Auditory Development Between 7 And 11 Years: An Event .

3y ago
992.37 KB
11 Pages
Last View : 24d ago
Last Download : 6m ago
Upload by : Anton Mixon

Auditory Development between 7 and 11 Years: AnEvent-Related Potential (ERP) StudyDorothy V. M. Bishop1,3*, Mike Anderson1,4, Corinne Reid2,4, Allison M. Fox1,41 School of Psychology, University of Western Australia, Perth, Australia, 2 School of Psychology, Murdoch University, Perth, Australia, 3 Department of ExperimentalPsychology, University of Oxford, Oxford, United Kingdom, 4 Neurocognitive Development Unit, University of Western Australia, Perth, AustraliaAbstractBackground: There is considerable uncertainty about the time-course of central auditory maturation. On some indices,children appear to have adult-like competence by school age, whereas for other measures development follows aprotracted course.Methodology: We studied auditory development using auditory event-related potentials (ERPs) elicited by tones in 105children on two occasions two years apart. Just over half of the children were 7 years initially and 9 years at follow-up,whereas the remainder were 9 years initially and 11 years at follow-up. We used conventional analysis of peaks in theauditory ERP, independent component analysis, and time-frequency analysis.Principal Findings: We demonstrated maturational changes in the auditory ERP between 7 and 11 years, both usingconventional peak measurements, and time-frequency analysis. The developmental trajectory was different for temporal vs.fronto-central electrode sites. Temporal electrode sites showed strong lateralisation of responses and no increase of lowfrequency phase-resetting with age, whereas responses recorded from fronto-central electrode sites were not lateralisedand showed progressive change with age. Fronto-central vs. temporal electrode sites also mapped onto independentcomponents with differently oriented dipole sources in auditory cortex. A global measure of waveform shape proved to bethe most effective method for distinguishing age bands.Conclusions/Significance: The results supported the idea that different cortical regions mature at different rates. The ICCmeasure is proposed as the best measure of ‘auditory ERP age’.Citation: Bishop DVM, Anderson M, Reid C, Fox AM (2011) Auditory Development between 7 and 11 Years: An Event-Related Potential (ERP) Study. PLoS ONE 6(5):e18993. doi:10.1371/journal.pone.0018993Editor: Thomas Koenig, University of Bern, SwitzerlandReceived November 8, 2010; Accepted March 25, 2011; Published May 9, 2011Copyright: ß 2011 Bishop et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permitsunrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Funding: Funding support was provided by the Australian Research Council DP0665616 ( and the School of Psychology, University ofWestern Australia. DB is supported by a Principal Research Fellowship affiliated with Wellcome Trust Programme Grant 053335 ( Thefunders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Competing Interests: Dorothy Bishop is an academic editor for PLoS.* E-mail: how far improvement in auditory functioning through childhoodis a reflection of non-auditory factors affecting task performance,or whether it is indicative of physiological changes in underlyingbrain systems.Auditory event-related potentials (ERPs) can provide complementary information to that from behavioural and imagingstudies. However, there have been few developmental studiescovering a wide age range of school-aged children. Three of thelargest studies, by Ponton et al. [6], [7], [8], Albrecht et al. [9] andSharma et al. [10] documented substantial changes in the auditoryERP, to click trains, tones and syllables respectively, from earlychildhood to adolescence, continuing into adulthood. However,inspection of their data suggested relatively little change inwaveforms for children between 7 and 11 years. Bishop et al.[11] reanalysed data from Albrecht et al [9] and found that theauditory ERP to simple sounds appeared to follow a step functionrather than gradual change, with substantial changes in theobserved waveform at the start and end of adolescence. Given thatthe period from 7 to 11 years is one where there is substantialcognitive growth and brain development, this observation raisesIntroductionTwo contrasting models of auditory maturation betweenchildhood and adulthood are suggested by behavioral and imagingstudies. The first is the stability model, which predicts that auditorydevelopment is complete by middle childhood. This seemssupported by findings that detection of auditory signals andfrequency discrimination are near adult-like by 6 years of age [1],[2]. Such stability is consistent with findings that Heschl’s gyrus(the site of primary auditory cortex) is functionally mature by 7years of age [3]. An alternative is the incremental model, whichpredicts gradual improvement in auditory function from childhoodto adulthood. This is supported by evidence that some higherorder auditory functions, such as ability to discriminate speech innoise, continue to develop in the teenage years [4]. Furthermore,alterations in myelination and synaptic pruning in secondaryauditory cortex continue well into adolescence [3]. Nevertheless, ithas been suggested that at least part of the improvement inauditory discrimination with age could be due to developing use oftop-down skills affecting task performance [2] ,[5]. A key questionPLoS ONE www.plosone.org1May 2011 Volume 6 Issue 5 e18993

