit development in humans remains limited. Here, periodic EEG power features and aperiodic components were examined from longitudinal EEGs collected from 592 healthy 2-44 month-old infants, revealing age-dependent nonlinear changes suggestive of distinct milestones in early brain maturation. Developmental changes in periodic peaks include (1) the presence and then absence of a 9-10 Hz alpha peak between 2-6 months, (2) nonlinear changes in high beta peaks (20-30 Hz) between 4-18 months, and (3) the emergence of a low beta peak (12-20 Hz) in some infants after six months of age. We hypothesized that the emergence of the low beta peak may reflect maturation of thalamocortical network development. Infant anesthesia studies observe that GABA-modulating anesthetics do not induce thalamocortical mediated frontal alpha coherence until 10-12 months of age. Using a small cohort of infants (n = 23) with EEG before and during GABA-modulating anesthesia, we provide preliminary evidence that infants with a low beta peak have higher anesthesia-induced alpha coherence compared to those without a low beta peak.

Decomposing neurophysiological underpinnings of age-related decline in visual working memory

Exploring the neural basis of age-related decline in working memory is vital in our aging society. Previous electroencephalographic studies suggested that the contralateral delay activity (CDA) may be insensitive to age-related decline in lateralized visual working memory (VWM) performance. Instead, recent evidence indicated that task-induced alpha power lateralization decreases in older age. However, the relationship between alpha power lateralization and age-related decline of VWM performance remains unknown, and recent studies have questioned the validity of these findings due to confounding factors of the aperiodic signal. Using a sample of 134 participants, we replicated the age-related decrease of alpha power lateralization after adjusting for the aperiodic signal. Critically, the link between task performance and alpha power lateralization was found only when correcting for aperiodic signal biases. Functionally, these findings suggest that age-related declines in VWM performance may be related to the decreased ability to prioritize relevant over irrelevant information. Conversely, CDA amplitudes were stable across age groups, suggesting a distinct neural mechanism possibly related to preserved VWM encoding or early maintenance.

Human sensorimotor resting state beta events and aperiodic activity show good test-retest reliability

OBJECTIVE

Diseases affecting sensorimotor function impair physical independence. Reliable functional clinical biomarkers allowing early diagnosis or targeting treatment and rehabilitation could reduce this burden. Magnetoencephalography (MEG) non-invasively measures brain rhythms such as the somatomotor 'rolandic' rhythm which shows intermittent high-amplitude beta (14-30 Hz) 'events' that predict behavior across tasks and species and are altered by sensorimotor neurological diseases.

METHODS

We assessed test-retest stability, a prerequisite for biomarkers, of spontaneous sensorimotor aperiodic (1/f) signal and beta events in 50 healthy human controls across two MEG sessions using the intraclass correlation coefficient (ICC). Beta events were determined using an amplitude-thresholding approach on a narrow-band filtered amplitude envelope obtained using Morlet wavelet decomposition.

RESULTS

Resting sensorimotor characteristics showed good to excellent test-retest stability. Aperiodic component (ICC 0.77-0.88) and beta event amplitude (ICC 0.74-0.82) were very stable, whereas beta event duration was more variable (ICC 0.55-0.7). 2-3 minute recordings were sufficient to obtain stable results. Analysis automatization was successful in 86%.

CONCLUSIONS

Sensorimotor beta phenotype is a stable feature of an individual's resting brain activity even for short recordings easily measured in patients.

SIGNIFICANCE

Spontaneous sensorimotor beta phenotype has potential as a clinical biomarker of sensorimotor system integrity.

