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. 2026 Mar 11;29(3):e70146. doi: 10.1111/desc.70146

MEG Evidence of Concurrent Bilateral and Hemisphere‐Specific Developmental Patterns in Auditory Cortex

Tiina Parviainen 1,3,✉, Pyry Heikkinen 1, Anni Hänninen 1, Anna Christina Nobre 2,3
PMCID: PMC12979710  PMID: 41814452

ABSTRACT

Children demonstrate remarkably different temporal characteristics, as well as higher interindividual variability, in the cortical auditory responses as compared with adults. The detailed dynamics at the level of underlying current generators are, however, not established. We used magnetoencephalography (MEG) to explore the age‐related differences in the cortical currents probed by simple auditory stimuli across age groups in children between 6 and 13.5 years and adults, with a specific focus on the time course of hemispheric differences. Two concurrent but independent changes by age were evident. First, linearly increasing adoption of early transient automatic processing appeared developmentally earlier in the right than in the left hemisphere, replicating previous findings. Second, enhanced reactivity of the cortex, in bilaterally symmetric manner at > 200 ms, was strongest in the mid to late childhood thus developmentally following an inverted u‐curve. Our findings support the idea of increased responsiveness of the sensory cortices in a specific developmental stage, that may underlie sensitive periods for perceptual learning. The delayed emergence of transient responses in left hemisphere may reflect its role in attuning to the rich language input during extended period of development.

Summary

  • Auditory cortex responses to tones show a shift in emphasis from P50m to N100m.

  • N100m emerges earlier in development in the right than in the left hemisphere.

  • Children in mid‐childhood show prominent bilateral late reactivity, suggesting a period of heightened sensitivity to stimulation.

Keywords: auditory, development, Magnetoencephalography, N100m, N250m, P50m

1. Introduction

Cortical circuits in children's brains show a remarkable capacity to change in response to interaction with environment. Although changing synaptic connections enable learning throughout lifespan, even the basic sensory circuits are strongly malleable in children's brains. One of the regions evidencing protracted developmental trajectory is the superior temporal cortex (STG) (Gogtay et al. 2004; Bethlehem et al.). The STG houses the cortical areas for auditory‐based information processing and likely contributes significantly to language learning and literacy (Zhao et al. 2022; Parviainen et al. 2011; Hämäläinen et al. 2013). Although most of the anatomical development of auditory cortices occurs during the first years of life (Yakovlev and Lecours 1967; Zhang et al. 2007), the development of the cortical auditory system microstructure continues into late adolescence and adulthood (Giedd et al. 1999; Gogtay et al. 2004; Paus et al. 1999). The electrophysiological response properties of the auditory cortex in children offer the opportunity to understand the development of the cortical perceptual processes and the unique cortical responsivity to external stimulation characteristic to the child brain .

The cortically measured electric responses to simple sensory probes reflect the progression of synaptic signaling in the (subcortical and) cortical pathways (Hari 1990; Eggermont 2001; Parviainen and Kujala 2023). Age‐related changes in evoked responses therefore provide a non‐invasive, temporally precise window into the development of this pathway—the underlying synaptic connections within cortical circuits (Eggermont 1988). Developmental changes in synaptic signaling properties determine the manner in which cortical circuits can re‐organize in response to external input (Hensch et al. 1998). This is particularly relevant for understanding changes in the capacity for perceptual learning—hence the notion of ‘critical’ or sensitive periods.

Substantial age‐related changes were described in the morphology of the auditory evoked response measured by electroencephalography (EEG) already 30 years ago (Courchesne 1990; Ceponiene et al. 1998; Ponton et al. 2000; Sharma et al. 1997; for review Wunderlich and Cone‐Wesson 2006). The auditory response to passive stimulation in the adult brain is characterized by a transient response, the P50/N100, which subsides by about 100 ms. This pattern emerges gradually over the childhood years, with an age‐related decrease in strength and latency for the P50/P50m (Cunningham et al. 2000; Takeshita et al. 2002) and a decrease in latency but an increase in strength for the N100/N100m (Green et al. 2022; Edgar et al. 2014; Cunningham et al. 2000), albeit with some conflicting findings (Fuchigami et al. 1993; Ponton et al. 2000). Besides the emergence of transient P50/N100 adult‐like responses, children across a wide age range consistently exhibit a distinct, developmentally specific longer‐latency response (N250/N250m) (Parviainen et al. 2019; Cunningham et al. 2000; Johnstone et al. 1996; Kraus et al. 1993; Ponton et al. 2000; Sussman et al. 2008; Edgar et al. 2020; Fujioka et al. 2006), which shows either a decrease (Ceponiene et al. 2002; Ponton et al. 2000) or no change (van Bijnen et al. 2022) in strength depending on the studied age group. These age‐related patterns suggest that auditory cortical activity undergoes complex reorganization across development, with different response features maturing along distinct trajectories.

However, the changes in proportional emphasis of these three different response types has not been systematically approached, particularly during the period of considerable cognitive and psychological maturation of mid‐childhood years. Gaining a deeper understanding of both typical and atypical age‐related changes in cortical properties—especially in humans—will require moving beyond sensor‐level evoked responses toward source‐informed approaches that better capture underlying neurobiological processes.

