Auditory processing indexed by stimulus-induced alpha desynchronization in children

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International Journal of Psychophysiology 68 (2008) 130 – 140 www.elsevier.com/locate/ijpsycho

Auditory processing indexed by stimulus-induced alpha desynchronization in children Takako Fujioka ⁎, Bernhard Ross Rotman Research Institute, Baycrest Centre, University of Toronto, Canada Received 1 August 2007; received in revised form 22 October 2007; accepted 3 December 2007 Available online 8 February 2008

Abstract By means of magnetoencephalography (MEG), we investigated event-related synchronization and desynchronization (ERS/ERD) in auditory cortex activity, recorded from twelve children aged four to six years, while they passively listened to a violin tone and a noise-burst stimulus. Time–frequency analysis using Wavelet Transform was applied to single-trials of source waveforms observed from left and right auditory cortices. Stimulus-induced changes in non-phase-locked activities were evident. ERS in the beta range (13–30 Hz) lasted only for 100 ms after stimulus onset. This was followed by prominent alpha ERD, which showed a clear dissociation between the upper (12 Hz) and lower (8 Hz) alpha range in both left and right auditory cortices for both stimuli. The time courses of the alpha ERD (onset around 300 ms, peak at 500 ms, offset after 1500 ms) were similar to those previously found for older children and adults with auditory memory related tasks. For the violin tone only, the ERD lasted longer in the upper than the lower alpha band. The findings suggest that induced alpha ERD indexes auditory stimulus processing in children without specific cognitive task requirement. The left auditory cortex showed a larger and longer-lasting upper alpha ERD than did the right auditory cortex, likely reflecting hemispheric differences in maturational stages of neural oscillatory mechanisms. © 2008 Elsevier B.V. All rights reserved. Keywords: Children; Auditory cortex; Oscillations; Hemispheric asymmetry; Magnetoencephalography; Alpha-band

1. Introduction Spontaneous rhythms in the electroencephalogram (EEG) and the magnetoencephalogram (MEG), which are thought to reflect neuronal circuitry between thalamic nuclei and the neocortex (Steriade and Llinas, 1988), can be modulated by sensory stimulation, motor events, or cognitive tasks. This modulation is expressed as a change in the number of synchronously oscillating neurons and consequently, as an increase or decrease in the amplitude of electromagnetic brain activity in various frequency bands. Such dynamic changes can be assessed by transforming the recorded EEG or MEG data from the time-domain into the frequency-domain, or a time–frequency continuum representation (Hari and Salmelin, 1997; Pfurtscheller and Lopes da Silva, ⁎ Corresponding author. Rotman Research Institute, 3560 Bathurst Street, Toronto, Ontario, Canada M6A 2E1. Tel.: +1 416 785 2500x3413; fax: +1 416 785 2862. E-mail address: [email protected] (T. Fujioka). 0167-8760/$ - see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.ijpsycho.2007.12.004

1999). The relative increase or decrease in signal power of cortical oscillations in certain frequency bands in a post-stimulus time interval, compared to a pre-stimulus interval, is called eventrelated synchronization (ERS) or desynchronization (ERD), respectively (Pfurtscheller and Lopes da Silva, 1999). The changes in the EEG or MEG signal described with ERS or ERD include brain activity not strictly phase-locked to the event (i.e., it may occur with slightly varying time delay from one trial to another). These non-phase-locked evoked responses, called ‘induced’ oscillations, are cancelled out when the evoked response is extracted by the conventional time-average technique (Tallon-Baudry and Bertrand, 1999). ERS and ERD in different frequency bands, together with spatial information of their generators, index various sensory and cognitive processes. For example, theta frequency oscillation (4–7 Hz) in frontal areas is modulated by memory demands (Gevins et al., 1997; Jensen and Tesche, 2002; Kahana et al., 2001). Beta activity (13–30 Hz), when coupled with alpha range activity (˜10 Hz) called mu rhythm, is observed in

