Magnetoencephalography in the study of children with autism spectrum disorder

Paediatric magnetoencephalography and its role in neurodevelopmental disorders

Magnetoencephalography (MEG) is a non-invasive neuroimaging technique that assesses neurophysiology through the detection of the magnetic fields generated by neural currents. In this way, it is sensitive to brain activity, both in individual regions and brain-wide networks. Conventional MEG systems employ an array of sensors that must be cryogenically cooled to low temperature, in a rigid one-size-fits-all helmet. Systems are typically designed to fit adults and are therefore challenging to use for paediatric measurements. Despite this, MEG has been employed successfully in research to investigate neurodevelopmental disorders, and clinically for presurgical planning for paediatric epilepsy. Here, we review the applications of MEG in children, specifically focussing on autism spectrum disorder and attention-deficit hyperactivity disorder. Our review demonstrates the significance of MEG in furthering our understanding of these neurodevelopmental disorders, while also highlighting the limitations of current instrumentation. We also consider the future of paediatric MEG, with a focus on newly developed instrumentation based on optically pumped magnetometers (OPM-MEG). We provide a brief overview of the development of OPM-MEG systems, and how this new technology might enable investigation of brain function in very young children and infants.

Regional-Asymmetric Adaptive Graph Convolutional Neural Network for Diagnosis of Autism in Children With Resting-State EEG

Currently, resting-state electroencephalography (rs-EEG) has become an effective and low-cost evaluation way to identify autism spectrum disorders (ASD) in children. However, it is of great challenge to extract useful features from raw rs-EEG data to improve diagnosis performance. Traditional methods mainly rely on the design of manual feature extractors and classifiers, which are separately performed and cannot be optimized simultaneously. To this end, this paper proposes a new end-to-end diagnostic method based on a recently emerged graph convolutional neural network for the diagnosis of ASD in children. Inspired by related neuroscience findings on the abnormal brain functional connectivity and hemispheric asymmetry characteristics observed in autism patients, we design a new Regional-asymmetric Adaptive Graph Convolutional Neural Network (RAGNN). It utilizes a hierarchical feature extraction and fusion process to learn separable spatiotemporal EEG features from different brain regions, two hemispheres, and a global brain. In the temporal feature extraction section, we utilize a convolutional layer that spans from the brain area to the hemisphere. This allows for effectively capturing temporal features both within and between brain areas. To better capture spatial characteristics of multi-channel EEG signals, we employ adaptive graph convolutional learning to capture non-Euclidean features within the brain's hemispheres. Additionally, an attention layer is introduced to highlight different contributions of the left and right hemispheres, and the fused features are used for classification. We conducted a subject-independent cross-validation experiment on rs-EEG data from 45 children with ASD and 45 typically developing (TD) children. Experimental results have shown that our proposed RAGNN model outperformed several existing deep learning-based methods (ShaollowNet, EEGNet, TSception, ST-GCN, and CGRU-MDGN).

A Potential Biomarker of Brain Activity in Autism Spectrum Disorders: A Pilot fNIRS Study in Female Preschoolers

Autism spectrum disorder (ASD) refers to a neurodevelopmental condition whose detection still remains challenging in young females due to the heterogeneity of the behavioral phenotype and the capacity of camouflage. The availability of quantitative biomarkers to assess brain function may support in the assessment of ASD. Functional Near-infrared Spectroscopy (fNIRS) is a non-invasive and flexible tool that quantifies cortical hemodynamic responses (HDR) that can be easily employed to describe brain activity. Since the study of the visual phenotype is a paradigmatic model to evaluate cerebral processing in many neurodevelopmental conditions, we hypothesized that visually-evoked HDR (vHDR) might represent a potential biomarker in ASD females. We performed a case-control study comparing vHDR in a cohort of high-functioning preschooler females with ASD (fASD) and sex/age matched peers. We demonstrated the feasibility of visual fNIRS measurements in fASD, and the possibility to discriminate between fASD and typical subjects using different signal features, such as the amplitude and lateralization of vHDR. Moreover, the level of response lateralization was correlated to the severity of autistic traits. These results corroborate the cruciality of sensory symptoms in ASD, paving the way for the validation of the fNIRS analytical tool for diagnosis and treatment outcome monitoring in the ASD population.