Aperiodic and Hurst EEG exponents across early human brain development: A systematic review

In electroencephalographic (EEG) data, power-frequency slope exponents (1/f_β) can provide non-invasive markers of in vivo neural activity excitation-inhibition (E:I) balance. E:I balance may be altered in neurodevelopmental conditions; hence, understanding how 1/fβ evolves across infancy/childhood has implications for developing early assessments/interventions. This systematic review (PROSPERO-ID: CRD42023363294) explored the early maturation (0-26 yrs) of resting-state EEG 1/f measures (aperiodic [AE], power law [PLE] and Hurst [HE] exponents), including studies containing ≥1 1/f measures and ≥10 typically developing participants. Five databases (including Embase and Scopus) were searched during March 2023. Forty-two studies were identified (Nparticipants=3478). Risk of bias was assessed using the Quality Assessment with Diverse Studies tool. Narrative synthesis of HE data suggests non-stationary EEG activity occurs throughout development. Age-related trends were complex, with rapid decreases in AEs during infancy and heterogenous changes thereafter. Regionally, AE maxima shifted developmentally, potentially reflecting spatial trends in maturing brain connectivity. This work highlights the importance of further characterising the development of 1/f measures to better understand how E:I balance shapes brain and cognitive development.

Tracking the neurodevelopmental trajectory of beta band oscillations with optically pumped magnetometer-based magnetoencephalography

Neural oscillations mediate the coordination of activity within and between brain networks, supporting cognition and behaviour. How these processes develop throughout childhood is not only an important neuroscientific question but could also shed light on the mechanisms underlying neurological and psychiatric disorders. However, measuring the neurodevelopmental trajectory of oscillations has been hampered by confounds from instrumentation. In this paper, we investigate the suitability of a disruptive new imaging platform - optically pumped magnetometer-based magnetoencephalography (OPM-MEG) - to study oscillations during brain development. We show how a unique 192-channel OPM-MEG device, which is adaptable to head size and robust to participant movement, can be used to collect high-fidelity electrophysiological data in individuals aged between 2 and 34 years. Data were collected during a somatosensory task, and we measured both stimulus-induced modulation of beta oscillations in sensory cortex, and whole-brain connectivity, showing that both modulate significantly with age. Moreover, we show that pan-spectral bursts of electrophysiological activity drive task-induced beta modulation, and that their probability of occurrence and spectral content change with age. Our results offer new insights into the developmental trajectory of beta oscillations and provide clear evidence that OPM-MEG is an ideal platform for studying electrophysiology in neurodevelopment.

Age-Related Characteristics of Resting-State Electroencephalographic Signals and the Corresponding Analytic Approaches: A Review

The study of the effects of aging on neural activity in the human brain has attracted considerable attention in neurophysiological, neuropsychiatric, and neurocognitive research, as it is directly linked to an understanding of the neural mechanisms underlying the disruption of the brain structures and functions that lead to age-related pathological disorders. Electroencephalographic (EEG) signals recorded during resting-state conditions have been widely used because of the significant advantage of non-invasive signal acquisition with higher temporal resolution. These advantages include the capability of a variety of linear and nonlinear signal analyses and state-of-the-art machine-learning and deep-learning techniques. Advances in artificial intelligence (AI) can not only reveal the neural mechanisms underlying aging but also enable the assessment of brain age reliably by means of the age-related characteristics of EEG signals. This paper reviews the literature on the age-related features, available analytic methods, large-scale resting-state EEG databases, interpretations of the resulting findings, and recent advances in age-related AI models.

The Necessity of Taking Culture and Context into Account When Studying the Relationship between Socioeconomic Status and Brain Development

Decades of research has revealed a relationship between childhood socioeconomic status (SES) and brain development at the structural and functional levels. Of particular note is the distinction between income and maternal education, two highly correlated factors which seem to influence brain development through distinct pathways. Specifically, while a families' income-to-needs ratio is linked with physiological stress and household chaos, caregiver education influences the day-to-day language environment a child is exposed to. Variability in either one of these environmental experiences is related to subsequent brain development. While this work has the potential to inform public policies in a way that benefits children, it can also oversimplify complex factors, unjustly blame low-SES parents, and perpetuate a harmful deficit perspective. To counteract these shortcomings, researchers must consider sociodemographic differences in the broader cultural context that underlie SES-based differences in brain development. This review aims to address these issues by (a) identifying how sociodemographic mechanisms associated with SES influence the day-to-day experiences of children, in turn, impacting brain development, while (b) considering the broader cultural contexts that may differentially impact this relationship.