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somatosensory and somatomotor cortices when associated with various motor tasks (Neuper et al., 1999; Salmelin et al., 1995; Salmelin and Hari, 1994; Toro et al., 1994). Gamma activities (low gamma: 28–60 Hz, high gamma: N 60 Hz) related to feature binding are observed in the visual modality (Bertrand and Tallon-Baudry, 2000; Tallon-Baudry, 2003). Alpha (8– 13 Hz), identified since the beginning of EEG research, is known to be suppressed not only by eye-opening but also by sensory input and mental activity. Thus ongoing alpha has been proposed as an index of cortical idling (Pfurtscheller et al., 1996). Recent research with more sophisticated EEG and MEG techniques and analysis methods has revealed a complex and dynamic behaviour of alpha activities associated with a variety of cognitive processes which involve multiple cortical regions (Basar et al., 2001; Klimesch, 1999). From human and animal research, a current hypothesis is that oscillatory synchrony between distant cortical regions may play a key role in integration and transmission of information (Varela et al., 2001). Early studies of electromagnetic brain activity have already shown that auditory stimuli produce event-related power changes in the absence of a specific cognitive task. The suppression of ongoing alpha activity by an auditory stimulus was first described in the pioneering studies of Berger (1969). A continuous sound stimulus caused not only alpha suppression but also a slight increment in alpha frequency (Mimura et al., 1962). The synchronization of alpha activity was first suggested as a mechanism for the generation of the auditory evoked response by Sayers et al. (1974). With MEG, which is more advantageous for analysing sources of cortical activities than EEG, Tiihonen et al. (1991) first demonstrated that the alpha suppression after sound presentation originated in the auditory cortex. In their experiment, healthy adult subjects listened passively to sound stimuli while asked to open or close their eyes. Alpha activity, which was clearly observed in a pre-stimulus period, decreased after stimulus onset. The sources of ERD were modeled as equivalent current dipoles vertically oriented in the supratemporal planes of the auditory cortices, adjacent to the centre of the neuronal generator of the prominent auditory evoked response at 100 ms. This alpha activity of auditory cortex origin was not affected by eye-opening, eye-closing, or hand movement, thereby demonstrating a dissociation from occipital alpha in the visual cortex (Salmelin and Hari, 1994; Vanni et al., 1997) and the rolandic mu rhythms in somatomotor cortex (Salmelin et al., 1995; Tiihonen et al., 1989). Using MEG, Kaufman et al. (1992), observed alpha suppression over the temporal lobes in a memory search task using tones, and found a positive correlation between memory load (e.g., the number of tones presented for memorization prior to a probe tone for recognition) and the duration of the alpha ERD, ranging from 1 to 3 s. Another example of alpha suppression in a modality-specific brain area was found in the visual system as elicited by a visual stimulus or a visual imagery task (Salenius et al., 1995). Furthermore, with a whole-head MEG, Makinen et al. (2004) showed suppression of activity around 16 Hz (just above the alpha band) occurring 250–500 ms after sound onset in passive listening. Their data demonstrated underlying generators in bilateral temporal areas with contributions from bilateral parietal regions. Recently, electrocortico-

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graphic (ECoG) recordings in adults revealed that in an auditory discrimination task, speech sounds elicited longer-lasting alpha ERD than did tones, as well as a short burst of low (35–45 Hz) and high (80–100 Hz) gamma ERS in the left auditory cortex (Crone et al., 2001). It is well known that neural activity in the sensory cortices change with variations in the physical features of the stimuli. For auditory stimuli, relevant features include frequency, intensity and complex spectral or temporal patterns. Since the signal recorded in EEG and MEG is the sum of complex neural activities, the signal also reflects which neural populations are involved and what strategies are used for stimulus processing. One unresolved issue regarding these strategies used in the auditory system is hemispheric specialization. Neuroimaging research has shown that the left auditory cortex responds more readily to speech-like sounds with a rapid temporal pattern, while the right auditory cortex responds to music-like sounds with a rich spectral pattern (Binder et al., 1996; Zatorre et al., 1992). The high temporal resolution of the MEG provides a more detailed picture of neural specificity and response timing in phase-locked activity when obtained by time-domain averaging. In adults, a speech sound elicits a larger auditory evoked field at 100 ms in the left auditory cortex when compared to a non-speech complex sound or a musical sound (Gootjes et al., 1999; Parviainen et al., 2005). Other studies have reported that hemispheric differences are more prominent in long latency activities (Eulitz et al., 1995) or are observed only in latency differences (Tiitinen et al., 1999). The developmental status of the brain affects auditory stimulus processing. The morphology of the auditory evoked response in MEG/EEG undergoes a process of development that begins at birth and spans 20 years before reaching adult status (Courchesne, 1990; Gomes et al., 2001; Ponton et al., 2000; Tonnquist-Uhlen et al., 2003). The maturational process, which is mainly characterized by decreased peak amplitudes and latencies, is likely a reflection of accelerated information processing. This is suggested by increased synaptic efficacy, maturation in myelination of layer-specific neuronal populations (Eggermont, 1992; Moore and Guan, 2001), and optimized synaptic connections for specialized neural networks as a result of synaptic pruning (Huttenlocher and Dabholkar, 1997). Furthermore, individual experience is known to influence the maturational change in the auditory evoked response. For example, the response to speech sounds in children, matures more rapidly than responses to non-speech sound (Pang and Taylor, 2000), or exhibits larger amplitudes (Ceponiene et al., 2001; Wunderlich et al., 2006). In addition, specific training such as music practice differentiates trained and untrained subjects in terms of hemispheric lateralization of responses to the specific (musical) sound stimulus. The 100-ms response to musical tones was right-lateralized in non-musicians (Gootjes et al., 1999; Kuriki et al., 2007), and when compared to age-matched naïve subjects, young adult musicians demonstrated a larger response to musical tones (Pantev et al., 1998). The enhancement in the musicians showed greater expression in the left than the right auditory cortex (Kuriki et al., 2006). This is a sharp contrast to the data collected from children aged 4–6 years, which showed that a musical tone and a noise burst, elicited larger responses in the left

