elifeeLifeeLife2050-084XeLife Sciences Publications, Ltd9456110.7554/eLife.9456110.7554/eLife.94561.1reviewed preprint1.1NeuroscienceThe neurodevelopmental trajectory of beta band oscillations: an OPM-MEG studyhttp://orcid.org/0000-0002-4560-8787RierLukas1*+RhodesNatalie12+PakenhamDaisie3BotoElena14http://orcid.org/0000-0002-0561-8366HolmesNiall14HillRyan M.14RiveroGonzalo Reina1ShahVishal5DoyleCody5OsborneJames5BowtellRichard1TaylorMargot J.2http://orcid.org/0000-0002-8687-8185BrookesMatthew J.14Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UKDiagnostic Imaging,The Hospital for Sick Children, 555 University Avenue, Toronto, M5G 1X8, CanadaClinical Neurophysiology, Nottingham University Hospitals NHS Trust, Queens Medical Centre, Derby Rd, Lenton, Nottingham NG7 2UH, UKCerca Magnetics Limited, 7-8 Castlebridge Office Village, Kirtley Drive, Nottingham, NG7 1LD, Nottingham, UKQuSpin Inc. 331 South 104th Street, Suite 130, Louisville, Colorado, 80027, USALuoHuanReviewing EditorPeking UniversityBeijingChinaColginLaura LSenior EditorUniversity of Texas at AustinAustinUnited States of AmericaCorrespondence to: Dr Lukas Rier, Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom E-mail: lukas.rier@nottingham.ac.uk
Indicates authors who made equal contributions to this manuscript.
Neural oscillations mediate coordination of activity within and between brain networks, supporting cognition and behaviour. How these processes develop throughout childhood is not only a critical neuroscientific question but could also shed light on the mechanisms underlying neurological and psychiatric disorders. However, measuring the neurodevelopmental trajectory of oscillations has been hampered by confounds from instrumentation. In this paper, we investigate the suitability of a disruptive new imaging platform – Optically Pumped Magnetometer-based magnetoencephalography (OPM-MEG) – to study oscillations during brain development. We show how a unique 192-channel OPM-MEG device, which is adaptable to head size and robust to participant movement, can be used to collect high-fidelity electrophysiological data in individuals aged between 2 and 34 years. Data were collected during a somatosensory task, and we measured both stimulus-induced modulation of beta oscillations in sensory cortex, and whole-brain connectivity, showing that both modulate significantly with age. Moreover, we show that pan-spectral bursts of electrophysiological activity drive beta oscillations throughout neurodevelopment, and how their probability of occurrence and spectral content changes with age. Our results offer new insights into the developmental trajectory of oscillations and provide the first clear evidence that OPM-MEG is an ideal platform for studying electrophysiology in children.
V.S. is the founding director of QuSpin, a commercial entity selling OPM magnetometers. J.O. and C.D. are employees of QuSpin. E.B. and M.J.B. are directors of Cerca Magnetics Limited, a spin-out company whose aim is to commercialise aspects of OPM-MEG technology. E.B., M.J.B., R.B., N.H. and R.H. hold founding equity in Cerca Magnetics Limited. HS, FW, and TH are employees of Cerca Magnetics Limited.
Introduction
Neural oscillations are a fundamental component of brain function. They enable coordination of electrophysiological activity within and between neural assemblies and this underpins cognition and behaviour. Oscillations in the beta range (13-30 Hz) are typically associated with sensorimotor processes 1; they are prominent over the sensorimotor cortices, diminished in amplitude during sensory stimulation or motor execution, and increased in amplitude (above a baseline level) following stimulus cessation, termed the post-movement beta rebound (PMBR)2. Beta oscillations and their modulation by tasks are robustly measured neuroscientific phenomena and their critical importance is highlighted by studies showing abnormalities across a range of disorders – e.g. autism 3, multiple sclerosis 4, Parkinson’s disease 5 and Schizophrenia 6. Despite this, little is known about the mechanistic role of beta oscillations, and most of what is known comes from studies applying non-invasive neuroimaging techniques to adult populations. Whilst the sensorimotor system changes little in adulthood, there are marked changes in childhood and a complete characterisation of the neurodevelopmental trajectory of beta oscillations, particularly how they underpin behavioural milestones, might offer a new understanding of their role in healthy and abnormal brain function.
Several studies have investigated how neural oscillations change with age: Gaetz et al. 7 measured beta modulation during index finger movement, showing that the post-movement beta rebound (PMBR) was diminished in children compared to adults. Kurz et al. 8 reported a similar effect when studying 11-19 year olds executing lower limb movement. Trevarrow et al. 9 found an age related increase in the PMBR amplitude in healthy 9-15 year olds, and further that the decrease in beta power during movement execution did not modulate with age. Finally, Vakhtin et al.10 showed an increase in PMBR amplitude between adolescence and adulthood, and that this trajectory was abnormal in autism. A separate body of work has assessed neural oscillations in the absence of a task, demonstrating that there is a redistribution of oscillatory power across frequency bands as the brain matures. Specifically, low frequency activity tends to decrease, and high frequency activity increases with age 11–13. These changes are spatially specific, with increasing beta power most prominent in posterior parietal and occipital regions 14,15. Beta oscillations are also implicated in long range connectivity 16,17 and previous studies have demonstrated increased connectivity strength with age 18, particularly in attentional networks 19. In sum, there is accord between studies that show increases in task induced beta modulation and connectivity as well as a redistribution of spectral power, with increasing age. Despite this progress, neurodevelopmental studies remain hindered by instrumental limitations.
Neural oscillations can be measured non-invasively by either magnetoencephalography (MEG) or electroencephalography (EEG). MEG detects magnetic fields generated by neural currents, providing assessment of electrical activity with sub-centimetre spatial, and millisecond temporal precision. However, the sensors traditionally used for field detection operate at low temperature, necessitating the use of fixed ‘one-size-fits-all’ sensor arrays. Because the signal declines with the square of distance, smaller head size leads to a reduction in signal20. In addition, movement relative to fixed sensors degrades data quality. These limitations mean scanning young children with traditional MEG systems/SQUIDs is challenging. Similarly, there are challenges in EEG. EEG measures differences in electrical potential across the scalp. The electrode array adapts to head shape and moves with the head, making it ‘wearable’. However, the resistive properties of the scalp and skull distort signal topography, limiting spatial resolution. EEG is also more susceptible to interference from muscles than MEG 21, particularly during movement. In sum, both EEG and MEG are limited; MEG is confounded by head size, EEG has poor spatial accuracy, and both are degraded by movement. However, in recent years novel magnetic field sensors – Optically Pumped Magnetometers (OPMs) – have inspired a new generation of MEG system 22. OPMs are small, lightweight and have similar sensitivity to conventional MEG sensors but do not require cryogenics. This enables construction of a wearable MEG system and because sensors can get closer to the head, it provides improved sensitivity and spatial specificity compared to both conventional MEG and EEG 23. OPM-MEG is, ostensibly, ideal for children; for example, Hill et al. showed the viability of OPM-MEG in a 2 year old 24 and Feys et al. showed advantages for epileptic spike detection in children 25. However, no studies have yet used OPM-MEG in large groups to measure neurodevelopment.