Functional connectivity based machine learning approach for autism detection in young children using MEG signals

Objective.Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder, and identifying early autism biomarkers plays a vital role in improving detection and subsequent life outcomes. This study aims to reveal hidden biomarkers in the patterns of functional brain connectivity as recorded by the neuro-magnetic brain responses in children with ASD.Approach.We recorded resting-state magnetoencephalogram signals from thirty children with ASD (4-7 years) and thirty age and gender-matched typically developing (TD) children. We used a complex coherency-based functional connectivity analysis to understand the interactions between different brain regions of the neural system. The work characterizes the large-scale neural activity at different brain oscillations using functional connectivity analysis and assesses the classification performance of coherence-based (COH) measures for autism detection in young children. A comparative study has also been carried out on COH-based connectivity networks both region-wise and sensor-wise to understand frequency-band-specific connectivity patterns and their connections with autism symptomatology. We used artificial neural network (ANN) and support vector machine (SVM) classifiers in the machine learning framework with a five-fold CV technique.Main results.To classify ASD from TD children, the COH connectivity feature yields the highest classification accuracy of 91.66% in the high gamma (50-100 Hz) frequency band. In region-wise connectivity analysis, the second highest performance is in the delta band (1-4 Hz) after the gamma band. Combining the delta and gamma band features, we achieved a classification accuracy of 95.03% and 93.33% in the ANN and SVM classifiers, respectively. Using classification performance metrics and further statistical analysis, we show that ASD children demonstrate significant hyperconnectivity.Significance.Our findings support the weak central coherency theory in autism detection. Further, despite its lower complexity, we show that region-wise COH analysis outperforms the sensor-wise connectivity analysis. Altogether, these results demonstrate the functional brain connectivity patterns as an appropriate biomarker of autism in young children.

A Multimodal Approach for Identifying Autism Spectrum Disorders in Children

Identification of autism spectrum disorder (ASD) in children is challenging due to the complexity and heterogeneity of ASD. Currently, most existing methods mainly rely on a single modality with limited information and often cannot achieve satisfactory performance. To address this issue, this paper investigates from internal neurophysiological and external behavior perspectives simultaneously and proposes a new multimodal diagnosis framework for identifying ASD in children with fusion of electroencephalogram (EEG) and eye-tracking (ET) data. Specifically, we designed a two-step multimodal feature learning and fusion model based on a typical deep learning algorithm, stacked denoising autoencoder (SDAE). In the first step, two SDAE models are designed for feature learning for EEG and ET modality, respectively. Then, a third SDAE model in the second step is designed to perform multimodal fusion with learned EEG and ET features in a concatenated way. Our designed multimodal identification model can automatically capture correlations and complementarity from behavior modality and neurophysiological modality in a latent feature space, and generate informative feature representations with better discriminability and generalization for enhanced identification performance. We collected a multimodal dataset containing 40 ASD children and 50 typically developing (TD) children to evaluate our proposed method. Experimental results showed that our proposed method achieved superior performance compared with two unimodal methods and a simple feature-level fusion method, which has promising potential to provide an objective and accurate diagnosis to assist clinicians.

Impact of Antiepileptic Drugs on Cognition and Neuromagnetic Activity in Childhood Epilepsy With Centrotemporal Spikes: A Magnetoencephalography Study

Objective: Childhood epilepsy with centrotemporal spikes (CECTS), the most common childhood epilepsy, still lacks longitudinal imaging studies involving antiepileptic drugs (AEDs). In order to examine the effect of AEDs on cognition and brain activity. We investigated the neuromagnetic activities and cognitive profile in children with CECTS before and after 1 year of treatment.

Methods: Fifteen children with CECTS aged 6–12 years underwent high-sampling magnetoencephalography (MEG) recordings before treatment and at 1 year after treatment, and 12 completed the cognitive assessment (The Wechsler Intelligence Scale for Children). Next, magnetic source location and functional connectivity (FC) were investigated in order to characterize interictal neuromagnetic activity in the seven frequency sub-bands, including: delta (1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (12–30 Hz), gamma (30–80 Hz), ripple (80–250 Hz), and fast ripple (250–500 Hz).