Auditory Development between 7 and 11 Yearsquestions about the underlying causes and functional significanceof changes in the auditory ERP. Before addressing those questions,it seems important, however, to ask how robust is the evidence fora step function. The auditory ERP in children can be stronglyinfluenced by the type of stimulus and rate of stimuluspresentation, and developmental trends may also differ dependingon the electrode sites from which recordings are taken. Theanalysis by Bishop et al. [11], though based on a relatively largesample, was restricted to cross-sectional data and focused only oncomparisons of waveform shape. Furthermore, the rate of stimuluspresentation was relatively rapid, with stimulus-onset asynchrony(SOA) of 1 s. In the current study, we recruited a new sample andemployed a longer interval between tones to increase thelikelihood of observing an adult-like negativity around 100 mspost-stimulus onset (N1) in the waveform [12].We also focused specifically on two aspects of the auditory ERPthat have been distinguished in the literature and appear torepresent activity in parallel auditory pathways [7], [8]. These arecomponents measured in the first 150 ms after presentation of anauditory signal, which are generally regarded as obligatory sensorypotentials whose characteristics are determined primarily byphysical and temporal characteristics of the stimuli, rather thanby their psychological significance to the listener [13]. The first ofthese, the P1, which peaks around 50 ms in adults, is recordedover a wide frontocentral area. Although P1 is much larger inchildren than in adults, Bishop et al. [11] found little developmental change in this component before adolescence. The secondcomponent, Ta, is a later positivity that is evident at temporalelectrode sites. In adults, Ta peaks around 100 ms post-stimulusonset, and is the first part of the T-complex, described by Wolpawand Penry [14]. Because it occurs around the same time as the N1at the vertex, it is sometimes regarded as arising from the samesource. However, Wolpaw and Penry noted that the auditoryresponse recorded at temporal electrode sites had a very differentmorphology from that at the vertex. Electrode location is not agood indicator of the source of the measured activity, andsubsequent research has shown that the T-complex representsactivity in radially-oriented dipole sources, whereas the vertexresponse has tangentially-oriented generators [7], [15], bothlocated in auditory cortex in the temporal lobe. Although it hasbeen known about for 35 years, the T-complex has been largelyneglected in the literature, perhaps because it is relatively small inadults. However, in children the T-complex is a prominent featureof the auditory ERP [16], [17].As well as conducting conventional analysis on ERP waveforms,we also used time-frequency analysis. This approach is gaining inpopularity as a method for analysing event-related electrophysiological responses [18], because it provides insight into underlyingmechanisms that might throw light on developmental change [19].Time-frequency analysis adopts a radically different perspective onthe ERP from the traditional view, where the peaks and troughs ina waveform are treated as signals extracted, by averaging, from abackground of noise [18]. The focus of time-frequency analysis ison oscillations, which are readily detected in the EEG when afrequency decomposition is performed. Incoming stimuli can leadto synchronisation of phase of oscillations at a given frequency,and this can be detected by computing phase relations acrosssuccessive trials. Here we focus on two complementary indices: (i)inter-trial coherence (ITC), a measure of the extent to whichphase-locking occurs, and (ii) event-related spectral perturbation(ERSP), a measure of the increase in power in a frequency bandafter presentation of a signal, relative to baseline. These measuresare not just an alternative way of representing data: they aresensitive to features in the data that can get averaged out byPLoS ONE www.plosone.orgconventional methods of analysis [18]. For instance, if anincoming signal leads to a boost in power at a given frequency,but the phase of the response is random, then this would not