Developmental changes in spontaneous electrocortical activity and network organization from early to late childhood

Constrained Functional Connectivity Dynamics in Pediatric Surgical Patients Undergoing General Anesthesia

BACKGROUND

Functional connectivity in cortical networks is thought to be important for consciousness and can be disrupted during the anesthetized state. Recent work in adults has revealed dynamic connectivity patterns during stable general anesthesia but whether similar connectivity state transitions occur in the developing brain remains undetermined. We tested the hypothesis that anesthetic-induced unconsciousness is associated with disruption of functional connectivity in the developing brain and that, like adults, there are dynamic shifts in connectivity patterns during the stable maintenance phase of general anesthesia.

METHODS

This was a preplanned analysis of a previously reported single-center, prospective, cross-sectional study of healthy (ASA I or II) children aged 8-16 years undergoing surgery with general anesthesia (n=50) at Michigan Medicine. Whole scalp (16-channel), wireless electroencephalographic data were collected from the preoperative period through the recovery of consciousness. Functional connectivity was measured using weighted phase lag index and discrete connectivity states were classified using cluster analysis.

RESULTS

Changes in functional connectivity were associated with anesthetic state transitions across multiple regions and frequency bands. An increase in prefrontal-frontal alpha (median[25th, 75th]; baseline 0.070[0.049, 0.101] vs. maintenance 0.474[0.286, 0.606], p<0.001) and theta connectivity (0.038[0.029, 0.048] vs 0.399[0.254, 0.488], p<0.001), and decrease in parietal-occipital alpha connectivity (0.171[0.145, 0.243] vs. 0.089[0.055, 0.132], p<0.001) were among those with the greatest effect size. Contrary to our hypothesis, connectivity patterns during the maintenance phase of general anesthesia were dominated by stable theta and alpha prefrontal-frontal and alpha frontal-parietal connectivity, and exhibited high between-cluster similarity (r = 0.75 to 0.87).

CONCLUSIONS

Changes in functional connectivity are associated with anesthetic state transitions but, unlike adults, connectivity patterns are constrained during general anesthesia in late childhood and early adolescence.

Developmental trajectory of MEG resting-state oscillatory activity in children and adolescents: a longitudinal reliability study

Neural oscillations may be sensitive to aspects of brain maturation such as myelination and synaptic density changes. Better characterization of developmental trajectories and reliability is necessary for understanding typical and atypical neurodevelopment. Here, we examined reliability in 110 typically developing children and adolescents (aged 9-17 years) across 2.25 years. From 10 min of magnetoencephalography resting-state data, normalized source spectral power and intraclass correlation coefficients were calculated. We found sex-specific differences in global normalized power, with males showing age-related decreases in delta and theta, along with age-related increases in beta and gamma. Females had fewer significant age-related changes. Structural magnetic resonance imaging revealed that males had more total gray, subcortical gray, and cortical white matter volume. There were significant age-related changes in total gray matter volume with sex-specific and frequency-specific correlations to normalized power. In males, increased total gray matter volume correlated with increased theta and alpha, along with decreased gamma. Split-half reliability was excellent in all frequency bands and source regions. Test-retest reliability ranged from good (alpha) to fair (theta) to poor (remaining bands). While resting-state neural oscillations can have fingerprint-like quality in adults, we show here that neural oscillations continue to evolve in children and adolescents due to brain maturation and neurodevelopmental change.

Covariate-adjusted hybrid principal components analysis for region-referenced functional EEG data

Electroencephalography (EEG) studies produce region-referenced functional data via EEG signals recorded across scalp electrodes. The high-dimensional data can be used to contrast neurodevelopmental trajectories between diagnostic groups, for example between typically developing (TD) children and children with autism spectrum disorder (ASD). Valid inference requires characterization of the complex EEG dependency structure as well as covariate-dependent heteroscedasticity, such as changes in variation over developmental age. In our motivating study, EEG data is collected on TD and ASD children aged two to twelve years old. The peak alpha frequency, a prominent peak in the alpha spectrum, is a biomarker linked to neurodevelopment that shifts as children age. To retain information, we model patterns of alpha spectral variation, rather than just the peak location, regionally across the scalp and chronologically across development. We propose a covariate-adjusted hybrid principal components analysis (CA-HPCA) for EEG data, which utilizes both vector and functional principal components analysis while simultaneously adjusting for covariate-dependent heteroscedasticity. CA-HPCA assumes the covariance process is weakly separable conditional on observed covariates, allowing for covariate-adjustments to be made on the marginal covariances rather than the full covariance leading to stable and computationally efficient estimation. The proposed methodology provides novel insights into neurodevelopmental differences between TD and ASD children.

Sleep restriction and age effects on waking alpha EEG activity in adolescents

Study Objectives

To understand how sleep need changes across adolescence our laboratory is carrying out a longitudinal dose-response study on the effects of sleep duration on daytime sleepiness and performance. This report focuses on the relation of the waking alpha (8-12 Hz) electroencephalogram (EEG) to prior sleep duration, whether this relation changes with age, and whether decreased waking alpha power is related to changes in daytime sleepiness, vigilance, and executive functioning.

Methods

Study participants (n = 77) entered the study at ages ranging from 9.86 to 13.98 years and were studied annually for 3 years. Each year participants completed each of three time in bed (TIB) conditions (7, 8.5, or 10 h) for four consecutive nights. Waking EEG was recorded on the day following the fourth night.

Results

TIB restriction and resultant sleep loss were associated with reduced alpha power with the effect being stronger for the eyes closed condition. TIB restriction altered the power spectrum within the alpha range by increasing the frequency of maximum alpha power. Alpha power decreased with age, but the effect of TIB restriction did not decrease with age. Reduced alpha power was associated with small but significant increases in subjective and objective sleepiness but was not associated with changes in vigilance or executive functioning.

Conclusions

We interpret the alpha depression following sleep loss as incomplete sleep dependent recuperation that contributes to daytime sleepiness. The absence of a decrease in TIB effects with age indicates that this sleep need measure does not decrease over early to mid-adolescence.

EEG functional brain connectivity strengthens with age during attentional processing to faces in children

Studying functional connectivity may generate clues to the maturational changes that occur in children, expressed by the dynamical organization of the functional network assessed by electroencephalographic recordings (EEG). In the present study, we compared the EEG functional connectivity pattern estimated by linear cross-correlations of the electrical brain activity of three groups of children (6, 8, and 10 years of age) while performing odd-ball tasks containing facial stimuli that are chosen considering their importance in socioemotional contexts in everyday life. On the first task, the children were asked to identify the sex of faces, on the second, the instruction was to identify the happy expressions of the faces. We estimated the stable correlation pattern (SCP) by the average cross-correlation matrix obtained separately for the resting state and the task conditions and quantified the similarity of these average matrices comparing the different conditions. The accuracy improved with higher age. Although the topology of the SCPs showed high similarity across all ages, the two older groups showed a higher correlation between regions associated with the attentional and face processing networks compared to the youngest group. Only in the youngest group, the similarity metric decreased during the sex condition. In general, correlation values strengthened with age and during task performance compared to rest. Our findings indicate that there is a spatially extended stable brain network organization in children like that reported in adults. Lower similarity scores between several regions in the youngest children might indicate a lesser ability to cope with tasks. The brain regions associated with the attention and face networks presented higher synchronization across regions with increasing age, modulated by task demands.

