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Neuroscience 164 (2009) 1108–1118

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LECTROPHYSIOLOGICAL STUDY OF AUDITORY DEVELOPMENT

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Centre de recherche du Centre Hospitalier Universitaire Sainte-Jus-ine, Montréal, Québec, Canada

Centre de Recherche en Neuropsychologie et Cognition, Universitée Montréal, Montréal, Québec, Canada

Neurostatistics Department, Cuban Neuroscience Centre, La Ha-ana, Cuba

bstract—Cortical auditory evoked potential (CAEP) testing,non-invasive technique, is widely employed to study audi-

ory brain development. The aim of this study was to inves-igate the development of the auditory electrophysiologicalignal without addressing specific abilities such as speech orusic discrimination. We were interested in the temporal and

pectral domains of conventional auditory evoked potentials.e analyzed cerebral responses to auditory stimulation

broadband noises) in 40 infants and children (1 month to 5ears 6 months) and 10 adults using high-density electro-hysiological recording. We hypothesized that the adult au-itory response has precursors that can be identified in in-ant and child responses. Results confirm that complex adultAEP responses and spectral activity patterns appear after 5ears, showing decreased involvement of lower frequenciesnd increased involvement of higher frequencies. In addition,ime-locked response to stimulus and event-related spectralertubation across frequencies revealed alpha and beta bandontributions to the CAEP of infants and toddlers before muta-ion to the beta and gamma band activity of the adult response.

detailed analysis of electrophysiological responses to a per-eptual stimulation revealed general development patterns andevelopmental precursors of the adult response. © 2009 IBRO.ublished by Elsevier Ltd. All rights reserved.

ey words: maturation, electrophysiology, infant, spectral,rocessing, perception.

vent-related potential (ERP) testing is a noninvasiveechnique that is widely employed to study brain develop-ent. In particular, cortical auditory evoked potential

CAEP) testing has been used extensively to investigateiscriminative abilities in young infants, including musical

eature discrimination (Trainor et al., 2003; Ceponiene etl., 2002; Leppänen and Lyytinen, 1997; Morr et al., 2002),honemic identity (Cheour et al., 1998; Dehaene-Lambertznd Baillet, 1998; Dehaene-Lambertz and Gliga, 2004;ehaene-Lambertz and Dehaene, 1994) and gap detec-

Correspondence to: S. Lippé, Ste-Justine University Hospital, Researchentre, University of Montreal, 3175 Côte Ste-Catherine, Montreal, QC3T 1C5, Canada. Tel: �514-343-6111 (ext. 46040); fax: �514-343-787.-mail address: sarah.lippe@umontreal.ca (S. Lippé).bbreviations: BBN, broadband noises; CAEPs, cortical auditoryvoked potentials; EEG, electroencephalography; ERPs, event related

iotentials; ERSPs, event-related spectral perturbations; ITC, inter-trialoherence; MLR, middle latency responses.

306-4522/09 $ - see front matter © 2009 IBRO. Published by Elsevier Ltd. All rightoi:10.1016/j.neuroscience.2009.07.066

1108

ion (Trehub et al., 1995). The aim of this study was tonvestigate auditory system development without address-ng specific abilities such as speech or music discrimina-ion. We focused specifically on developmental changes inhe temporal and spectral domains of the electrophysio-ogical signal occurring from zero to 5 years. We hypoth-sized that adult CAEP components have identifiable pre-ursors.

The CAEP components P1, N1, P2 and N2 are definedy their polarity and latency. CAEPs are generated fromuditory thalamo–cortical and corticocortical pathways, in-luding the primary and association cortices (Ponton et al.,002; Eggermont and Ponton, 2002; Näätänen and Picton,987). These long latency responses may originate fromeveral spatially distinct areas (Näätänen and Picton,987; Arezzo et al., 1975). P1 with a mature latency of0–60 ms was recorded in the Heschl’s gyrus (Godey etl., 2001) and the secondary auditory cortex of adultsLiegeois-Chauvel et al., 1994). N1, whose adult latency is0–110 ms, has been largely studied. It consists of at leasthree components (Näätänen and Picton, 1987). The T-omplex N1, which involves Na and Nc, is largest at laterallectrodes (McCallum and Curry, 1980; Giard et al., 1994),hereas N1b is a fronto-central negativity originating bilater-lly in the superior temporal cortex from the Heschl’s gyrusnd the planum temporale (Godey et al., 2001). Other au-

hors have described a second frontal component peaking atround 40 ms after N1b, attributed to anterior cingulate acti-ation (Mulert et al., 2001; Alcaini et al., 1994). In adults, the2 component has a latency of 150–200 ms and is followedy the N2. It has been suggested that these components areodulated by sound complexity (Ceponiene et al., 2001).