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than right auditory cortex at a latency range of 100–300 ms, regardless of the children’s music training experience (Fujioka et al., 2006). The difference in laterality between children and adults could be due to the shorter duration of experience, and the change of hemispheric asymmetry in neural connectivity between different cortical regions at different ages (Thatcher et al., 1987). However, the relation between neural oscillations and stimulus processing in the developing auditory brain is largely unknown. Earlier studies in children assessed the phase-locked components of neural oscillations related to tasks requiring higher cognitive processes, and found involvement of theta, alpha and beta oscillations (Kolev et al., 2001; Yordanova and Kolev, 1996a,b; Yordanova et al., 2002). In a recent study, which investigated both phase-locked and non-phase-locked activities, ERS in the alpha band occurred in response to auditory stimuli in children aged 10 to 14 years (Krause et al., 2007) regardless of memory encoding or retrieval stages. A previous report by the same group (Krause et al., 2001) showed that the time courses of alpha ERS followed by ERD in children closely resembled those of adults. These studies suggest that, auditory alpha band activities in children, as in adults, can be related to auditory stimulus processing, without requiring the involvement of a memory process, although higher cognitive processes can modulate the activities. To our knowledge, no study has investigated auditory oscillatory activities in normal developing children using MEG. Thus, the current study was aimed to characterize specifically nonphase-locked ERS and ERD in 4–6 year-old children, who passively listened to sound stimuli. This was a retrospective analysis of MEG data collected during a longitudinal study using four time recordings over one year (Fujioka et al., 2006). In the averaged evoked fields, left hemisphere dominance was present in the magnetic peaks at 100 ms and 320 ms consistently across multiple sessions for two acoustic stimuli (a violin tone and a noise burst). However, the maturational change was differently expressed according to the stimulus type. Moreover, the stimulus effect on the maturational change interacted with the child’s musical experience. Our interest here was to examine ERS and ERD in the auditory cortices in terms of stimulus processing and its possible interactions with hemispheric asymmetry, experience and maturation.

2.2. Auditory stimuli Two auditory stimuli were used. One was a violin sound (A4, open string at 440 Hz fundamental frequency) with a duration of 850 ms. The other was a burst of white noise of 500 ms duration including linear ramps of 5 ms for rise and decay. Fig. 1 shows the time envelope of the two stimuli. The stimuli were stored in Windows as wave files using 16 bit resolution and 44,100 Hz sampling rate. Stimulus presentation was controlled by STIM software (Neuroscan Inc., El Paso, TX, USA) in blocks of 100 repetitions at a stimulus-onset-asynchrony (SOA) of 3 s, resulting in a block duration of about 5 min. The sounds were delivered binaurally through Etymotic ER3A transducers connected with 1.5 m of matched plastic tubing and foam earplugs to the subject's ears. Below 2000 Hz the frequency characteristic of the sound transmission system was relatively flat (±6 dB) and the phase characteristic was linear. The stimulus intensity was set to 60 dB SL (above individual sensation thresholds), which were measured immediately before each MEG recording for the violin tone. The noise intensity was set 3 dB lower as the threshold for the noise was found to be on average 3 dB higher than for the violin sound in ten young adults. The order of stimulus blocks was counter-balanced within each group and each session. Each stimulus block was replicated if subjects agreed to extend the recordings. The magnetic field responses were recorded with a 151-channel whole-head magnetometer (OMEGA, VSM MedTech, Coquitlam, Canada) in a quiet magnetically shielded room. The data were recorded with 100-Hz low-pass filtering and a sample rate of 312.5 Hz in stimulus-related epochs of 2.4 s duration including a pre-stimulus interval of 400 ms. The subjects lay in a supine position on the bed and were instructed to stay awake but not to pay specific attention to the stimuli. In order to maintain alertness, the children watched a subtitled movie of their own choice. The subject's compliance was verified by video monitoring. 2.3. Data analysis

2.1. Subjects and experimental design

Trials contaminated with eye-blink or movement artefacts were identified by visual inspection and rejected prior to averaging. The locations and orientations of equivalent current dipoles (ECD) in the left and right auditory cortices were estimated from the evoked magnetic field distribution. The spatiotemporal fitting approach (Scherg and Von Cramon, 1986)

After obtaining informed consent from their parents, twelve children of normal development (aged four to six years at the start of the study, mean 6.08, S.D. 0.69) participated in the study in which within one year four MEG recordings were performed for each child with approximately 3 to 4 months between each session. Half of the children took music lessons beginning no earlier than three months prior to the first MEG recording, and continued the program until the last recording. The other half did not participate in any musical activities outside of regular school programs. Each subgroup contained one left-handed child. Two children in each subgroup failed to attend either the second or third session. Detailed profiles of the children have been documented previously (Fujioka et al., 2006).

