Developmental change in the resting state electroencephalogram: Insights into cognition and the brain

Electroencephalography estimates brain age in infants with high precision: Leveraging advanced machine learning in healthcare

Changes in the pace of neurodevelopment are key indicators of atypical maturation during early life. Unfortunately, reliable prognostic tools rely on assessments of cognitive and behavioral skills that develop towards the second year of life and after. Early assessment of brain maturation using electroencephalography (EEG) is crucial for clinical intervention and care planning. We developed a reliable methodology using conventional machine learning (ML) and novel deep learning (DL) networks to efficiently quantify the difference between chronological and biological age, so-called brain age gap (BAG) as a marker of accelerated/decelerated biological brain development. In this cross-sectional study, EEG from 219 typically-developing infants aged from three to 14-months was used. For DL networks, the input samples were increased to 2628 recordings. We further validated the BAG tool in a population at clinical risk with abnormal brain growth (macrocephaly) to capture deviation from normal aging. Our results indicate that DL networks outperform conventional ML models, capturing complex non-monotonic EEG characteristics and predicting the biological age with a mean absolute error of only one month (MAE = 1 month, 95 %CI:0.88-1.15, r = 0.82, 95 %CI:0.78-0.85). Additionally, the developing brain follows a trajectory characterized by increased non-linearity and complexity in which alpha rhythm plays an important role. BAG could detect group-level maturational delays between typically-developing and macrocephaly (pvalue=0.009). In macrocephaly, BAG negatively correlated with the general adaptive composite of the ABAS-II (pvalue=0.04) at 18-months and the information processing speed scale of the WPSSI-IV at age four (pvalue=0.006). The EEG-based BAG score offers a reliable non-invasive measure of brain maturation, with significant advantages and implications for developmental neuroscience and clinical practice.

Electroencephalogram activity related to psychopathological and neuropsychological symptoms in institutionalised minors: a systematic review

OBJECTIVE

This systematic review aims to update the current evidence on the effects of institutionalisation in minors living in residential care homes, specifically focusing on alterations in neuronal systems and their association with psychopathological and neuropsychological outcomes.

METHODS

Searches were conducted in the Web of Science, Scopus, PubMed, and Google Scholar databases, following PRISMA methodology for peer-reviewed empirical articles. The final selection comprised 10 studies that met the inclusion criteria: (1) published articles with quantitative data, (2) aimed at observing the relationship between psychological and neuropsychological symptoms and the electroencephalogram (EEG) activity in institutionalised children, (3) published between 2016 and 2023, and (4) examining institutionalised minors in residential care homes.

RESULTS

The articles show that these children exhibit general immaturity in EEG patterns, with a predominance of slow waves (primarily in the theta band). They also demonstrate poorer performance in executive functions (e.g. working memory, inhibition, and processing speed) and cognitive processes, along with a higher risk of externalising problems. However, current evidence does not allow definitive conclusions on whether early EEG abnormalities predict long-term neuropsychological deficits, despite data showing associations between EEG changes and certain cognitive dysfunctions at the time of evaluation.

CONCLUSION

The reviewed evidence suggests that EEG alterations in institutionalised minors are linked to executive dysfunction and increased psychopathological risk. These findings highlight the value of EEG in identifying at-risk children and inform the design of preventive interventions. Longitudinal studies are needed to clarify causal relationships.

Severity of anhedonia is associated with hyper-synchronization of the salience-default mode network in non-clinical individuals: a resting state EEG connectivity study

Anhedonia is a core transnosographic symptom in several neuropsychiatric disorders. Recently, the Triple Network (TN) model has been proposed as a useful neurophysiological paradigm for conceptualizing anhedonia, providing new insights to clinicians and researchers. Despite this, the relationship between the functional dynamics of TN and the severity of anhedonia has been relatively understudied in non-clinical samples, especially in the resting state (RS) condition. Therefore, in the current study, we investigated this relationship using electroencephalography (EEG) functional connectivity. Eighty-two participants (36 males; mean age: 24.28 ± 7.35 years) underwent RS EEG recording with eyes-closed and completed the Beck Depression Inventory-derived 4-item anhedonia scale (BDI-Anh4) and the Brief Symptoms Inventory (BSI). EEG data on functional connectivity were analyzed using the exact low-resolution electromagnetic tomography (eLORETA). A significant positive correlation was observed between the BDI-Anh4 total score and salience-default mode network connectivity in the beta frequency band (r = 0.409; p = 0.010). The results of the hierarchical linear regression analysis also showed that this connectivity pattern was positively and independently associated (β = 0.358; p < 0.001) with the BDI-Anh4 total score and explained an additional 11% of the anhedonia variability. The association between anhedonia severity and increased salience-default mode network synchronization detected in the current study may reflect difficulty disengaging from internal/self-related mental contents, which consequently impairs the processing of other stimuli, including rewarding stimuli.

Cross brain reshaping in congenital visual or hearing impairment: triple-network dysfunction

This research examines how congenital visual or hearing impairment reshapes brain function using EEG. The study involved 40 children with congenital visual impairment, 40 with hearing impairment and 42 age and gender-matched normal children as controls. The investigation included assessments of visual and auditory abilities, along with comprehensive EEG evaluations. Techniques such as source localization, functional connectivity and cross-frequency coupling were used to analyse variations in brain activity. Machine learning methods, specifically support vector machines, were utilized to identify key reshaping characteristics associated with congenital impairments. Results showed reduced activation in the visual cortex for visually impaired children and decreased activation in the auditory cortex for hearing-impaired children compared with the control group. Both impairment groups demonstrated significant reductions in functional connectivity across various brain regions, including the visual and auditory cortices, insula, parahippocampal gyrus, posterior cingulate gyrus and frontal cortex. The machine learning model highlighted aberrant connectivity between the visual/auditory cortex and the right insula, the medial prefrontal cortex and dorsolateral prefrontal cortex and the visual and auditory cortex in children with these impairments in the alpha frequency band. Spatially similar patterns of cross-frequency coupling of rhythmic activity were also observed. The study concludes that congenital visual and hearing impairments significantly impact brain development, identifying distinct functional characteristics and shared reshaping patterns. The consistent presence of dysrhythmic activity and reduced functional connectivity suggest the existence of a triple network anomaly.

Neurophysiological signatures of ageing: compensatory and compromised neural mechanisms

Spatiotemporal patterns of neural oscillations change with ageing, even in the cognitively unimpaired individual. Whether these neurophysiological changes represent ageing-related vulnerabilities or mechanisms that support cognitive resilience remains largely unknown. In this study, we used magnetoencephalography imaging to examine age-related changes of resting-state whole-brain neurophysiology in a well-characterized cohort of cognitively unimpaired individuals (n = 70; age range 52-87 years). We quantified spatial patterns of age-related changes in band-limited spectral power within delta-theta (2-7 Hz), alpha (8-12 Hz) and beta (13-30 Hz) bands and the spectral aperiodic slope (15-50 Hz), and examined how spectral changes are associated with cognitive abilities in healthy ageing. In a subset of individuals (n = 40) who were evaluated with a uniform battery of cognitive tests, using a partial least square regression approach, we examined the associations between age-related spectral changes and cognitive performance. We found that, with advancing age, delta-theta and beta spectral power reduces, while alpha spectral power increases. A periodic slope also showed reductions with ageing. Better cognitive scores were positively correlated with delta-theta reductions and alpha power increases associated with ageing, suggesting that these may represent compensatory neural mechanisms. Beta power reductions and spectral aperiodic slope changes, in contrast, correlated negatively with higher cognitive scores, suggesting that these may represent compromised neural mechanisms of ageing. Our findings highlighted that the neurophysiological changes that occur during later decades of life were distinct from the previously known lifespan changes. This study demonstrates the trajectories of neurophysiological changes in cognitive ageing explicitly relating to conserved and impaired neural mechanisms with important implications for identifying specific spectral changes in neurodegenerative processes in the context of ageing.