ˇ eponiene et al. (2005) further suggest that the two peaksre sensitive to distinct stimulus types: P2 would be moreelated to non-phonetic stimuli, whereas N2 would be relatedo syllabic stimuli (Ceponiene et al., 2005).

Typically, from 8 years to adulthood, a child’s P1 com-onent decreases in latency and amplitude, N1 increases

n amplitude until it becomes a discrete component atdulthood, P2 increases in latency and decreases in am-litude, and N2 decreases in both latency and amplitudeSussman et al., 2008). In infants, CAEP morphology islearly different. It is defined by a large positive componentrominent over the frontocentral scalp area at around00–250 ms (Picton and Taylor, 2007; Ohlrich, 1978; Rot-eveel et al., 1985, 1987; Ceponiene et al., 2002; Egger-ont and Ponton, 2002; Ponton et al., 1996). Within thisonophasic response, authors have described a negativeeflection at 160–200 ms from 3 months onward, whichhey called N1M (Novak et al., 1989). In newborns, follow-

ng a large positivity, a late negativity occurs at 600–700s reserved.

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S. Lippé et al. / Neuroscience 164 (2009) 1108–1118 1109

s, which increases in amplitude and decreases in latencyith age (Kurtzberg et al., 1984). From 3 to 6 months, twoegative components of low amplitude follow this greatrominent positivity (at around 280 and 500 ms) (Kushne-enko et al., 2002). Nevertheless, the frontocentral positiv-ty changes very slowly and becomes the P1, N1 and P2esponses. The N1b response is described at 3 years byaetau et al. (1995), but in most studies it has been better

dentified in late childhood (9 years) (Ceponiene et al.,998; Wunderlich et al., 2006; Sussman et al., 2008; Ton-quist-Uhlen, 1996; Pang and Taylor, 2000). Although itsources have been less studied, this component also ap-ears to originate from both temporal lobes (Pang et al.,003; Lütkenhöner and Steinsträter, 1998).

In perceptual and cognitive protocols, N1b has beenelated to orientation and integration of informationNäätänen and Picton, 1987). The absence of N1 in infantsnd children has therefore raised speculations. For exam-le, Sussman et al. (2008) recently hypothesized that the1 refractory period is longer in childhood than in adult-ood, given that increasing the stimulus rate in adultsissipates N1. Other authors posit that the appearance of1 is linked to the formation of functioning synaptic con-ections in the upper layers of the auditory cortex (re-iewed in Picton and Taylor, 2007; Shahin et al., 2004),hereas activity in the deeper layers would create the

arge positive wave of the infant. Although N1 appears toave a lengthy maturation, it may have electrophysiologi-al precursors, which can be measured either temporallyr spectrally in infant and child signals.

Spectral domain development has been addressed byeveral authors in studies on resting-state and evokedotentials (e.g., Lippé et al., 2007; Thatcher, 1992) show-

ng reduced low frequency density and increased higherrequency density with age. The shift from a clear domi-ance of slow band activity to an electroencephalographicEEG) signal involving all bands more evenly has beenell documented using power spectrum density analysis.ast activity may reflect biological and functional fine markersf development. However, from scalp EEG, the presence of

ocal low-amplitude and fast activity may be difficult to capturen the infant’s signal characterized by slow activity and highmplitude. Little is known about the development of time andrequency changes, and most studies, even recent ones,ave focused on the alpha band (e.g., Thatcher et al., 2008),ith few attempts to identify higher frequency activity. Al-

hough low in proportion in the infant signal, it is unlikely thatigh frequency activity is noncontributive to the evoked re-ponse, especially given its important role in the adult re-ponse (e.g., Chorlian et al., 2006).

Spectral manipulation of stimuli can induce specific spec-ral responses (Merzenich and Brugge, 1973; Formisano etl., 2003). In our study, we used an auditory stimulus includ-

ng a large frequency window that can be processed at a veryoung age. Broadband noises (BBNs), which are known tongage low and high frequency hearing areas, have beenhown to enhance cortical response amplitude (Riquelme etl., 2006). Given that a slower stimulus rate results in more

dult-like features of the waveforms in older children (Suss- s

an et al., 2008), we presented an auditory stimulus havinglarge spectrum at one cycle per second to ensure maxi-um brain processing at any age (Ceponiene et al., 1998).