Fig. 1. Time courses of auditory stimuli. Top: violin sound, bottom: noise sound.

2. Methods

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was used based on a single-sphere model with the radius between 7.5 and 8.2 cm approximated to the individual digitized head shape. A pair of single dipoles in each hemisphere was fitted to the averaged MEG data in the time interval of 0–800 ms after stimulus onset. The resulting source coordinates, which were defined in a Cartesian coordinate system based on fiducials at the nasion and left and right pre-auricular points, represented the centre of neural activity in the auditory cortices. In all subjects at every session, at least one pair of dipoles was accepted according to the criterion that the goodness of fit was better than 70%, with the constraints of source orientation (upward around 100 ms corresponding to the vertex-positive evoked potential) and location (within the general auditory areas). Since there were no significant differences across subject groups, sessions and stimuli, individual models of auditory cortex sources were obtained from mean locations and orientations across stimuli, and from repeated measurements in each subject (for more details, see Fujioka et al., 2006). Based on these dipole models, the source waveforms for all single-trials in both left and right auditory cortices were calculated from the raw MEG signals using the signal space projection method (Tesche et al., 1995). Time–frequency decompositions were calculated for each single-trial source waveform for each subject, in both the left and right auditory cortices, and for each stimulus, using a Morlet wavelet transform (Bertrand et al., 1994). The wavelet was designed such that the half maximum width was equal to 2 periods of the centre frequency (e.g., 200 ms at 10 Hz). In order to calculate estimates for the evoked response for each point in

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time (− 0.4 to 2.4 s) and frequency (4 to 40 Hz), the sum of all wavelet coefficients was calculated across all subjects and all repeated measurements separately for each stimulus and hemisphere. Accordingly, the induced activity, also termed the mean signal power, was calculated as the mean variance across trials, 1/N Σ(x − xm)2, where N is the total number of trials, x an observed waveform, and xm the averaged signal. From this result, the event-related synchronization was calculated for each frequency by normalizing the mean signal power to the mean signal power in the pre-stimulus interval. The resulting power ratio was expressed in decibels and visualized with color-coded time–frequency maps. A frequency spectrum for the main effect of ERD was calculated as the mean ERD time–frequency coefficients across the time interval from 400 to 800 ms after stimulus onset. The time courses of alpha ERD were obtained from the ERD time–frequency coefficients at 8 and 12 Hz. The mean theta ERS (4–7 Hz) in the 0–200 ms time interval between 4 and 7 Hz, as well as the mean upper and lower alpha ERD (8 Hz and 12 Hz, respectively) in the 400–800 ms time interval, were calculated for all subjects, stimuli, and both the left and right auditory sources. Two-way ANOVAs were performed separately for these ERS and ERD measures with the factors ‘stimulus type’ (violin / noise) and ‘hemisphere’ (left/right). 3. Results Time–frequency transform on single-trial source activity was successfully performed in all subjects and all repeated

Fig. 2. Time–frequency representation and time series of the auditory evoked magnetic field response for the violin sound (top) and noise stimulus (bottom) in both hemispheres. The responses were obtained from source waveforms of equivalent dipoles located in left and right auditory cortices. Note that the polarity of the source waveforms is set to be consistent with auditory evoked potentials recorded by EEG from frontal–midline electrodes. Thus, the first dominant peak with latency around 100 ms is the positive peak, termed the P100 m, typically seen in young children. The contour lines are equidistant at 2 nAm.

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Fig. 3. Time–frequency representation of event-related synchronization and desynchronization (ERS/ERD) for the violin sound (top) and the noise stimulus (bottom) in both hemispheres. Color-coded is the power change (red: increase; blue: decrease) in non-phase-locked cortical activity both pre-stimulus and post-stimulus. The isocontour lines are given in steps of 0.8 dB above 0.24 and below − 0.24 dB. The horizontal grid lines indicate the low (around 8 Hz) and high (12 Hz) alpha bands.