Results: After 1 year of treatment, children with CECTS had increased scores on full-scale intelligence quotient, verbal comprehension index (VCI) and perceptual reasoning index (PRI). Alterations of neural activity occurred in specific frequency bands. Source location, in the 30–80 Hz frequency band, was significantly increased in the posterior cingulate cortex (PCC) after treatment. Moreover, FC analysis demonstrated that after treatment, the connectivity between the PCC and the medial frontal cortex (MFC) was enhanced in the 8–12 Hz frequency band. Additionally, the whole-brain network distribution was more dispersed in the 80–250 Hz frequency band.

Conclusion: Intrinsic neural activity has frequency-dependent characteristic. AEDs have impact on regional activity and FC of the default mode network (DMN). Normalization of aberrant DMN in children with CECTS after treatment is likely the reason for improvement of cognitive function.

Cortical Auditory Processing of Simple Stimuli Is Altered in Autism: A Meta-analysis of Auditory Evoked Responses

BACKGROUND

Auditory perceptual abnormalities are common in persons on the autism spectrum. The neurophysiologic underpinnings of these differences have frequently been studied using auditory event-related potentials (ERPs) and event-related magnetic fields (ERFs). However, no study to date has quantitatively synthesized this literature to determine whether early auditory ERP/ERF latencies or amplitudes in autistic persons differ from those of typically developing control subjects.

METHODS

We searched PubMed and ProQuest for studies comparing 1) latencies/amplitudes of P1/M50, N1b, N1c, M100, P2/M200, and/or N2 ERP/ERF components evoked by pure tones and 2) paired-click sensory gating (P1/N1b amplitude suppression) in autistic individuals and typically developing control subjects. Effects were synthesized using Bayesian 3-level meta-analysis.

RESULTS

In response to pure tones, autistic individuals exhibited prolonged P1/M50 latencies (g = 0.341 [95% credible interval = 0.166, 0.546]), prolonged M100 latencies (g = 0.319 [0.093, 0.550]), reduced N1c amplitudes (g = -0.812 [-1.278, -0.187]), and reduced N2 amplitudes (g = -0.374 [-0.633, -0.179]). There were no practically significant group differences in P2/M200 latencies, N2 latencies, P1/M50 amplitudes, N1b amplitudes, M100 amplitudes, or P2/M200 amplitudes. Paired-click sensory gating was also reduced in autistic individuals (g = -0.389 [-0.619, -0.112]), although this effect was primarily driven by smaller responses to the first click stimulus.

CONCLUSIONS

Relative to typically developing control subjects, autistic individuals demonstrate multiple alterations in early cortical auditory processing of simple stimuli. However, most group differences were modest in size and based on small numbers of heterogeneous studies with variable quality. Future work is necessary to understand whether these neurophysiologic measures can predict clinically meaningful outcomes or serve as stratification biomarkers for the autistic population.

MEG Theta during Lexico-Semantic and Executive Processing Is Altered in High-Functioning Adolescents with Autism

Neuroimaging studies have revealed atypical activation during language and executive tasks in individuals with autism spectrum disorders (ASD). However, the spatiotemporal stages of processing associated with these dysfunctions remain poorly understood. Using an anatomically constrained magnetoencephalography approach, we examined event-related theta oscillations during a double-duty lexical decision task that combined demands on lexico-semantic processing and executive functions. Relative to typically developing peers, high-functioning adolescents with ASD had lower performance accuracy on trials engaging selective semantic retrieval and cognitive control. They showed an early overall theta increase in the left fusiform cortex followed by greater activity in the left-lateralized temporal (starting at ~250 ms) and frontal cortical areas (after ~450 ms) known to contribute to language processing. During response preparation and execution, the ASD group exhibited elevated theta in the anterior cingulate cortex, indicative of greater engagement of cognitive control. Simultaneously increased activity in the ipsilateral motor cortex may reflect a less lateralized and suboptimally organized motor circuitry. Spanning early sensory-specific and late response selection stages, the higher event-related theta responsivity in ASD may indicate compensatory recruitment to offset inefficient lexico-semantic retrieval under cognitively demanding conditions. Together, these findings provide further support for atypical language and executive functions in high-functioning ASD.