In addition to instrumental limitations, most neurodevelopmental studies have used an approach to data analysis where signals are averaged over trials. This has led to the idea that sensory induced beta modulation comprises a drop in oscillatory amplitude during movement and a smooth increase on movement cessation. However, recent studies 26–28 investigating unaveraged signals show that, rather than a smooth oscillation, the beta rhythm is, in part, driven by discrete punctate events, known as “bursts”. Bursts occur with a characteristic probability, which is altered by a task29,30, and are not confined to the beta band but are pan-spectral, with components falling across many frequencies 6,30. There is also evidence that functional connectivity is driven by bursts that are coincident in time across spatially separate regions 30. Recent work using EEG has found that, even in children as young as 12 months, beta band activity is driven by bursts 31. These studies have changed the way that the research community thinks about oscillations 32 and a full understanding of beta dynamics and their age dependence must be placed in the context of the burst model.
Here, we combine OPM-MEG with a burst analysis based on a Hidden Markov Model (HMM) 30,33,34 to investigate beta dynamics during a somatosensory task in a large range of young children. Our study addresses two objectives: First, we test the viability of a novel 192-channel triaxial OPM-MEG system for use in paediatric populations, investigating its practicality in young children (from age 2 years) and assessing whether previously observed age-related changes in task-induced beta modulation and functional connectivity can be reliably measured using OPM-MEG. Second, we investigate how task-induced beta modulation in the sensorimotor cortices is related to the occurrence of pan-spectral bursts, and how the characteristics of those bursts change with age.
Results
Our OPM-MEG system comprised a maximum of 64 OPMs (QuSpin Inc., Colorado, USA) each capable of measuring magnetic field independently in three orthogonal orientations, meaning data were recorded using up to 192-channels. Sensors were mounted in 3D-printed helmets of differing size (Cerca Magnetics Ltd. Nottingham, UK), allowing adaptation to the participant’s head (Figure 1A). The total weight of the helmet ranged from ∼856 g (in the smallest case) to ∼906 g (in the largest case). The system was integrated into a magnetically shielded room (MSR) equipped with an active field control system (see “coils” in Figure 1A-B; Cerca Magnetics Ltd. Nottingham, UK) which allowed reduction of background field to <1 nT. This was to ensure that participants were able to move during a scan without compromising sensor operation 35,36. A schematic of the system is shown in Figure 1B.
Experimental setup and beta band modulation during sensory task.
(A) 4-year-old child wearing an OPM-MEG helmet (consent and authorisation for publication was obtained). (B) Schematic diagram of the whole system inside the shielded room. (C) Schematic illustration of stimulus timings and a photo of the somatosensory stimulators. “Braille” stimulators each comprise 8 pins, which can be controlled independently; all 8 were used simultaneously to deliver the stimuli.
27 children (aged 2-13 years) and 26 adults (aged 21-34 years) took part in the study. All participants performed a task in which two stimulators (Figure 1C) delivered somatosensory stimulation to either the index or little finger of the right hand sequentially. Stimuli lasted 0.5 s, occurred every 3.5 s, and comprised three taps on the fingertip. This pattern of stimulation was repeated, alternating 42 times between both fingers. Throughout the experiment, participants could watch their favourite TV show. Following data preprocessing, high fidelity data were available in 27 children and 24 adults Two datasets were excluded from further analysis as data quality was not sufficient to perform our Hidden Markov Model analysis (see Methods). We removed 19 ± 12 % (mean ± standard deviation) of trials in children, and 9 ± 5 % of trials in adults due to excessive interference. On average we had 160 ± 10 channels with high quality data available (note that not all sensors were available for every measurement – see also Discussion).
Beta band modulation with age
Figure 2 shows beta band modulation during the task for a single representative child (7 years old). Panel A shows the estimated brain anatomy (see Methods) with the locations of the largest beta desynchronisation – contrasted between a stimulus period (0.3-0.8 s relative to stimulus onset) and rest (2.5-3 s) – for index and little finger simulation (derived using a beamformer analysis (see Methods)) overlaid in blue and red respectively. The largest effects fall in the sensorimotor cortices as expected. Panel B shows time frequency spectra depicting the temporal evolution of the amplitude of neural oscillations. Blue represents a decrease in oscillatory amplitude relative to baseline (2.5-3 s); yellow represents an increase. As expected, there is a reduction in beta amplitude during stimulation
Data from a single participant:
(A) Brain plots show slices through the left motor cortex, with a pseudo-T-statistical map of beta modulation for a single 7-year-old participant. The blue peaks indicate locations of largest beta amplitude reduction during stimulation for index finger trials (digit 2/D2), while the red peaks show the little finger (digit 5/D5). (B) Time frequency spectra showing neural oscillatory amplitude modulation (fractional change in spectral amplitude relative to baseline measured in the 2.5-3 s window) for both fingers, using data extracted from the location of peak beta modulation (left sensorimotor cortex). Note the beta amplitude reduction during stimulation, as expected.
Group averaged beta dynamics are shown in Figure 3. Here, for visualisation, the children were split into three groups of 9: youngest (aged 2 – 6 years), middle (6 to 10 years), and oldest (10 to 13 years). Data were averaged within each group, and across all 24 adults (21 – 34 years) for comparison. The brain plots show group averaged pseudo-T statistical maps of stimulus induced beta band modulation. In all groups, the peak modulation appeared in the left sensorimotor cortex. We observed no significant difference in the location of peak beta desynchronisation between index and little finger stimulation (see also Discussion). The time-frequency spectrograms (TFSs) are also shown for each group. Here, we observe a drop in beta amplitude during stimulation for all 3 groups, however this was most pronounced in adults and was weaker in younger children. For statistical analysis, we estimated the difference in beta-band amplitude between the stimulation (0.3-0.8 s) and post-stimulation (1-1.5 s) windows and plotted this as a function of age (Figure 3B) with Pearson correlation suggesting a significant (𝑅2 = 0.29, 𝑝 = 4 × 10−5) relationship. These data agree strongly with previous studies showing increased task induced beta modulation with age. However, they are acquired using a fundamentally new wearable technology, and in younger participants.