The magnetic field recorded from outside the head can be analyzed at the level of their underlying current generators, with distinct temporal dynamics and spatial distribution (Hari et al. 2010). This is a critical feature for examining developmental changes, as the surface‐recorded evoked components cannot be directly compared between age groups. Response components with opposite polarity (in EEG) or current direction (in MEG) are generally considered to reflect different stages of processing in the underlying neuronal circuitry, but the co‐occurrence of P50 in children and N100 in adults (both at around 100 ms) (Parviainen et al. 2019; Sussman et al. 2008; Fujioka et al. 2006; Orekhova et al. 2013) makes it unreliable to use pure latency‐amplitude measures in describing developmental change. Both focal source estimates (equivalent current dipole, ECD, Hämäläinen et al. 1993) and distributed source estimates (minimum norm estimate, MNE, Gramfort et al. 2014) can include current direction, in addition to its time‐varying amplitude, in the estimate and provide thus feasible ways to approach more specific developmental changes in the auditory system.

Interestingly, the early transient activation in children showed more adult‐like characteristics in the right auditory cortex (Parviainen et al. 2019) suggesting faster functional maturation of the right hemisphere at school age (Parviainen et al. 2011, Parviainen et al. 2019; Kotecha et al. 2009; Fujioka and Ross 2008; van Bijnen et al. 2022; Edgar et al. 2014; Green et al. 2022). Post‐mortem and DTI‐studies have shown developmental lag in structural properties of the left hemisphere, showing earlier emergence of gyral complexity in the area of planum temporale (Chi et al. 1977) and faster “pre”‐myelination in arcuate fasciculus (Dubois et al. 2008; Glasel et al. 2011) in the right hemisphere. However, age‐related differences in hemispheric balance across the time course of auditory evoked responses have not yet been systematically investigated—particularly regarding the developmental interplay between early transient (P50m/N100m) and later sustained (N250m) components within the same study.

Here, we build on the generally accepted assumption that the obligatory auditory evoked responses measured with MEG reflect the characteristics of the synaptic signaling pathway (Baillet 2017; Hari et al. 2010). We map the age‐related differences in the cortical signaling underlying passive sensory‐perceptual processing, particularly the changes in balance between fast transient reactivity and late prolonged pattern across mid childhood years. We utilize source‐modeling algorithms that allow reliable comparison across age groups. We specifically focus on the age‐related differences, and hemispheric lateralization, of the prolonged reactivity of the cortex (N250m) within different age groups of children and between children and adults. We also test the hypothesis that the two hemispheres show differences in the maturational emergence of adult‐like (N100m) features in the auditory evoked response, as has been suggested in earlier studies focusing on a narrower age range (Poulsen et al. 2009; Parviainen et al. 2019; Green et al. 2022).

2. Methods

2.1. Participants

Participants were recruited from schools and via university mailing lists in the Oxford area in the UK. All eligible and willing pupils from the participating schools were invited. They were required to speak English as their mother tongue, be right‐handed, and have normal hearing and no history of neurological disorders. In the original sample, 48 participants were recruited, but four were excluded due to disrupted or missing data resulting in 44 participants in the final analysis. Participants were evenly distributed across narrow age ranges to capture major developmental stages from early to late childhood: Group A (6–7.5 years, n = 13, two females), Group B (9–10.5 years, n = 10, six females), Group C (12–13.5 years, n = 12, eight females) and Group D (adults; 19–26 years, n = 9, six females). Although dividing participants into narrowly defined age groups necessarily reduced the number of participants within each group, this structure allowed age group to be included as a factor in the repeated‐measures ANOVA, thereby enabling the detection of systematic age‐related effects even with relatively modest group sizes. Prior to testing, an informed consent form was collected from all participants and, in the case of children, from their parents. Participants were reimbursed for the expenses incurred by their participation. All participants attended two separate research sessions (behavioral testing and MEG measurement). The study was approved by the Central University Research Ethics Committee at the University of Oxford and was carried out in accordance with The Declaration of Helsinki.

2.2. Behavioural Tests

All participants were tested with a cognitive test battery to confirm typical cognitive development. The test battery included tests for linguistic and non‐linguistic skills, reading‐related skills, and verbal short‐term memory (Vocabulary and Matrix Reasoning of a short‐form administration of WISC‐III, Wechsler 1974; The Test of Word Reading Efficiency (Towre), Torgesen et al. 1999).

2.3. MEG Recordings and Stimuli

The magnetic signals were recorded with the VectorViewTM system (Elekta Neuromag, Helsinki, Finland) in Oxford Center for Human Brain Activity at the Warneford Hospital in Oxford. The MEG helmet has 306 sensors distributed across 122 locations, with triple‐sensors elements of two orthogonally oriented planar gradiometers and one magnetometer. The position of each participant's head in the MEG helmet was defined by using four head position indicator (HPI) coils attached to the scalp (Ahlfors and Ilmoniemi 1989; Fuchs et al. 1995). Before the measurement, their locations were measured with the Polhemus Fastrak digitizer (Colchester, VT, USA), together with three anatomical landmarks (nasion, left and right pre‐auricular reference points), to define head‐based MEG coordinate system with x‐axis passing through the pre‐auricular points from left to right, the y‐axis passing through the nasion perpendicular to the x‐axis, and the z‐axis pointing upwards. Extra points were collected covering the scalp morphology. To determine the location of the HPI coils in the MEG helmet, the coils were briefly activated at the beginning of the recordings. Continuous head position tracking (cHPI) was used with youngest children to compensate for effects of the head movements on the MEG data (Taulu et al. 2004). Further, horizontal and vertical eye movements (electro‐oculogram) were recorded to detect eye movements and blinks.