Aperiodic EEG and 7T MRSI evidence for maturation of E/I balance supporting the development of working memory through adolescence

Adolescence has been hypothesized to be a critical period for the development of human association cortex and higher-order cognition. A defining feature of critical period development is a shift in the excitation: inhibition (E/I) balance of neural circuitry, however how changes in E/I may enhance cortical circuit function to support maturational improvements in cognitive capacities is not known. Harnessing ultra-high field 7 T MR spectroscopy and EEG in a large, longitudinal cohort of youth (N = 164, ages 10-32 years old, 347 neuroimaging sessions), we delineate biologically specific associations between age-related changes in excitatory glutamate and inhibitory GABA neurotransmitters and EEG-derived measures of aperiodic neural activity reflective of E/I balance in prefrontal association cortex. Specifically, we find that developmental increases in E/I balance reflected in glutamate:GABA balance are linked to changes in E/I balance assessed by the suppression of prefrontal aperiodic activity, which in turn facilitates robust improvements in working memory. These findings indicate a role for E/I-engendered changes in prefrontal signaling mechanisms in the maturation of cognitive maintenance. More broadly, this multi-modal imaging study provides evidence that human association cortex undergoes physiological changes consistent with critical period plasticity during adolescence.

Developmental trajectories of EEG aperiodic and periodic components: Implications for understanding thalamocortical development during infancy

The development of neural circuits has long-lasting effects on brain function, yet our understanding of early circuit development in humans remains limited. Here, periodic EEG power features and aperiodic components were examined from longitudinal EEGs collected from 592 healthy 2-44 month-old infants, revealing age-dependent nonlinear changes suggestive of distinct milestones in early brain maturation. Consistent with the transient developmental progression of thalamocortical circuitry, we observe the presence and then absence of periodic alpha and high beta peaks across the three-year period, as well as the emergence of a low beta peak (12-20Hz) after six months of age. We present preliminary evidence that the emergence of the low beta peak is associated with higher thalamocortical-dependent, anesthesia-induced alpha coherence. Together, these findings suggest that early age-dependent changes in alpha and beta periodic peaks may reflect the state of thalamocortical network development.

Wake EEG oscillation dynamics reflect both sleep need and brain maturation across childhood and adolescence

An objective measure of brain maturation is highly insightful for monitoring both typical and atypical development. Slow wave activity, recorded in the sleep electroencephalogram (EEG), reliably indexes changes in brain plasticity with age, as well as deficits related to developmental disorders such as attention-deficit hyperactivity disorder (ADHD). Unfortunately, measuring sleep EEG is resource-intensive and burdensome for participants. We therefore aimed to determine whether wake EEG could likewise index developmental changes in brain plasticity. We analyzed high-density wake EEG collected from 163 participants 3-25 years old, before and after a night of sleep. We compared two measures of oscillatory EEG activity, amplitudes and density, as well as two measures of aperiodic activity, intercepts and slopes. Furthermore, we compared these measures in patients with ADHD (8-17 y.o., N=58) to neurotypical controls. We found that wake oscillation amplitudes behaved the same as sleep slow wave activity: amplitudes decreased with age, decreased after sleep, and this overnight decrease decreased with age. Oscillation densities were also substantially age-dependent, decreasing overnight in children and increasing overnight in adolescents and adults. While both aperiodic intercepts and slopes decreased linearly with age, intercepts decreased overnight, and slopes increased overnight. Overall, our results indicate that wake oscillation amplitudes track both development and sleep need, and overnight changes in oscillation density reflect some yet-unknown shift in neural activity around puberty. No wake measure showed significant effects of ADHD, thus indicating that wake EEG measures, while easier to record, are not as sensitive as those during sleep.