Other developmental studies have used broadbandtimuli such as clicks (Rotteveel et al., 1987; Pasman etl., 1999; Ponton et al., 2000), but have focused onhanges occurring between 5 years and adulthood orithin the first year of life only (Kushnerenko et al., 2002).e examined infants from 1 month to 5 years 6 months andgroup of young adults and compared morphological, am-

litude and latency changes in CAEP throughout develop-ent. Considering the amount of structural changes occur-

ing in the first years of life, we felt it was necessary to covern extended age range (0–5 years) to obtain a thoroughnderstanding of auditory development. To our knowledge,uditory electrophysiological and spectral development hasever been examined throughout this age span.

In the present study, we examined spectral densityevelopment for each frequency band (delta, theta, alpha,eta1, beta2 and gamma). We specifically aimed at de-omposing the event-related EEG signal in the time-fre-uency domain. CAEPs are thought to be mainly formedy phase-locked activity, evoked and time-locked to thevent (Makeig et al., 2004). We used inter-trial coherenceITC), a phase-locking measure between EEG processes,nd the occurrence of the event across trials (Makeig et al.,002) to assess phase coherence across time-locked tri-ls. In addition to evoked activity, oscillatory changes cane induced by experimental events and should involveime-frequency power changes. Event-related spectralerturbation (ERSP) was also investigated, as it measureshe average of the dynamic changes in amplitude of theEG spectrum with time (Makeig, 1993) that may be com-lementary to the evoked response. We hypothesized thatpectral maturation underlies the mechanisms involved inhe appearance of developing CAEP components.

EXPERIMENTAL PROCEDURES

articipants

orty infants and children aged from 1 month to 5 years 6 monthsere divided into five age groups (Table 1) of seven to 10 subjectsach. Subjects were chosen from a cohort of 79 tested infants andhildren. Data on 39 of these children were excluded from theriginal sample due to their arousal state (crying or sleeping babies),ovement artifacts and other non-neurological activity (e.g. electricaloise). In addition, 11 adults aged 20–30 years were tested. Datarom one adult were rejected due to excessive artifacts.

Developmental information was gathered from interviews anddevelopmental questionnaire completed by the parents. All sub-

ects were born at term and had no history of neurological illness.e tested 58% of the children for intellectual development using

wo Intellectual Development Scales, according to the child’s age.n average of 100.78 (standard deviation 10.26) standard scoreas obtained on the Mental Development Index of the Bayleycale of Infant Development, 2nd ed. (Bayley, 1993) for infants–24 months or on the Stanford Binet Intelligence Scale, 4th ed.Thorndike et al., 1994) for children 24–66 months. Thus, thehildren tested were well within the average developmental andntellectual quotient range. Parents and adult participants signedhe consent form authorized by the ethics, administrative and

cientific committees of the Ste-Justine’s University Hospital Re-

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S. Lippé et al. / Neuroscience 164 (2009) 1108–11181110

earch Centre and the University of Montreal. Testing wastopped whenever children reacted negatively.

pparatus and stimuli

BNs of 50 ms duration were presented in a soundproof room infree-field setting at a randomly distributed interstimulus interval

arying from 1200 to 1400 ms at 70 dB SPL intensity and 16-bitesolution. Stimuli were generated by a Dell GX150 PC using-Prime 2000 software (Psychology Software Tools Inc., Pitts-urgh, PA, USA). BBNs were delivered through two speakersOptimus XTS 24, Boston, MA, USA) located laterally at aistance of 30 cm from each of the subject’s ears. Young

nfants were seated on their parent’s lap. Their attention wasirected centrally using small coloured objects held by thexperimenter. Older subjects were asked to sit still and face thecreen in front of them. EEG was recorded continuously from28 electrodes (Electrical Geodesics System Inc., Eugene, OR,SA). Recordings were made only when the children were stillnd their gaze was centered to ensure equivalent distancesetween the speakers and the subjects’ ears. Reference was athe vertex and impedance was maintained below 40 K ohms, asuggested by Tucker (Tucker, 1993). The EEG signal wasmplified and analog bandpass filtered from 0.1 to 100 Hz. Theignal was digitized at 250 Hz in 1024 ms epochs. A G4acintosh computer was used for data acquisition.