Participants were instructed to sit still and watch silent cartoons without paying special attention to the sound stimuli. Stimuli were 50‐ms 1000‐Hz pure tones with 15‐ms rise and fall times, created in Adobe Audition 1.5. Tones were presented monaurally to the left and right ears, alternately between the ears via inserted earphones using Presentation software (Neurobehavioral System Inc., San Fransisco, CA). The interstimulus interval (ISI) was randomized between 0.8 and 1.2 s. The individual auditory threshold was obtained for each participant to ensure that all participants had normal hearing. During the MEG recordings, the tones were presented 65 dB above hearing level.

2.4. Preprocessing

Prior to further analysis, the signal separation (SSS) method (Taulu 2005b) was applied to improve data quality by suppression of interfering signals outside of the MEG sensor array. When continuous HPI tracking was used, SSS was applied with movement compensation (Taulu 2005a). The MEG signals were bandpass filtered at 0.03–.330 Hz and sampled at 600 Hz.

2.5. Sensor Level Analysis

The data were first examined at the sensor level. For this, epochs of −200 to 800 ms, with baseline correction set at −200 to 0 ms were extracted, and epochs contaminated by eye movements were removed. On average 105 (minimum 60) artifact‐free epochs per category were extracted for each participant. Two subsequent time windows of activation were evident in the 1‐s interval after stimulus onset: at around 100 ms (later referred to as early time‐window, <200 ms) and after 200 ms (late time‐window, >200 ms) post stimulus (see Figure 1). These time windows closely matched the timing of main peaks of electrophysiological activity to passive stimulation in the earlier literature (Sussman et al. 2008; Parviainen et al. 2011; Parviainen et al. 2019; van Bijnen et al. 2023; Takeshita et al. 2002; Johnstone et al. 1996) and were used as the basis for further analysis.

FIGURE 1.

FIGURE 1

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Auditory evoked responses (AER) in one sensor. AERs over the left and right temporal cortex (black) in the four age groups (A = children 6–7.5 years, B = children 9–10.5 years, C = children 12–13.5 years, D = adults 19–26 years). Left‐ and right‐ear sounds are shown separately.

2.6. Source‐Level Analysis

Minimum Norm Estimation (MNE Python, version 16) was used to reconstruct the distribution of cortical current sources in the early and late time‐windows and to study the differences in strength of activation between groups and hemispheres. In addition to the distributed source analysis within the time windows of interest, focal equivalent current dipoles (ECD's) were used to model the dominant current underlying the evoked responses in both time windows in each age group (Hämäläinen et al. 1993) (See Appendix A). The ECD's were used to extract the timing (peak latency) of the current activity in the main time‐windows.

2.6.1. MNE Source Analysis

MNE source analysis was performed on the maxfiltered data. Data were first filtered with low pass of 40 Hz and a high pass of 0.1 Hz. Blink and heartbeat artifacts were removed using ICA, with a semi‐automatic procedure: the topographies and time courses of components intended for removal were inspected to ensure they represented typical patterns for eye‐ and heart‐generated signals. Data were epoched from −200 to 800 ms, with baseline correction set at −200 to 0 ms and epochs with peak‐to‐peak values exceeding 4000e‐13 for gradiometers and 4e‐12 for magnetometers were rejected from the analysis. The source space used consisted of 4098 (spacing: 4.9 mm, area: 24mm2) octahedron‐shaped (oct6) active vertices at the grey‐white matter boundary, for each hemisphere. Importantly, all preprocessing steps (including filtering, ICA, artifact rejection, epoching, and baseline correction) were applied identically across all age groups to ensure full methodological consistency (Gross et al. 2013).

The normalized covariance matrix, utilized for whitening, was calculated individually from epoch baselines (−200 to 0 ms) for both left and right ear stimulation. The best estimator was chosen utilizing a cross‐validation paradigm employed in the MNE software, with two possible outcomes (shrunk, empirical). For the inverse operator a loose orientation constraint (0.2) and depth weighting (of 0.8) was applied to acquire a more precise estimate. In the final source estimate, source orientation was set normal to the white matter boundary, to get a more accurate representation that maintains the sign (direction of current) of the activity.

ROI selection was based on the localization of auditory sensory activation in the temporal cortex both in the previous literature (e.g. Hari et al. 1980; Shvarts et al. 2020), as well as in the visualized distribution of activation in the current study (see Figure 2). For further analysis, a region of interest (ROI), including Heschl's gyrus and sulcus and planum temporale, was defined based on the Destrieux atlas (Destrieux et al. 2010; Fischl et al. 2004), in each hemisphere separately. In the absence of individual MRI, templates from the McGill pediatric atlases (Fonov et al. 2011) and Fsaverage template (Dale et al. 1999; Fischl 2012; Fischl et al. 1999) were used to build cortically constrained source estimates for the children and adult groups, respectively, as this approach has adequate accuracy when dealing with sources close to the scalp (Beg et al. 2009). The data of each participant were morphed to their respective age‐appropriate template: kids 4–8 years for group A; Kids 7–11 years for group B; kids 10–14 years for group C; and fsaverage for group D. The templates were morphed for each individual head shape and position in 3 dimensions using MNE Coreg software, based on fiducial landmarks and additional points providing head shape information, digitized during data acquisition. When additional digitized points were unavailable, the uniform transformation was done based on the fiducial points. A boundary element model consisting of one surface (conductivity value: 0.3) based on the inner skull surface was created during this process for each individual.