Peak Alpha Frequency and Thalamic Structure in Children with Typical Development and Autism Spectrum Disorder

Associations between age, resting-state (RS) peak-alpha-frequency (PAF = frequency showing largest amplitude alpha activity), and thalamic volume (thalamus thought to modulate alpha activity) were examined to understand differences in RS alpha activity between children with autism spectrum disorder (ASD) and typically-developing children (TDC) noted in prior studies. RS MEG and structural-MRI data were obtained from 51 ASD and 70 TDC 6- to 18-year-old males. PAF and thalamic volume maturation were observed in TDC but not ASD. Although PAF was associated with right thalamic volume in TDC (R² = 0.12, p = 0.01) but not ASD (R² = 0.01, p = 0.35), this group difference was not large enough to reach significance. Findings thus showed unusual maturation of brain function and structure in ASD as well as an across-group thalamic contribution to alpha rhythms.

Evolution of Cortical Functional Networks in Healthy Infants

During normal childhood development, functional brain networks evolve over time in parallel with changes in neuronal oscillations. Previous studies have demonstrated differences in network topology with age, particularly in neonates and in cohorts spanning from birth to early adulthood. Here, we evaluate the developmental changes in EEG functional connectivity with a specific focus on the first 2 years of life. Functional connectivity networks (FCNs) were calculated from the EEGs of 240 healthy infants aged 0-2 years during wakefulness and sleep using a cross-correlation-based measure and the weighted phase lag index. Topological features were assessed via network strength, global clustering coefficient, characteristic path length, and small world measures. We found that cross-correlation FCNs maintained a consistent small-world structure, and the connection strengths increased after the first 3 months of infancy. The strongest connections in these networks were consistently located in the frontal and occipital regions across age groups. In the delta and theta bands, weighted phase lag index networks decreased in strength after the first 3 months in both wakefulness and sleep, and a similar result was found in the alpha and beta bands during wakefulness. However, in the alpha band during sleep, FCNs exhibited a significant increase in strength with age, particularly in the 21-24 months age group. During this period, a majority of the strongest connections in the networks were located in frontocentral regions, and a qualitatively similar distribution was seen in the beta band during sleep for subjects older than 3 months. Graph theory analysis suggested a small world structure for weighted phase lag index networks, but to a lesser degree than those calculated using cross-correlation. In general, graph theory metrics showed little change over time, with no significant differences between age groups for the clustering coefficient (wakefulness and sleep), characteristics path length (sleep), and small world measure (sleep). These results suggest that infant FCNs evolve during the first 2 years with more significant changes to network strength than features of the network structure. This study quantifies normal brain networks during infant development and can serve as a baseline for future investigations in health and neurological disease.

The development of brain rhythms at rest and its impact on vocabulary acquisition

A long-standing question in developmental science is how the neurodevelopment of the brain influences cognitive functions. Here, we examined the developmental change of resting EEG power and its links to vocabulary acquisition in school-age children. We further explored what mechanisms may mediate the relation between brain rhythm maturation and vocabulary knowledge. Eyes-opened resting-state EEG data were recorded from 53 typically-developing Chinese children every 2 years between the ages of 7 and 11. Our results showed first that delta, theta, and gamma power decreased over time, whereas alpha and beta power increased over time. Second, after controlling for general cognitive abilities, age, home literacy environment, and phonological skills, theta decreases explained 6.9% and 14.4% of unique variance in expressive vocabulary at ages 9 and 11, respectively. We also found that beta increase from age 7 to 9 significantly predicted receptive vocabulary at age 11. Finally, theta decrease predicted expressive vocabulary through the effects of phoneme deletion at age 9 and tone discrimination at age 11. These results substantiate the important role of brain oscillations at rest, especially theta rhythm, in language development. The developmental change of brain rhythms could serve as sensitive biomarkers for vocabulary development in school-age children, which would be of great value in identifying children at risk of language impairment.

Development of Frontal EEG Differences Between Eyes-Closed and Eyes-Open Resting Conditions in Children: Data From a Single-Channel Dry-Sensor Portable Device

Changes in EEG when moving from an eyes-closed to an eyes-open resting condition result from bottom-up sensory processing and have been referred to as activation. In children, activation is characterized by a global reduction in alpha, frontally present reductions for delta and theta, and a frontal increase for beta. The present study aimed to replicate frontal EEG activation effects using single-channel, dry-sensor EEG, and to extend current understanding by examining developmental change in children. Frontal EEG was recorded using a single-channel, dry-sensor EEG device while 182 children aged 7 to 12 years completed eyes-closed resting (EC), eyes-open resting (EO), and focus (FO) tasks. Results indicated that frontal delta, theta, and alpha power were reduced, and frontal beta power was increased, in the EO compared with the EC condition. Exploratory analysis of a form of top-down activation showed that frontal beta power was increased in the FO compared with to the EO condition, with no differences for other bands. The activation effects were robust at the individual level. The bottom-up activation effects reduced with age for frontal delta and theta, increased for frontal alpha, with no developmental change for top-down or bottom-up frontal beta activation. These findings contribute further to validation of the single-channel, dry-sensor, frontal EEG and provide support for use in a range of medical, therapeutic, and clinical domains.

Resting-state aperiodic neural dynamics predict individual differences in visuomotor performance and learning

An emerging body of work has demonstrated that resting-state non-oscillatory, or aperiodic, 1/f neural activity is a functional and behaviorally relevant marker of cognitive function capacity. In the motor domain, previous work has only applied 1/f analyses to investigations of motor coordination and performance measures. The value of aperiodic resting-state neural dynamics as a marker of individual visuomotor performance capacity remains unknown. Accordingly, the aim of this work was to investigate if individual 1/f intercept and slope parameters of aperiodic resting-state neural activity predict reaction time and perceptual sensitivity in an immersive virtual reality marksmanship task. The marksmanship task required speeded selection of target stimuli and avoidance of selecting non-target stimuli. Motor and perceptual demands were incrementally increased across task blocks and participants performed the task across three training sessions spanning one week. When motor demands were high, steeper individual 1/f slope predicted shorter reaction time. This relationship did not change with practice. Increased 1/f intercept and a steeper 1/f slope were associated with higher perceptual sensitivity, measured as d'. However, this association was only observed under the highest levels of perceptual demand and only in the initial exposure to these conditions. Individuals with a lower 1/f intercept and a shallower 1/f slope demonstrated the greatest gains in perceptual sensitivity from task practice. These findings demonstrate that individual differences in motor and perceptual performance can be accounted for with resting-state aperiodic neural dynamics. The 1/f aperiodic parameters are most informative in predicting visuomotor performance under complex and demanding task conditions. In addition to predicting capacity for high visuomotor performance with a novel task, 1/f aperiodic parameters might also be useful in predicting which individuals might derive the most improvements from practice.

Cortical signatures of precision grip force control in children, adolescents, and adults

Human dexterous motor control improves from childhood to adulthood, but little is known about the changes in cortico-cortical communication that support such ontogenetic refinement of motor skills. To investigate age-related differences in connectivity between cortical regions involved in dexterous control, we analyzed electroencephalographic data from 88 individuals (range 8-30 years) performing a visually guided precision grip task using dynamic causal modelling and parametric empirical Bayes. Our results demonstrate that bidirectional coupling in a canonical 'grasping network' is associated with precision grip performance across age groups. We further demonstrate greater backward coupling from higher-order to lower-order sensorimotor regions from late adolescence in addition to differential associations between connectivity strength in a premotor-prefrontal network and motor performance for different age groups. We interpret these findings as reflecting greater use of top-down and executive control processes with development. These results expand our understanding of the cortical mechanisms that support dexterous abilities through development.