Oscillatory But Not Aperiodic Frontal Brain Activity Predicts the Development of Executive Control From Infancy to Toddlerhood

Executive control (EC) emerges in the first year of life, with the ability to inhibit prepotent responses (inhibitory control [IC]) and to flexibly readapt (cognitive flexibility [CF]) steadily improving. Simultaneously, electrophysiological brain activity undergoes profound reconfiguration, which has been linked to individual variability in EC. However, most studies exploring this relationship have used relative/absolute power and tasks that combine different executive processes. In addition, brain activity conflates aperiodic and oscillatory activity, which hinders the interpretation of the relationship between power and cognition. In the current study, we used the Early Childhood Inhibitory Touchscreen Task (ECITT) to examine the development of EC skills from 9 to 16 months in a longitudinal sample, and related performance of the task to resting-state EEG (rs-EEG) power, separating oscillatory and aperiodic activity. Our results showed improvement in IC but not in CF with age. In addition, alpha and theta oscillatory activity were concurrent (9-mo.) and longitudinal predictors of CF in toddlerhood, whereas the aperiodic exponent of the EEG signal did not contribute to EC. These findings demonstrate the relevance of oscillatory brain activity for cognitive development and provide an early brain marker for the early development of EC.

Measuring and interpreting individual differences in fetal, infant, and toddler neurodevelopment

As scientists interested in fetal, infant, and toddler (FIT) neurodevelopment, our research questions often focus on how individual children differ in their neurodevelopment and the predictive value of those individual differences for long-term neural and behavioral outcomes. Measuring and interpreting individual differences in neurodevelopment can present challenges: Is there a "standard" way for the human brain to develop? How do the semantic, practical, or theoretical constraints that we place on studying "development" influence how we measure and interpret individual differences? While it is important to consider these questions across the lifespan, they are particularly relevant for conducting and interpreting research on individual differences in fetal, infant, and toddler neurodevelopment due to the rapid, profound, and heterogeneous changes happening during this period, which may be predictive of long-term outcomes. This article, therefore, has three goals: 1) to provide an overview about how individual differences in neurodevelopment are studied in the field of developmental cognitive neuroscience, 2) to identify challenges and considerations when studying individual differences in neurodevelopment, and 3) to discuss potential implications and solutions moving forward.

Resting-State Electroencephalogram and Speech Perception in Young Children with Developmental Language Disorder

BACKGROUND/OBJECTIVES

Endogenous oscillations reflect the spontaneous activity of brain networks involved in cognitive processes. In adults, endogenous activity across different bands correlates with, and can even predict, language and speech perception processing. However, it remains unclear how this activity develops in children with typical and atypical development.

METHODS

We investigated differences in resting-state EEG between preschoolers with developmental language disorder (DLD), their age-matched controls with typical language development (TLD), and a group of adults.

RESULTS

We observed significantly lower oscillatory power in adults than in children (p < 0.001 for all frequency bands), but no differences between the groups of children in power or hemispheric lateralisation, suggesting that oscillatory activity reflects differences in age, but not in language development. The only measure that differed between the children's groups was theta/alpha band ratio (p = 0.004), which was significantly smaller in TLD than in DLD children, although this was an incidental finding. Behavioural results also did not fully align with previous research, as TLD children performed better in the filtered speech test (p = 0.01), but not in the speech-in-babble one, and behavioural test scores did not correlate with high-frequency oscillations, lateralisation indices, or band ratio measures.

CONCLUSIONS

We discuss the suitability of these resting-state EEG measures to capture group-level differences between TLD/DLD preschoolers and the relevance of our findings for future studies investigating neural markers of typical and atypical language development.

Bilingualism, sleep, and cognition: An integrative view and open research questions

Sleep and language are fundamental to human existence and have both been shown to substantially affect cognitive functioning including memory, attentional performance, and cognitive control. Surprisingly, there is little-to-no research that examines the shared impact of bilingualism and sleep on cognitive functions. In this paper, we provide a general overview of existing research on the interplay between bilingualism and sleep with a specific focus on executive functioning. First, we highlight their interconnections and the resulting implications for cognitive performance. Second, we emphasize the need to explore how bilingualism and sleep intersect at cognitive and neural levels, offering insights into potential ways of studying the interplay between sleep, language learning, and bilingual language use. Finally, we suggest that understanding these relationships could enhance our knowledge of reserve and its role in mitigating age-related cognitive decline.

Socioeconomic status (SES) and cognitive outcomes are predicted by resting-state EEG in school-aged children

Children's socioeconomic status (SES) is related to patterns of intrinsic resting-state brain function that subserve relevant cognitive processes over the course of development. Although infant research has demonstrated the association between children's environments, cognitive outcomes, and resting-state electroencephalography (rsEEG), it remains unknown how these aspects of their environment, tied to SES, impact neural and cognitive development throughout the school years. To address this gap, we applied a multivariate pattern analysis (MVPA) to rsEEG data to identify which neural frequencies at rest are differentially associated with unique aspects of socioeconomic status (SES; income and maternal education) and cognitive (vocabulary, working memory) outcomes among school-aged children (8-15 years). We find that the alpha frequency is associated with both income and maternal education, while lower gamma and theta fluctuations are tied to dissociable aspects of SES and cognitive outcomes. Specifically, changes in the gamma frequency are predictive of both maternal education and vocabulary outcome, while changes in the theta frequency are related to both income and working memory ability. The current findings extend our understanding of unique pathways by which SES influences cognitive and neural development in school-aged children.

Prefrontal excitation/inhibition balance supports adolescent enhancements in circuit signal to noise ratio

The development and refinement of neuronal circuitry allow for stabilized and efficient neural recruitment, supporting adult-like behavioral performance. During adolescence, the maturation of PFC is proposed to be a critical period (CP) for executive function, driven by a break in balance between glutamatergic excitation and GABAergic inhibition (E/I) neurotransmission. During CPs, cortical circuitry fine-tunes to improve information processing and reliable responses to stimuli, shifting from spontaneous to evoked activity, enhancing the SNR, and promoting neural synchronization. Harnessing 7 T MR spectroscopy and EEG in a longitudinal cohort (N = 164, ages 10-32 years, 283 neuroimaging sessions), we outline associations between age-related changes in glutamate and GABA neurotransmitters and EEG measures of cortical SNR. We find developmental decreases in spontaneous activity and increases in cortical SNR during our auditory steady state task using 40 Hz stimuli. Decreases in spontaneous activity were associated with glutamate levels in DLPFC, while increases in cortical SNR were associated with more balanced Glu and GABA levels. These changes were associated with improvements in working memory performance. This study provides evidence of CP plasticity in the human PFC during adolescence, leading to stabilized circuitry that allows for the optimal recruitment and integration of multisensory input, resulting in improved executive function.

Maturational changes in frontal EEG alpha and theta activity from infancy into early childhood and the relation with self-regulation in boys and girls

There is increasing interest in examining the development of frontal EEG power in relation to self-regulation in early childhood. However, the majority of previous studies solely focuses on the brain's alpha rhythm and little is known about the differences between young boys and girls. The aim of the current study was therefore to gain more insight into the neural mechanisms involved in the emergence of self-regulation. The sample consisted of 442 children and data were collected at approximately 5 months, 10 months, and around 3 years of age. Latent growth curve models indicated that,while the neurobiological foundations of self-regulation are established during infancy,it is the maturation of the frontal alpha rhythm that contributes to variations in both observed and parent-reported self-regulation. In addition, it appears that boys might have a greater reliance on external regulation than girls during early childhood, as evident by higher scores of girls on both measures of self-regulation. More insight into the role of external regulators in brain maturation can help to implement interventions aimed at establishing bottom-up self-regulatory skills early in life, in order to provide the necessary foundations for the emergence of top-down self-regulatory skills in the preschool period.