FIGURE 2.

FIGURE 2

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Distribution of activation in the early and late time‐windows. Grand‐average of the MNE source distributions in left and right hemisphere at 100 ms and at 250 ms post auditory stimulation in the four age‐groups. In adults (group D), the source current at around 50 ms is also shown. Source activation to the left‐ and right ‐ear sounds are shown in separate columns.

2.7. Quantifying the Strength of Activation

For each individual participant, the MNE source waveform was used to extract activation strength for the early (<200 ms) and late (>200 ms) time‐windows separately for the left and right hemisphere ROI and for left and right ear sounds (see Figure 3). The mean activation was calculated for all the vertices within the ROIs, and from these values the maximum (for P50m and P100m), and minimum (for N100m and N250m) was collected for the statistical analysis. The specific time window of 30–120 ms was used to extract the strength of the early peaks, i.e. P100m and N100m responses in children, and the N100m response in adults. For adults a shorter time window of 30–60 ms was used for P50m, to reliably capture the small peak prior to the prominent N100m response: the longer time window was contaminated by the amplitude values reflecting the rise‐time of the N100m, from the vertices in the opposite bank of the HG (thus with ‘positive’ sign) (see Figure 2). The time window of 200–300 ms was used to extract the strength of the N250 response.

FIGURE 3.

FIGURE 3

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Time‐course of activation in left and right auditory ROIs. MNE source waveforms in the left and right hemisphere ROIs for the contra‐ and ipsilateral stimuli (shown for each individual voxel). The data from different age groups are shown in separate rows.

2.8. Statistical Analysis

Differences in activation strength between age groups in the two hemispheres were tested using repeated measures ANOVA, separately for the three current types (P50m/P100m, N100m, and N250m). Age group was used as a between‐subject factor and hemisphere (left, right) as a within‐subject factor. Post‐hoc pairwise comparisons were conducted with Bonferroni correction to test the difference between individual levels. Assumption of normality (by Shapiro‐Wilks test) was met in most of the variables and Levene's test of equality of error variance indicated that groups had the same variance with few exceptions. Small violations of these assumptions do not weaken the reliability of results from analyses of variance (Nummenmaa 2009). The threshold of statistical significance for difference was set at p < 0.05. Differences in activation latency were calculated using ECD maximum latency (see Appendix A).

3. Results

3.1. General Pattern of Activation at MEG Sensors

Figure 1 shows the MEG signals averaged over participants in each group in one planar gradiometer pair over left and right temporal cortices (depicted in black in the helmet). In adults, the strongest transient peak was evoked around 100 ms after stimulus onset in both left and right hemispheres (N100m). Preceding this peak, there was a weaker response at around 50 ms (P50m). In children, the most prominent deflection in both left and right hemispheres was detected significantly later at around 250 ms (N250m). This deflection was typically preceded by a transient peak at a post‐stimulus latency around 100 ms, coinciding with the timing of typical adult N100m deflection, and reflecting either the same or opposite direction as the prominent N250m response.

The response waveform from the gradiometer sensors reflects the strength of the magnetic field in the inferior–superior direction, in a plane tangential to the cortical surface. Examination of the sensor‐level data in children at the grand‐average level provides an indication of the progression and hemispheric differences in the underlying current. At around 250 ms, the direction of the underlying current is similar between the hemispheres. At around 100 ms, the left hemisphere shows a roughly opposite direction of current flow compared to the 250 ms response. In contrast, in the right hemisphere, the 100 ms response reflects a similar current direction to that at 250 ms, at least in the oldest child groups. Current direction significantly influences the amplitude at the individual sensor level, and more detailed information on group differences requires analysis at the source level.

3.2. Current Source Distribution Across Hemispheres and Age‐Groups

Figure 2 displays the location of activation as reflected by MNE sources. The MNE source distribution indicate persistent activity of the supratemporal area, but the emphasis between adults and children in the MNE source strength is in different time‐windows. The strongest activation occurred in the late (>200‐ms) time window in child participants but in the early (< 200‐ms) time window in adults. Figure 3 shows the MNE source waveforms, in the auditory cortex ROI in the left and right hemispheres for contra‐ and ipsilateral stimulation.

The ECD's (see Appendix A, Figure A1) were localized to areas comparable to the MNE sources across the two time‐windows in all age groups. The dominant direction of the current activity in each time window is illustrated by the ECD arrow in Figure A1b (typical sequence of currents for one participant in each age group) and A1c (distribution across all participants), and by the blue and yellow color (indicating current ‘source’ and ‘sink’, respectively) for the MNE analysis in Figure 2.

In adults, the strongest activation at around 100 ms reflected a typical N100m current, in both hemispheres, preceded by weak P50m current. In children, the source analysis revealed three different current types that diverged from each other in timing and/or orientation (see methods). In the early time‐window, roughly at 100 ms, the ECDs in children reflected either P100m current (28/35 children) with anterior/superior direction, or N100m current (15/35) with posterior/inferior direction. In the late > 200 ms time‐window the ECD reflected N250m current with posterior/inferior direction. The late time‐window N250m was the most systematic response across children and hemispheres (in 35/35 children).