Development of the Alpha Rhythm Is Linked to Visual White Matter Pathways and Visual Detection Performance

Alpha is the strongest electrophysiological rhythm in awake humans at rest. Despite its predominance in the EEG signal, large variations can be observed in alpha properties during development, with an increase in alpha frequency over childhood and adulthood. Here, we tested the hypothesis that these changes in alpha rhythm are related to the maturation of visual white matter pathways. We capitalized on a large diffusion MRI (dMRI)-EEG dataset (dMRI n = 2,747, EEG n = 2,561) of children and adolescents of either sex (age range, 5-21 years old) and showed that maturation of the optic radiation specifically accounts for developmental changes of alpha frequency. Behavioral analyses also confirmed that variations of alpha frequency are related to maturational changes in visual perception. The present findings demonstrate the close link between developmental variations in white matter tissue properties, electrophysiological responses, and behavior.

Resting-state EEG signatures of Alzheimer's disease are driven by periodic but not aperiodic changes

Electroencephalography (EEG) has shown potential for identifying early-stage biomarkers of neurocognitive dysfunction associated with dementia due to Alzheimer's disease (AD). A large body of evidence shows that, compared to healthy controls (HC), AD is associated with power increases in lower EEG frequencies (delta and theta) and decreases in higher frequencies (alpha and beta), together with slowing of the peak alpha frequency. However, the pathophysiological processes underlying these changes remain unclear. For instance, recent studies have shown that apparent shifts in EEG power from high to low frequencies can be driven either by frequency specific periodic power changes or rather by non-oscillatory (aperiodic) changes in the underlying 1/f slope of the power spectrum. Hence, to clarify the mechanism(s) underlying the EEG alterations associated with AD, it is necessary to account for both periodic and aperiodic characteristics of the EEG signal. Across two independent datasets, we examined whether resting-state EEG changes linked to AD reflect true oscillatory (periodic) changes, changes in the aperiodic (non-oscillatory) signal, or a combination of both. We found strong evidence that the alterations are purely periodic in nature, with decreases in oscillatory power at alpha and beta frequencies (AD < HC) leading to lower (alpha + beta) / (delta + theta) power ratios in AD. Aperiodic EEG features did not differ between AD and HC. By replicating the findings in two cohorts, we provide robust evidence for purely oscillatory pathophysiology in AD and against aperiodic EEG changes. We therefore clarify the alterations underlying the neural dynamics in AD and emphasize the robustness of oscillatory AD signatures, which may further be used as potential prognostic or interventional targets in future clinical investigations.

Positive or negative environmental modulations on human brain development: the morpho-functional outcomes of music training or stress

In the last couple of decades, the study of human living brain has benefitted of neuroimaging and non-invasive electrophysiological techniques, which are particularly valuable during development. A number of studies allowed to trace the usual stages leading from pregnancy to adult age, and relate them to functional and behavioral measurements. It was also possible to explore the effects of some interventions, behavioral or not, showing that the commonly followed pathway to adulthood may be steered by external interventions. These events may result in behavioral modifications but also in structural changes, in some cases limiting plasticity or extending/modifying critical periods. In this review, we outline the healthy human brain development in the absence of major issues or diseases. Then, the effects of negative (different stressors) and positive (music training) environmental stimuli on brain and behavioral development is depicted. Hence, it may be concluded that the typical development follows a course strictly dependent from environmental inputs, and that external intervention can be designed to positively counteract negative influences, particularly at young ages. We also focus on the social aspect of development, which starts in utero and continues after birth by building social relationships. This poses a great responsibility in handling children education and healthcare politics, pointing to social accountability for the responsible development of each child.

Developmental trajectories of EEG aperiodic and periodic power: Implications for understanding the timing of thalamocortical development during infancy

The development of neural circuits over the first years of life has long-lasting effects on brain function, yet our understanding of early circuit development in humans remains limited. Here, aperiodic and periodic EEG power features were examined from longitudinal EEGs collected from 592 healthy 2-44 month-old infants, revealing age-dependent nonlinear changes suggestive of distinct milestones in early brain maturation. Consistent with the transient developmental progression of thalamocortical circuitry, we observe the presence and then absence of periodic alpha and high beta peaks across the three-year period, as well as the emergence of a low beta peak (12-20Hz) after six months of age. We present preliminary evidence that the emergence of the low beta peak is associated with thalamocortical connectivity sufficient for anesthesia-induced alpha coherence. Together, these findings suggest that early age-dependent changes in alpha and beta periodic peaks may reflect the state of thalamocortical network development.