Relationship between preinduction electroencephalogram patterns and propofol sensitivity in adult patients

PURPOSE

To determine the precise induction dose, an objective assessment of individual propofol sensitivity is necessary. This study aimed to investigate whether preinduction electroencephalogram (EEG) data are useful in determining the optimal propofol dose for the induction of general anesthesia in healthy adult patients.

METHODS

Seventy healthy adult patients underwent total intravenous anesthesia (TIVA), and the effect-site target concentration of propofol was observed to measure each individual's propofol requirements for loss of responsiveness. We analyzed preinduction EEG data to assess its relationship with propofol requirements and conducted multiple regression analyses considering various patient-related factors.

RESULTS

Patients with higher relative delta power (ρ = 0.47, p < 0.01) and higher absolute delta power (ρ = 0.34, p = 0.01) required a greater amount of propofol for anesthesia induction. In contrast, patients with higher relative beta power (ρ = -0.33, p < 0.01) required less propofol to achieve unresponsiveness. Multiple regression analysis revealed an independent association between relative delta power and propofol requirements.

CONCLUSION

Preinduction EEG, particularly relative delta power, is associated with propofol requirements during the induction of general anesthesia. The utilization of preinduction EEG data may improve the precision of induction dose selection for individuals.

Resting-state EEG data before and after cognitive activity across the adult lifespan and a 5-year follow-up

This dataset consists of 64-channels resting-state EEG recordings of 608 participants aged between 20 and 70 years, 61.8% female, as well as follow-up measurements after approximately 5 years of 208 participants, starting 2021. The EEG was measured for three minutes with eyes open and eyes closed before and after a 2-hour block of cognitive experimental tasks. The data set is part of the Dortmund Vital Study, a prospective study on the determinants of healthy cognitive aging. The dataset can be used for (1) analyzing cross-sectional resting-state EEG of healthy individuals across the adult life span; (2) generating normalization data sets for comparison of resting-state EEG data of patients with clinically relevant disorders; (3) studying effects of performing cognitive tasks on resting-state EEG and age; (4) exploring intra-individual changes in resting-state EEG and effects of task performance over a time period of about 5 years. The data are provided in Brain Imaging Data Structure (BIDS) format and are available on OpenNeuro.

Diurnal biological effects of correlated colour temperature and its exposure timing on alertness, cognition, and mood in an enclosed environment

Artificial lighting, which profits from the non-visual effects of light, is a potentially promising solution to support residents' psychophysiological health and performance at specific times of the day in enclosed environments. However, few studies have investigated the non-visual effects of daytime correlated colour temperature (CCT) and its exposure timing on human alertness, cognition, and mood. However, the neural mechanisms underlying these effects are largely unknown. The current study evaluated the effects of daytime CCT and its exposure timing on markers of subjective experience, cognitive performance, and cerebral activity in a simulated enclosed environment. Forty-two participants participated a single-blind laboratory study with a 4 within (CCT: 4000 K vs. 6500 K vs. 8500 K vs. 12,000 K) × 2 between (exposure timing: morning vs. afternoon) mixed design. The results showed time of the day dependent benefits of the daytime CCT on subjective experience, vigilant attention, response inhibition, working memory, emotional perception, and risk decisions. The results of the electroencephalogram (EEG) revealed that lower-frequency EEG bands, including theta, alpha, and alpha-theta, were quite sensitive to daytime CCT intervention, which provides a valuable reference for trying to establish the underlying mechanisms that support the performance-enhancement effects of exposure to CCT in the daytime. However, the results revealed no consistent intervention pattern across these measurements. Therefore, future studies should consider personalised optimisation of daytime CCT for different cognitive demands.

Age-Related Differences in Prestimulus EEG Affect ERPs and Behaviour in the Equiprobable Go/NoGo Task

Detailed studies of the equiprobable auditory Go/NoGo task have allowed for the development of a sequential-processing model of the perceptual and cognitive processes involved. These processes are reflected in various components differentiating the Go and NoGo event-related potentials (ERPs). It has long been established that electroencephalography (EEG) changes through normal lifespan development. It is also known that ERPs and behaviour in the equiprobable auditory Go/NoGo task change from children to young adults, and again in older adults. Here, we provide a novel examination of links between in-task prestimulus EEG, poststimulus ERPs, and behaviour in three gender-matched groups: children (8-12 years), young adults (18-24 years), and older adults (59-74 years). We used a frequency Principal Component Analysis (f-PCA) to estimate prestimulus EEG components and a temporal Principal Component Analysis (t-PCA) to separately estimate poststimulus ERP Go and NoGo components in each age group to avoid misallocation of variance. The links between EEG components, ERP components, and behavioural measures differed markedly between the groups. The young adults performed best and accomplished this with the simplest EEG-ERP-behaviour brain dynamics pattern. The children performed worst, and this was reflected in the most complex brain dynamics pattern. The older adults showed some reduction in performance, reflected in an EEG-ERP-behaviour pattern with intermediate complexity between those of the children and young adults. These novel brain dynamics patterns hold promise for future developmental research.

Prefrontal Excitation/ Inhibition Balance Supports Adolescent Enhancements in Circuit Signal to Noise Ratio

The development and refinement of neuronal circuitry allow for stabilized and efficient neural recruitment, supporting adult-like behavioral performance. During adolescence, the maturation of PFC is proposed to be a critical period (CP) for executive function, driven by a break in balance between glutamatergic excitation and GABAergic inhibition (E/I) neurotransmission. During CPs, cortical circuitry fine-tunes to improve information processing and reliable responses to stimuli, shifting from spontaneous to evoked activity, enhancing the SNR, and promoting neural synchronization. Harnessing 7T MR spectroscopy and EEG in a longitudinal cohort (N = 164, ages 10-32 years, 283 neuroimaging sessions), we outline associations between age-related changes in glutamate and GABA neurotransmitters and EEG measures of cortical SNR. We find developmental decreases in spontaneous activity and increases in cortical SNR during our auditory steady state task using 40 Hz stimuli. Decreases in spontaneous activity were associated with glutamate levels in DLPFC, while increases in cortical SNR were associated with more balanced Glu and GABA levels. These changes were associated with improvements in working memory performance. This study provides evidence of CP plasticity in the human PFC during adolescence, leading to stabilized circuitry that allows for the optimal recruitment and integration of multisensory input, resulting in improved executive function.

Maternal heart rate variability at 3-months postpartum is associated with maternal mental health and infant neurophysiology

Previous research has demonstrated a critical link between maternal mental health and infant development. However, there is limited understanding of the role of autonomic regulation in postpartum maternal mental health and infant outcomes. In the current study, we tested 76 mother-infant dyads from diverse socioeconomic backgrounds when infants were 3-months of age. We recorded simultaneous ECG from dyads while baseline EEG was collected from the infant; ECG heart rate variability (HRV) and EEG theta-beta ratio and alpha asymmetry were calculated. Dyadic physiological synchrony was also analyzed to better understand the role of autonomic co-regulation. Results demonstrated that lower maternal HRV was associated with higher self-reported maternal depression and anxiety. Additionally, mothers with lower HRV had infants with lower HRV. Maternal HRV was also associated with higher infant theta-beta ratios, but not alpha asymmetry. Exploratory analyses suggested that for mother-infant dyads with greater physiological synchrony, higher maternal HRV predicted increased infant theta-beta ratio via infant HRV. These findings support a model in which maternal mental health may influence infant neurophysiology via alterations in autonomic stress regulation and dyadic physiological co-regulation.