Beta band modulation with age:
(A) Brain plots show slices through the left motor cortex, with a pseudo-T-statistical map of beta modulation (blue/black) overlaid on the standard brain. Time frequency spectrograms show modulation of the amplitude of neural oscillations (fractional change in spectral amplitude relative to the baseline measured in the 2.5-3 s window). In all cases results were extracted from the location of peak beta desynchronisation (in the left sensorimotor cortex). Note the clear beta amplitude reduction during stimulation. (B) Difference in beta-band amplitude (0.3-0.8 s window vs 1-1.5 s window) plotted as a function of age (i.e., each data point shows a different participant; triangles represent children, circles represent adults). Note significant correlation (R2=0.29, p=0.00004*). Also, all data here relate to the index finger stimulation; similar results are available for the little finger stimulation in supplementary information Figure S1.
Functional connectivity in the beta band
Whole brain beta-band functional connectivity was estimated by calculating amplitude envelope correlation (AEC)37 between (unaveraged) beta-band signals extracted from 78 cortical regions. Figure 4A shows connectome matrices averaged across participants in each of the four groups; each matrix element represents the strength of a connection between two brain regions. In the “glass brains”, the red lines show the 150 connections with the highest connectivity. In adults, the connectome is in strong agreement with those from previous studies18,38, with prominent sensorimotor, posterior-parietal- and fronto-parietal connections. However, connectivity patterns in children differed in both strength and spatial signature, with the visual network showing the strongest connectivity. To statistically test the relationship between connectivity and age, we plotted global connectivity (i.e., the sum of all matrix elements) versus age (Figure 4B). Pearson correlation suggested a significant (𝑅2 = 0.42, 𝑝 = 2.67 × 10−7) relationship with older participants having increased connectivity. We also probed how this relationship changes across brain regions: Figure 4D shows example scatter plots of node degree (i.e., how connected a specific region is to the rest of the brain) for two pairs of homologous frontal and occipital regions. Note that the gradient of the fit in the frontal regions (0.27 𝑎𝑔𝑒−1, 𝑅2 = 0.44, 𝑝 = 1.2 × 10−7 and 0.27 𝑎𝑔𝑒−1, 𝑅2 = 0.50, 𝑝 = 5.8 × 10−9) is much larger than that in the occipital regions (0.10 𝑎𝑔𝑒−1, 𝑅2 = 0.18, 𝑝 = 2.0 × 10−3, 0.12 𝑎𝑔𝑒−1, 𝑅2 = 0.29, 𝑝 = 4.2 × 10−5.). This is delineated for all brain regions in Figure 4C, where each region is coloured according to the gradient of the fit. The regions showing the largest change with age are frontal and parietal areas, with visual cortex demonstrating the smallest effect.
Functional connectivity – estimated using Amplitude Envelope Correlation (AEC) – varies with age.
(A) Connectivity matrices constructed using 78 regions of the AAL atlas and glass brains showing average connectomes across groups and corresponding glass brains showing the strongest 150 connections. AEC was estimated across the entire task recording. (B) Global average connectivity increases significantly with age (𝑅2 = 0.42, 𝑝 = 2.67 × 10−7*). (C) Age-related changes in connectivity vary spatially. Brain plot shows the linear fit gradient of node degree (the sum across the rows of the connectivity matrices) against age. Node degree varies less in occipital regions while frontal regions become more strongly connected with increasing age. (D) Example plots show node degree against age for left and right frontal and occipital regions. Pearson correlation yielded (from left to right): (𝑅2 = 0.44, 𝑝 = 1.2 × 10−7, 𝐷𝑒𝑔𝑟𝑒𝑒 = 0.27 · 𝑎𝑔𝑒 + 0.26); (𝑅2 = 0.50, 𝑝 = 5.8 × 10−9, 𝐷𝑒𝑔𝑟𝑒𝑒 = 0.28 · 𝑎𝑔𝑒 + 0.17); (𝑅2 = 0.18, 𝑝 = 2.0 × 10−3, 𝐷𝑒𝑔𝑟𝑒𝑒 = 0.10 · 𝑎𝑔𝑒 + 2.92); (𝑅2 = 0.29, 𝑝 = 4.2 × 10−5, 𝐷𝑒𝑔𝑟𝑒𝑒 = 0.12 · 𝑎𝑔𝑒 + 2.38).
Burst interpretation of beta dynamics
To assess pan-spectral bursts, we applied a univariate, 3-state HMM to the broadband (1-48 Hz) electrophysiological signal extracted from the location of largest beta modulation. This enabled us to identify the times at which bursts occurred in sensorimotor cortex 30,39.
Figure 5A shows a raster plot of burst occurrence for all individual task trials in all participants. White represents time points and trials where bursts are occurring; black represents the absence of a burst. Participants are separated by the red lines and groups are separated by the blue lines. Burst absence is more likely in the 0.3 s to 0.8 s time-period (during stimulation), indicating a task-induced decrease in burst- probability. Figure 5B shows group averaged burst-probability as a function of time. In all age groups, bursts were less likely during stimulation, though this modulation changes with age, with the younger group demonstrating the least pronounced effect. This is tested statistically in Figure 5C which shows a significant (𝑅2 = 0.13, 𝑝 = 8.9 × 10−3*) positive Pearson correlation between the modulation of burst probability and age. Figure 5D shows the association between beta amplitude and burst-probability modulation. Here the significant (𝑅2 = 0.50, 𝑝 = 5.2 × 10−9*) positive relationship supports a hypothesis that the observed change in task induced beta modulation with age (shown in Figure 3) is driven by changes in the modulation of burst probability.
The relationship between beta-band amplitude modulation and pan-spectral burst probability.
(A) Raster plot showing burst occurrence (white) as a function of time for all trials and participants combined (participants sorted by increasing age). (B) Trial averaged burst probability time-courses across the four participant groups. Shaded areas indicate the standard error within groups. (C) Stimulus-to post-stimulus modulation of burst probability (0.3-0.8 s vs 1-1.5 s) plotted against age. Note significant (R2=0.13, p=0.0089*) positive correlation. (D) Beta amplitude modulation plotted against burst probability. Note again significant correlation (𝑅2 = 0.5, 𝑝 = 5.2 × 10−9*). (Values for both measures were z-transformed within the Children and Adult group respectively to mitigate the age confound). Triangles and circles denote Children and Adults respectively.