Sensitive period for the plasticity of alpha activity in humans

Visual experience is crucial for the development of neural processing. For example, alpha activity development is a vision-dependent mechanism. Indeed, studies report no alpha activity is present in blind adults. Nevertheless, studies have not investigated the developmental trajectory of this activity in infants and children with blindness. Here, we hypothesize that the difference in neural activity of blind compared to sighted subjects is: absent at birth, progressive with age, specifically occipital and linked to a gradual motor impairment. Therefore, we consider spectral power of resting-state EEG and its association with motor impairment indices, in blind subjects and in sighted controls between 0 and 11 years of age. Blind subjects show posterior alpha activity during the first three years of life, although weaker and slower maturing compared to sighted subjects. The first great differentiation between blind and sighted subjects occurs between 3 and 6 years of age. Starting in this period, reduced alpha activity increases the probability of motor impairment in blind subjects, likely because of impaired perception/interaction. These results show that visual experience mediates the neural mechanisms generating alpha oscillations during the first years of life, suggesting that it is a sensitive period for the plasticity of this process.

12-Month peak alpha frequency is a correlate but not a longitudinal predictor of non-verbal cognitive abilities in infants at low and high risk for autism spectrum disorder

Although studies of PAF in individuals with autism spectrum disorder (ASD) report group differences and associations with non-verbal cognitive ability, it is not known how PAF relates to familial risk for ASD, and whether similar associations with cognition in are present in infancy. Using a large multi-site prospective longitudinal dataset of infants with low and high familial risk for ASD, metrics of PAF at 12 months were extracted and growth curves estimated for cognitive development between 12-36 months. Analyses tested whether PAF 1) differs between low and high risk infants, 2) is associated with concurrent non-verbal/verbal cognitive ability and 3) predicts developmental change in non-verbal/verbal ability. Moderation of associations between PAF and cognitive ability by familial risk status was also tested. No differences in 12-month PAF were found between low and high risk infants. PAF was associated with concurrent non-verbal cognitive ability, but did not predict change in non-verbal cognitive over development. No associations were found between PAF and verbal ability, along with no evidence of moderation. PAF is not related to familial risk for ASD, and is a neural marker of concurrent non-verbal cognitive ability, but not verbal ability, in young infants at low and high risk for ASD.

Alterations in coordinated EEG activity precede the development of seizures in comatose children

OBJECTIVE

We aimed to test the hypothesis that computational features of the first several minutes of EEG recording can be used to estimate the risk for development of acute seizures in comatose critically-ill children.

METHODS

In a prospective cohort of 118 comatose children, we computed features of the first five minutes of artifact-free EEG recording (spectral power, inter-regional synchronization and cross-frequency coupling) and tested if these features could help identify the 25 children who went on to develop acute symptomatic seizures during the subsequent 48 hours of cEEG monitoring.

RESULTS

Children who developed acute seizures demonstrated higher average spectral power, particularly in the theta frequency range, and distinct patterns of inter-regional connectivity, characterized by greater connectivity at delta and theta frequencies, but weaker connectivity at beta and low gamma frequencies. Subgroup analyses among the 97 children with the same baseline EEG background pattern (generalized slowing) yielded qualitatively and quantitatively similar results.

CONCLUSIONS

These computational features could be applied to baseline EEG recordings to identify critically-ill children at high risk for acute symptomatic seizures.

SIGNIFICANCE

If confirmed in independent prospective cohorts, these features would merit incorporation into a decision support system in order to optimize diagnostic and therapeutic management of seizures among comatose children.

Cortical maturation from childhood to adolescence is reflected in resting state EEG signal complexity

Endogenous cortical fluctuations captured by electroencephalograms (EEGs) reflect activity in large-scale brain networks that exhibit dynamic patterns over multiple time scales. Developmental changes in the coordination and integration of brain function leads to greater complexity in population level neural dynamics. In this study we examined multiscale entropy, a measure of signal complexity, in resting-state EEGs in a large (N = 405) cross-sectional sample of children and adolescents (9–16 years). Our findings showed consistent age-dependent increases in EEG complexity that are distributed across multiple temporal scales and spatial regions. Developmental changes were most robust as the age gap between groups increased, particularly between late childhood and adolescence, and were most prominent over fronto-central scalp regions. These results suggest that the transition from late childhood to adolescence is characterized by age-dependent changes in the underlying complexity of endogenous brain networks.

Absolute and relative variability changes of the resting state brain rhythms from childhood and adolescence to young adulthood

The present report aimed to analyze the possible relationship of spontaneous EEG power variability across epochs in individual subjects (absolute and relative) with age. For this purpose, the resting state EEG of a sample of 258 healthy subjects (6-29 years old) in open and closed eyes experimental conditions were recorded. The power spectral density (PSD) was calculated from 0.5-45 Hz. Three electrodes with the highest PSD in each band were selected, and linear and inverse regression of the mean, standard deviation (SD), and coefficient of variation CV of the PSD vs age were computed. The results showed that the EEG absolute variability (SD) decreases with age, and in contrast, the relative variability (CV) increased, except for high frequencies in which it remains stable during maturation. We conclude that the variability in the EEG PSD when is not influenced by the mean PSD tends to increase from childhood and adolescence to young adulthood. Present results complement the extensive literature on changes of EEG power in different brain rhythms with the changes in EEG power variability during maturation.

Bouncing the network: A dynamical systems model of auditory-vestibular interactions underlying infants' perception of musical rhythm

Previous work suggests that auditory-vestibular interactions, which emerge during bodily movement to music, can influence the perception of musical rhythm. In a seminal study on the ontogeny of musical rhythm, Phillips-Silver and Trainor (2005) found that bouncing infants to an unaccented rhythm influenced infants' perceptual preferences for accented rhythms that matched the rate of bouncing. In the current study, we ask whether nascent, diffuse coupling between auditory and motor systems is sufficient to bootstrap short-term Hebbian plasticity in the auditory system and explain infants' preferences for accented rhythms thought to arise from auditory-vestibular interactions. First, we specify a nonlinear, dynamical system in which two oscillatory neural networks, representing developmentally nascent auditory and motor systems, interact through weak, non-specific coupling. The auditory network was equipped with short-term Hebbian plasticity, allowing the auditory network to tune its intrinsic resonant properties. Next, we simulate the effect of vestibular input (e.g., infant bouncing) on infants' perceptual preferences for accented rhythms. We found that simultaneous auditory-vestibular training shaped the model's response to musical rhythm, enhancing vestibular-related frequencies in auditory-network activity. Moreover, simultaneous auditory-vestibular training, relative to auditory- or vestibular-only training, facilitated short-term auditory plasticity in the model, producing stronger oscillator connections in the auditory network. Finally, when tested on a musical rhythm, models which received simultaneous auditory-vestibular training, but not models that received auditory- or vestibular-only training, resonated strongly at frequencies related to their "bouncing," a finding qualitatively similar to infants' preferences for accented rhythms that matched the rate of infant bouncing.

The Sensor Hub for Detecting the Developmental Characteristics in Reading in Children on a White vs. Colored Background/Colored Overlays

This study investigated the influence of white vs. 12 background and overlay colors on the reading process in twenty-four school-age children. Previous research reported that colors could affect reading skills as an important factor in the emotional and physiological state of the body. The aim of the study was to assess developmental differences between second and third grade students of an elementary school, and to evaluate differences in electroencephalography (EEG), ocular, electrodermal activities (EDA) and heart rate variability (HRV). Our findings showed a decreasing trend with age regarding EEG power bands (Alpha, Beta, Delta, Theta) and lower scores of reading duration and eye-tracking measures in younger children compared to older children. As shown in the results, HRV parameters showed higher scores in 12 background and overlay colors among second than third grade students, which is linearly correlated to the level of stress and is readable from EDA measures as well. Our study showed the calming effect on second graders of turquoise and blue background colors. Considering other colors separately for each parameter, we assumed that there are no systematic differences in reading duration, EEG power band, eye-tracking and EDA measures.