Degree of multilingual engagement modulates resting state oscillatory activity across the lifespan

Multilingualism has been demonstrated to lead to a more favorable trajectory of neurocognitive aging, yet our understanding of its effect on neurocognition across the lifespan remains limited. We collected resting state EEG recordings from a sample of multilingual individuals across a wide age range. Additionally, we obtained data on participant multilingual language use patterns alongside other known lifestyle enrichment factors. Language experience was operationalized via a modified multilingual diversity (MLD) score. Generalized additive modeling was employed to examine the effects and interactions of age and MLD on resting state oscillatory power and coherence. The data suggest an independent modulatory effect of individualized multilingual engagement on age-related differences in whole brain resting state power across alpha and theta bands, and an interaction between age and MLD on resting state coherence in alpha, theta, and low beta. These results provide evidence of multilingual engagement as an independent correlational factor related to differences in resting state EEG power, consistent with the claim that multilingualism can serve as a protective factor in neurocognitive aging.

Pupillometry reveals resting state alpha power correlates with individual differences in adult auditory language comprehension

Although individual differences in adult language processing are well-documented, the neural basis of this variability remains largely unexplored. The current study addressed this gap in the literature by examining the relationship between resting state alpha activity and individual differences in auditory language comprehension. Alpha oscillations modulate cortical excitability, facilitating efficient information processing in the brain. While resting state alpha oscillations have been tied to individual differences in cognitive performance, their association with auditory language comprehension is less clear. Participants in the study were 80 healthy adults with a mean age of 25.8 years (SD = 7.2 years). Resting state alpha activity was acquired using electroencephalography while participants looked at a benign stimulus for 3 min. Participants then completed a language comprehension task that involved listening to 'syntactically simple' subject-relative clause sentences and 'syntactically complex' object-relative clause sentences. Pupillometry measured real-time processing demand changes, with larger pupil dilation indicating increased processing loads. Replicating past research, comprehending object relative clauses, compared to subject relative clauses, was associated with lower accuracy, slower reaction times, and larger pupil dilation. Resting state alpha power was found to be positively correlated with the pupillometry data. That is, participants with higher resting state alpha activity evidenced larger dilation during sentence comprehension. This effect was more pronounced for the 'complex' object sentences compared to the 'simple' subject sentences. These findings suggest the brain's capacity to generate a robust resting alpha rhythm contributes to variability in processing demands associated with auditory language comprehension, especially when faced with challenging syntactic structures. More generally, the study demonstrates that the intrinsic functional architecture of the brain likely influences individual differences in language comprehension.

Patterns of spontaneous neural activity associated with social communication abilities among infants and toddlers showing signs of autism

Early disruptions to social communication development, including delays in joint attention and language, are among the earliest markers of autism spectrum disorder (autism, henceforth). Although social communication differences are a core feature of autism, there is marked heterogeneity in social communication-related development among infants and toddlers exhibiting autism symptoms. Neural markers of individual differences in joint attention and language abilities may provide important insight into heterogeneity in autism symptom expression during infancy and toddlerhood. This study examined patterns of spontaneous electroencephalography (EEG) activity associated with joint attention and language skills in 70 community-referred 12- to 23-month-olds with autism symptoms and elevated scores on an autism diagnostic instrument. Data-driven cluster-based permutation analyses revealed significant positive associations between relative alpha power (6-9 Hz) and concurrent response to joint attention skills, receptive language, and expressive language abilities. Exploratory analyses also revealed significant negative associations between relative alpha power and measures of core autism features (i.e., social communication difficulties and restricted/repetitive behaviors). These findings shed light on the neural mechanisms underlying typical and atypical social communication development in emerging autism and provide a foundation for future work examining neural predictors of social communication growth and markers of intervention response.

Prenatal family income, but not parental education, is associated with resting brain activity in 1-month-old infants

Childhood socioeconomic disadvantage is associated with disparities in development and health, possibly through adaptations in children's brain function. However, it is not clear how early in development such neural adaptations might emerge. This study examined whether prenatal family socioeconomic status, operationalized as family income and average years of parental education, prospectively predicts individual differences in infant resting electroencephalography (EEG; theta, alpha, beta, and gamma power) at approximately 1 month of age (N = 160). Infants of mothers reporting lower family income showed more lower-frequency (theta) and less higher-frequency (beta and gamma) power. These associations held when adjusting for other prenatal and postnatal experiences, as well as infant demographic and health-related factors. In contrast, parental education was not significantly associated with infant EEG power in any frequency band. These data suggest that lower prenatal family income is associated with developmental differences in brain function that are detectable within the first month of life.

Association between gait speed deterioration and EEG abnormalities

Physical and cognitive decline at an older age is preceded by changes that accumulate over time until they become clinically evident difficulties. These changes, frequently overlooked by patients and health professionals, may respond better than fully established conditions to strategies designed to prevent disabilities and dependence in later life. The objective of this study was twofold; to provide further support for the need to screen for early functional changes in older adults and to look for an early association between decline in mobility and cognition. A cross-sectional cohort study was conducted on 95 active functionally independent community-dwelling older adults in Havana, Cuba. We measured their gait speed at the usual pace and the cognitive status using the MMSE. A value of 0.8 m/s was used as the cut-off point to decide whether they presented a decline in gait speed. A quantitative analysis of their EEG at rest was also performed to look for an associated subclinical decline in brain function. Results show that 70% of the sample had a gait speed deterioration (i.e., lower than 0.8 m/s), of which 80% also had an abnormal EEG frequency composition for their age. While there was no statistically significant difference in the MMSE score between participants with a gait speed above and below the selected cut-off, individuals with MMSE scores below 25 also had a gait speed<0.8 m/s and an abnormal EEG frequency composition. Our results provide further evidence of early decline in older adults-even if still independent and active-and point to the need for clinical pathways that incorporate screening and early intervention targeted at early deterioration to prolong the years of functional life in older age.

Rhythmic Attention and ADHD: A Narrative and Systematic Review

In recent decades, a growing body of evidence has confirmed the existence of rhythmic fluctuations in attention, but the effect of inter-individual variations in these attentional rhythms has yet to be investigated. The aim of this review is to identify trends in the attention deficit/hyperactivity disorder (ADHD) literature that could be indicative of between-subject differences in rhythmic attention. A narrative review of the rhythmic attention and electrophysiological ADHD research literature was conducted, and the commonly-reported difference in slow-wave power between ADHD subjects and controls was found to have the most relevance to an understanding of rhythmic attention. A systematic review of the literature examining electrophysiological power differences in ADHD was then conducted to identify studies with conditions similar to those utilised in the rhythmic attention research literature. Fifteen relevant studies were identified and reviewed. The most consistent finding in the studies reviewed was for no spectral power differences between ADHD subjects and controls. However, the strongest trend in the studies reporting power differences was for higher power in the delta and theta frequency bands and lower power in the alpha band. In the context of rhythmic attention, this trend is suggestive of a slowing in the frequency and/or increase in the amplitude of the attentional oscillation in a subgroup of ADHD subjects. It is suggested that this characteristic electrophysiological modulation could be indicative of a global slowing of the attentional rhythm and/or an increase in the rhythmic recruitment of neurons in frontal attention networks in individuals with ADHD.