Although MNE source reconstruction is not very intuitive in revealing direction of the current, the distributed activation depicted in Figure 2 reflect similar pattern as the ECD analysis: In some stimulation conditions the pattern of sink (blue) vs source (yellow) reversed between the early and late time‐windows (corresponding a change from P100m to N250m, e.g. group A, left hemisphere), and in other stimulation conditions the pattern stayed the same between the early and late time‐windows (corresponding a change from N100m to N250m, e.g. groups B and C, right hemisphere). As illustrated in Figure 2 and Figure A1, even though the source orientation clearly differs between the time‐windows, the source location did not systematically change between the time‐windows nor across age (Figure 2, Figure A1a).

Earlier literature evidence continuous developmental decrease of P50 latency from around 100 ms to the adult latencies of about 50 ms, and thus we based our analysis on the assumption that P100m and P50m reflect similar underlying current configuration. The statistical analysis of group differences both for activation strength and activation timing was thus performed separately to P100m/P50m currents, N100m currents and N250m currents.

3.3. Activation Strength Across Hemispheres and Age‐Groups

The mean MNE source strength in the early (P100m/P50m and N100m) and late (N250m) time windows for different groups, hemispheres, and ears of presentation is represented in Figure 4.

FIGURE 4.

FIGURE 4

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The mean (+ SEM) strength for P50m/P100m (a), N100m (b) and N250m (c) source activation in the auditory ROIs. Responses in the left hemisphere are given in the two leftmost columns (left and right ear stimulation) and responses in the right hemisphere are given in the two rightmost columns (left and right ear stimulation). The different age groups are represented with different colour and statistically significant differences for hemi, group, and ear are given.

3.3.1. Early Time‐Window: P50M/P100M

For the early activation with superior/anterior current direction (P50m response in adults, P100m response in children) there was no significant difference in the strength of activation between age groups. However, the effect of ipsi‐ vs. contralateral stimulation was apparent, indicated by the significant hemi x ear interaction (F(1,40) = 9.81, p < 0.01, ηp 2 = 0.19). Contralateral current showed stronger amplitude than ipsilateral current in all groups alike.

3.3.2. Early Time‐Window: N100M

For the early activation with inferior/posterior current direction (N100m), the activation was stronger in adults than in child groups. Interestingly, the pattern differed between hemispheres (effect of age group F(3,40) = 7,49, p < 0.001, ηp 2 = 0.36, interaction age group by hemisphere F(3,40) = 4,95, p < 0.01, ηp 2 = 0,27). In the left hemisphere, the difference was significant between the adult group D and all the child groups A (p < 0.05), B (p < 0.01) and C (p < 0.01). The amplitude values for the child groups were almost identical to each other. In the right hemisphere, there was an apparent increase in amplitude with increasing age, and the N100m amplitude differed significantly also between youngest child group A and oldest child group C (p < 0.05). The difference was also significant between adults and the child groups A (p < 0.001), B (0.003) and C (0.013).

The amplitudes were in general stronger in the right than in the left hemisphere (F(1,40) = 16,27, p < 0.001, ηp 2 = 0.28), but this difference was significant only in adults (p < 0.01) and the oldest child group (p <0.01) (interaction group by hemi F(3,40) = 4,95, p < 0.01). The difference between ipsi‐ vs. contralateral stimulation was evident in all groups, as indicated by significant hemi x ear interaction (F(1,40) = 17.91, p < 0.001, ηp 2 = 0.31) and no three‐way interaction between hemisphere, ear, and group, with stronger amplitudes to contralateral sounds in both hemispheres.

3.3.3. Late Time‐Window: N250M

Activation in the late time window, with inferior/posterior current direction (N250m), was stronger in children than in adults (F(3,40) = 3.40, p < 0.05, ηp 2 = 0.20). Unlike for N100m current activation, there was no interaction between group and hemisphere. Pair‐wise comparison indicated nonlinear age‐related changes. The two older child groups B and C showed stronger amplitude than the youngest group A (A vs. B p < 0.05; A vs. C, p < 0.05) and the adult group D (B vs. D p < 0.05; C vs. D p < 0.05) but did not differ from each other. A significant effect of hemisphere (F(1,40) = 9.89, p < 0.01, ηp 2 = 0.19) indicated stronger current in the right than in the left hemisphere, independent of group, and the effect of stimulated ear was significant in both hemispheres (hemi x stim interaction F(1,40) = 14.56, p < 0.001, ηp 2 = 0.27), with stronger amplitude to the contralateral stimulation.

4. Discussion

We characterized age‐related differences in the auditory cortical reactivity between hemispheres, probed by simple sound stimuli. The results suggested two concurrent developmental processes in the auditory cortex circuitry. A process indexing enhanced reactivity of the cortex at long latencies showed an inverted u‐curve. These currents were most prominent in mid to late childhood. The other process revealed a more linear development, or stepwise adoption of, early transient automatic processing. While the transient N1 response emerged earlier in the right than in the left hemisphere, confirming earlier findings (Edgar et al. 2014; Parviainen et al. 2011, Parviainen et al. 2019), the age‐related changes in the later, prolonged response were bilaterally symmetric.