Dynamics of Word Production in the Transition from Adolescence to Adulthood

Changes in word production occur across the lifespan. Previous studies have shown electrophysiological, temporal, and functional differences between children and adults accompanying behavioral changes in picture-naming tasks (Laganaro, Tzieropoulos, Fraunfelder, & Zesiger, 2015). Thus, a shift toward adult-like processes in referential word production occurs somewhere between the ages of 13 and 20. Our aim was to investigate when and how children develop adult-like behavior and brain activation in word production. Toward this aim, performance and event-related potentials (ERP) in a referential word production task were recorded and compared for two groups of adolescents (aged 14 to 16 and 17 to 18), children (aged 10 to 13), and young adults (aged 20 to 30). Both groups of adolescents displayed adult-like production latencies, which were longer only for children, while accuracy was lower in the younger adolescents and in children, compared to adults. ERP waveform analysis and topographic pattern analysis revealed significant intergroup differences in key time-windows on stimulus-locked ERPs, both early (150-220 ms)-associated with pre-linguistic processes-and late (280-330 ms)-associated with lexical processes. The results indicate that brain activation underlying referential word production is completely adult-like in 17-year-old adolescents, whereas an intermediate pattern is still observed in adolescents aged 14 to 16 years old, although their production speed, but not their accuracy, is already adult-like.

Brain Arousal as Measured by EEG-Assessment Differs Between Children and Adolescents With Attention-Deficit/Hyperactivity Disorder (ADHD) and Depression

Objective: Disturbed regulation of vigilance in the wake state seems to play a key role in the development of mental disorders. It is assumed that hyperactivity in adult ADHD is an attempt to increase a general low vigilance level via external stimulation in order to avoid drowsiness. For depression, the avoidance of stimulation is interpreted as a reaction to a tonic increased vigilance state. Although ADHD is assumed to start during childhood, this vigilance model has been barely tested with children diagnosed for ADHD so far. Methods: Resting-state EEG (8 min) measures from two groups of children diagnosed with either ADHD [N = 76 (16 female, 60 male), age: (mean/SD) 118/33 months] or depression [N = 94 (73 female, 21 male), age: 184/23 months] were analyzed. Using the VIGALL toolbox, EEG patterns of vigilance level, and regulation were derived and compared between both groups. In correlation analysis, the relations between vigilance measures, attentional test performance (alertness and inhibition), and mental health symptoms were analyzed. Results: Children with ADHD differed from children with most prominent depressive symptoms in brain arousal regulation and level, but EEG vigilance was not related to behavior problems and not related to the attentional test performance. Brain arousal was dependent on the age of the participant in the whole sample; younger children showed lower vigilance stages than teenagers; this effect was not present when analyzed separately for each diagnostic group. EEG assessment time and received medication had no effect on the EEG vigilance. Discussion: Although based on a small sample, this explorative research revealed that EEG vigilance level is different between children with ADHD and with depression. Moreover, even the standard procedure of the clinical routine EEG (resting state) can be used to differentiate brain arousal states between participants with ADHD and depression. Because routine EEG is not specialized to vigilance assessment, it may not be sufficiently sensitive to find vigilance-symptomatology associations. Further research should address developmental changes in EEG measurements in children and use bigger samples of participants within the same age range.

Effects of perinatal dioxin exposure on neonatal electroencephalography (EEG) activity of the quiet sleep stage in the most contaminated area from Agent Orange in Vietnam

OBJECTIVE

To investigate the effects of perinatal dioxin exposure indicated by dioxins in breast milk on neonatal electroencephalography (EEG) power in the quiet sleep stage, and associations with neurodevelopmental outcomes at 2 years of age.

STUDY DESIGN

Fifty-one mother-newborn pairs were enrolled for neonatal EEG analysis in the quiet sleep stage from a birth cohort recruited at a prefecture hospital in Bien Hoa city, Vietnam. Relative EEG power in intra-burst-intervals and high-voltage-bursts in the trace alternant pattern were computed from EEG data during the quiet sleep stage. Forty-three mother-child pairs participated in a 2-year follow-up survey to examine neurodevelopment using the Bayley-III scale and gaze behavior exhibited by fixation duration on the face of a child talking in videos. The general linear model and regression linear model were used for data analysis after adjusting for confounding factors.

RESULTS

Perinatal dioxin exposure, particularly 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) exposure, influenced relative EEG power values mainly in the intra-burst-interval part of the trace alternant pattern in the quiet sleep stage. In intra-burst-intervals, decreased frontal delta power and increased frontal and parietal alpha power values in the left hemisphere and temporal beta power values in the right hemisphere were associated with increased TCDD exposure, with significant dose-response relationships. Almost none of the relative power values in these brain regions were associated with Bayley III scores, but relative delta power values were significantly associated with face fixation duration in left frontal and parietal regions at 2 years of age.

CONCLUSION

Perinatal dioxin exposure influences neuronal activity in the quiet sleep stage, leading to poor communication ability indicated by gaze behavior in early childhood.

Altered Resting-State Neural Oscillations and Spectral Power in Children with Fetal Alcohol Spectrum Disorder

BACKGROUND

Consumption of alcohol during pregnancy impacts fetal development and may lead to a variety of physical, cognitive, and behavioral abnormalities in childhood collectively known as fetal alcohol spectrum disorder (FASD). The FASD spectrum includes children with fetal alcohol syndrome (FAS), partial fetal alcohol syndrome (pFAS), and alcohol-related neurodevelopmental disorder (ARND). Children with a FASD or prenatal alcohol exposure (PAE) have impaired white matter, reduced structural volumes, impaired resting-state functional connectivity when measured with fMRI, and spectral hypersynchrony as infants. Magnetoencephalography (MEG) provides high temporal resolution and good spatial precision for examining spectral power and connectivity patterns unique from fMRI. The impact of PAE on MEG resting-state spectral power in children remains unknown.

METHODS

We collected 2 minutes of eyes-open and eyes-closed resting-state data in 51 children (8 to 12 years of age) with 3 subgroups included: 10 ARND/PAE, 15 FAS/pFAS, and 26 controls (TDC). MEG data were collected on the Elekta Neuromag system. The following spectral metrics were compared between subgroups: power, normalized power, half power, 95% power, and Shannon spectral entropy (SSE). MEG spectral data were correlated with behavioral measures.

RESULTS

Our results indicate children with FAS/pFAS had reduced spectral power and normalized power, particularly within the alpha frequency band in sensor parietal and source superior parietal and lateral occipital regions, along with elevated half power, 95% power, and SSE. We also found select hemisphere specific effects further indicating reduced corpus callosum connectivity in children with a FASD. Interestingly, while the ARND/PAE subgroup had significant differences from the FAS/pFAS subgroup, in many cases spectral data were not significantly different from TDC.

CONCLUSIONS

Our results were consistent with previous studies and provide new insight into resting-state oscillatory differences both between children with FAS and TDC, and within FASD subgroups. Further understanding of these resting-state variations and their impact on cognitive function may help provide early targets for intervention and enhance outcomes for individuals with a FASD.

Functional neural network configuration in late childhood varies by age and cognitive state

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fMRI data from 60 children aged 9–12 during resting and tasks involving decision making, visual perception, and working memory were examined.

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At rest, the child brain exhibited network organization similar to adults though the degree of similarity was age- and network-dependent.