Theta activity and cognitive functioning: Integrating evidence from resting-state and task-related developmental electroencephalography (EEG) research

The theta band is one of the most prominent frequency bands in the electroencephalography (EEG) power spectrum and presents an interesting paradox: while elevated theta power during resting state is linked to lower cognitive abilities in children and adolescents, increased theta power during cognitive tasks is associated with higher cognitive performance. Why does theta power, measured during resting state versus cognitive tasks, show differential correlations with cognitive functioning? This review provides an integrated account of the functional correlates of theta across different contexts. We first present evidence that higher theta power during resting state is correlated with lower executive functioning, attentional abilities, language skills, and IQ. Next, we review research showing that theta power increases during memory, attention, and cognitive control, and that higher theta power during these processes is correlated with better performance. Finally, we discuss potential explanations for the differential correlations between resting/task-related theta and cognitive functioning, and offer suggestions for future research in this area.

Effects of 90 dB pure tone exposure on auditory and cardio-cerebral system functions in macaque monkeys

Excessive noise exposure presents significant health risks to humans, affecting not just the auditory system but also the cardiovascular and central nervous systems. This study focused on three male macaque monkeys as subjects. 90 dB sound pressure level (SPL) pure tone exposure (frequency: 500Hz, repetition rate: 40Hz, 1 min per day, continuously exposed for 5 days) was administered. Assessments were performed before exposure, during exposure, immediately after exposure, and at 7-, 14-, and 28-days post-exposure, employing auditory brainstem response (ABR) tests, electrocardiograms (ECG), and electroencephalograms (EEG). The study found that the average threshold for the Ⅴ wave in the right ear increased by around 30 dB SPL right after exposure (P < 0.01) compared to pre-exposure. This elevation returned to normal within 7 days. The ECG results indicated that one of the macaque monkeys exhibited an RS-type QRS wave, and inverted T waves from immediately after exposure to 14 days, which normalized at 28 days. The other two monkeys showed no significant changes in their ECG parameters. Changes in EEG parameters demonstrated that main brain regions exhibited significant activation at 40Hz during noise exposure. After noise exposure, the power spectral density (PSD) in main brain regions, particularly those represented by the temporal lobe, exhibited a decreasing trend across all frequency bands, with no clear recovery over time. In summary, exposure to 90 dB SPL noise results in impaired auditory systems, aberrant brain functionality, and abnormal electrocardiographic indicators, albeit with individual variations. It has implications for establishing noise protection standards, although the precise mechanisms require further exploration by integrating pathological and behavioral indicators.

Association of Neighborhood Opportunity with Infant Brain Activity and Cognitive Development

BACKGROUND AND OBJECTIVES

Neighborhood socioeconomic disadvantage is associated with lower neurocognitive scores and differences in brain structure among school-age children. Associations between positive neighborhood characteristics, infant brain activity, and cognitive development are underexplored. We examined direct and indirect associations between neighborhood opportunity, brain activity, and cognitive development.

METHODS

This longitudinal cohort study included infants from 2 primary care clinics in Boston and Los Angeles. Using a sample of 65 infants, we estimated path models to examine associations between neighborhood opportunity (measured by the Child Opportunity Index), infant electroencephalography (EEG) at 6 months, and infant cognitive development (measured using the Mullen Scales of Early Learning) at 12 months. A mediation model tested whether EEG power explained associations between neighborhood opportunity and infant cognition.

RESULTS

Neighborhood opportunity positively predicted infant absolute EEG power across multiple frequency bands: low ( b = 0.12, 95% CI 0.01-0.24, p = 0.04, = 0.21); high ( b = 0.11, 95% CI 0.01-0.21, p = 0.03, = 0.23); ( b = 0.10, 95% CI 0.00-0.19, p = 0.04, = 0.20); and ( b = 0.12, 95% CI 0.02-0.22, p = 0.02, = 0.24). The results remained statistically significant after applying a Benjamini-Hochberg false discovery rate of 0.10 to adjust for multiple comparisons. No significant associations emerged between neighborhood opportunity, relative EEG power, and infant cognition. Mediation was not significant.

CONCLUSION

Neighborhood opportunity is positively associated with some forms of infant brain activity, suggesting that positive neighborhood characteristics may play a salient role in early development.

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.

Associations between EEG power and coherence with cognition and early precursors of speech and language development across the first months of life

The neural processes underpinning cognition and language development in infancy are of great interest. We investigated EEG power and coherence in infancy, as a reflection of underlying cortical function of single brain region and cross-region connectivity, and their relations to cognition and early precursors of speech and language development. EEG recordings were longitudinally collected from 21 infants with typical development between approximately 1 and 7 months. We investigated relative band power at 3-6Hz and 6-9Hz and EEG coherence of these frequency ranges at 25 electrode pairs that cover key brain regions. A correlation analysis was performed to assess the relationship between EEG measurements across frequency bands and brain regions and raw Bayley cognitive and language developmental scores. In the first months of life, relative band power is not correlated with cognitive and language scales. However, 3-6Hz coherence is negatively correlated with receptive language scores between frontoparietal regions, and 6-9Hz coherence is negatively correlated with expressive language scores between frontoparietal regions. The results from this preliminary study contribute to the existing literature on the relationship between electrophysiological development, cognition, and early speech precursors in this age group. Future work should create norm references of early development in these domains that can be compared with infants at risk for neurodevelopmental disabilities.

Evolutionary origin of alpha rhythms in vertebrates

The purpose of this review extends beyond the traditional triune brain model, aiming to elucidate the evolutionary aspects of alpha rhythms in vertebrates. The forebrain, comprising the telencephalon (pallium) and diencephalon (thalamus, hypothalamus), is a common feature in the brains of all vertebrates. In mammals, evolution has prioritized the development of the forebrain, especially the neocortex, over the midbrain (mesencephalon) optic tectum, which serves as the prototype for the visual brain. This evolution enables mammals to process visual information in the retina-thalamus (lateral geniculate nucleus)-occipital cortex pathway. The origin of posterior-dominant alpha rhythms observed in mammals in quiet and dark environments is not solely attributed to cholinergic pontine nuclei cells functioning as a 10 Hz pacemaker in the brainstem. It also involves the ability of the neocortex's cortical layers to generate traveling waves of alpha rhythms with waxing and waning characteristics. The utilization of alpha rhythms might have facilitated the shift of attention from external visual inputs to internal cognitive processes as an adaptation to thrive in dark environments. The evolution of alpha rhythms might trace back to the dinosaur era, suggesting that enhanced cortical connectivity linked to alpha bands could have facilitated the development of nocturnal awakening in the ancestors of mammals. In fishes, reptiles, and birds, the pallium lacks a cortical layer. However, there is a lack of research clearly observing dominant alpha rhythms in the pallium or organized nuclear structures in fishes, reptiles, or birds. Through convergent evolution, the pallium of birds, which exhibits cortex-like fiber architecture, has not only acquired advanced cognitive and motor abilities but also the capability to generate low-frequency oscillations (4-25 Hz) resembling alpha rhythms. This suggests that the origins of alpha rhythms might lie in the pallium of a common ancestor of birds and mammals.

A growth chart of brain function from infancy to adolescence based on EEG

BACKGROUND

In children, objective, quantitative tools that determine functional neurodevelopment are scarce and rarely scalable for clinical use. Direct recordings of cortical activity using routinely acquired electroencephalography (EEG) offer reliable measures of brain function.

METHODS

We developed and validated a measure of functional brain age (FBA) using a residual neural network-based interpretation of the paediatric EEG. In this cross-sectional study, we included 1056 children with typical development ranging in age from 1 month to 18 years. We analysed a 10- to 15-min segment of 18-channel EEG recorded during light sleep (N1 and N2 states).