Visuomotor Activation of Inhibition-Processing in Pediatric Obsessive Compulsive Disorder: A Magnetoencephalography Study

Background: Response inhibition engages the cortico-striato-thalamo-cortical (CSTC) circuit, which has been implicated in children, and youth with obsessive compulsive disorder (OCD). This study explored whether CSTC engagement during response inhibition, measured using magnetoencephalography (MEG), differed in a sample of medication-naïve youth with OCD, compared to typically developing controls (TDC). Methods: Data was analyzed in 17 medication-naïve children and youth with OCD (11.7 ± 2.2 SD years) and 13 TDC (12.6 ± 2.2 SD years). MEG was used to localize and characterize neural activity during a Go/No-Go task. Task performance on Go/No-Go conditions and regional differences in amplitude of activity during Go and No-Go condition between OCD vs. TDC were examined using two-sample t-tests. Post-hoc analysis with Bayesian t-tests was used to estimate the certainty of outcomes. Results: No differences in Go/No-Go performance were found between OCD and TDC groups. In response to the visual cue presented during the Go condition, participants with OCD showed significantly increased amplitude of activity in the primary motor (MI) cortex compared to TDC. In addition, significantly reduced amplitude of PCu was found following successful stopping to No-Go cues in OCD vs. TDC during No-Go task performance. Bayesian t-tests indicated high probability and large effect sizes for the differences in MI and PCu amplitude found between groups. Conclusion: Our preliminary study in a small medication-naïve sample extends previous work indicating intact response inhibition in pediatric OCD. While altered neural response in the current study was found during response inhibition performance in OCD, differences localized to regions outside of the CSTC. Our findings suggest that additional imaging research in medication-naïve samples is needed to clarify regional differences associated with OCD vs. influenced by medication effects, and suggest that MEG may be sensitive to detecting such differences.

A Spectrotemporal Correlate of Language Impairment in Autism Spectrum Disorder

This study introduces an objective neurophysiological marker of language ability, the integral of event-related desynchronization in the 5-20 Hz band during 0.2-1 seconds post auditory stimulation with interleaved word/non-word tokens. This measure correlates with clinical assessment of language function in both ASD and neurotypical pediatric populations. The measure does not appear related to general cognitive ability nor autism symptom severity (beyond degree of language impairment). We suggest that this oscillatory brain activity indexes lexical search and thus increases with increased search in the mental lexicon. While specificity for language impairment in ASD remains to be determined, such an objective index has potential utility in low functioning individuals with ASD and young children during language acquisition.

Magnetic Source Imaging and Infant MEG: Current Trends and Technical Advances

Magnetoencephalography (MEG) is known for its temporal precision and good spatial resolution in cognitive brain research. Nonetheless, it is still rarely used in developmental research, and its role in developmental cognitive neuroscience is not adequately addressed. The current review focuses on the source analysis of MEG measurement and its potential to answer critical questions on neural activation origins and patterns underlying infants’ early cognitive experience. The advantages of MEG source localization are discussed in comparison with functional magnetic resonance imaging (fMRI) and functional near-infrared spectroscopy (fNIRS), two leading imaging tools for studying cognition across age. Challenges of the current MEG experimental protocols are highlighted, including measurement and data processing, which could potentially be resolved by developing and improving both software and hardware. A selection of infant MEG research in auditory, speech, vision, motor, sleep, cross-modality, and clinical application is then summarized and discussed with a focus on the source localization analyses. Based on the literature review and the advancements of the infant MEG systems and source analysis software, typical practices of infant MEG data collection and analysis are summarized as the basis for future developmental cognitive research.

Lesser suppression of response to bright visual stimuli and visual abnormality in children with autism spectrum disorder: a magnetoencephalographic study

BACKGROUND

Visual abnormality is a common sensory impairment in autism spectrum disorder (ASD), which may cause behavioral problems. However, only a few studies exist on the neural features corresponding to the visual symptoms in ASD. The purpose of this study was to investigate the relationship between cortical responses to visual stimuli and visual abnormality to examine the neurophysiological mechanisms of the visual abnormality in ASD.