We estimated the spectral content of the bursts identified by the HMM. In Figure 6A the burst spectra are shown for all 4 participant groups. In adults, the spectral power diminishes with increasing frequency, with additional peaks in the alpha and beta band. In children, high frequencies are diminished, and low frequencies are enhanced, compared to adults. This is also shown in Figure 6B where, for every frequency, we perform a linear fit to a scatter plot of spectral density versus age. Here, positive values indicate that spectral power increases with age; negative power means it decreases. The inset scatter plots show example age relationships at 3 Hz, 9 Hz, 21 Hz, and 37 Hz. We see a clear decrease in low-frequency spectral content and increasing high-frequency content, with age. Interestingly, spectral content in the alpha band appeared stable with no significant correlation with age. Similar trends for changes in frequency content with age were found for the non-burst states (See Figure S2).
Spectral content of the burst state varies with age.
(A) Average burst-state spectra across groups. Shaded areas indicate standard error on the group mean. (B) Pearson correlation coefficient for the PSD values in (A) against age across all frequency values. Red shaded areas indicate 𝑝 < 0.01 (uncorrected). The four inset plots show example scatters of PSD values with age at select frequencies (3 Hz, 9 Hz, 21 Hz, and 37 Hz). Low-frequency spectral content decreases with age while high-frequency content increases. No significant correlation was observed in the high theta and alpha bands.
Discussion
There are few practical, non-invasive neuroimaging platforms capable of measuring brain function in children with good spatial and temporal resolution. Functional magnetic resonance imaging (fMRI) 40 tracks brain activity with millimetre spatial resolution, but the mechanism of detection is indirect (based on haemodynamic responses) and consequently fMRI has limited temporal precision. Participants must also lie immobile in a large scanner while being exposed to high acoustic noise; many children find this environment challenging and it is difficult to implement naturalistic experimental paradigms. Functional near infra-red spectroscopy (fNIRS) 41 also measures haemodynamics, but provides a wearable platform which allows scanning of almost any participant during any conceivable experiment. However, fNIRS has limited temporal resolution since measurements are driven by changes in blood flow and metabolism. fNIRS also has limited (∼1 cm) spatial resolution and is only sensitive to superficial sources. EEG 42 measures electrophysiological activity in neural networks and thus offers millisecond temporal precision. In addition, EEG is wearable, adaptable to any participant, and therefore enables naturalistic experiments. However, spatial resolution is restricted due to the inhomogeneous conductivity profile of the head (a problem made more challenging in very young (<18 months) children due to additional inhomogeneities caused by the fontanelle). EEG is also highly susceptible to artefacts from electrical activity in muscles. Conventional MEG 43 offers both excellent spatial and temporal resolution for non-invasive measurement of brain electrophysiology but is nevertheless limited in both performance and practicality – particularly in young people – due to the fixed nature of the sensor array. It therefore follows that the technologies currently in use for neurodevelopmental assessment are limited by either practicality, performance, or both. Development of new techniques for use in this area is therefore of high importance. In principle, OPM-MEG offers the performance of MEG, with the practicality of fNIRS or EEG, making it extremely attractive for use in children. Here, our first aim was to test the feasibility of this platform for neurodevelopmental studies.
We designed our OPM-MEG system for lifespan compliance. The helmets were relatively lightweight, ranging from ∼856 g (in the smallest case) to ∼906 g (in the largest case). While this is heavier than, for example, a child’s bicycle helmet (the average weight of which is ∼300-350 g) they were well tolerated by our cohort, most likely due to the relatively short scanning duration of < 5 minutes. Multiple sizes of helmet meant we could select the best fitting size for any given participant, reducing the confounds of small head size which are associated with conventional MEG. Heat from the sensors (which require elevated temperature to maintain operation in the spin exchange relaxation free regime 44) was controlled via both convection cooling, with air being able to flow through the helmet lattice, and an insulating material cap worn under the helmet by all participants (See Figure 1A). Together, these ensured that participants remained comfortable throughout data recording.
Whilst the helmet allows sensors to move with the head, the sensors are perturbed by background fields (e.g., if a sensor rotates in a uniform background field, or translates in a field gradient, it will see a changing field which can obfuscate brain activity and, in some cases, stop the sensors working 23). For this reason, our system also employed active field control 36 which enabled us to reduce the field to a level where sensors work reliably, even in the presence of head movements. This meant that, although we did not encourage our participants to move, they were completely unrestrained. The sensors themselves are robust to head motion: every sensor is a self-contained unit connected to its own control electronics by a cable that can accommodate rapid and uncontrolled movement. Another challenge when imaging children is the proximity of the brain to the scalp – the brain-scalp separation is 15 mm in adults but can be as little as 5 mm in children. Previous work 45 has shown that, when using radially oriented field measurements, a combination of finite sampling and the proximity of the brain can lead to inhomogeneous coverage (i.e. spatial aliasing). For this reason, our system was designed with triaxial sensors which helps to prevent this confound (though not directly related to scanning children, we also note that triaxial sensors enable improved noise rejection 46,47). Finally, our system was housed in a large MSR which allowed children to be accompanied by a parent throughout the scan. All these design features led to a system that enables acquisition of high-quality MEG data and is also well tolerated. We were able to obtain usable data in 27 out of 27 children and 24 out of 26 adults. Our findings of increased beta modulation and whole brain connectivity with age support previous studies7–9,18, and in this way provide a validation for this technology.
Importantly, most prior studies of neurodevelopmental trajectory were carried out in older children – for example Kurz et al. 8 showed a similar effect in 11-19 year olds; Trevarrow et al. 9 employed a cohort of 9-15 year olds and our own previous work also scanned a cohort of 9-15 year olds 19. In the present study, we were able to successfully scan children from age 2 years. There are important reasons for moving to younger participants: from a neuroscientific viewpoint, many critical milestones in development occur in the first few years (even months) of life – such as learning to walk and talk. If we can use OPM-MEG technology to measure the brain activities that underpin these developmental milestones, this would offer not only a new understanding of brain function but also new avenues to explore in atypically developing children. More importantly, many disorders strike in the first years of life – for example autism can be diagnosed in children as young as two years and epilepsy has a high incidence in children, including in the neonatal and infant period 48. In those where seizures cannot be controlled by drugs, surgery is often a viable option for treatment; the younger the patient, the more successful the outcome49. For these reasons, the development of a platform that enables the assessment of brain electrophysiology, with high spatiotemporal precision, in young people is a significant step forward and one that has potential to impact multiple neuroscientific and clinical areas.