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During tasks, brain network configurations showed task-induced and age-related changes in integration.

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Frontoparietal network showed flexible connectivity pattern across states while networks for sensory and motor processing remained stable.

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Findings demonstrate that network connectivity characteristics may serve as markers for neural and cognitive maturation.

Late childhood and early adolescence is characterized by substantial brain maturation which contributes to both adult-like and age-dependent resting-state network connectivity patterns. However, it remains unclear whether these functional network characteristics in children are subject to differential modulation by distinct cognitive demands as previously found in adults. We conducted network analyses on fMRI data from 60 children (aged 9–12) during resting and during three distinct tasks involving decision making, visual perception, and spatial working memory. Graph measures of network architecture, functional integration, and flexibility were calculated for each of the four states. During resting state, the children’s network architecture was similar to that in young adults (N = 60, aged 20–23) but the degree of similarity was age- and network-dependent. During the task states, the children's whole-brain network exhibited enhanced integration in response to increased cognitive demand. Additionally, the frontoparietal network showed flexibility in connectivity patterns across states while networks implicated in motor and visual processing remained relatively stable. Exploratory analyses suggest different relationships between behavioral performance and connectivity profiles for the working memory and perceptual tasks. Together, our findings demonstrate state- and age-dependent features in functional network connectivity during late childhood, potentially providing markers for brain and cognitive development.

Magnetoencephalography Research in Pediatric Autism Spectrum Disorder

Magnetoencephalography (MEG) research indicates differences in neural brain measures in children with autism spectrum disorder (ASD) compared to typically developing (TD) children. As reviewed here, resting-state MEG exams are of interest as well as MEG paradigms that assess neural function across domains (e.g., auditory, resting state). To date, MEG research has primarily focused on group-level differences. Research is needed to explore whether MEG measures can predict, at the individual level, ASD diagnosis, prognosis (future severity), and response to therapy.

Lifespan associated global patterns of coherent neural communication

Healthy ageing is accompanied by changes to spontaneous electromagnetic oscillations. At the macroscopic scale, previous studies have quantified the basic features, e.g., power and frequencies in rhythms of interest from the perspective of attention, perception, learning and memory. On the other hand, signatures and modes of neural communication have recently been argued to be identifiable from global measures applied on neuro-electromagnetic data such as global coherence that quantifies the degree of togetherness of distributed neural oscillations and metastability that parametrizes the transient dynamics of the network switching between successive stable states. Here, we demonstrate that global coherence and metastability can be informative measures to track healthy ageing dynamics over lifespan and together with the traditional spectral measures provides an attractive explanation of neuronal information processing. Finding normative patterns of brain rhythms in resting state MEG would naturally pave the way for tracking task relevant metrics that could crucially determine cognitive flexibility and performance. While previously reported observations of a reduction in peak alpha frequency and increased beta power in older adults are reflective of changes at individual sensors (during rest and task), global coherence and metastability pinpoint the underlying coordination dynamics over multiple brain areas across the entire lifespan. In addition to replication of the previous observations in a substantially larger lifespan cohort than what was previously reported, we also demonstrate, for the first time to the best of our knowledge, age related changes in coherence and metastability in signals over time scales of neuronal processing. Furthermore, we observed a marked frequency dependence in changes in global coordination dynamics, which, coupled with the long-held view of specific frequency bands subserving different aspects of cognition, hints at differential functional processing roles for slower and faster brain dynamics.

Magnetoencephalography for Schizophrenia

Schizophrenia (Sz) is a chronic mental disorder characterized by disturbances in thought (such as delusions and confused thinking), perception (hearing voices), and behavior (lack of motivation). The lifetime prevalence of Sz is between 0.3% and 0.7%, with late adolescence and early adulthood, the peak period for the onset of psychotic symptoms. Causal factors in Sz include environmental and genetic factors and especially their interaction. About 50% of individuals with a diagnosis of Sz have lifelong impairment.

Speech Features and Electroencephalogram Parameters in 4- to 11-Year-Old Children

The goal of the study is to investigate a correlation between different levels of speech organization, indicating the physiological processes of maturation of the vocal tract structures and brain regions associated with speech and language, and basic electroencephalogram (EEG) rhythms, reflecting the age-related dynamics of maturation of brain structures in children aged 4-11 years. The complex method of analysis, including EEG registration, clinical and spectral analysis of EEG; dichotic listening, identifying the profile of functional lateral asymmetry (PFLA), and phonemic hearing of the child; recording, linguistic, and acoustic analysis of child speech; and identification of speech characteristics reflecting the formation of its different levels, was used. Two complementary experimental series were conducted: the correlation between EEG parameters, speech features, dichotic listening, the PFLA, and phonemic hearing of the child in the age dynamics of 4-11 years (first); the specificity of EEG patterns in children at different stages of reading skills formation (second). The result of this study showed the correlation between acoustic and linguistic features of child speech and brain activity. The analysis of EEG and acoustic features of child speech revealed the correlation between pitch and pitch range values in spontaneous speech and theta-rhythm intensity in EEG. High values of pitch and its variation in younger children (4-6 years) are related to the intensity of theta rhythm in the EEG pattern, as this rhythm is most expressed in younger children. It was revealed that the alpha rhythm is asymmetrically localized in children with clear pronunciation of words (which determines the intelligibility of their speech) that is typical for 6.5- to 11-year-old children. The formation of reading skills in a child is associated with a change in the characteristics of the alpha rhythm-from irregular, unstable, low frequency, and low amplitude in children at the beginning of reading skills mastering to medium and low amplitude, regular, asymmetrically localized in children reading words and phrases. The specifics of the relation between brain activity and different levels of speech formation at different child's age periods are discussed.

Adolescent cognitive control and mediofrontal theta oscillations are disrupted by neglect: Associations with transdiagnostic risk for psychopathology in a randomized controlled trial

Children that have experienced psychosocial neglect display impairments in self-monitoring and controlling their behavior (cognitive control) and are at broad, transdiagnostic risk for psychopathology. However, the neural underpinnings of such effects remain unclear. Event-related mediofrontal theta oscillations reflect a neural process supporting cognitive control that may relate to transdiagnostic psychopathology risk. Recent work demonstrates reduced mediofrontal theta in rodent models of neglect; however, similar findings have not been reported in humans. Here, 136 children reared in Romanian institutions were randomly assigned to either a high-quality foster care intervention and placed with families or remained in institutions; 72 never-institutionalized children served as a comparison group. The intervention ended at 54 months; event-related mediofrontal theta and psychopathology were assessed at 12- and 16-year follow-up assessments. Institutional rearing (neglect) predicted reduced mediofrontal theta by age 16, which was linked to heightened transdiagnostic risk for psychopathology (P factor); no specific associations with internalizing/externalizing factors were present once transdiagnostic risk was accounted for. Earlier placement into foster care yielded greater mediofrontal activity by age 16. Moreover, foster care placement was associated with the developmental trajectory of mediofrontal theta across the adolescent period (ages 12–16), which was, in turn, associated with greater reductions in transdiagnostic risk across this same period. These data reflect the first experimental evidence that the development of mediofrontal theta is impacted by removal from situations of neglect in humans, and further characterizes the importance of studying developmental change in mediofrontal theta during the adolescent period.