METHODS

Twenty-two high-functioning children with ASD (10.95 ± 2.01 years old) and 23 age-matched typically developing (TD) children (10.13 ± 2.80 years old) participated in this study. We measured the cortical responses (i.e., activated intensity and attenuation ratio) elicited by the Original visual image and other two types of bright images (the Dot noise or Blind image, which includes overlapped particles onto the Original image or the enhanced-brightness version of the Original image, respectively) using magnetoencephalography.

RESULTS

The severity of visual abnormalities was significantly associated with behavioral problems in children with ASD. In addition, we found the increased cortical activation in response to the Original image in the left supramarginal gyrus (SMG) and middle temporal gyrus in children with ASD. However, there were no inter-group differences in the primary visual and medial orbitofrontal cortices. Furthermore, when we compared cortical responses according to the type of images, children with ASD showed lesser attenuation of the activated intensities than children with TD in response to the bright images compared with the Original image in the right SMG. These attenuation ratios (Dot noise/Original and Blind/Original) were also associated with the severity of visual abnormalities.

CONCLUSIONS

Our results show that dysfunction of stimulus-driven neural suppression plays a crucial role in the neural mechanism of visual abnormality in children with ASD. To the best of our knowledge, this is the first magnetoencephalography study to demonstrate the association between the severity of visual abnormality and lower attenuation ratios in children with ASD. Our results contribute to the knowledge of the mechanisms underlying visual abnormality in children with ASD, and may therefore lead to more effective diagnosis and earlier intervention.

Abnormal auditory mismatch fields are associated with communication impairment in both verbal and minimally verbal/nonverbal children who have autism spectrum disorder

Abnormal auditory discrimination neural processes, indexed by mismatch fields (MMFs) recorded by magnetoencephalography (MEG), have been reported in verbal children with ASD. Association with clinical measures indicates that delayed MMF components are associated with poorer language and communication performance. At present, little is known about neural correlates of language and communication skills in extremely language impaired (minimally-verbal/non-verbal) children who have ASD: ASD-MVNV. It is hypothesized that MMF delays observed in language-impaired but nonetheless verbal children with ASD will be exacerbated in ASD-MVNV. The present study investigated this hypothesis, examining MMF responses bilaterally during an auditory oddball paradigm with vowel stimuli in ASD-MVNV, in a verbal ASD cohort without cognitive impairment and in typically developing (TD) children. The verbal ASD cohort without cognitive impairment was split into those demonstrating considerable language impairment (CELF core language index <85; "ASD-LI") versus those with less or no language impairment (CELF CLI >85; "ASD-V"). Eighty-four participants (8-12 years) were included in final analysis: ASD-MVNV: n = 9, 9.67 ± 1.41 years, ASD: n = 48, (ASD-V: n = 27, 10.55 ± 1.21 years, ASD-LI: n = 21, 10.67 ± 1.20 years) and TD: n = 27, 10.14 ± 1.38 years. Delayed MMF latencies were found bilaterally in ASD-MVNV compared to verbal ASD (both ASD-V and ASD-LI) and TD children. Delayed MMF responses were associated with diminished language and communication skills. Furthermore, whereas the TD children showed leftward lateralization of MMF amplitude, ASD-MVNV and verbal ASD (ASD-V and ASD-LI) showed abnormal rightward lateralization. Findings suggest delayed auditory discrimination processes and abnormal rightward laterality as objective markers of language/communication skills in both verbal and MVNV children who have ASD. Autism Res 2019, 12: 1225-1235. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Brain imaging showed abnormal auditory discrimination processes in minimally-verbal/non-verbal children (MVNV) who have autism spectrum disorder (ASD). Delays in auditory discrimination were associated with impaired language and communication skills. Findings suggest these auditory neural measures may be objective markers of language and communication skills in both verbal and, previously-understudied, MVNV children who have ASD.