In addition to providing a new platform for neurodevelopmental investigation, our study also provides insights into coordinated brain activity and its maturation with age. Beta band oscillations are thought to mediate top-down influence on primary cortices, with regions of high beta amplitude likely being inhibited (for a review see Barone and Rossiter 1). Whilst most evidence is based on studies of movement, there is significant supporting evidence from somatosensory studies in adults; for example Bauer et al. 50 showed that, when one attends to events relating to the left hand, a relative decrease in beta amplitude is seen in the contralateral sensory cortex (right) and an increase in ipsilateral cortex – suggesting the brain is inhibiting the sensory representation of the non-relevant hand. Given this strong link to attentional mechanisms and top-down processing, it is unsurprising that beta oscillations are not fully developed in children, and consequently change with age. Interestingly, Figure 3 implies that the well-known post stimulus beta signal – the PMBR – appears to be absent in children but can be seen in adults. The rebound, as well as being linked to top-down inhibition of the sensorimotor cortex, is associated with long range connectivity 51. The lack of rebound is therefore in agreement with the connectivity findings shown in Figure 4. We failed to see a significant difference in the spatial location of the cortical representations of the index and little finger; there are three potential reasons for this. First, the system was not designed to look for such a difference – sensors were sparsely distributed to achieve whole head coverage (rather than packed over sensory cortex to achieve the best spatial resolution in one area). Second, our “pseudo-MRI” approach to head modelling is less accurate than acquisition of participant-specific MRIs, and so may mask subtle spatial differences. Finally, previous work 52 suggested that, for a motor paradigm in adults, only the beta rebound, and not the power reduction during stimulation, mapped motortopically. Nevertheless, it remains the case that by placing sensors closer to the scalp, OPM-MEG should offer improved spatial resolution in children and adults; this should be the topic of future work.
The burst model of beta dynamics is relatively new, yet significant evidence already shows that the neurophysiological signal is driven by punctate bursts of activity, whose probability of occurrence changes depending on the task phase. Our study provides the first evidence that neurodevelopmental changes in the amplitude of task induced beta modulation can also be explained by the burst model. Specifically, we showed that task induced modulation of burst probability changes significantly as a function of age, suggesting bursts in somatosensory cortex are less likely to occur during stimulation of older participants compared to younger participants. We also showed that the “classical” beta band modulation exhibited a significant linear relationship with burst-probability modulation. In addition, when bursts do occur, they tend to have different spectral properties in younger participants. Specifically, younger participants have increased low-frequency activity and decreased high frequency activity, compared to adults. It is likely that a combination of the change in burst probability with age, and the change in dominant frequency (away from the canonical beta band) drives the observation from previous studies of changing beta modulation with age.
Our connectivity finding is also of note, showing a significant increase in functional connectivity with age. This is in good agreement with previous literature – for example Schäfer et al. 18 showed quantitatively similar data in conventional MEG, albeit again by scanning older children (ages 6 and up). Here we also showed that connectivity changes with age are most prominent in the frontal and parietal areas, and weakest in the visual cortex. It makes intuitive sense that the largest changes in connectivity over the age range studied should occur in the parietal and frontal regions – these regions are typically associated with both cognitive and attentional networks and are implicated in the networks that develop most between childhood and adulthood. Here, we observed a relative lack of age-related change in the visual regions; this could relate to the nature of the task – recall that all volunteers watched their favourite TV show and so the visual regions were being stimulated throughout, driving coordinated network activity in occipital cortex. The visual system is also early to mature compared to frontal cortex.
There are four limitations of our system which warrant discussion. Firstly, the range of available helmets was limited, and future studies may aim to use more sizes to better accommodate variation in head size and shape. Also, even the lightweight helmet used here may be too heavy for younger participants; whilst in general it was tolerated very well, anecdotally, some of the very young participants commented that it was heavy. This indicates that further optimisation of helmet-weight is needed if we want to move towards younger (< 2 years) participants; in babies a fundamentally different solution must be found. Further optimisation is possible since, whilst the total weight is approximately 900 g, the combined sensor weight is just 250g. Similarly, the warmth generated by the sensors was controlled by a combination of convection and insulation. However, for systems with a higher channel count, where more heat may be generated, active cooling (e.g., air forced through the helmet) may be required. Second, the number of sensors available varied across participants – this was mainly for pragmatic purposes (the system was experimental and not all OPMs were available for every recording). Whilst we always ensured good coverage of sensorimotor cortex, and tried to optimise whole brain coverage as much as we could, the system is likely to have diminished sensitivity around the temporal cortex, and this may explain why there was relatively little change in connectivity with age in those regions. In future, the inclusion of more sensors, particularly around the cheekbone would be a natural extension. Thirdly, magnetic field control was only available over a region encompassing the head, whilst participants were seated (i.e., participants had to be sat in a chair for the scanner to work). This was key to ensuring participants were unconstrained. However, in future studies, particularly in younger participants, it may be desirable to accommodate different positions (e.g., participants seated on the floor) and a greater range of motion (e.g., children crawling or walking). This may be possible with newly developing coil technology 53. Finally, here we chose to study sensory stimulation. There are many other systems to choose – and whether the findings here regarding beta bursts and the changes with age also extend to other brain networks remains an open question that could be explored in future studies.
Conclusion
Characterising how neural oscillations change with age is a key step towards understanding the developmental trajectory of coordinated brain function, and the deviation of that trajectory in disorders. However, limitations of conventional, non-invasive approaches to measuring electrophysiology have led to confounds when scanning children. Here, we have demonstrated a new platform for neurodevelopmental assessment. Using OPM-MEG, we have been able to provide evidence – supported by previous studies – that shows both task-induced beta modulation and whole brain functional connectivity increase with age. In addition, we have shown that the classically observed beta power drop during stimulation can be explained by a lower burst probability, and that modulation of burst probability changes with age. We further showed that the frequency content of bursts changes with age. Our results offer new insights into the developmental trajectory of beta oscillations and provide the first clear evidence that OPM-MEG is an ideal platform to study electrophysiology in children.
MethodsParticipants and Experiment
The study received ethical approval from the University of Nottingham Research Ethics Committee and written consent was obtained from the parents of each participant. Consent and authorisation for publication of Figure 1A were also obtained.