sessions, without any exclusion of data. Because no systematic differences in the ERS/ERD were found between the two groups or between the four subsequent sessions, all the data were combined in order to improve the signal-to-noise ratio for analyses of the effects of stimulus type and hemisphere. 3.1. Phase-locked activities Time–frequency maps of phase-locked activities in combination with the corresponding waveforms of the time-domain averaged evoked response are shown in Fig. 2. The evoked activity is spread across theta (4–7 Hz), alpha (8–13 Hz), and lower beta (13–20 Hz) frequency bands during the first 200 ms which corresponds to the P100–N250 m peaks in the evoked response. Longer-lasting low frequency activity corresponds to the long lasting negativity in the evoked response. Both, the peak magnitudes of the phase-locked oscillatory activities during the first 200 ms and the amplitude of the P100 m peak, show a similar dependency on stimulus type and hemisphere. These activities were larger during the violin sound compared to the noise stimulus for both hemispheres; however, the left showed more activity than the right hemisphere, regardless of stimulus type. The ANOVA applied to the mean amplitude in the time interval between 0 and 200 ms and the frequency interval between 4 and 7 Hz (theta), revealed significant effects of stimulus type (F(1,44) = 13.1, p b 0.0008) and hemisphere (F (1,44) = 22.3, p b 0.0001), but no interaction between the factors. One common characteristic in the phase-locked activity maps

shown in Fig. 2 is that there is no alpha or beta activity evident in the latency range beyond 300 ms. 3.2. Non-phase-locked ERS and ERD The time–frequency maps for ERS/ERD at a location of peak source activity in the left and right auditory cortices for violin and noise stimuli are shown in Fig. 3. Most prominent is the desynchronization in the alpha band with the strongest effects around 8 and 12 Hz. This begins about 300 ms after stimulus onset, and returns to baseline as late as 1500 ms after stimulus onset. Increase in low frequency activity (b 5 Hz) with maximum around 200 ms is evident for stimuli in both left and right auditory sources. This peak coincides in time with the low frequency maximum in the evoked response (Fig. 2). Although the averaged response was eliminated in the ERS calculation, the early ERS likely reflects inter-trial variability in the evoked response. Short bursts of ERS at higher alpha and beta ranges (13–30 Hz) occurred immediately after stimulus onset in the left auditory source for the violin sound, and in the right auditory source for the noise sound. However, these ERS were far less consistent than the alpha ERD. 3.3. Upper and lower alpha ERD The closed isocontour lines in the alpha band in the ERS/ ERD time–frequency maps (Fig. 3) point to distinct troughs around 8 Hz (lower alpha) and 12 Hz (upper alpha). For the

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Fig. 4. Time courses for event-related desynchronization at lower alpha (left) and upper alpha (right) frequency bands for the violin (top) and noise (bottom) stimuli.

violin sound, the left 8-Hz ERD returned to baseline about 500 ms earlier than did the 12-Hz ERD. The time courses of upper and lower alpha ERD are summarized in Fig. 4. A stronger decrease (around 12 Hz) is clearly visible in the left compared to the right hemisphere for both stimuli over the entire time interval of the activity. In contrast, the lower alpha ERD showed no systematic difference between the left and right auditory cortices. Furthermore, the morphologies of the ERD time courses showed differences between stimuli and hemispheres. The left hemisphere ERD for the violin showed a sharp peak at 12 Hz before 500 ms, whereas the corresponding peak for the noise was shallower and later. However, statistical analysis on the peak latencies could not be performed due to insufficient signal-to-noise ratio. The differences in magnitude between upper and lower alpha ERD were apparent across stimulus type and hemisphere, particularly in the peak around 500 ms (Fig. 4). Fig. 5 illustrates the frequency spectra of the ERD, which was obtained as an average of the time–frequency coefficients in the 400–800 ms time interval. Two troughs at 8 and 12 Hz were consistently evident for both left and right auditory sources for both stimuli.

Whereas the magnitude of the 8-Hz ERD was almost identical for violin and noise in both sources, the 12-Hz ERD dissociated between stimuli and hemispheres. The ANOVA applied to the 12-Hz ERD in the 400–800 ms time interval, revealed main effects for hemisphere (F(1,44) = 6.0, p b 0.02) and stimulus type (F(1,44) = 5.6, p b 0.04), but no interaction between factors. Neither factor showed significant effects at 8 Hz. 4. Discussion 4.1. Maturational changes and influence of experience in ERS/ ERD The present analysis focusing on non-phase-locked ERS and ERD did not show maturational changes or training-related differences, unlike the previous analysis of the auditory evoked response obtained from the same data. Three explanations are possible. First, the analysis of oscillatory activity used here may be less sensitive than the evoked response. The power change in ERS and ERD is based on single-trial data, which contain “non-

Fig. 5. Frequency spectrum of the mean value of event-related synchronization in the latency interval from 400 to 800 ms for the violin sound (left) and noise stimulus (right). In the alpha frequency range (8–13 Hz), two distinct peaks of decrease are obvious at 8 Hz (lower alpha) and 12 Hz (upper alpha). For both stimulus types, the ERD at 12 Hz is more prominent in left than right hemisphere.