Overlaps and distinctions between attention deficit/hyperactivity disorder and autism spectrum disorder in young adulthood: Systematic review and guiding framework for EEG-imaging research

Attention deficit/hyperactivity disorders (ADHD) and autism spectrum disorders (ASD) frequently co-occur. However, we know little about the neural basis of the overlaps and distinctions between these disorders, particularly in young adulthood - a critical time window for brain plasticity across executive and socioemotional domains. Here, we systematically review 75 articles investigating ADHD and ASD in young adult samples (mean ages 16-26) using cognitive tasks, with neural activity concurrently measured via electroencephalography (EEG) - the most accessible neuroimaging technology. The majority of studies focused on event-related potentials (ERPs), with some beginning to capitalise on oscillatory approaches. Overlapping and specific profiles for ASD and ADHD were found mainly for four neurocognitive domains: attention processing, performance monitoring, face processing and sensory processing. No studies in this age group directly compared both disorders or considered dual diagnosis with both disorders. Moving forward, understanding of ADHD, ASD and their overlap in young adulthood would benefit from an increased focus on cross-disorder comparisons, using similar paradigms and in well-powered samples and longitudinal cohorts.

A theoretical rut: revisiting and critically evaluating the generalized under/over-connectivity hypothesis of autism

In 2004, two papers proposed that pervasive functional under-connectivity (Just et al., ) or a trade-off between excessive local connectivity at the cost of distal under-connectivity (Belmonte et al., ) characterizes atypical brain organization in autism. Here, we take stock of the most recent and rigorous functional and structural connectivity findings with a careful eye toward evaluating the extent to which they support these original hypotheses. Indeed, the empirical data do not support them. From rsfMRI studies in adolescents and adults, there is an emerging consensus regarding long-range functional connections indicating cortico-cortical under-connectivity, specifically involving the temporal lobes, combined with subcortical-cortical over-connectivity. In contrast, there is little to no consensus regarding local functional connectivity or findings from task-based functional connectivity studies. The structural connectivity data suggest that white matter tracts are pervasively weak, particularly in the temporal lobe. Together, these findings are revealing how deeply complex the story is regarding atypical neural network organization in autism. In other words, distance and strength of connectivity as individual factors or as interacting factors do not consistently explain the patterns of atypical neural connectivity in autism. Therefore, we make several methodological recommendations and highlight developmental considerations that will help researchers in the field cultivate new hypotheses about the nature and mechanisms of potentially aberrant functional and structural connectivity in autism.

Detection of atypical network development patterns in children with autism spectrum disorder using magnetoencephalography

Autism spectrum disorder (ASD) is a developmental disorder that involves developmental delays. It has been hypothesized that aberrant neural connectivity in ASD may cause atypical brain network development. Brain graphs not only describe the differences in brain networks between clinical and control groups, but also provide information about network development within each group. In the present study, graph indices of brain networks were estimated in children with ASD and in typically developing (TD) children using magnetoencephalography performed while the children viewed a cartoon video. We examined brain graphs from a developmental point of view, and compared the networks between children with ASD and TD children. Network development patterns (NDPs) were assessed by examining the association between the graph indices and the raw scores on the achievement scale or the age of the children. The ASD and TD groups exhibited different NDPs at both network and nodal levels. In the left frontal areas, the nodal degree and efficiency of the ASD group were negatively correlated with the achievement scores. Reduced network connections were observed in the temporal and posterior areas of TD children. These results suggested that the atypical network developmental trajectory in children with ASD is associated with the development score rather than age.

Development of Auditory Evoked Responses in Normally Developing Preschool Children and Children with Autism Spectrum Disorder