The paradigm comprised tactile stimulation of the tips of the index and little fingers using two braille stimulators (METEC, Germany) (See Figure 1C). Each stimulator comprised 8 independently controlled pins which could be raised or lowered to tap the participant’s finger. A single trial comprised 0.5 s of stimulation (during which the finger was tapped 3 times using all 8 pins) followed by 3 s rest and the finger stimulated (index or little) was alternated between trials. There was a total of 42 trials for each finger, meaning the experiment lasted a total of 294 s. Throughout the experiment, participants watched a television program of their choice (presented via projection onto a screen in the MSR). All children were accompanied inside the MSR by a parent and one experimenter throughout their visit.
Data collection and co-registration
The sensor array comprised 64 triaxial OPMs (QuSpin Inc, Colorado, USA, Zero Field Magnetometer, Third Generation, Triaxial Variant) which enabled a maximum of 192 measurements of magnetic field around the scalp (192-channels). The OPMs could be mounted in one of four 3D-printed helmets of different sizes (Cerca Magnetics Ltd., Nottingham, UK); this affords (approximate) whole-head coverage and adaptation to the participants head size. All participants wore a thin aerogel cap underneath the helmet to control heat from the sensors (which operate with elevated temperature). The system is housed in a magnetically shielded room (MSR) equipped with degaussing coils 54 and active magnetic field control 36 (Cerca Magnetics Ltd., Nottingham, UK). Prior to data collection, the MSR was demagnetised and the magnetic field compensation coils were enabled (using currents based on previously obtained field maps, the demagnetisation procedure ensures a repeatable background field and the magnetically quiet campus location of our MSR ensures field drifts <0.05 nT/min 55,56). This procedure, which results in a background field of ∼0.6 nT 56, is important to enable free head motion during a scan 57. All OPMs were equipped with on-board coils which were used for sensor calibration. MEG data were collected at a sampling rate of 1,200 Hz using a National Instruments (NI, Texas, USA) data acquisition system interfaced with LabVIEW (NI).
Following data collection, two 3D digitisations of the participant’s head, with and without the OPM helmet, were generated using a 3D structured light metrology scanner (Einscan H, SHINING 3D, Hangzhou, China). Participants wore a swimming cap to flatten hair during the ‘head-only’ scan. The head-only digitisation was used to measure head size and shape, and an age-matched T1-weighted template MRI scan was selected from a database 58 and warped to fit the digitisation, using FLIRT in FSL 59,60. This procedure resulted in a “pseudo-MRI” which provided an approximation of the subject’s brain anatomy. Sensor locations and orientations relative to this anatomy were found by aligning it to the digitisation of the participant wearing the sensor helmet, and adding the known geometry of the sensor locations and orientations within the helmet 61–63. This was done using MeshLab 64.
MEG Data Preprocessing
We used a preprocessing pipeline described previously 63. Briefly, broken or excessively noisy channels were identified by manual visual inspection of channel power spectra; any channels that were either excessively noisy, or had zero amplitude, were removed. Bad trials were defined as those with variance greater than 3 standard deviations from the mean trial variance, and automatically removed. A visual inspection was also carried out and any remaining trials with excess artefacts were removed. Notch filters at the powerline frequency (50 Hz) and 2 harmonics, and a 1-150 Hz band pass filter, were applied. Finally, eye blink and cardiac artefacts were removed using ICA (implemented in FieldTrip 65) and homogeneous field correction (HFC) was applied to reduce interference 66.
Source Reconstruction and Beta-Modulation
For source estimation, we used a LCMV beamformer spatial filter 67. For all analyses, covariance matrices were generated using data acquired throughout the whole experiment (excluding bad channels and trials). Covariance matrices were regularised using the Tikhonov method with a regularisation parameter equal to 5% of the maximum eigenvalue of the unregularized matrix. The forward model was based on a single shell volumetric conductor 68.
To construct the Pseudo-T statistical images, data were filtered to the beta-band (13-30 Hz) and narrow band data covariance matrixes generated. Voxels were placed on both an isotropic 4-mm grid covering the whole brain, and a 1-mm grid covering the contralateral sensorimotor regions. For each voxel, we contrasted power in active (0.3-0.8 s) and control (2.5-3 s) time windows to generate images showing the spatial signature of beta band modulation. Separate images were derived for index and little finger trials. To generate time frequency spectra, we used broad-band (1-150 Hz) data and covariance matrices. The beamformer was used to produce a time course of neural activity (termed a “virtual electrode”) at the voxel with maximum beta-band desynchronisation. The resulting projected broad-band data were frequency filtered into a set of overlapping bands, and a Hilbert transform used to derive the analytic signal for each band. The absolute value of this was computed to give the envelope of oscillatory amplitude (termed the Hilbert envelope). This was averaged across trials, concatenated in frequency, baseline corrected and normalised yielding a time frequency spectrogram showing relative change in spectral power (from baseline) as a function of time and frequency. Finally, to quantify the magnitude of beta-modulation, we filtered the virtual electrode to the beta band, calculated the Hilbert envelope and measured the mean difference in amplitude between stimulation (0.3-0.8 s) and post-stimulus (1-1.5 s) time windows. These values (derived for every participant) were plotted against age and a relationship assessed via Pearson correlation.
Functional Connectivity Analysis
To estimate connectivity, we first parcellated the brain into distinct regions. To this end, estimated brain anatomies were co-registered to the MNI standard brain using FSL FLIRT 59,60 and divided into 78 cortical regions according to the Automated Anatomical Labelling (AAL) atlas 69–71. Virtual electrode timecourses were generated at the centre of mass of each of these 78 regions, and the beta band Hilbert envelope derived. We then computed Amplitude Envelope Correlation (AEC) as an estimate of functional connectivity between all possible pairs of AAL regions 17,37. Prior to AEC, we applied pairwise orthogonalisation to reduce source leakage 72,73. This resulted in a single connectome matrix per participant. We estimated “Global Connectivity” as the mean connectivity value across all elements in the connectome. This was plotted against age and the relationship assessed using Pearson correlation. To visualise the spatial variation in age-related connectivity changes, we also estimated the correlation between node degree (i.e., the column-wise sum of connectome matrix elements) and age, for each of the 78 AAL regions.
Beta Bursts and Hidden Markov Modelling
To estimate beta burst timings we employed a three-state, time-delay embedded univariate HMM34. This method has been described extensively in previously papers 30,39. Briefly, virtual electrode time series were frequency filtered between 1-48 Hz. The HMM was used to divide this timecourse into three “states” each depicting repeating patterns of activity with similar temporo-spectral signatures. The output was three timecourses representing the likelihood of each state being active as a function of time. These were binarized (using a threshold of 2/3) and used to generate measures of the probability of state occurrence as a function of time in a single trial. The state whose probability of occurrence modulated most with the task was defined as the “burst state”. We estimated age-related changes in burst probability modulation and the relationship between burst probability modulation and classical beta-modulation (see above) using Pearson correlation. Further, we investigated the spectral content of the burst state and its modulation with age using multi-taper estimation of the power spectral density (PSD) 34. Having derived the spectral content of the burst state we used Pearson correlation to measure how the PSD magnitude, for every frequency, changes with age.