The prominence of the late and prolonged response in children aligns with earlier finding (Sussman et al. 2008; Ponton et al. 2000; Wunderlich and Cone‐Wesson 2006; Takeshita et al. 2002; Albrecht et al. 2000; Ceponiene et al. 1998; Sharma et al. 1997; Parviainen et al. 2011, Parviainen et al. 2019; van Bijnen et al. 2022; Wunderlich and Cone‐Wession 2006). Despite abundant research in the field, the nature of the late response, and especially its underlying cortical generators, are not known. This response is evoked by passive presentation of pure tones (Albrecht et al. 2000; Takeshita et al. 2002; Parviainen et al. 2019) or click sounds (Ponton et al. 2000). It also appears to be unaffected by task conditions (van Bijnen et al. 2019). These findings suggest that the response reflects mere cortical reactivity to external (auditory) input in the developing brain. Interestingly, similarly increased amplitude in later time‐window has been shown by probing motor (Määttä et al. 2017) and prefrontal (Määttä et al. 2019) cortices in children with “artificial” magnetic stimulation. The prolonged reactivity in our study could thus reflect increased excitability (and hence, decreased inhibition) in the late time‐window also to natural (auditory) stimulation in developing brain. However, the translation from cortical recordings to the underlying synaptic signaling is likely to be more complicated, as we shall elaborate shortly.

Earlier reports on maturational changes in the N250m response propose linear decrease with age (Albrecht et al. 2000; Ceponiene et al. 2002; Cunningham et al. 2000; Takeshita et al. 2002), no change (Johnstone et al. 1996; VanBijnen et al. 2022), or increase up to late childhood and decrease only thereafter (Ponton et al. 2000). Our results, albeit from cross‐sectional sample, support the latter. The level of response strength in 6‐year‐olds and adults did not differ, but the children between 9 and 13.5 years showed stronger N250 responses. These findings imply mechanisms in the cortical circuitry, that are particularly pronounced during mid‐childhood, and, presumably, disappear by adulthood.

Increase in cortical reactivity may be meaningful for the changes in perceptual capacity at the age of major developmental changes in cognitive skills. Indeed, shorter N250m duration in 7‐to 8‐year olds was linked with better (reading) efficiency (Parviainen et al. 2011). Furthermore, stronger N250m response amplitude has been associated with higher stability in response inhibition task in typically developing 7–12‐year‐olds children (van Bijnen et al. 2022) and with higher language skills in 7–8‐year‐old children with SLI (van Bijnen et al. 2019) and 8–10‐year‐old children with dyslexia (Lohvansuu et al. 2014).

Our findings concerning the early time window are in general agreement with earlier results showing an increase in the prominent N1 response amplitude along with maturation (Sussman et al. 2008; Cunningham et al. 2000; Takeshita et al. 2002) and together suggest speed‐up of early cortical signaling. Our source analysis revealed that while the early transient currents in adults organize in orderly sequenced and precisely timed P50m and N100m complex, children evidence two currents with opposite directions and overlap in timing. Originating in the bilateral supratemporal plane, but with opposite directions (superior and anterior P100m current vs. inferior and posterior N100m current), these currents most likely reflect synaptic signaling arising from different laminar compartments in the same or close‐by cortical neuronal populations. Unlike in adults, considerable inter‐individual variability occurred in the peak latency for both responses in children (see Figure A2) and the evoked activity was dominated by either one or the other. In line with earlier reports, from the age of 9–10.5 years onwards, the N100m component started to dominate the 100 ms time‐window, but with longer latency (see Ceponiene et al. 1998; Cunningham et al. 2000; Sharma et al. 1997) and weaker amplitudes (see Albrecht et al. 2000; Ponton et al. 2000; Sharma et al. 1997) than in adults.

The separation (or lack thereof) of these essentially opposite but temporally overlapping current generators (in MEG) or topographies (in EEG), together with high interindividual variability, may explain some of the discrepancies concerning the early time‐window in earlier studies (e.g. Albrecht et al. 2000; Ponton et al. 2002; Takeshita et al. 2002). Large individual variance in the timing of P100m and in the age of emergence of N100m may lead two separate studies to give a different view on auditory cortical development (see also Parviainen et al. 2019; Orekhova et al. 2013). Besides the subject population, the appearance may depend on whether the selected analysis pipeline uses global power measures, that ignore the orientation, or spatially more specific measures. Our data (along with some earlier reports e.g. by Edgar et al. 2014 and Green et al. 2022) suggest that using RMS amplitude may prevent from identifying developmentally informative phenomena in the M/EEG data. Altogether, current findings imply that it is not only timing, but also the relative contribution of underlying synaptic events along the cortical laminae that may shift with age, given the differences in the dominant current orientation.