Developmental changes in individual alpha frequency: Recording EEG data during public engagement events

Statistical power in cognitive neuroimaging experiments is often very low. Low sample size can reduce the likelihood of detecting real effects (false negatives) and increase the risk of detecting non-existing effects by chance (false positives). Here, we document our experience of leveraging a relatively unexplored method of collecting a large sample size for simple electroencephalography (EEG) studies: by recording EEG in the community during public engagement and outreach events. We collected data from 346 participants (189 females, age range 6-76 years) over 6 days, totalling 29 hours, at local science festivals. Alpha activity (6-15 Hz) was filtered from 30 seconds of signal, recorded from a single electrode placed between the occipital midline (Oz) and inion (Iz) while the participants rested with their eyes closed. A total of 289 good-quality datasets were obtained. Using this community-based approach, we were able to replicate controlled, lab-based findings: individual alpha frequency (IAF) increased during childhood, reaching a peak frequency of 10.28 Hz at 28.1 years old, and slowed again in middle and older age. Total alpha power decreased linearly, but the aperiodic-adjusted alpha power did not change over the lifespan. Aperiodic slopes and intercepts were highest in the youngest participants. There were no associations between these EEG indexes and self-reported fatigue, measured by the Multidimensional Fatigue Inventory. Finally, we present a set of important considerations for researchers who wish to collect EEG data within public engagement and outreach environments.

The maturation of aperiodic EEG activity across development reveals a progressive differentiation of wakefulness from sleep

During development, the brain undergoes radical structural and functional changes following a posterior-to-anterior gradient, associated with profound changes of cortical electrical activity during both wakefulness and sleep. However, a systematic assessment of the developmental effects on aperiodic EEG activity maturation across vigilance states is lacking, particularly regarding its topographical aspects. Here, in a population of 160 healthy infants, children and teenagers (from 2 to 17 years, 10 subjects for each year), we investigated the development of aperiodic EEG activity in wakefulness and sleep. Specifically, we parameterized the shape of the aperiodic background of the EEG Power Spectral Density (PSD) by means of the spectral exponent and offset; the exponent reflects the rate of exponential decay of power over increasing frequencies and the offset reflects an estimate of the y-intercept of the PSD. We found that sleep and development caused the EEG-PSD to rotate over opposite directions: during wakefulness the PSD showed a flatter decay and reduced offset over development, while during sleep it showed a steeper decay and a higher offset as sleep becomes deeper. During deep sleep (N2, N3) only the spectral offset decreased over age, indexing a broad-band voltage reduction. As a result, the difference between values in deep sleep and those in both light sleep (N1) and wakefulness increased with age, suggesting a progressive differentiation of wakefulness from sleep EEG activity, most prominent over the frontal regions, the latest to complete maturation. Notably, the broad-band spectral exponent values during deep sleep stages were entirely separated from wakefulness values, consistently across developmental ages and in line with previous findings in adults. Concerning topographical development, the location showing the steepest PSD decay and largest offset shifted from posterior to anterior regions with age. This shift, particularly evident during deep sleep, paralleled the migration of sleep slow wave activity and was consistent with neuroanatomical and cognitive development. Overall, aperiodic EEG activity distinguishes wakefulness from sleep regardless of age; while, during development, it reveals a postero-anterior topographical maturation and a progressive differentiation of wakefulness from sleep. Our study could help to interpret changes due to pathological conditions and may elucidate the neurophysiological processes underlying the development of wakefulness and sleep.