Off-line analyses were performed with Brain Vision Ana-yzer software version 1.05 (Brain Products, Munich, Germany).ata were digitally filtered with a 0.5–50 Hz (24 dB/October)andpass filter and re-referenced to average. The EEG wasubjected to algorithmic artefact rejection of voltage exceeding100 �V. Eye movement artifacts were corrected using theratton and Cole’s algorithm (Gratton et al., 1983). Visualxamination of the segmented (�500 –1000 ms) data were alsoerformed and segments with artifacts were manually rejected.n average of 52.3 (�27.33) artifact-free EEG epochs of 1500s, time-locked to the stimulus onset, was analyzed for each

ubject.

uditory evoked potentials analysis

rtifact-free segments were baseline corrected and averaged.

able 1. Averages and standard deviations of each peak per age gro

P1 1st negative

Amp Lat. Amp

r1 n�51 month) �2.82 (1.5)�7r2 n�7

2 months) �1.76 (2.32)�8r3 n�4

3–6 months) �2.07 (1.69)�7r4 n�7

7–18 months) �1.95 (1.44)�8r5 n�7

24–66 months) �1.31 (1.01)�10r6 1.5 52.80 �1.46

adults) (sd 0.6) (sd 20.7) (sd 1.5)

The first negativity was not found in 10 children across groups. Me

mplitudes and latencies were measured at electrode six, cor- a

esponding to FCz in the Electrical Geodesics system. Thislectrode was chosen because it appeared to show the highest2 amplitude on brain topographies from infancy to adulthood.mplitudes were defined from baseline (500 ms pre-stimulus)

o the highest amplitude for each component (P1, N1, P2 and2). Latencies were defined from stimulus onset to the highestmplitude for each component. Group averages for AEPs and to-ographies indicated the approximate time windows from whichomponents were visually selected, separately for each subject. Forach subject, latency of the first dominant peak was first identified.hen, the first negative deflection was found before the onset of theominant peak, and the second negative peak latency was foundfter the dominant peak deflection. Topographies of every peak werelso examined individually to ensure consistent scalp distributioncross subjects. See the supplementary material for group averagesor scalp topographies.

In addition, using a MATLAB script, we performed paired-tests with a Bonferroni correction for multiple comparisons at 12

ime points within a 50 ms window centered at the mean latencyf each peak (Table 1) for each age group. Averaged latencieshown in Table 1 are in good agreement with other studies (e.g.,iegeois-Chauvel et al., 1994; Näätänen and Picton, 1987; Pictonnd Taylor, 2007). T-tests were therefore applied to averaged

rials of each individual for each group to determine whetheromponents differed significantly from zero (P�0.05). Standardeviations were calculated for each group (Fig. 1). Although com-onent morphologies were generally comparable within ageroups, the first negative deflection could not be extracted in 10hildren, as no deflections were observed (Table 1). Statisticalnalyses were performed using SPSS software. Regression anal-ses using curve estimations and analyses of variance were usedo quantify developmental changes. Regression analyses wereerformed on the data from children aged one to 66 months only.ogarithmic transformations were applied to the age variable andhe latency and amplitude variables.

Brain topographies were performed using Brain Vision Ana-yzer software version 1.05 (Brain Products, Munich, Germany).hey were computed at the maximal amplitude of the visuallyetermined component window at electrode FCz. All in one maps90° of maximal angles), interpolated by spherical splines, and

2nd positive 2nd negative

Amp Lat. Amp Lat.

13.23 243 �1.79 615.410.7) (sd 6.7) (sd 72.1) (sd 2.7) (sd 88.2)

11.42 217 �1.9 682.520.62) (sd 6.3) (sd 23) (sd 1.2) (sd 110)

12.66 198.8 �0.9 47319.89) (sd 6.54) (sd 42.4) (sd 4.08) (sd 89.6)

8.5 174 �2.89 318.517.15) (sd 2.9) (sd 26.9) (sd 2.7) (sd 27.8)

7.8 134.8 �5.33 3247.66) (sd 3.1) (sd 16.9) (sd 3.2) (sd 82.6)

2.54 169 �2.07 2885) (sd 1.3) (sd 16.8) (sd 1.39) (sd 40.5)

for the first negative were calculated on adjusted samples.

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S. Lippé et al. / Neuroscience 164 (2009) 1108–1118 1111

nd right side views were chosen as representative of the datasee supplementary Fig. 1).

pectral density analysis

pectral density analyses were performed on the auditoryvoked potential artifact-free EEG data. Using Brain Visionnalyzer software (1.05), a mean of 52.3 (�27.33) 1000 msrtifact-free EEG epochs, time-locked to the stimulus onset,as chosen for each subject. All epochs were re-sampled at56 Hz and submitted to fast fourier transformation (FFT) with10% Hanning window to obtain 1 Hz frequency resolution.

he sum of spectral power was averaged across segments andxtracted for the following EEG bands: delta: 1–3 Hz; theta:– 8 Hz; alpha: 8 –13 Hz; beta1: 13–22 Hz; beta2: 22–32 Hz;nd gamma: 32–50 Hz. Data were averaged across five elec-rodes over the frontocentral region.