Our results thus suggest two concordant but independent developmental processes in the auditory cortex reactivity, as indicated by the early transient and late prolonged response patterns to simple tones. MEG reflects the summed effect of synaptic currents in the underlying circuitry and allows us to speculate on the underlying processes contributing to developmental change in evoked activity. In general, excitatory neurotransmission in the apical dendrites of the pyramidal neurons (see Hansen, Kringlebach, Salmelin, 2010; Hämäläinen et al. 1993) are considered as the primary generator of MEG evoked fields. However, many other electrical events in the laminar organization, including inhibitory neurotransmission and calcium spikes, may contribute to the extracellularly recorded fields (Buzsáki et al., 2012, May 18). This is relevant for understanding brain development since age‐related changes in excitatory and inhibitory synaptic connections, for example, evidence divergent nonlinear trajectories that contribute to the changes in functional properties of the circuitry at different ages. In the auditory cortex specifically, studies in rodents have shown that the development of inhibitory connections lags behind that of excitatory ones (Chang et al. 2005; Dorrn et al. 2010; but see also Sun et al. 2010). This developmental increase in the overlap between excitation and inhibition shortens the integration window and sharpens synaptic signal transmission (Oswald and Reyes 2011).

Myelinization in axons is also likely to impact in a linear, but region‐specific, manner, to the speedup of information transfer in the cortex (Yakolev and Lecours 1967; Bethlehem et al. 2022. Lebel and Leoni 2018). These micro‐level changes presumably increase synchronization of neural activity and thus decrease the latency and increase the amplitude of MEG response (Stufflebeam et al. 2008; Dockstader et al. 2012; Roberts et al. 2013). They may also influence the morphology (such as orientation) of the responses, as the inhibitory and excitatory synaptic contacts are differently distributed along the dendrite of pyramidal neuron (Ahlfors et al. 2015). Besides feedforward pathways to higher‐level processing areas, the evoked activity can arise also from recurrent and feedback signaling in the early sensory cortices, that may underlie macro‐level responses particularly at longer latencies, such as the N250m (Ponton et al. 2000).

The pattern of change with increased reactivity during mid/late childhood is particularly interesting from the point of view of perceptual plasticity. Indeed, our results are in line with the general notion from animal models suggesting that the maturation of neural circuits in sensory cortices follows an inverted U‐shaped trajectory in plasticity (Hensch 2005). This framework has also been applied to explain neurocognitive development in humans (Reh et al. 2020; Larsen et al. 2023; Johnson 2005; Werker and Hensch 2015). At specific period of development, namely sensitive (or critical) periods, the neural circuitry is particularly prone to modify in response to external stimulation. We propose that by probing the reactivity of the cortical auditory pathway with simple tones, MEG captures the temporal dynamics of the synaptic currents, that reflect the structural changes in the circuitry during sensitive periods of development. Building on current findings and earlier interpretations (e.g. Albrecht et al. 2000) of the N250m response, we suggest that the developmental expression of the slower, prolonged response pattern represents the stage of ‘laborious’ processing of external input, allowing more readily plastic changes in the brain. This pattern is gradually replaced by a less effortful and automatic transient process, as reflected by the adult P50‐N100 complex.

Utilizing the knowledge provided by studies where magnetic stimulation of the cortex was applied together with selectively modulating the excitatory dopaminergic synapses or the inhibitory GABAergic synapses, the >200 ms electric activity have been attributed to GABA‐B mediated signaling. The role of GABA‐B transporters particularly during development is not well characterized, but as metabotropic slow‐acting transporters they show decay times matching with the N250m timing and are known to be involved in long‐range communication across cortical areas. The disinhibitory role of GABA‐B signaling in the cortical network would also match with the idea of the stage of increased plasticity as reflected by heightened responsivity to external stimuli. Future research should clarify the developmental trajectory of slow‐acting GABAergic synapses and make a linkage between micro‐ and macro‐level electrophysiology to provide evidence for this hypothesis.

It is curious that the slower response appeared bilaterally symmetric, while the emergence of transient responses reflects faster maturation of the right hemisphere. Our findings replicated and extended earlier findings for speech sounds (Parvianen et al. 2011) and for simple sine‐wave tones (Poulsen et al. 2009; Edgar et al. 2014; Parviainen et al. 2019, Green et al. 2022; van Bijnen et al. 2022; Kotecha et al. 2009; Paetau et al. 1995; Orekhova et al. 2013). The source analysis of our data can reconcile the varying latency values reported for N100m or P100m (Paetau et al. 1995; Orekhova et al. 2013): While the right hemisphere evidenced linear increase in the N100m current strength from 6 to 13 years, left hemisphere was dominated by the P50m (P100m) current up to 13 years, and the “growth burst” of N100m current is yet to be expected after 13 years of age. The rate and age of auditory response maturation has been suggested as significant for identifying atypical development in children (Green et al. 2022). Our results indicate that hemisphere‐specific measures would be needed to achieve higher sensitivity for such classification (Roberts et al. 2010, but see Port et al. 2016 Apr 6).

The maturational lag in the left hemisphere may be linked to its recognized role in language (Binder et al. 1996; Zatorre et al. 1992; Dorsaint‐Pierre et al. 2006). Language acquisition has been suggested to rely on synaptic changes driving opening, duration, and closure of sensitive periods, experimentally studied in rodents (Werker and Hensch 2015). Continuing skill‐learning demands in motor domain (Kumpulainen et al. 2015), and modified sensory input in auditory domain (Chang and Merzenich 2003; Peck 1995; Stiles 2000) are known to retain plasticity in the cortical networks in question. Perhaps due to innate differences in the (temporal) processing characteristics of the two hemispheres (Neopythou et al. 2022; Giroud et al. 2020; Hickok and Poeppel 2007) the richly varying linguistic input makes differing demands to the hemispheres leading the left hemisphere to retain immature characteristics longer than right, further supporting in the wiring of its networks optimally for communicative input. Thus, the more immature state of the left compared to the right hemisphere could reflect the demands imposed on the left hemisphere by it being exposed to spoken language. However, the current results using tones are not to be unambiguously generalized to auditory speech processing.