Atypical Relationships Between Spontaneous EEG and fMRI Activity in Autism

Autism spectrum disorders (ASDs) have been linked to atypical communication among distributed brain networks. However, despite decades of research, the exact nature of these differences between typically developing (TD) individuals and those with ASDs remains unclear. ASDs have been widely studied using resting-state neuroimaging methods, including both functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). However, little is known about how fMRI and EEG measures of spontaneous brain activity are related in ASDs. In the present study, two cohorts of children and adolescents underwent resting-state EEG (n = 38 per group) or fMRI (n = 66 ASD, 57 TD), with a subset of individuals in both the EEG and fMRI cohorts (n = 17 per group). In the EEG cohort, parieto-occipital EEG alpha power was found to be reduced in ASDs. In the fMRI cohort, blood oxygen level-dependent (BOLD) power was regionally increased in right temporal regions and there was widespread overconnectivity between the thalamus and cortical regions in the ASD group relative to the TD group. Finally, multimodal analyses indicated that while TD children showed consistently positive relationships between EEG alpha power and regional BOLD power, these associations were weak or negative in ASDs. These findings suggest atypical links between alpha rhythms and regional BOLD activity in ASDs, possibly implicating neural substrates and processes that coordinate thalamocortical regulation of the alpha rhythm.

Identifying electrophysiological markers of autism spectrum disorder and schizophrenia against a backdrop of normal brain development

An examination of electroencephalographic and magnetoencephalographic studies demonstrates how age-related changes in brain neural function temporally constrain their use as diagnostic markers. A first example shows that, given maturational changes in the resting-state peak alpha frequency in typically developing children but not in children who have autism spectrum disorder (ASD), group differences in alpha-band activity characterize only a subset of children who have ASD. A second example, auditory encoding processes in schizophrenia, shows that the complication of normal age-related brain changes on detecting and interpreting group differences in neural activity is not specific to children. MRI studies reporting group differences in the rate of brain maturation demonstrate that a group difference in brain maturation may be a concern for all diagnostic brain markers. Attention to brain maturation is needed whether one takes a DSM-5 or a Research Domain Criteria approach to research. For example, although there is interest in cross-diagnostic studies comparing brain measures in ASD and schizophrenia, such studies are difficult given that measures are obtained in one group well after and in the other much closer to the onset of symptoms. In addition, given differences in brain activity among infants, toddlers, children, adolescents, and younger and older adults, creating tasks and research designs that produce interpretable findings across the life span and yet allow for development is difficult at best. To conclude, brain imaging findings show an effect of brain maturation on diagnostic markers separate from (and potentially difficult to distinguish from) effects of disease processes. Available research with large samples already provides direction about the age range(s) when diagnostic markers are most robust and informative.

Covariate-Adjusted Hybrid Principal Components Analysis

Electroencephalography (EEG) studies produce region-referenced functional data in the form of EEG signals recorded across electrodes on the scalp. The high-dimensional data capture underlying neural dynamics and it is of clinical interest to model differences in neurodevelopmental trajectories between diagnostic groups, for example typically developing (TD) children and children with autism spectrum disorder (ASD). In such cases, valid group-level inference requires characterization of the complex EEG dependency structure as well as covariate-dependent heteroscedasticity, such as changes in variation over developmental age. In our motivating study, resting state EEG is collected on both TD and ASD children aged two to twelve years old. The peak alpha frequency (PAF), defined as the location of a prominent peak in the alpha frequency band of the spectral density, is an important biomarker linked to neurodevelopment and is known to shift from lower to higher frequencies as children age. To retain the most amount of information from the data, we model patterns of alpha spectral variation, rather than just the peak location, regionally across the scalp and chronologically across development for both the TD and ASD diagnostic groups. We propose a covariate-adjusted hybrid principal components analysis (CA-HPCA) for region-referenced functional EEG data, which utilizes both vector and functional principal components analysis while simultaneously adjusting for covariate-dependent heteroscedasticity. CA-HPCA assumes the covariance process is weakly separable conditional on observed covariates, allowing for covariate-adjustments to be made on the marginal covariances rather than the full covariance leading to stable and computationally efficient estimation. A mixed effects framework is proposed to estimate the model components coupled with a bootstrap test for group-level inference. The proposed methodology provides novel insights into neurodevelopmental differences between TD and ASD children.

Covariate-adjusted region-referenced generalized functional linear model for EEG data

Electroencephalography (EEG) studies produce region-referenced functional data in the form of EEG signals recorded across electrodes on the scalp. It is of clinical interest to relate the highly structured EEG data to scalar outcomes such as diagnostic status. In our motivating study, resting-state EEG is collected on both typically developing (TD) children and children with autism spectrum disorder (ASD) aged 2 to 12 years old. The peak alpha frequency (PAF), defined as the location of a prominent peak in the alpha frequency band of the spectral density, is an important biomarker linked to neurodevelopment and is known to shift from lower to higher frequencies as children age. To retain the most amount of information from the data, we consider the oscillations in the spectral density within the alpha band, rather than just the peak location, as a functional predictor of diagnostic status (TD vs ASD), adjusted for chronological age. A covariate-adjusted region-referenced generalized functional linear model is proposed for modeling scalar outcomes from region-referenced functional predictors, which utilizes a tensor basis formed from one-dimensional discrete and continuous bases to estimate functional effects across a discrete regional domain while simultaneously adjusting for additional nonfunctional covariates, such as age. The proposed methodology provides novel insights into differences in neural development of TD and ASD children. The efficacy of the proposed methodology is investigated through extensive simulation studies.

Early patterns of functional brain development associated with autism spectrum disorder in tuberous sclerosis complex

Tuberous sclerosis complex (TSC) is a rare genetic disorder that confers a high risk for autism spectrum disorders (ASD), with behavioral predictors of ASD emerging early in life. Deviations in structural and functional neural connectivity are highly implicated in both TSC and ASD. For the first time, we explore whether electroencephalographic (EEG) measures of neural network function precede or predict the emergence of ASD in TSC. We determine whether altered brain function (a) is present in infancy in TSC, (b) differentiates infants with TSC based on ASD diagnostic status, and (c) is associated with later cognitive function. We studied 35 infants with TSC (N = 35), and a group of typically developing infants (N = 20) at 12 and 24 months of age. Infants with TSC were later subdivided into ASD and non-ASD groups based on clinical evaluation. We measured features of spontaneous alpha oscillations (6-12 Hz) that are closely associated with neural network development: alpha power, alpha phase coherence (APC), and peak alpha frequency (PAF). Infants with TSC demonstrated reduced interhemispheric APC compared to controls at 12 months of age, and these differences were found to be most pronounced at 24 months in the infants who later developed ASD. Across all infants, PAF at 24 months was associated with verbal and nonverbal cognition at 36 months. Associations between early network function and later neurodevelopmental and cognitive outcomes highlight the potential utility of early scalable EEG markers to identify infants with TSC requiring additional targeted intervention initiated very early in life. Autism Res 2019, 12: 1758-1773. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Approximately half of infants with tuberous sclerosis complex (TSC) develop autism. Here, using EEG, we find that there is a reduction in communication between brain regions during infancy in TSC, and that the infants who show the largest reductions are those who later develop autism. Being able to identify infants who show early signs of disrupted brain development may improve the timing of early prediction and interventions in TSC, and also help us to understand how early brain changes lead to autism.