All data were transferred to SPSS software. To calculate relativepectral density, we divided the spectral density of each band by totalpectral density. Regression analyses (curve estimations) were ap-lied to each band ratio on the log-transformed age variable, exclud-

ng adults. Logarithmic transformations were applied when normalistribution was not observed (age, theta and alpha).

Time-frequency analyses were performed with inter-trial co-erence (ICT) and ERSP measures using EEGlab software v5.03Swartz Center for Computational Neuroscience) on all partici-ants’ data from the FCz electrode (electrode six). In light of thepectral density ratio results (see below), higher frequency activityas of special interest. Therefore, we used a three-cycle Morletavelet at the lowest frequency and a 6.4-cycles wavelet at theighest frequencies. Frequencies were estimated from 11.7 to

One month

-500 -250 0 250 500 750-20

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3-6 months

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24-66 months

-500 -250 0 250 500 750-10

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Adults

time (ms)

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-500 -250 0 250 500 750-5

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ig 1. Grand averaged CAEPs per age groups. The grey shadow shoere applied on averaged trials of each individual for each group,

nterpretation of the references to color in this figure legend, the read

0.0 Hz. Inter-trial coherence (ITC) and ERSP were computed on (

rtifact-free trials. Each trial contains samples from �500 msefore to 996 ms after the time-locking event. Only significantalues (bootstrap P�0.05) were assigned values, and non-signif-cant values were set to zero.

First, ITC and ERSP results were examined individually fornfants, toddlers and adults. A group analysis was then performedy computing the binomial probability for each time-frequencyoint, as suggested by Onton et al. (2005). This approach as-umes that each subject represents an independent observation,nd it determines how many subjects show a significant time-requency point. The significance probability of each subject isiven by the bootstrap threshold (P�0.05). Therefore, points thatre significant in most subjects have a very low binomial proba-ility. In this paper, significant points across subjects are defineds having a binomial probability below 0.05. For visualizationurposes, we plotted the negative logarithm of the binomial prob-bility in the time-frequency plane for significant ERSP and ITCoints only, while non-significant points are set to zero. In order toistinguish between significant ERSP increases and decreases,e applied signs to the negative logarithm of the binomial proba-ility (which is always positive) according to the sign of the aver-ge ERSP across subjects.

RESULTS

ortical auditory evoked potentials

he CAEP responses presented in Fig. 1 and Table 1 areverages for seven to 10 normal individuals per age group

2 months

-500 -250 0 250 500 750-20

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20

7-18 months

-500 -250 0 250 500 750-10

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10

Adults

time (ms)-500 -250 0 250 500 750-5

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5

andard deviation whereas the grey line shows t-tests results. T-Testsif components were significantly different from zero (P�0.05). Forrred to the Web version of this article.

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S. Lippé et al. / Neuroscience 164 (2009) 1108–11181112

AEPs

he data confirm the late maturation of CAEPs. In fact, the1, N1, P2, N2 complex becomes identifiable only in thedult group (Fig. 1). The T-test results on specific 50 msindows for each peak and each group indicate signifi-ance (P�0.05) for only the P2 component in all groups.egression analyses suggest that this dominant positiveeflection diminishes in amplitude and latency until adult-ood (amplitude: F�2.2173, R2�0.107 03, P�0.000; la-ency: F�22.99, R2�0.657 14, P�0.000; Fig. 2A, B). The-tests with Bonferroni correction did not reveal the pres-nce of the second negative peak in our infant and childata. This may be due to the highly variable latency within theame age group. However, standard deviations (Fig. 1) showhe presence of second negative deflections in some indi-iduals from 7 months of age, and more so from 24onths of age. A small decrease in latency and ampli-

ude follows a cubic function (N2 amplitude: F�4.234,2�0.2608, P�0.000; N2 latency: F�37.418, R2�0.7571,�0.000). No developmental changes are evident for1, as it is neither consistently nor reliably identifiable

N1 amplitude: F�0.848 12, P�0.3629) in our infant andhild data.

pectral analysis: spectral density

o investigate the developmental gain in density of fre-uency bands and to fairly compare children to adults, in

ig. 2. (A, B) Regression analyses of amplitudes (A) and latencies (B)or each peak on log of age for participants from 1 month to 5–1/2ears. Adult results are also shown. For interpretation of the refer-

mnces to color in this figure legend, the reader is referred to the Webersion of this article.