In addition to the maturational changes in overall morphology, contralateral preference in amplitude was observed in both hemispheres in both adults and children. These findings align with earlier work in mature humans (see e.g. Mäkelä 1993) and animals (Kelly and Judge 1994; Mrsic‐Flogel et al. 2006; Phillips and Irvine 1983), as well as 7–8 ‐year‐old human children (Parviainen et al. 2019). In rodents, contralateral preference is not innate but starts to emerge at the age of 49–51 days as an increased response to contralateral sounds (Mrsic‐Flogel et al. 2006). Whether in humans this characteristic represents innate or prenatally structuring property of the auditory system would require studies at younger children. Our results suggest stronger cortical processing of contralateral auditory input at least from 6 years onwards.

It is possible that our results reflect individual variance in developmental stage, and the relatively small age‐group sizes—together with the considerable pubertal heterogeneity typical for the 12–14‐year range—may further contribute to such variability. Indeed, the relatively small sample size is a clear limitation that may constrain the generalizability of the findings and increase sensitivity to individual developmental differences. Larger sample sizes with narrow age ranges and longitudinal follow‐up will be needed for more detailed characterization of the developmental changes. It would be particularly relevant to explore the developmental trajectory of the prolonged reactivity (N250 response) beyond 14 years of age, to fill the gap in its appearance between teen and adult years. Moreover, the behavioral advantage, if any, of the child‐typical cortical reactivity is yet to be shown. Ours and others’ earlier findings indicate the relevance of this response type for behavioral skills (e.g. Parviainen et al. 2011; vanBijnen et al. 2022; Lohvansuu et al. 2014). Importantly, the interpretation on MEG‐derived responses depends on assumptions on the generators of the extra‐cortically measured fields. These are necessarily only approximations and animal models and/or pharmacological manipulations are necessary to establish the developmental changes in the circuitry elements, e.g., in synaptic inhibitory and excitatory signaling, that underlie the changes in evoked response morphology.

5. Conclusions

To conclude, our results imply two concurrent developmental processes in the electric response properties of auditory cortex. First, enhanced, and prolonged current amplitude in later > 200 ms time‐window appears to follow an inverted u‐curve peaking at around mid to late childhood and being largely absent in adults. Second, amplitude (and mere emergence) of adult‐type transient responses evidence linear increase across age groups between 6 and 13.5 years, but only in the right hemisphere while left hemisphere show more immature characteristics at these ages. Cortical current predominantly visible in a specific developmental stage is in line with the notion of increased plasticity at given age during development, leading to sensitive periods e.g. for perceptual learning. Similar developmental trajectory may govern also other cortical areas, as implied by recent findings using TMS‐evoked responses in children in the motor cortex.

Author Contributions

Tiina Parviainen: conceptualization, formal analysis, funding acquisition, investigation, writing – original draft. Pyry Heikkinen: formal analysis, visualization, writing – review and editing. Anni Hänninen: investigation, formal analysis, writing – review and editing. Anna Christina Nobre: conceptualization, resources, supervision, writing – review and editing.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Supporting File 1: desc70146‐sup‐0001‐AppendixA.docx

DESC-29-e70146-s004.docx (1.7MB, docx)

Supporting File 2: desc70146‐sup‐0002‐Appendix‐FigA1.jpg

DESC-29-e70146-s002.jpg (691.9KB, jpg)

Supporting File 3: desc70146‐sup‐0003‐Appendix‐FigA2.pdf

DESC-29-e70146-s001.pdf (99.2KB, pdf)

Supporting File 4: desc70146‐sup‐0004‐Appendix‐FigA3.pdf

DESC-29-e70146-s003.pdf (497.9KB, pdf)

Acknowledgments

We thank Joseph de Martino for assisting in MEG data acquisition and in cognitive testing of the children. This work was supported by the Academy of Finland grant to Tiina Parviainen (129060) and University of Jyväskylä (311877).

Open access publishing facilitated by Jyvaskylan yliopisto, as part of the Wiley ‐ FinELib agreement.

Data Availability Statement

The data supporting the findings of this study are available upon reasonable request from the authors and with permission of the Ethics Committee of the University of Jyväskylä.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supporting File 1: desc70146‐sup‐0001‐AppendixA.docx

DESC-29-e70146-s004.docx (1.7MB, docx)

Supporting File 2: desc70146‐sup‐0002‐Appendix‐FigA1.jpg

DESC-29-e70146-s002.jpg (691.9KB, jpg)

Supporting File 3: desc70146‐sup‐0003‐Appendix‐FigA2.pdf

DESC-29-e70146-s001.pdf (99.2KB, pdf)

Supporting File 4: desc70146‐sup‐0004‐Appendix‐FigA3.pdf

DESC-29-e70146-s003.pdf (497.9KB, pdf)

Data Availability Statement

The data supporting the findings of this study are available upon reasonable request from the authors and with permission of the Ethics Committee of the University of Jyväskylä.

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