Acknowledgements
This work was supported by an Engineering and Physical Sciences Research Council (EPSRC) Healthcare Impact Partnership Grant (EP/V047264/1) and an Innovate UK germinator award (Grant number 1003346). We also acknowledge support from the UK Quantum Technology Hub in Sensing and Timing, funded by EPSRC (EP/T001046/1), a Wellcome Collaborative Award in Science (203257/Z/16/Z and 203257/B/16/Z) and the National Institutes of Health (Grant Number R01EB028772). Sensor development was made possible by funding from the National Institutes of Health (R44MH110288).
Conflicts of interest
V.S. is the founding director of QuSpin, a commercial entity selling OPM magnetometers. J.O. and C.D. are employees of QuSpin. E.B. and M.J.B. are directors of Cerca Magnetics Limited, a spin-out company whose aim is to commercialise aspects of OPM-MEG technology. E.B., M.J.B., R.B., N.H. and R.H. hold founding equity in Cerca Magnetics Limited. HS, FW, and TH are employees of Cerca Magnetics Limited.
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Beta band modulation with age for little finger stimulation:
(A) Brain plots show slices through the left motor cortex, with a pseudo-T-statistical map of beta modulation (blue/black) overlaid on the standard brain. Time frequency spectrograms show modulation of the amplitude of neuraloscillations (fractional change in spectral power relative to the baseline measured in the 2.5-3 s window). In all cases results were extracted from the location of peak beta power reduction during stimulation (in the left sensorimotor cortex). (B) Difference in beta-band amplitude (0.3-0.8 s window vs 1-1.5 s window) plotted as a function of age (i.e., each data point shows a different participant; triangles represent children, circles represent adults). Note the significant correlation (R2=0.23, p=0.00032*).
Spectral content of the non-burst states.
(A) Average non-burst-state spectra across groups. Shaded areas indicate standard error on the group mean. (B) Pearson correlation coefficient for the PSD values in (A) against age across all frequency values. Red shaded areas indicate 𝑝 < 0.01 (uncorrected). The four inset plots show example scatters of PSD values with age at select frequencies (3 Hz, 9 Hz, 21 Hz, and 37 Hz). Low-frequency spectral content decreases with age while high-frequency content increases. Results broadly mirror the frequency content and age relationships found in the burst state, however, features in the spectra corresponding to classical alpha and beta peaks are less prominent outside the burst state.
This study provides important evidence supporting the ability of a new type of neuroimaging, OPM-MEG system, to measure beta-band oscillation in sensorimotor tasks on 2-14 years old children and to demonstrate the corresponding development changes, since neuroimaging methods with high spatiotemporal resolution that could be used on small children are quite limited. The evidence supporting the conclusion is solid but lacks clarifications about the much-discussed advantages of OPM-MEG system (e.g., motion tolerance), control analyses (e.g., trial number), and rationale for using sensorimotor tasks. This work will be of interest to the neuroimaging and developmental science communities.
Compared with conventional SQUID-MEG, OPM-MEG offers theoretical advantages of sensor configurability (that is, sizing to suit the head size) and motion tolerance (the sensors are intrinsically in the head reference frame). This study purports to be the first to experimentally demonstrate these advantages in a developmental study from age 2 to age 34.
In short, while the theoretical advantages of OPM-MEG are attractive - both in terms of young child sensitivity and in terms of motion tolerance - neither was in fact demonstrated in this manuscript. We are left with a replication of SQUID-MEG observations, which certainly establishes OPM-MEG as "substantially equivalent" to conventional technology but misses the opportunity to empirically demonstrate the much-discussed theoretical advantages/opportunities.
Strengths:
A replication of SQUID-MEG observations, which certainly establishes OPM-MEG as "substantially equivalent" to conventional technology but misses the opportunity to empirically demonstrate the much-discussed theoretical advantages/opportunities.
Weaknesses:
The authors describe 64 tri-axial detectors, which they refer to as 192 channels. This is in keeping with some of the SQUID-MEG description, but possibly somewhat disingenuous. For the scientific literature, perhaps "64 tri-axial detectors" is a more parsimonious description.
A small fraction (<20%) of trials were eliminated for analysis because of "excess interference" - this warrants further elaboration.
Figure 3 shows a reduced beta ERD in the youngest children. Although the authors claim that OPM-MEG would be similarly sensitive for all ages and that SQUID-MEG would be relatively insensitive to young children, one trivial counterargument that needs to be addressed is that OPM has NOT in fact increased the sensitivity to young child ERD. This can possibly be addressed by analogous experiments using a SQUID-based system. An alternative would be to demonstrate similar sensitivity across ages using OPM to a brain measure such as evoked response amplitude. In short, how does Figure 3 demonstrate the (theoretical) sensitivity advantage of OPM MEG in small heads ?
The data do not make a compelling case for the motion tolerance of OPM-MEG. Although an apparent advantage of a wearable system, an empirical demonstration is still lacking. How was motion tracked in these participants?
Furthermore, while the introduction discusses at some length the phenomenon of PMBR, there is no demonstration of the recording of PMBR (or post-sensory beta rebound). This is a shame because there is literature suggesting an age-sensitivity to this, that the optimal sensitivity of OPM-MEG might confirm/refute. There is little evidence in Figure 3 for adult beta rebound. Is there an explanation for the lack of sensitivity to this phenomenon in children/adolescents ? Could a more robust paradigm (button-press) have shed light on this?
Data on functional connectivity are valuable but do not rely on OPM recording. They further do not add strength to the argument that OPM MEG is more sensitive to brain activity in smaller heads - in fact, the OPM recordings seem plagued by the same insensitivity observed using conventional systems.
The discussion of burst vs oscillations, while highly relevant in the field, is somewhat independent of the OPM recording approach and does not add weight to the OPM claims.
In short, while the theoretical advantages of OPM-MEG are attractive - both in terms of young child sensitivity and in terms of motion tolerance, neither was in fact demonstrated in this manuscript. We are left with a replication of SQUID-MEG observations, which certainly establishes OPM-MEG as "substantially equivalent" to conventional technology but misses the opportunity to empirically demonstrate the much-discussed theoretical advantages/opportunities.