hom amplitudes differ, ratios of each band power overhe sum of all band powers were calculated for each par-icipant and averaged within age groups. Regression anal-ses were applied to the infant and child data on therontocentral region, our area of interest. All frequencyands were found to change significantly with ageP�0.000 05) (Fig. 3A, B). In fact, although delta band ratioecreases with age (cubic: F�31.94203, R2 0.72691,�0.0005), all other band ratios increase significantly (the-

a: cubic F�23.81686, R2 0.664 96, P�0.0000; alpha:ubic F�41.72181 R2�0.77663, P�0.0000). The highestrequency band ratios, from 13 to 50 Hz, show cubic anduadratic increase (cubic: beta1 F�15.385 24, R2�0.4544, P�0.0000; beta2 P�0.05; gamma P�0.05) and showmall increase between 1 month and 5 years, with large

ncrements at adulthood.

TC and ERSP

TC is a measure of EEG phase consistency across trials,ver time and at each frequency (Makeig et al., 2002).onsidered a measure of phase-locked EEG activity, ITCay reflect the specific contribution of each frequency to

he CAEP. ITCs were calculated from 11.7 to 50 Hz forach subject and group statistics were performed (Fig 5).owever, due to inter-subject variability, only representa-

ive examples are shown in Fig. 4A, B.As a representative sample of the adult data, Fig. 4A

emonstrates strong ITC from 11.7 to 50 Hz frequencies. Its noteworthy that ITC of high frequencies around 45–90

s appear to correspond in time to P1, peaking at 73 ms.n contrast, N1, P2 and N2 appear to correspond in timeith ITC mainly in the high alpha range. Corresponding

ncrease in power (ERSP) occurs within the first 200 ms. In1 year-old child, ITC of beta and gamma activity is also

bserved, but at later latency. It is noteworthy that activityn the gamma frequency band appears to correspond inime with an emerging negative deflection visible in the00–150 ms range (Fig. 4B).

The time-frequency group analyses show a general trendor both ERSP and ITC. Although not consistently significant,e observe two clusters of high alpha and beta activity in the

nfant and toddler data (1–24 months), one at the beginningf the dominant peak and one at the maximum amplitude of

he same peak. At 1 month, the first cluster appears in timeorrespondence with an early negativity (Fig. 4B). In adults,he first cluster occurs very early and within the gamma band,n time correspondence with the P1 and N1 peaks.

Group analyses also show that increases in ITC wereccompanied by significant or nearly significant increases inower (ERSP). ERSP shows more within-group variability,specially from three to 24 months. At 1 month, waves ofRSP decreases and increases in the beta and gamma bandre well organized around the first cluster of ITC and ERSPhanges. From two to 66 months, this early organization is

ost. The organized pattern of ERSP increase and decreases optimal in the adult group: very early decrease in power attimulus onset is followed by an increase in power during 200

s, with a decrease thereafter in beta/gamma power (Fig 5).

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S. Lippé et al. / Neuroscience 164 (2009) 1108–1118 1113

DISCUSSION

e characterized the electrophysiological development ofuditory processing without addressing specific abilitiesuch as speech or music discrimination in children agedne to 66 months. A thorough understanding of the pre-ursors of the typical adult response (P1, N1, P2 and N2)equired a fine analysis of the CAEPs. We determinedaturational changes in auditory responses by describingAEPs by amplitude and latency. In addition, we investi-ated developmental changes in auditory responses in the

requency domain using three measures: relative spectralower, phase-locking activity and event-related spectralerturbation. Our purpose was to better describe brainesponses related to CAEPs in the time and frequencyomains throughout development.

Results confirm the presence of maturational changeshroughout infancy, changes that are still incomplete at 5ears. In fact, using neither vocal or verbal stimuli, complexAEPs with P1, N1, P2 and N2 components only emerge

rom 5 years to adulthood. In the infant response, a large

ig. 3. (A, B) Relative spectral density values for each band. Relative vpectral density. Regression analyses were performed for each band ondividually on the right side of the graph. For interpretation of the refef this article.

ositive wave is reliably dominant. The second negative t

eflection is present, but with variable latencies. It cane better discriminated as latency is reduced and am-litude increases. Finally, the P1 and N1 componentsre barely identifiable in infants and children.