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phase-locked” physiological noise from other cortical areas, blood vessels, muscles and movements. Consequently, the signal-to-noise ratio in induced oscillatory activities is substantially smaller compared to the evoked response. Second, the neural populations which contributed to the changes in the evoked response could be different from those which contributed to the observed ERS and ERD. In fact, the observed maturational changes and group differences in the evoked response were mainly characterized by the changes at the latency range between 100 and 400 ms (between the positive peak at 100 ms and the following negative peak around 250 ms shown in Fig. 2). This time interval corresponds to a transition from the short alpha and beta ERS to the slow onset of alpha ERD (Fig. 3). This apparent lack of a direct link between two phenomena makes further speculation difficult. Third, changes during the studied age range in spontaneous and event-related oscillatory activities might have been too subtle when compared to those in the evoked response. It appears that previous literature supports this explanation. For example, the evoked response data of Ponton et al. (2000) showed that between the ages of 5–6 and 19–20, the first positive component decreases in latency by almost a half while its amplitude decreases down to a quarter. In a developing brain, increased myelination in axons from deep to surface cortical layers (Moore and Guan, 2001) and optimized synaptic connection by synaptic pruning (Huttenlocher and Dabholkar, 1997) likely contribute to these changes in the auditory evoked response (Eggermont, 1992). In contrast, spontaneous oscillatory activities show a much slower rate of maturation. The occipital alpha rhythm demonstrates the most pronounced changes in spontaneous oscillation throughout the lifespan; it undergoes a steep acceleration from 1-year (5–6 Hz) to 3-years of age (8 Hz), and is followed by a slow linear increase reaching 10 Hz around the age of 16 years (Kellaway, 1990). The magnitude of this alpha decreases with increasing age (Petersen and Eeg-Olofsson, 1971); for example, the mean amplitude of alpha during an eyes-closed condition decreased 2–3 μV from age of 7 to 11 (Samson-Dollfus and Goldberg, 1979). Moreover, Wada and colleagues found that when comparing children aged of 4, 9, and 12 years, age-related changes of the alpha oscillation was dominated by changes in spatial distribution (from occipitalpredominance towards increased power in frontal areas), but not in frequency composition (Wada et al., 1996). In a longitudinal EEG study in children between 4 and 17 years old, the relative power of theta, upper and lower alpha bands recorded at central electrodes, compared to broadband power, was markedly stable in individuals between age 4 to 7 (the age range in the present study) (Benninger et al., 1984). Collectively, it appears that neural oscillations index fundamental network connectivity already operative by the age of 3 as indicated by established 8-Hz alpha activity. This underlying mechanism may not change rapidly enough in the 4–6 year age range to be captured within a one-year study.

time–frequency representation of phase-locked activities contained no substantial alpha contribution (Fig. 2), whereas alpha desynchronization (Fig. 3) both in the upper (8 Hz) and lower (12 Hz) bands, increased after 200 ms and reached its maximum at around 500 ms. In the literature, a similar time course of induced alpha ERD in response to auditory stimuli has been reported in adults when subjects were required to compare a probe stimulus to a set of previously heard stimuli (Karrasch et al., 1998; Krause et al., 1995, 2001) and when subjects had to detect a target stimulus in an oddball paradigm (Mazaheri and Picton, 2005). These experiments commonly showed that alpha ERD began approximately 300– 600 ms after stimulus onset, followed by a peak of ERD around 600–800 ms, and ended around 1500–2000 ms. It appears that one difference between the data in children and previous data in adults is the magnitude. The alpha ERD during the detection of the target stimuli was on the order of 25% in adults (Mazaheri and Picton, 2005) while our ERD was smaller, between 5% and 12%. This reduction of alpha ERD magnitude in children (aged 10–12 years), compared to adults, has been also reported mainly for the lower alpha band at 8– 10 Hz (Krause et al., 2001), although the time course of alpha ERD was similar in both age groups. Since their data were related to the involvement of cognitive processes such as memory encoding and retrieval, the comparison of our data to theirs must be taken cautiously. On the other hand, the long lasting alpha ERD has been observed in visual experiments (Sterman, 1996; Woertz et al., 2004). Thus, it is possible that modality-specific processing in sensory cortices may follow a common principle of neural oscillation and its dynamics. We tentatively hypothesize that the formation of an internal stimulus representation could be related to alpha desynchronization and the established representation could be subsequently utilized for higher cognitive processes. To our knowledge, induced alpha ERD in children at age 4– 6 years has not been previously reported. An EEG study in younger children showed that the phase-locked or non-phaselocked alpha desynchronization following the auditory stimulus was absent at the age of three years (Basar-Eroglu et al., 1994). However, before concluding that children after three years of age develop neural oscillatory mechanism related to auditory sensory processing, it has to be shown that the differences in the findings were not caused by differences in recording techniques. It is possible that specific oscillatory phenomena became evident with the higher spatial resolution of MEG and was overlooked in the EEG recordings. Also in the present study, we combined the data from repeated recording sessions, likely achieving a higher signal-to-noise ratio than in other studies with young children. Since the data in the current study showed a stimulus-related modulation without memory task involvement, we propose that the observed dynamics of neural oscillation reflect basic stages of stimulus processing (e.g., feature analysis and registration of mental representation).