The cortical responses to auditory stimuli undergo rapid and dramatic changes during the first 3 years of life in normally developing (ND) children, with decreases in latency and changes in amplitude in the primary peaks. However, most previous studies have focused on children >3 years of age. The analysis of data from the early stages of development is challenging because the temporal pattern of the evoked responses changes with age (e.g., additional peaks emerge with increasing age) and peak latency decreases with age. This study used the topography of the auditory evoked magnetic field (AEF) to identify the auditory components in ND children between 6 and 68 months (n = 48). The latencies of the peaks in the AEF produced by a tone burst (ISI 2 ± 0.2 s) during sleep decreased with age, consistent with previous reports in awake children. The peak latencies of the AEFs in ND children and children with autism spectrum disorder (ASD) were compared. Previous studies indicate that the latencies of the initial components of the auditory evoked potential (AEP) and the AEF are delayed in children with ASD when compared to age-matched ND children >4 years of age. We speculated whether the AEF latencies decrease with age in children diagnosed with ASD as in ND children, but with uniformly longer latencies before the age of about 4 years. Contrary to this hypothesis, the peak latencies did not decrease with age in the ASD group (24-62 months, n = 16) during sleep (unlike in the age-matched controls), although the mean latencies were longer in the ASD group as in previous studies. These results are consistent with previous studies indicating delays in auditory latencies, and they indicate a different maturational pattern in ASD children and ND children. Longitudinal studies are needed to confirm whether the AEF latencies diverge with age, starting at around 3 years, in these 2 groups of children.

Mu rhythm suppression reflects mother-child face-to-face interactions: a pilot study with simultaneous MEG recording

Spontaneous face-to-face interactions between mothers and their children play crucial roles in the development of social minds; however, these inter-brain dynamics are still unclear. In this pilot study, we measured MEG mu suppression during face-to-face spontaneous non-linguistic interactions between mothers and their children with autism spectrum disorder (ASD) using the MEG hyperscanning system (i.e., simultaneous recording). The results demonstrated significant correlations between the index of mu suppression (IMS) in the right precentral area and the traits (or severity) of ASD in 13 mothers and 8 children (MEG data from 5 of the children could not be obtained due to motion noise). In addition, higher IMS values (i.e., strong mu suppression) in mothers were associated with higher IMS values in their children. To evaluate the behavioral contingency between mothers and their children, we calculated cross correlations between the magnitude of the mother and child head-motion during MEG recordings. As a result, in mothers whose head motions tended to follow her child's head motion, the magnitudes of mu suppression in the mother's precentral area were large. Further studies with larger sample sizes, including typically developing children, are necessary to generalize this result to typical interactions between mothers and their children.

The maturation of auditory responses in infants and young children: a cross-sectional study from 6 to 59 months

Background: An understanding of the maturation of auditory cortex responses in typically developing infants and toddlers is needed to later identify auditory processing abnormalities in infants at risk for neurodevelopmental disorders. The availability of infant and young child magnetoencephalography (MEG) systems may now provide near optimal assessment of left and right hemisphere auditory neuromagnetic responses in young populations. To assess the performance of a novel whole-head infant MEG system, a cross-sectional study examined the maturation of left and right auditory cortex responses in children 6- to 59-months of age.

Methods: Blocks of 1000 Hz (1st and 3rd blocks) and 500 Hz tones (2nd block) were presented while MEG data were recorded using an infant/young child biomagnetometer (Artemis 123). Data were obtained from 29 children (11 males; 6- to 59-months). Latency measures were obtained for the first positive-to-negative evoked response waveform complex in each hemisphere. Latency and age associations as well as frequency and hemisphere latency differences were examined. For the 1000 Hz tone, measures of reliability were computed.

Results: For the first response—a response with a “P2m” topography—latencies decreased as a function of age. For the second response—a response with a “N2m” topography—no N2m latency and age relationships were observed. A main effect of tone frequency showed earlier P2m responses for 1st 1000 Hz (150 ms) and 2nd 1000 Hz (148 ms) vs. 500 Hz tones (162 ms). A significant main effect of hemisphere showed earlier N2m responses for 2nd 1000 Hz (226 ms) vs. 1st 1000 Hz (241 ms) vs. 500 Hz tones (265 ms). P2m and N2m interclass correlation coefficient latency findings were as follows: left P2m (0.72, p < 0.001), right P2m (0.84, p < 0.001), left N2m (0.77, p < 0.001), and right N2m (0.77,p < 0.01).

Conclusions: Findings of strong age and latency associations, sensitivity to tone frequency, and good test-retest reliability support the viability of longitudinal infant MEG studies that include younger as well as older participants as well as studies examining auditory processing abnormalities in infants at risk for neurodevelopmental disorders.