Increased segregation of functional networks in developing brains

A growing literature conceptualises typical brain development from a network perspective. However, largely due to technical and methodological challenges inherent in paediatric functional neuroimaging, there remains an important gap in our knowledge regarding the typical development of functional brain networks in "preschool" childhood (i.e., children younger than 6 years of age). In this study, we recorded brain oscillatory activity using age-appropriate magnetoencephalography in 24 children, including 14 preschool children aged from 4 to 6 years and 10 school children aged from 7 to 12 years. We compared the topology of the resting-state brain networks in these children, estimated using minimum spanning tree (MST) constructed from phase synchrony between beamformer-reconstructed time-series, with that of 24 adults. Our results show that during childhood the MST topology shifts from a star-like (centralised) toward a more line-like (de-centralised) configuration, indicating the functional brain networks become increasingly segregated. In addition, the increasing global network segregation is frequency-independent and accompanied by decreases in centrality (or connectedness) of cortical regions with age, especially in areas of the default mode network. We propose a heuristic MST model of "network space", which posits a clear developmental trajectory for the emergence of complex brain networks. Our results not only revealed topological reorganisation of functional networks across multiple temporal and spatial scales in childhood, but also fill a gap in the literature regarding neurophysiological mechanisms of functional brain maturation during the preschool years of childhood.

Visual temporal integration windows are adult-like in 5- to 7-year-old children

The visual system must organize dynamic input into useful percepts across time, balancing between stability and sensitivity to change. The temporal integration window (TIW) has been hypothesized to underlie this balance: If two or more stimuli fall within the same TIW, they are integrated into a single percept; those that fall in different windows are segmented (Arnett & Di Lollo, 1979; Wutz, Muschter, van Koningsbruggen, Weisz, & Melcher, 2016). Visual TIWs have been studied in adults, showing average windows of 65 ms (Wutz et al., 2016); however, it is unclear how windows develop through early childhood. Here we measured TIWs in 5- to 7-year-old children and adults, using a variant of the missing dot task (Di Lollo, 1980; Wutz et al. 2016), in which integration and segmentation thresholds were measured within the same participant, using the same stimuli. Participants saw a sequence of two displays separated by an interstimulus interval (ISI) that determined the visibility of a visual search target. Longer ISIs increased the likelihood of detecting a segmentation target (but decreased detection for the integration target) although shorter ISIs increased the likelihood of detecting the integration target (but decreased detection of the segmentation target). We could then estimate the TIW by measuring the point at which these two functions intersect. Children's TIWs (M = 68 ms) were comparable to adults' (M = 73 ms) with no appreciable age trend within our sample, indicating that TIWs reach adult levels by approximately 5 years of age.

Delayed cortical maturation at the centrotemporal brain regions in patients with benign childhood epilepsy with centrotemporal spikes (BCECTS)

Benign childhood epilepsy with centrotemporal spikes (BCECTS) is an epilepsy syndrome commonly found in child and adolescent. Although the prognosis is mostly favorable as long as the seizure is well controlled. However, they are often suffering from the cognitive and behavioral problems which might be the consequences of the initial insults. It is still not clear whether the initial epileptiform discharges has long term impact on the resting-state brain activities at later ages. This study investigated the resting-state brain activities in BCECTS patients with clinical seizure remission stage (n = 16; 11 males) and compared with the non-epileptic, age-matched control subjects. Quantitative electroencephalography (qEEG) revealed a significantly higher absolute power of the theta and alpha waves in BCECTS patients with clinical seizure remission as compared with the non-epileptic control subjects. Interestingly, the differences were observed mainly over the centrotemporal electrodes which are the common sites of the initial epileptiform discharges. The differences were more significant in patients with bilateral epileptiform discharges than those with the unilateral epileptic activities. Typically, the brain wave power continuously decreases with increasing ages. Therefore, higher absolute powers of the brain waves indicate more delayed in cortical maturation compared with the non-epileptic control group. These findings indicated that BCECTS patients have delay cortical maturation at the centrotemporal brain regions even at the clinical seizure remission phase.

Abnormal maturation of the resting-state peak alpha frequency in children with autism spectrum disorder

Age-related changes in resting-state (RS) neural rhythms in typically developing children (TDC) but not children with autism spectrum disorder (ASD) suggest that RS measures may be of clinical use in ASD only for certain ages. The study examined this issue via assessing RS peak alpha frequency (PAF), a measure previous studies, have indicated as abnormal in ASD. RS magnetoencephalographic (MEG) data were obtained from 141 TDC (6.13-17.70 years) and 204 ASD (6.07-17.93 years). A source model with 15 regional sources projected the raw MEG surface data into brain source space. PAF was identified in each participant from the source showing the largest amplitude alpha activity (7-13 Hz). Given sex differences in PAF in TDC (females > males) and relatively few females in both groups, group comparisons were conducted examining only male TDC (N = 121) and ASD (N = 183). Regressions showed significant group slope differences, with an age-related increase in PAF in TDC (R²  = 0.32) but not ASD (R²  = 0.01). Analyses examining male children below or above 10-years-old (median split) indicated group effects only in the younger TDC (8.90 Hz) and ASD (9.84 Hz; Cohen's d = 1.05). In the older ASD, a higher nonverbal IQ was associated with a higher PAF. In the younger TDC, a faster speed of processing was associated with a higher PAF. PAF as a marker for ASD depends on age, with a RS alpha marker of more interest in younger versus older children with ASD. Associations between PAF and cognitive ability were also found to be age and group specific.

Assaying neural activity of children during video game play in public spaces: a deep learning approach

OBJECTIVE

Understanding neural activity patterns in the developing brain remains one of the grand challenges in neuroscience. Developing neural networks are likely to be endowed with functionally important variability associated with the environmental context, age, gender, and other variables. Therefore, we conducted experiments with typically developing children in a stimulating museum setting and tested the feasibility of using deep learning techniques to help identify patterns of brain activity associated with different conditions.

APPROACH

A four-channel dry EEG-based Mobile brain-body imaging data of children at rest and during videogame play (VGP) was acquired at the Children's Museum of Houston. A data-driven approach based on convolutional neural networks (CNN) was used to describe underlying feature representations in the EEG and their ability to discern task and gender. The variability of the spectral features of EEG during the rest condition as a function of age was also analyzed.

MAIN RESULTS

Alpha power (7-13 Hz) was higher during rest whereas theta power (4-7 Hz) was higher during VGP. Beta (13-18 Hz) power was the most significant feature, higher in females, when differentiating between males and females. Using data from both temporoparietal channels to classify between VGP and rest condition, leave-one-subject-out cross-validation accuracy of 67% was obtained. Age-related changes in EEG spectral content during rest were consistent with previous developmental studies conducted in laboratory settings showing an inverse relationship between age and EEG power.

SIGNIFICANCE

These findings are the first to acquire, quantify and explain brain patterns observed during VGP and rest in freely behaving children in a museum setting using a deep learning framework. The study shows how deep learning can be used as a data driven approach to identify patterns in the data and explores the issues and the potential of conducting experiments involving children in a natural and engaging environment.

Spatial and spectral trajectories in typical neurodevelopment from childhood to middle age

Detailed characterization of typical human neurodevelopment is key if we are to understand the nature of mental and neurological pathology. While research on the cellular processes of neurodevelopment has made great advances, in vivo human imaging is crucial to understand our uniquely human capabilities, as well as the pathologies that affect them. Using magnetoencephalography data in the largest normative sample currently available (324 participants aged 6-45 years), we assess the developmental trajectory of resting-state oscillatory power and functional connectivity from childhood to middle age. The maturational course of power, indicative of local processing, was found to both increase and decrease in a spectrally dependent fashion. Using the strength of phase-synchrony between parcellated regions, we found significant linear and nonlinear (quadratic and logarithmic) trajectories to be characterized in a spatially heterogeneous frequency-specific manner, such as a superior frontal region with linear and nonlinear trajectories in theta and gamma band respectively. Assessment of global efficiency revealed similar significant nonlinear trajectories across all frequency bands. Our results link with the development of human cognitive abilities; they also highlight the complexity of neurodevelopment and provide quantitative parameters for replication and a robust footing from which clinical research may map pathological deviations from these typical trajectories.