Resting-state EEG signatures of Alzheimer's disease are driven by periodic but not aperiodic changes

Electroencephalography (EEG) has shown potential for identifying early-stage biomarkers of neurocognitive dysfunction associated with dementia due to Alzheimer's disease (AD). A large body of evidence shows that, compared to healthy controls (HC), AD is associated with power increases in lower EEG frequencies (delta and theta) and decreases in higher frequencies (alpha and beta), together with slowing of the peak alpha frequency. However, the pathophysiological processes underlying these changes remain unclear. For instance, recent studies have shown that apparent shifts in EEG power from high to low frequencies can be driven either by frequency specific periodic power changes or rather by non-oscillatory (aperiodic) changes in the underlying 1/f slope of the power spectrum. Hence, to clarify the mechanism(s) underlying the EEG alterations associated with AD, it is necessary to account for both periodic and aperiodic characteristics of the EEG signal. Across two independent datasets, we examined whether resting-state EEG changes linked to AD reflect true oscillatory (periodic) changes, changes in the aperiodic (non-oscillatory) signal, or a combination of both. We found strong evidence that the alterations are purely periodic in nature, with decreases in oscillatory power at alpha and beta frequencies (AD < HC) leading to lower (alpha + beta) / (delta + theta) power ratios in AD. Aperiodic EEG features did not differ between AD and HC. By replicating the findings in two cohorts, we provide robust evidence for purely oscillatory pathophysiology in AD and against aperiodic EEG changes. We therefore clarify the alterations underlying the neural dynamics in AD and emphasise the robustness of oscillatory AD signatures, which may further be used as potential prognostic or interventional targets in future clinical investigations.

Associations between EEG trajectories, family income, and cognitive abilities over the first two years of life

We sought to characterize developmental trajectories of EEG spectral power over the first 2 years after birth and examine whether family income or maternal education alter those trajectories. We analyzed EEGs (n = 161 infants, 534 EEGs) collected longitudinally between 2 and 24 months of age, and calculated frontal absolute power across 7 canonical frequency bands. For each frequency band, a piecewise growth curve model was fit, resulting in an estimated intercept and two slope parameters from 2 to 9 months and 9-24 months of age. Across 6/7 frequency bands, absolute power significantly increased over age, with steeper slopes in the 2-9 month period compared to 9-24 months. Increased family income, but not maternal education, was associated with higher intercept (2-3 month power) across delta-gamma bands (p range = 0.002-0.04), and reduced change in power between 2 and 9 months of age in lower frequency bands (delta-alpha, p range = 0.01-0.02). There was no significant effect of income on slope between 9 and 24 months. EEG intercept and slope measures did not mediate relationships between income and 24-month verbal and nonverbal development. These results add to growing literature concerning the role of socioeconomic factors in shaping brain trajectories.

Decomposing age effects in EEG alpha power

Increasing life expectancy is prompting the need to understand how the brain changes during healthy aging. Research utilizing electroencephalography (EEG) has found that the power of alpha oscillations decrease from adulthood on. However, non-oscillatory (aperiodic) components in the data may confound results and thus require re-investigation of these findings. Thus, the present report analyzed a pilot and two additional independent samples (total N = 533) of resting-state EEG from healthy young and elderly individuals. A newly developed algorithm was utilized that allows the decomposition of the measured signal into periodic and aperiodic signal components. By using multivariate sequential Bayesian updating of the age effect in each signal component, evidence across the datasets was accumulated. It was hypothesized that previously reported age-related alpha power differences will largely diminish when total power is adjusted for the aperiodic signal component. First, the age-related decrease in total alpha power was replicated. Concurrently, decreases of the intercept and slope (i.e. exponent) of the aperiodic signal component were observed. Findings on aperiodic-adjusted alpha power indicated that this general shift of the power spectrum leads to an overestimation of the true age effects in conventional analyses of total alpha power. Thus, the importance of separating neural power spectra into periodic and aperiodic signal components is highlighted. However, also after accounting for these confounding factors, the sequential Bayesian updating analysis provided robust evidence that aging is associated with decreased aperiodic-adjusted alpha power. While the relation of the aperiodic component and aperiodic-adjusted alpha power to cognitive decline demands further investigation, the consistent findings on age effects across independent datasets and high test-retest reliabilities support that these newly emerging measures are reliable markers of the aging brain. Hence, previous interpretations of age-related decreases in alpha power are reevaluated, incorporating changes in the aperiodic signal.