FINDINGS

The FBA had a weighted mean absolute error (wMAE) of 0.85 years (95% CI: 0.69-1.02; n = 1056). A two-channel version of the FBA had a wMAE of 1.51 years (95% CI: 1.30-1.73; n = 1056) and was validated on an independent set of EEG recordings (wMAE = 2.27 years, 95% CI: 1.90-2.65; n = 723). Group-level maturational delays were also detected in a small cohort of children with Trisomy 21 (Cohen's d = 0.36, p = 0.028).

INTERPRETATION

A FBA, based on EEG, is an accurate, practical and scalable automated tool to track brain function maturation throughout childhood with accuracy comparable to widely used physical growth charts.

FUNDING

This research was supported by the National Health and Medical Research Council, Australia, Helsinki University Diagnostic Center Research Funds, Finnish Academy, Finnish Paediatric Foundation, and Sigrid Juselius Foundation.

Resting-state electroencephalography delta and theta bands as compensatory oscillations in chronic neuropathic pain: a secondary data analysis

Chronic neuropathic pain (CNP) remains a significant clinical challenge, with complex neurophysiological underpinnings that are not fully understood. Identifying specific neural oscillatory patterns related to pain perception and interference can enhance our understanding and management of CNP. To analyze resting electroencephalography data from individuals with chronic neuropathic pain to explore the possible neural signatures associated with pain intensity, pain interference, and specific neuropathic pain characteristics. We conducted a secondary analysis from a cross-sectional study using electroencephalography data from a previous study, and Brief Pain Inventory from 36 patients with chronic neuropathic pain. For statistical analysis, we modeled a linear or logistic regression by dependent variable for each model. As independent variables, we used electroencephalography data with such brain oscillations: as delta, theta, alpha, and beta, as well as the oscillations low alpha, high alpha, low beta, and high beta, for the central, frontal, and parietal regions. All models tested for confounding factors such as age and medication. There were no significant models for Pain interference in general activity, walking, work, relationships, sleep, and enjoyment of life. However, the model for pain intensity during the past four weeks showed decreased alpha oscillations, and increased delta and theta oscillations were associated with decreased levels of pain, especially in the central area. In terms of pain interference in mood, the model showed high oscillatory Alpha signals in the frontal and central regions correlated with mood impairment due to pain. Our models confirm recent findings proposing that lower oscillatory frequencies, likely related to subcortical pain sources, may be associated with brain compensatory mechanisms and thus may be associated with decreased pain levels. On the other hand, higher frequencies, including alpha oscillations, may disrupt top-down compensatory mechanisms.

Early brain cognitive development in late preterm infants: an event-related potential and resting EEG study

BACKGROUND

Late preterm infants (LPIs) are at risk of neurodevelopmental delay. Research on their cognitive development is helpful for early intervention and follow-up.

METHODS

Event-related potential (ERP) and resting electroencephalography (RS-EEG) were used to study the brain cognitive function of LPIs in the early stage of life. The Gesell Developmental Scale (GDS) was used to track the neurodevelopmental status at the age of 1 year after correction, and to explore the neurophysiological indicators that could predict the outcome of cognitive development in the early stage.

RESULTS

The results showed that mismatch response (MMR) amplitude, RS-EEG power spectrum and functional connectivity all suggested that LPIs were lagging behind. At the age of 1 year after correction, high-risk LPIs showed no significant delay in gross motor function, but lagged behind in fine motor function, language, personal social interaction and adaptability. The ROC curve was used to evaluate the predictive role of MMR amplitude in the brain cognitive development prognosis at 1 year, showing a sensitivity of 80.00% and a specificity of 90.57%. The area under the curve (AUC) was 0.788, with a P-value of 0.007.

CONCLUSIONS

Based on our findings we supposed that the cognitive function of LPI lags behind that of full-term infants in early life. Preterm birth and perinatal diseases or high risk factors affected brain cognitive function in LPIs. MMR amplitude can be used as an early predictor of brain cognitive development in LPIs.

TRIAL REGISTRATION

This clinical trial is registered with the Chinese Clinical Trial Registry (ChiCTR).

TRIAL REGISTRATION NUMBER

ChiCTR2100041929. Date of registration: 2021-01-10. URL of the trial registry record: https://www.chictr.org.cn/ .

Frontal EEG alpha asymmetry in youth with autism: Sex differences and social-emotional correlates

In youth broadly, EEG frontal alpha asymmetry (FAA) associates with affective style and vulnerability to psychopathology, with relatively stronger right activity predicting risk for internalizing and externalizing behaviors. In autistic youth, FAA has been related to ASD diagnostic features and to internalizing symptoms. Among our large, rigorously characterized, sex-balanced participant group, we attempted to replicate findings suggestive of altered FAA in youth with an ASD diagnosis, examining group differences and impact of sex assigned at birth. Second, we examined relations between FAA and behavioral variables (ASD features, internalizing, and externalizing) within autistic youth, examining effects by sex. Third, we explored whether the relation between FAA, autism features, and mental health was informed by maternal depression history. In our sample, FAA did not differ by diagnosis, age, or sex. However, youth with ASD had lower total frontal alpha power than youth without ASD. For autistic females, FAA and bilateral frontal alpha power correlated with social communication features, but not with internalizing or externalizing symptoms. For autistic males, EEG markers correlated with social communication features, and with externalizing behaviors. Exploratory analyses by sex revealed further associations between youth FAA, behavioral indices, and maternal depression history. In summary, findings suggest that individual differences in FAA may correspond to social-emotional and mental health behaviors, with different patterns of association for females and males with ASD. Longitudinal consideration of individual differences across levels of analysis (e.g., biomarkers, family factors, and environmental influences) will be essential to parsing out models of risk and resilience among autistic youth.

Phonological acquisition depends on the timing of speech sounds: Deconvolution EEG modeling across the first five years

The late development of fast brain activity in infancy restricts initial processing abilities to slow information. Nevertheless, infants acquire the short-lived speech sounds of their native language during their first year of life. Here, we trace the early buildup of the infant phoneme inventory with naturalistic electroencephalogram. We apply the recent method of deconvolution modeling to capture the emergence of the feature-based phoneme representation that is known to govern speech processing in the mature brain. Our cross-sectional analysis uncovers a gradual developmental increase in neural responses to native phonemes. Critically, infants appear to acquire those phoneme features first that extend over longer time intervals-thus meeting infants' slow processing abilities. Shorter-lived phoneme features are added stepwise, with the shortest acquired last. Our study shows that the ontogenetic acceleration of electrophysiology shapes early language acquisition by determining the duration of the acquired units.

Does Electrophysiological Maturation Shape Language Acquisition?

Infants master temporal patterns of their native language at a developmental trajectory from slow to fast: Shortly after birth, they recognize the slow acoustic modulations specific to their native language before tuning into faster language-specific patterns between 6 and 12 months of age. We propose here that this trajectory is constrained by neuronal maturation-in particular, the gradual emergence of high-frequency neural oscillations in the infant electroencephalogram. Infants' initial focus on slow prosodic modulations is consistent with the prenatal availability of slow electrophysiological activity (i.e., theta- and delta-band oscillations). Our proposal is consistent with the temporal patterns of infant-directed speech, which initially amplifies slow modulations, approaching the faster modulation range of adult-directed speech only as infants' language has advanced sufficiently. Moreover, our proposal agrees with evidence from premature infants showing maturational age is a stronger predictor of language development than ex utero exposure to speech, indicating that premature infants cannot exploit their earlier availability of speech because of electrophysiological constraints. In sum, we provide a new perspective on language acquisition emphasizing neuronal development as a critical driving force of infants' language development.