At 1 month, a first negative component (80 ms) isrequently present in the auditory evoked potentials ofnfants, but this has rarely been addressed by researchers.onsidering the young age of the infants (1 month) and theody of literature (Sharma et al., 1997; Paetau et al., 1995;illey et al., 2005; Ceponiene et al., 2002) asserting that

he N1 component is not present before 4–5 years, it isnlikely that this component is the adult N1. It could bergued that this first negative component corresponds toiddle latency responses (MLR). Adult MLR range from 10

o 50 ms and are characterized by the Na (18 ms), Pa (30s), Nb (40 ms) and P1 or Pb (50 ms) components (Pictonnd Hillyard, 1974). The infant latency of 80 ms may be an

mmature form of N1b. Time-frequency analyses show aorresponding increase in power (ERSP) and phase-

ocked activity (ITC) in the alpha and beta bands. However,

obtained from the ratio of each band spectral density/sum of all bandsof age on infants and children’s data only. Adult data are also showncolor in this figure legend, the reader is referred to the Web version

alues aren the log

his early negativity is no longer present in group average

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ata from 2 months on. Nevertheless, in the infant andoddler groups, alpha and beta activity is observed at theime of the increment of the dominant positive wave, whichuggests that the increase in power and the phase-lockedctivity of the infant EEG signal may initiate the increase inmplitude of the dominant positive response.

In addition, the data obtained on certain individualubjects suggest a hypothetical role of high frequencyctivity in the CAEPs. In the adult, the P1/N1 appears toorrespond to the beta and gamma event-related activity.urthermore, at 12 months, we observe two clusters ofigh frequency activity, one early, at the beginning of theominant positive component, and one later, during therough within this same component. We should keep in

ig 4. Upper panel: ERSP. ERSP are the mean change in spectralower (in dB) compared to baseline. Green and blue lines below repre-ent the maximal and the minimal values. Lower panel: ITC. ITC is thetrength of phase coherence between time locked trials. The wave un-erneath the ITC panel is the corresponding EPs at FCz. (A) One adult.B) One year-old child. Note developmental changes in inter-trial coher-nce values (time-locked activity) expressed over time and spectral fre-uencies. Adults show high frequency time-locked activity early aftertimulus onset with corresponding ERSP. At 1 year of age, most of theime-locked activity is found within the lower frequencies. Higher fre-uency time-locked activity seems to correspond to a negative trough.

ind that this negative curve is often observed in infant s

ata, and was described from 3 months on by Novak et al.1989). It may therefore be that two clusters of spectral ac-ivity entangled in the infant’s response mutate with age intone early beta-gamma cluster of event-related activity. Otheruthors have suggested that the large positive deflection

ound in infants originates from the early maturing corticalnput layers (III and IV), whereas later appearing componentsP1 and N1b) originate from the late maturing superficialayers (reviewed in Shahin et al., 2004). Further research iseeded to elucidate how beta and gamma activity is related

o the development of P1 and N1 per se.

pectral domain

n conjunction with CAEP development, maturationalhanges in spectral activity appear to continue beyond 5ears. As revealed by relative spectral density measures,

nfant brain activity is dominated by slow frequencies. Withge, as the delta band is reduced, higher frequency bandshow a relative increase in activity. More precisely, theta,lpha and beta bands show substantial changes from 5onths on, whereas the gamma band shows greater in-

rease only after 5 years. This pattern of spectral activityevelopment does not appear to be exclusive to the audi-ory system, as it has also been found in the visual systemLippe, 2007). Similar results have been demonstrated intudies on resting state EEG (Matthis et al., 1980; Gassert al., 1988; Marshall et al., 2002; Stroganova et al., 1999),uggesting that low frequency activity is replaced through-ut development by higher frequency activity. Maturationalhanges in spectral density are therefore not specific to therocessing state or sensory system involved.

Time-frequency analyses confirm the increasing con-ribution of higher frequency at adulthood. Moreover,cross age groups, increase in phase coherence (ITC)as accompanied by an increase in power (ERSP). ERSPeasures are thought to reflect not only event-relatedesynchronization and synchronization, but also superim-osed evoked activity (Makeig et al., 2004). Changes inower (ERSP) are more variable in the infant and childroups than the adult group. In adults, the desynchroniza-ion and synchronization pattern of the 11.7–50 Hz activitys well defined, characterized by late desynchronization.ate and long components (�200 ms) may be associatedith increase in lower frequency power and decrease inigher frequency power. In children, brain maturation couldllow the signal to travel within multiple neural networks,hich are not yet efficient, multiplying brain sources ofesynchronization/synchronization and superimposed ac-ivity. It indeed has been proposed that multiplicity of brainources likely to be captured and the signal mixing pro-uced by volume conduction may explain some of theRSP variability (Makeig et al., 2004). In addition to ERSP,hase consistency (ITC) becomes more definite in timend frequency from childhood to adulthood. The increase

n phase shift and phase-locked periods with age found byhatcher et al. (2008) is in good agreement with a reduc-