4.2. Non-phased-locked alpha ERD and stimulus processing

4.3. Phase-locked and non-phase-locked ERS

We found a clear dissociation between phase-locked and nonphase-locked alpha activities. After 300 ms post-stimulus, the

We observed theta ERS with maximal size around 200 ms after stimulus onset consistently in both phase-locked activity

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(Fig. 2) and non-phase-locked activity (Fig. 3). The alpha ERS appeared only in phase-locked activity before 300 ms. It is very likely that the early ERS is related to the evoked response, even though the averaged signals were removed prior to calculation of the signal power. The reason is that this subtraction cannot eliminate completely the effect of the evoked response from signal power changes, unless the size and shape of the evoked activity were identical from trial to trial. Thus, we assume that inter-trial variance of the evoked response causes the early ERS. This is also supported by the fact that the significant lefthemispheric dominance of the P100 m component, a prominent feature of the evoked response, was shared by the theta ERS in the present results. Previous literature reported phased-locked alpha and theta ERS in children between 6 and 11 years of age during an auditory oddball paradigm (Yordanova and Kolev, 1996a,b). The close relationship between the sensory evoked response around 100 ms and alpha and theta synchronization has been pointed out by other authors (Bruneau et al., 1993; Gruber et al., 2005; Klimesch et al., 2005). The short burst of beta ERS (13–30 Hz) observed in the nonphase-locked activity in the first 100 ms was expressed differently in time course and centre frequency for both stimulus types in both hemispheres (Fig. 3). We speculate that the beta ERS might reflect the middle-latency responses (MLR). The MLR in adults consists of a Na–Pa–Nb–Pb complex between 10 and 80 ms and typically falls in the gamma frequency range between 30 and 40 Hz, while the MLR in children between 4 and 7 years of age exhibits a Pa–Nb complex with lower frequency content around 20 Hz (Suzuki and Hirabayashi, 1987; Suzuki et al., 1983). Also, the MLR is not always detectable before the age of 10 suggesting a great inter-subject variability (Kraus et al., 1985). Due to the large variability in latency and frequency, we believe that this component is only visible in non-phase-locked activity. 4.4. Distinction between upper and lower alpha ERD The alpha ERD was stronger in the upper than in the lower alpha band while the effect was more prominently expressed for the violin tone than for the noise (Fig. 4). In general, the upper alpha has been proposed to be associated with semantic processing in adults (e.g., Klimesch, 1999). Recent studies also have shown that memory load and task complexity promote greater dissociation between upper and lower alpha band activities (Fink et al., 2005; Krause et al., 2000). More relevant to the present results, it has been shown that even with the same auditory task design, the type of sound stimulus is an important factor in dissociating these two alpha bands. Using an auditory memory search task for tones and vowels separately, alpha ERD to a probe stimulus was larger for the vowel condition than for the tone condition, especially in the upper alpha frequency band (Krause et al., 1995). In children, the proposed involvement of semantic processes in the upper alpha band has been supported in a few studies. When performance on an auditory memory search task was matched, the alpha ERD in 10–12 year-old subjects was not significantly different from adults in the upper band but was smaller in the lower band (Krause et al., 2001). When reading words, the upper alpha ERD was observed in healthy control