The authors introduce a new 192-channel OPM system that can be configured using different helmets to fit individuals from 2 to 34 years old. To demonstrate the veracity of the system, they conduct a sensorimotor task aimed at mapping developmental changes in beta oscillations across this age range. Many past studies have mapped the trajectory of beta (and gamma) oscillations in the sensorimotor cortices, but these studies have focused on older children and adolescents (e.g., 9-15 years old) and used motor tasks. Thus, given the study goals, the choice of a somatosensory task was surprising and not justified. The authors recorded a final sample of 27 children (2-13 years old) and 24 adults (21-34 years) and performed a time-frequency analysis to identify oscillatory activity. This revealed strong beta oscillations (decreases from baseline) following the somatosensory stimulation, which the authors imaged to discern generators in the sensorimotor cortices. They then computed the power difference between 0.3-0.8 period and 1.0-1.5 s post-stimulation period and showed that the beta response became stronger with age (more negative relative to the stimulation period). Using these same time windows, they computed the beta burst probability and showed that this probability increased as a function of age. They also showed that the spectral composition of the bursts varied with age. Finally, they conducted a whole-brain connectivity analysis. The goals of the connectivity analysis were not as clear as prior studies of sensorimotor development have not conducted such analyses and typically such whole-brain connectivity analyses are performed on resting-state data, whereas here the authors performed the analysis on task-based data. In sum, the authors demonstrate that they can image beta oscillations in young children using OPM and discern developmental effects.
Strengths:
Major strengths of the study include the novel OPM system and the unique participant population going down to 2-year-olds. The analyses are also innovative in many respects.
Weaknesses:
Several weaknesses currently limit the impact of the study. First, the choice of a somatosensory stimulation task over a motor task was not justified. The authors discuss the developmental motor literature throughout the introduction, but then present data from a somatosensory task, which is confusing. Of note, there is considerable literature on the development of somatosensory responses so the study could be framed with that. Second, the primary somatosensory response actually occurs well before the time window of interest in all of the key analyses. There is an established literature showing mechanical stimulation activates the somatosensory cortex within the first 100 ms following stimulation, with the M50 being the most robust response. The authors focus on a beta decrease (desynchronization) from 0.3-0.8 s which is obviously much later, despite the primary somatosensory response being clear in some of their spectrograms (e.g., Figure 3 in older children and adults). This response appears to exhibit a robust developmental effect in these spectrograms so it is unclear why the authors did not examine it. This raises a second point; to my knowledge, the beta decrease following stimulation has not been widely studied and its function is unknown. The maps in Figure 3 suggest that the response is anterior to the somatosensory cortex and perhaps even anterior to the motor cortex. Since the goal of the study is to demonstrate the developmental trajectory of well-known neural responses using an OPM system, should the authors not focus on the best-understood responses (i.e., the primary somatosensory response that occurs from 0.0-0.3 s)?
Regarding the developmental effects, the authors appear to compute a modulation index that contrasts the peak beta window (.3 to .8) to a later 1.0-1.5 s window where a rebound is present in older adults. This is problematic for several reasons. First, it prevents the origin of the developmental effect from being discerned, as a difference in the beta decrease following stimulation is confounded with the beta rebound that occurs later. A developmental effect in either of these responses could be driving the effect. From Figure 3, it visually appears that the much later rebound response is driving the developmental effect and not the beta decrease that is the primary focus of the study. Second, these time windows are a concern because a different time window was used to derive the peak voxel used in these analyses. From the methods, it appears the image was derived using the .3-.8 window versus a baseline of 2.5-3.0 s. How do the authors know that the peak would be the same in this other time window (0.3-0.8 vs. 1.0-1.5)? Given the confound mentioned above, I would recommend that the authors contrast each of their windows (0.3-0.8 and 1.0-1.5) with the 2.5-3.0 window to compute independent modulation indices. This would enable them to identify which of the two windows (beta decrease from 0.3-0.8 s or the increase from 1.0-1.5 s) exhibited a developmental effect. Also, for clarity, the authors should write out the equation that they used to compute the modulation index. The direction of the difference (positive vs. negative) is not always clear.
Another complication of using a somatosensory task is that the literature on bursting is much more limited and it is unclear what the expectations would be. Overall, the burst probability appears to be relatively flat across the trial, except that there is a sharp decrease during the beta decrease (.3-.8 s). This matches the conventional trial-averaging analysis, which is good to see. However, how the bursting observed here relates to the motor literature and the PMBR versus beta ERD is unclear.
Another weakness is that all participants completed 42 trials, but 19% of the trials were excluded in children and 9% were excluded in adults. The number of trials is proportional to the signal-to-noise ratio. Thus, the developmental differences observed in response amplitude could reflect differences in the number of trials that went into the final analyses.
Finally, the discussion could be improved to focus on the somatosensory literature and how this contributes to that. Currently, the discussion includes very little from the somatosensory literature.
This study demonstrated the application of OPM-MEG in neurodevelopment studies of somatosensory beta oscillations and connections with children as young as 2 years old. It provides a new functional neuroimaging method that has a high spatial-temporal resolution as well wearable which makes it a new useful tool for studies in young children. They have constructed a 192-channel wearable OPM-MEG system that includes field compensation coils which allow free head movement scanning with a relatively high ratio of usable trials. Beta band oscillations during somatosensory tasks are well localized and the modulation with age is found in the amplitude, connectivity, and pan-spectral burst probability. It is demonstrated that the wearable OPM-MEG could be used in children as a quite practical and easy-to-deploy neuroimaging method with performance as good as conventional MEG. With both good spatial (several millimeters) and temporal (milliseconds) resolution, it provides a novel and powerful technology for neurodevelopment research and clinical applications not limited to somatosensory areas.
The conclusions of this paper are mostly well supported by data acquired under the proper method. However, some aspects of data analysis need to be improved and extended.
(1) The colour bars selected for the pseudo-T-static pictures of beta modulation in Figures 2 and 3, which are blue/black and red/black, are not easily distinguished from the anatomical images which are grey-scale. A colour bar without black/white would make these figures better. The peak point locations are also suggested to be marked in Figure 2 and averaged locations in Figure 3 with an error bar.
(2) The data points in plots are not constant across figures. In Figures 3 and 5, they are classified into triangles and circles for children and adults, but all are circles in Figures 4 and 6.
(3) Although MEG is much less susceptible to conductivity inhomogeneity of the head than EEG, the forward modulating may still be impacted by the small head profile. Add more information about source localization accuracy and stability across ages or head size.