ion of within-group brain signal variability with age.The classical view of underlying ERP mechanisms

uggests that the evoked (fixed latency of transient polarity

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S. Lippé et al. / Neuroscience 164 (2009) 1108–1118 1115

pecific) activity is superimposed on the background EEG.his view postulates an increase in power. In addition, thexed polarity and latency suggested by the classical vieway reduce intertrial variability, thereby mimicking phase

esetting. More recent hypotheses of underlying ERPechanisms have proposed the phase-reset model, in-

olving a reorganization of ongoing oscillations in the EEGFell et al., 2004; Jansen and Brandt, 1991; Tallon-Baudryt al., 1996; Brandt, 1997; Schack and Klimesch, 2002;nton et al., 2005; Gruber et al., 2005). ITC measuresave proven to be dependent on mean activity, and could

herefore reflect ongoing background activity if no concom-tant increase in power is observed (Martínez-Montes etl., 2008). Therefore, at this stage, our analysis does notllow us to draw a definite conclusion on the underlyingeurophysiological mechanisms in the infant, child anddult brain that process perceptual stimuli, or how they

ig 5. Group analyses for ERSP (A, C, E, G, I, K) and ITC (B, D, F,he binomial probability for each group. Non significant values (binomiaalues indicates that a sufficient number of subjects showed significane labeled as negative those points where the average ERSP in the grecreases across subjects in each group. Note scaling variation throu

hange with maturation. a

The differences in spectral activity response betweennfants and adults could also result from distinct perceptualnd cognitive processes as well as neuroanatomical cor-elates engaged during the task. The gamma phased-ocked activity occurring early after auditory stimulationas been reported by others (e.g. Yordanova et al., 1997).t was found to originate locally, in the auditory cortexBrosch et al., 2002), and its power has been linked toelective attention (Yordanova et al., 1997; Tiitinen et al.,993). Similarly, the gamma band response in children

ncreased in power and stability (phase-locked) when chil-ren 9–12 years old were selectively attentive to auditoryargets (Yordanova et al., 2000). Thus, the gradualhanges in time and frequency responses may also reflectttentional mechanisms.

We may therefore hypothesize that the neural devel-pment of mechanisms underlying higher band activity

rough binomial probability. The plots show the negative logarithm ofity less than 0.05, uncorrected) were set to zero (green), while nonzeroor ITC at a specific point in time and frequency. In the case of ERSPlso negative, in order to distinguish between significant increases andoups.

H, J, L) thl probabilt ERSP

llows more efficient oscillatory capacities, frequency

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G

G

G

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S. Lippé et al. / Neuroscience 164 (2009) 1108–11181116

lignment and transient evoked potentials, reflected byefined phase-locked activity and CAEP complexity in ourdult sample. In animal models, immature auditory regionsave been evidenced by broad receptive fields, sluggish

emporal processing, poor neural synchronization, lowensity and less developed inhibitory interneurons (de Vil-

ers-Sidani et al., 2008; Gao et al., 2000). The appearancef functional inhibitory interneurons may be an importanteature of the CAEPs, as it is known to increasingly con-ribute to the synchronization of oscillatory networks overhe post-natal period (Long et al., 2005).

CONCLUSION

o conclude, this study has shown tremendous electro-hysiological developmental changes in brain processing

n response to auditory stimulation. Our results underscorehe contribution of spectral activity development to CAEPeasures and confirm that mature spectral expression ofrain activity is not yet reached at 5 years. In light of our

ime-frequency results, we posit that the emergence of thedult P1 CAEP component may be related to specificamma frequency band activity. Further studies areeeded to clarify the meaning of the early alpha and betactivity observed at 1 month of age. A first possibility coulde they are the MLR. A second possibility could be that

hey represent spectral precursors of the P1/N1 found inhe early post-stimulus alpha–beta increased activity.ore studies are needed to fully understand the extent and

ignificance of the relationship of beta and gamma activityo auditory abilities and behavior.

cknowledgments—This work was supported by the Canadiannstitutes of Health Research (postgraduate scholarships to Lippend grants awarded to Lassonde), the Canada Research Chairrogram, the Natural Sciences and Engineering Research Coun-il of Canada, the Fonds de la Recherche en Santé du Québecnd the Canadian Foundation for Innovation (Lassonde). We also

hank Dr. Guido Hesselmann for his valuable input.

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APPENDIX

upplementary data

upplementary data associated with this article can be found, in

he online version, at doi: 10.1016/j.neuroscience.2009.07.066.

(Accepted 18 July 2009)(Available online 6 August 2009)