Analysis of EEG networks and their correlation with cognitive impairment in preschool children with epilepsy

OBJECTIVE

Cognitive impairment (CI) is common in children with epilepsy and can have devastating effects on their quality of life. Early identification of CI is a priority to improve outcomes, but the current gold standard of detection with psychometric assessment is resource intensive and not always available. This paper proposes exploiting network analysis techniques to characterize routine clinical electroencephalography (EEG) to help identify CI in children with early-onset epilepsy (CWEOE) (0-5 years old).

METHODS

Functional networks from routinely acquired EEGs of 51 newly diagnosed CWEOE were analyzed. Combinations of connectivity metrics with subnetwork analysis identified significant correlations between network properties and cognition scores via rank correlation analysis (Kendall's τ). Predictive properties were investigated using a cross-validated classification model with healthy cognition, mild/moderate CI, and severe CI classes.

RESULTS

Network analysis revealed phase-dependent connectivity having higher sensitivity to CI and significant functional network changes across EEG frequencies. Nearly 70.5% of CWEOE were aptly classified as having healthy cognition, mild/moderate CI, or severe CI using network features. These features predicted CI classes 55% better than chance and halved misclassification penalties.

CONCLUSIONS

Cognitive impairment in CWEOE can be detected with sensitivity at 85% (in identifying mild/moderate or severe CI) and specificity of 84%, by network analysis.

SIGNIFICANCE

This study outlines a data-driven methodology for identifying candidate biomarkers of CI in CWEOE from network features. Following additional replication, the proposed method and its use of routinely acquired EEG forms an attractive proposition for supporting clinical assessment of CI.

Developmental differences in the neural oscillations underlying auditory sentence processing in children and adults

Although very young children seem to process ongoing language quickly and effortlessly, neuroimaging and behavioral studies reveal that children continue to mature in their language skills through adolescence. During this prolonged development, children likely engage the same basic cognitive processes and neural mechanisms to perform language tasks as adults, but in somewhat different ways. In this study we used time frequency analysis of EEG to identify developmental differences in the engagement of neural oscillations between children (ages 10-12) and adults while listening to naturally-paced sentences. Adults displayed consistent beta changes throughout the sentence compared to children, thought to be related to efficient syntactic integration, and children displayed more broadly distributed theta changes than adults, thought to be related to more effortful semantic integration. Few differences in alpha, related to verbal working memory, existed between groups. These findings shed new light on developmental changes in the neuronal processes underlying language comprehension.

Novelty N2-P3a Complex and Theta Oscillations Reflect Improving Neural Coordination Within Frontal Brain Networks During Adolescence

Adolescents are easily distracted by novel items than adults. Maturation of the frontal cortex and its integration into widely distributed brain networks may result in diminishing distractibility with the transition into young adulthood. The aim of this study was to investigate maturational changes of brain activity during novelty processing. We hypothesized that during adolescence, timing and task-relevant modulation of frontal cortex network activity elicited by novelty processing improves, concurrently with increasing cognitive control abilities. A visual novelty oddball task was utilized in combination with EEG measurements to investigate brain maturation between 8–28 years of age (n = 84). Developmental changes of the frontal N2-P3a complex and concurrent theta oscillations (4–7 Hz) elicited by rare and unexpected novel stimuli were analyzed using regression models. N2 amplitude decreased, P3a amplitude increased, and latency of both components decreased with age. Pre-stimulus amplitude of theta oscillations decreased, while inter-trial consistency, task-related amplitude modulation and inter-site connectivity of frontal theta oscillations increased with age. Targets, intertwined in a stimulus train with regular non-targets and novels, were detected faster with increasing age. These results indicate that neural processing of novel stimuli became faster and the neural activation pattern more precise in timing and amplitude modulation. Better inter-site connectivity further implicates that frontal brain maturation leads to global neural reorganization and better integration of frontal brain activity within widely distributed brain networks. Faster target detection indicated that these maturational changes in neural activation during novelty processing may result in diminished distractibility and increased cognitive control to pursue the task.

Decreased occipital alpha oscillation in children who stutter during a visual Go/Nogo task

OBJECTIVE

Our goal was to discover attention- and inhibitory control-related differences in the main oscillations of the brain of children who stutter (CWS) compared to typically developed children (TDC).

METHODS

We performed a time-frequency analysis using wavelets, fast Fourier transformation (FFT) and the Alpha/Theta power ratio of EEG data collected during a visual Go/Nogo task in 7-9 year old CWS and TDC, including also the time window between consecutive tasks.

RESULTS

CWS showed significantly reduced occipital alpha power and Alpha/Theta ratio in the "resting" or preparatory period between visual stimuli especially in the Nogo condition.

CONCLUSIONS

The CWS demonstrate reduced inhibition of the visual cortex and information processing in the absence of visual stimuli, which may be related to problems in attentional gating.

SIGNIFICANCE

Occipital alpha oscillation is elementary in the control and inhibition of visual attention and the lack of occipital alpha modulation indicate fundamental differences in the regulation of visual information processing in CWS. Our findings support the view of stuttering as part of a wide-ranging brain dysfunction most likely involving also attentional and inhibitory networks.

Development of brain functional connectivity and its relation to infant sustained attention in the first year of life

The study of brain functional connectivity is crucial to understanding the neural mechanisms underlying the improved behavioral performance and amplified ERP responses observed during infant sustained attention. Previous investigations on the development of functional brain connectivity during infancy are primarily confined to the use of functional and structural MRI techniques. The current study examined the relation between infant sustained attention and brain functional connectivity and their development during infancy with high-density EEG recordings. Fifty-nine infants were tested at 6 (N = 15), 8 (N =14), 10 (N = 17), and 12 (N = 13) months. Infant sustained attention was defined by measuring infant heart rate changes during infants' looking. Functional connectivity was estimated from the electrodes on the scalp and with reconstructed cortical source activities in brain regions. It was found that infant sustained attention was accompanied by attenuated functional connectivity in the dorsal attention and default mode networks in the alpha band. Graph theory analyses showed that there was an increase in path length and a decrease in clustering coefficient during infant sustained attention. The functional connectivity within the visual, somatosensory, dorsal attention, and ventral attention networks and graph theory measures of path length and clustering coefficient were found to increase with age. These findings suggest that infant sustained attention is accompanied by distinct patterns of brain functional connectivity. The current findings also suggest the rapid development of functional connectivity in brain networks during infancy.

Effect of visual orientation on mu suppression in children: a comparative EEG study with adults

BACKGROUND

The human mirror neuron system exists in adults, and even in children. However, a significant, unanswered question in the literature concerns age differences in the effect of visual orientation of human body movements. The observation of actions performed by others is known to activate populations of neural cells called mirror neuron system. Moreover, the power of mu rhythms (8-13 Hz) in the EEG is known to decrease while performing and observing human movements. Therefore, the mu rhythm could be related to the activity of the mirror neuron system. This study investigated the effects of the visual perspective on electroencephalography responses to hand actions in two age groups.

METHODS

The participants were 28 elementary school students and 26 university students. Videos of the two hands operating switches were used as stimuli. The electroencephalogram mu rhythm (8-13 Hz) was measured during stimuli presentation as an index of mirror neuron system activity.

RESULTS

Adult participants showed significant mirror neuron system activation under both conditions, although no effect of visual perspectives was observed. On the other hand, children only reacted to egocentric stimuli and not to the others.

CONCLUSIONS

These findings confirmed the suggested differences in the activity of the mirror neuron system between different age groups. The demonstration that brain activities related to mirroring change during development could help explain previous findings in the literature.