Associations between maternal stress and infant resting brain activity among families residing in poverty in the U.S

Growing evidence suggests that maternal experiences of stress shape children's functional brain activity in the first years of life. Individuals living in poverty are more likely to experience stress from a variety of sources. However, it is unclear how stress is related to resting brain activity among children born into poverty. The present study examines whether infants born into households experiencing poverty show differences in brain activity associated with maternal reports of experiencing stress. The analytic sample comprised 247 mother-infant dyads who completed maternal questionnaires characterizing stress, and for whom recordings of infant resting brain activity were obtained at 1 year of age (M=12.93 months, SD=1.66; 50% female). Mothers (40% Black, non-Hispanic, 40% Hispanic, 12% White, non-Hispanic) who reported higher stress had infants who showed more resting brain activity in the lower end of the frequency spectrum (relative theta power) and less resting brain activity in the middle range of the frequency spectrum (relative alpha power). While statistically detectable at the whole-brain level, follow-up exploratory analyses revealed that these effects were most apparent in electrodes over frontal and parietal regions of the brain. These findings held after adjusting for a variety of potentially confounding variables. Altogether, the present study suggests that, among families experiencing low economic resources, maternal reports of stress are associated with differences in patterns of infant resting brain activity during the first year of life.

Early development of electrophysiological activity: Contribution of periodic and aperiodic components of the EEG signal

Brain function rapidly changes in the first 2 years of life. In the last decades, resting-state EEG has been widely used to explore those changes. Previous studies have focused on the relative power of the signal in established frequency bands (i.e., theta, alpha, and beta). However, EEG power is a mixture of a 1/f-like background power (aperiodic) in combination with narrow peaks that appear over that curve (periodic activity, e.g., alpha peak). Therefore, it is possible that relative power captures both, aperiodic and periodic brain activity, contributing to changes in electrophysiological activity observed in infancy. For this reason, we explored the early developmental trajectory of the relative power in theta, alpha, and beta frequency bands from infancy to toddlerhood and compared it with changes in periodic activity in a longitudinal study with three waves at age 6, 9, and 16 to 18 months. Finally, we tested the contribution of periodic activity and aperiodic components of the EEG to age changes in relative power. We found that relative power and periodic activity trajectories differed in this period in all the frequency bands but alpha. Furthermore, aperiodic EEG activity flattened between 6 and 18 months. More importantly, only alpha relative power was exclusively related to periodic activity, whereas aperiodic components of the signal significantly contributed to the relative power of activity in theta and beta bands. Thus, relative power in these frequencies is influenced by developmental changes of the aperiodic activity, which should be considered for future studies.

Conspiracy beliefs are associated with a reduction in frontal beta power and biases in categorizing ambiguous stimuli

Prior beliefs, such as conspiracy beliefs, significantly influence our perception of the natural world. However, the brain activity associated with perceptual decision-making in conspiracy beliefs is not well understood. To shed light on this topic, we conducted a study examining the EEG activity of believers, and skeptics during resting state with perceptual decision-making task. Our study shows that conspiracy beliefs are related to the reduced power of beta frequency band. Furthermore, skeptics tended to misclassify ambiguous face stimuli as houses more frequently than believers. These results help to explain the differences in brain activity between believers and skeptics, especially in how conspiracy beliefs impact the categorization of ambiguous stimuli.

Resting brain activity in early childhood predicts IQ at 18 years

Resting brain activity has been widely used as an index of brain development in neuroscience and clinical research. However, it remains unclear whether early differences in resting brain activity have meaningful implications for predicting long-term cognitive outcomes. Using data from the Bucharest Early Intervention Project (Zeanah et al., 2003), we examined the impact of institutional rearing and the consequences of early foster care intervention on 18-year IQ. We found that higher resting theta electroencephalogram (EEG) power, reflecting atypical neurodevelopment, across three assessments from 22 to 42 months predicted lower full-scale IQ at 18 years, providing the first evidence that brain activity in early childhood predicts cognitive outcomes into adulthood. In addition, both institutional rearing and later (vs. earlier) foster care intervention predicted higher resting theta power in early childhood, which in turn predicted lower IQ at 18 years. These findings demonstrate that experientially-induced changes in brain activity early in life have profound impact on long-term cognitive development, highlighting the importance of early intervention for promoting healthy development among children living in disadvantaged environments.

The developmental trajectory of functional excitation-inhibition balance relates to language abilities in autistic and allistic children

Autism is a neurodevelopmental condition that has been related to an overall imbalance between the brain's excitatory (E) and inhibitory (I) systems. Such an EI imbalance can lead to structural and functional cortical deviances and thus alter information processing in the brain, ultimately giving rise to autism traits. However, the developmental trajectory of EI imbalances across childhood and adolescence has not been investigated yet. Therefore, its relationship to autism traits is not well understood. In the present study, we determined a functional measure of the EI balance (f-EIB) from resting-state electrophysiological recordings for a final sample of 92 autistic children from 6 to 17 years of age and 100 allistic (i.e., non-autistic) children matched by age, sex, and nonverbal-IQ. We related the developmental trajectory of f-EIB to behavioral assessments of autism traits as well as language ability. Our results revealed differential EI trajectories for autistic compared to allistic children. Importantly, the developmental trajectory of f-EIB values related to individual language ability. In particular, elevated excitability in late childhood and early adolescence was linked to decreased listening comprehension. Our findings provide evidence against a general EI imbalance in autistic children when correcting for non-verbal IQ. Instead, we show that the developmental trajectory of EI balance shares variance with autism trait development at a specific age range. This is consistent with the proposal that the late development of inhibitory brain activity is a key substrate of autism traits.

Resting state electroencephalography power correlates with individual differences in implicit sequence learning

Neuroimaging resting state paradigms have revealed synchronised oscillatory activity is present even in the absence of completing a task or mental operation. One function of this neural activity is likely to optimise the brain's sensitivity to forthcoming information that, in turn, likely promotes subsequent learning and memory outcomes. The current study investigated whether this extends to implicit forms of learning. A total of 85 healthy adults participated in the study. Resting state electroencephalography was first acquired from participants before they completed a serial reaction time task. On this task, participants implicitly learnt a visuospatial-motor sequence. Permutation testing revealed a negative correlation between implicit sequence learning and resting state power in the upper theta band (6-7 Hz). That is, lower levels of resting state power in this frequency range were associated with superior levels of implicit sequence learning. This association was observed at midline-frontal, right-frontal and left-posterior electrodes. Oscillatory activity in the upper theta band supports a range of top-down processes including attention, inhibitory control and working memory, perhaps just for visuospatial information. Our results may be indicating that disengaging theta-supported top-down attentional processes improves implicit learning of visuospatial-motor information that is embedded in sensory input. This may occur because the brain's sensitivity to this type of information is optimally achieved when learning is driven by bottom-up processes. Moreover, the results of this study further demonstrate that resting state synchronised brain activity influences subsequent learning and memory.

Associations between EEG power and coherence and cognitive and language development across the first months of life

The neural processes underpinning cognition and language development in infancy are of great interest. We investigated EEG power and coherence in infancy, as a reflection of underlying cortical function of single brain region and cross-region connectivity, and their relations to cognition and language development. EEG recordings were longitudinally collected from 21 infants with typical development between 1 and 7 months. We investigated relative band power at theta (3-6Hz) and alpha (6-9Hz) and EEG coherence of these frequency bands at 25 electrode pairs that cover key brain regions. A correlation analysis was performed to assess the relationship between EEG measurements (frequency bands and brain regions) and raw Bayley cognitive and language developmental scores. In the first months of life, relative band power is not correlated with changes in cognitive and language scales. However, theta coherence is negatively correlated with receptive language scores between frontoparietal regions, and alpha coherence is negatively correlated with expressive language scores between frontoparietal regions. The results from this preliminary study are the first steps in identifying potential biomarkers of early cognitive and language development. In future work, we will confirm norm references of early cognitive and language development that can be compared with infants at risk for neurodevelopmental disabilities.