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children but not in subjects with dyslexia (Klimesch et al., 2001). Since we used a passive listening task, the difference between violin and noise sounds cannot be associated with cognitive demands directly. However, it is reasonable to assume that the violin sound is more meaningful compared to white noise, and thereby initiates more complex auditory processing which leads to the pronounced upper alpha ERD as shown in adults (Krause et al., 1995). Thus, we propose that the upper alpha ERD induced by an auditory stimulus can index processes of stimulus feature analysis and registration, as well as subsequent cognitive processes in both children and adults. The functional significance of the lower alpha activity, on the other hand, has not been as well demonstrated. Our data showed highly consistent amplitudes and time courses for the ERD in the lower alpha across hemispheres and stimulus types (Fig. 4). It has been proposed that the lower alpha ERD reflects attentional processes (e.g., anticipation, orienting) (Klimesch et al., 1998, 1992). Interestingly, this proposal was based on the observation that only the lower alpha band (8–10 Hz), not the upper alpha band (10–12 Hz), showed a decrement to warning tone stimuli which preceded visual stimuli. In particular, the time course of lower alpha in the study of Klimesch et al. (1998) appears to be similar to our result of 8-Hz ERD, specifically as both returned to baseline after 1000 ms. Thus, there may be a link between our results and these data, indicating that the lower alpha ERD might reflect non-specific obligatory processes in response to auditory inputs. 4.5. Left-lateralization in upper alpha ERD In addition to the stimulus effect mentioned above, the upper alpha ERD was also longer-lasting and more pronounced in the left than the right auditory cortex (Figs. 4 and 5). Since most of the ERS/ERD research has been done with scalp-recorded EEG, evidence for clearly identified hemispheric differences in source activity in the auditory cortices is difficult to find in the literature. The exception is a study by Kaufman et al. (Kaufman et al., 1992) which examined alpha ERD using simultaneous MEG and EEG recordings during an auditory memory search for musical tones in adults, and showed a greater correlation between reaction time and the magnitude of alpha ERD in the right auditory cortex than in the left. As well, the magnetic auditory evoked response at 100 ms co-varied with the memory load only in the right auditory cortex. These results appear to be consistent with our present data as both the evoked response and alpha ERD exhibited the same auditory cortex lateralization. We also found that the enhanced left source activities were more prominent to the violin tone than to the noise stimulus. However, this laterality is in contrast to previous MEG data in adults whose evoked responses at 100 ms and later to musical tones were right-lateralized (Eulitz et al., 1995; Gootjes et al., 1999; Kuriki et al., 2007). Moreover, the link between the evoked response and the alpha ERD is obscure. Using ECoG during an auditory discrimination task, speech sounds elicited greater alpha ERD in left auditory cortex compared to amplitude-modulated pure tones (Crone et al., 2001), although the evoked response at 100 ms did not show any stimulus

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modulation, unlike the result obtained by Kaufman et al. (1992). Clearly, more research is necessary to illustrate hemispheric differences in alpha suppression in the auditory cortices across a variety of tasks, stimuli and ages. Nevertheless, we maintain our previous proposal and suggest that the observed laterality both in upper alpha ERD and the evoked responses may reflect leftward advancement of cortical maturation in right-handed subjects (10 of 12 subjects) in this age range. As discussed above, the upper alpha ERD is thought to index stimulus processing with further cognitive processing. During development, absolute signal power of spontaneous oscillatory activities at rest increases in the upper alpha band and decreases at theta and delta (0.5–3 Hz) frequencies (Petersen and Eeg-Olofsson, 1971). Large absolute alpha power in the EEG is correlated with a pronounced decrease in overall event-related band power (Doppelmayr et al., 1998). Therefore, the degree to which the upper alpha ERD is expressed may also reflect the developmental progression in certain cortical areas. There is no clear indication of lateralization in spontaneous alpha activity in childhood. However, it is known that left and right hemisphere alpha activity develops at different rates and different age ranges. EEG phase correlation between frontal and temporal electrodes in the alpha band shows left-dominant development in children between 4 and 6 years (Thatcher et al., 1987) indicating an enhanced ability for network connections in the left hemisphere within this age group. This is consistent with results of structural brain imaging examinations, which showed that fibre tracts forming frontotemporal pathways were advanced in the left hemisphere (Paus et al., 1999). There are several factors that may have contributed to the lack of laterality in spontaneous activity in general. In a longitudinal follow-up study, a larger inter-individual variability was found than age-related changes in spontaneous activity (Benninger et al., 1984). To date, there has not been an MEG study examining spontaneous activity in children. With scalp EEG, which has been used with infants and children, the signal is typically dominated by occipital alpha, because the eyes-closed condition is often used for recording in this age range. Therefore, it is possible that potential hemispheric differences occurring locally in the auditory cortices were obscured. Alternatively, spontaneous neural activity might not be strongly lateralized compared to event-related neural responsiveness regardless of the influence of occipital alpha activities. Even when normally developing children (between 3 and 8 years, the majority of whom were right-handed) were fully awake and quietly attending to visual stimuli, only a weak leftward asymmetry was found in central electrodes (between C3 and C4) (Stroganova et al., 2007). This indicates that the asymmetry in spontaneous activities, in the temporal and parietal lobes, is subtle in the scalp-recorded EEG signals. Collectively, we propose that the upper alpha ERD selectively reflects the maturational status of particular neural networks, which are used to analyse different stimuli, and this is more advanced in the left auditory cortex in the present age group. Further work to test this hypothesis is necessary, as no other developmental data demonstrating lateralization of stimulus-induced oscillatory brain activity are currently avail-

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