Age-related differences in transient gamma band activity during working memory maintenance through adolescence

Adolescence is a stage of development characterized by neurodevelopmental specialization of cognitive processes. In particular, working memory continues to improve through adolescence, with increases in response accuracy and decreases in response latency continuing well into the twenties. Human electroencephalogram (EEG) studies indicate that gamma oscillations (35-65 Hz) during the working memory delay period support the maintenance of mnemonic information guiding subsequent goal-driven behavior, which decrease in power with development. Importantly, recent electrophysiological studies have shown that gamma events, more so than sustained activity, may underlie working memory maintenance during the delay period. However, developmental differences in gamma events during working memory have not been studied. Here, we used EEG in conjunction with a novel spectral event processing approach to investigate age-related differences in transient gamma band activity during a memory guided saccade (MGS) task in 164 10- to 30-year-olds. Total gamma power was found to significantly decrease through adolescence, replicating prior findings. Results from the spectral event pipeline showed age-related decreases in the mean power of gamma events and trial-by-trial power variability across both the delay period and fixation epochs of the MGS task. In addition, we found that while event number decreased with age during the fixation period, the developmental decrease during the delay period was more dramatic, resulting in an increase in event spiking from fixation to delay in adolescence but not adulthood. While average power of the transient gamma events was found to mediate age-related differences in total gamma power in the fixation and delay periods, the number of gamma events was related to total power in only the delay period, suggesting that the power of gamma events may underlie the sustained gamma activity seen in EEG literature while the number of events may directly support age-related improvements in working memory maintenance. Our findings provide compelling new evidence for mechanistic changes in neural processing characterized by refinements in neural function as behavior becomes optimized in adulthood.

Does the Frontal Brain Electrical Activity Mediate the Effect of Home Executive Function Environment and Screen Time on Children's Executive Function?

Executive functions play an important role in various developmental aspects of children; however, environmental factors influencing individual differences in children's executive function and their neural substructures, particularly in middle childhood, are rarely investigated. Therefore, the current study aimed to investigate the relationship between the home executive function environment (HEFE) and screen time with the executive function of children aged 8-12 years by employing the mediating variables of alpha, beta, and theta waves. The parents of 133 normal children completed Barkley Deficits in Executive Functioning, HEFE, and Screen Time Scales. Alpha, beta, and theta brain waves were also measured. Data were examined using correlational and path analysis. The results suggested a positive and significant relationship between home executive functions and the executive functions of children. Furthermore, the results indicated an inverse and significant relationship between screen time and executive function. The results also proved the mediating role of alpha, beta, and theta brain waves in the relationship between screen time and the children's executive function. Environmental factors (such as home environment and screen time) affect the function of brain waves and, thus, the daily executive function of children.

Maternal hair cortisol predicts periodic and aperiodic infant frontal EEG activity longitudinally across infancy

Maternal stress is known to be an important factor in shaping child development, yet the complex pattern of associations between stress and infant brain development remains understudied. To better understand the nuanced relations between maternal stress and infant neurodevelopment, research investigating longitudinal relations between maternal chronic physiological stress and infant brain function is warranted. In this study, we leveraged longitudinal data to disentangle between- from within-person associations of maternal hair cortisol and frontal electroencephalography (EEG) power at three time points across infancy at 3, 9, and 15 months. We analyzed both aperiodic power spectral density (PSD) slope and traditional periodic frequency band activity. On the within-person level, maternal hair cortisol was associated with a flattening of frontal PSD slope and an increase in relative frontal beta. However, on the between-person level, higher maternal hair cortisol was associated with steeper frontal PSD slope, increased relative frontal theta, and decreased relative frontal beta. The within-person findings may reflect an adaptive neural response to relative shifts in maternal stress levels, while the between-person results demonstrate the potentially detrimental effects of chronically elevated maternal stress. This analysis offers a novel, quantitative insight into the relations between maternal physiological stress and infant cortical function.

Inter-individual variability during neurodevelopment: an investigation of linear and nonlinear resting-state EEG features in an age-homogenous group of infants

Electroencephalography measures are of interest in developmental neuroscience as potentially reliable clinical markers of brain function. Features extracted from electroencephalography are most often averaged across individuals in a population with a particular condition and compared statistically to the mean of a typically developing group, or a group with a different condition, to define whether a feature is representative of the populations as a whole. However, there can be large variability within a population, and electroencephalography features often change dramatically with age, making comparisons difficult. Combined with often low numbers of trials and low signal-to-noise ratios in pediatric populations, establishing biomarkers can be difficult in practice. One approach is to identify electroencephalography features that are less variable between individuals and are relatively stable in a healthy population during development. To identify such features in resting-state electroencephalography, which can be readily measured in many populations, we introduce an innovative application of statistical measures of variance for the analysis of resting-state electroencephalography data. Using these statistical measures, we quantified electroencephalography features commonly used to measure brain development-including power, connectivity, phase-amplitude coupling, entropy, and fractal dimension-according to their intersubject variability. Results from 51 6-month-old infants revealed that the complexity measures, including fractal dimension and entropy, followed by connectivity were the least variable features across participants. This stability was found to be greatest in the right parietotemporal region for both complexity feature, but no significant region of interest was found for connectivity feature. This study deepens our understanding of physiological patterns of electroencephalography data in developing brains, provides an example of how statistical measures can be used to analyze variability in resting-state electroencephalography in a homogeneous group of healthy infants, contributes to the establishment of robust electroencephalography biomarkers of neurodevelopment through the application of variance analyses, and reveals that nonlinear measures may be most relevant biomarkers of neurodevelopment.

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.

Age related changes and sex related differences of functional brain networks in childhood: A high-density EEG study

OBJECTIVE

The aim of this study was to explore functional network age-related changes and sex-related differences during the early lifespan with a high-density resting state electroencephalography (rs-EEG).

METHODS

We analyzed two data sets of high-density rs-EEG in healthy children and adolescents. We recorded a 64-channel EEG and calculated functional connectomes in 27 participants aged 5-18 years. To validate our results, we used publicly available data and calculated functional connectomes in another 86 participants aged 6-18 years from a 128-channel rs-EEG. We were primarily interested in alpha frequency band, but we also analyzed theta and beta frequency bands.

RESULTS

We observed age-related increase of characteristic path, clustering coefficient and interhemispheric strength in the alpha frequency band of both data sets and in the beta frequency band of the larger validation data set. Age-related increase of global efficiency was seen in the theta band of the validation data set and in the alpha band of the test data set. Increase in small worldness was observed only in the alpha frequency band of the test data set. We also observed an increase of individual peak alpha frequency with age in both data sets. Sex-related differences were only observed in the beta frequency band of the larger validation data set, with females having higher values than same aged males.

CONCLUSIONS

Functional brain networks show indices of higher segregation, but also increasing global integration with maturation. Age-related changes are most prominent in the alpha frequency band.

SIGNIFICANCE

To the best of our knowledge, our study was the first to analyze maturation related changes and sex-related differences of functional brain networks with a high-density EEG and to compare functional connectomes generated from two diverse high-density EEG data sets. Understanding the age-related changes and sex-related differences of functional brain networks in healthy children and adolescents is crucial for identifying network abnormalities in different neurologic and psychiatric conditions, with the aim to identify possible markers for prognosis and treatment.