bedetected in an averaged ERP, but would be evident in the ERSPmeasure, derived from single trials. Similarly, if there is no increasein power when a signal is perceived, but the phase of oscillations isreset, the averaged ERP can give a misleading impression that theresponse involves additional power, for instance, increasedneuronal firing, when the ITC would show instead that the brainoscillations on individual trials have not changed in amplitude, buthave rather become aligned in phase to the signal onset. AsKlimesch and colleages have noted in the context of visual ERPs[20], if an ERP is generated by an increase in power in response tothe stimulus, we might expect to see an increase in phasealignment of the ERP across trials (ITC), but this would necessarilybe accompanied by an event-related increase in signal amplitudefor individual trials (ERSP). If, on the other hand the grandaverage ERP is the consequence of phase resetting of ongoingoscillations, we might see increased ITC accompanied by either anincrease in power in individual trials, no change in power, or anevent-related drop in amplitude (event-related desynchronisation).Furthermore, the pattern of phase synchronisation and amplitudechange may vary across frequencies. Therefore we can illuminateunderlying mechanisms of ERP generation by studying how ITCand ERSP in different frequency bands relate to the grand averageERP.The use of time-frequency analysis to investigate developmentof auditory processing is still in its infancy, but there is alreadyevidence to suggest that changes in ERPs between childhood andadolescence involve an increase in stimulus-induced phasesynchronisation [21], [22], [23]. Of particular interest are studieswith preadolescent children reporting enhancement of phaselocked responses in the theta range to sounds [24], [25] ,[26]. Asimilar, though non-significant, trend is apparent in plots shown byMüller et al.[22], with less theta phase-locking for children aged 9–10 years than for those aged 11–12 years.Several authors have noted the possibility of using ERPs toidentify children who have immature or abnormal auditorydevelopment. This is of potential value in investigations of theorigins of developmental impairments, especially in the area oflanguage [8], [17]. However, in order for auditory ERPs to beclinically useful, we need to know not only what the averagedevelopmental trajectory is for the auditory ERP, but also howmuch variation there is at a given age. One goal of our study wasto examine how well one could predict a child’s chronological agefrom a knowledge of the auditory ERP. The previous study byBishop et al. [11] suggested this may only be possible across verybroad age bands. In the current study, we considered whether itwould we could identify indicators of auditory ‘brain age’ thatwould discriminate levels of brain maturity in pre-adolescentschool-aged children.We used a mixed cross-sectional and longitudinal design; thisgives greater power to detect developmental change because itcontrols for within-group variability at a given age. We measuredERPs to pure tones on two occasions separated by two years. Justover half of the children were 7 years initially and 9 years atfollow-up, whereas the remainder were 9 years initially and 11years at follow-up. As well as measuring amplitude of peaks in thewaveform, we conducted time-frequency analysis to investigatedevelopment of phase-synchronisation in the evoked signal. Wethen did independent components analysis (ICA) [18] to identifyseparate sources of observed waveforms, confirming the distinctionbetween two sources for the auditory ERP. Finally, to quantifyhow far developmental aspects of the ERP could be used to index2May 2011 Volume 6 Issue 5 e18993

Auditory Development between 7 and 11 Yearsbrain maturation, we analysed waveform shape using methodsbased on Bishop et al.[11].ERP is typically complete by 600 ms post-onset. Responses to tonepairs will be reported elsewhere.Specific aims and predictionsEEG acquisition and analysisa)The electroencephalogram (EEG) was recorded continuously(0.5–30 Hz bandpass) from 33 scalp locations referenced to theright mastoid using an electrode cap (EasyCap, Montage 40,excluding TP9 and TP10). Electrodes were also placed above andbelow the left eye, and on the left mastoid, with an averagedmastoid reference digitally computed offline. Site AFz was used asground. Data were amplified with a NuAmps 40-channelamplifier, and digitized at a sampling rate of 250 Hz. Offlineanalysis was performed using SCAN 4.3 and EEGLAB [28].Ocular artifact reduction was performed on the continuousEEG using regression-based subtraction of the averaged blinkartefact identified in the bipolar VEOG channel [29]. Epochsencompassing an interval from 200 ms prior to the onset of thefirst tone in the pair to 800 ms post-stimulus were extracted andtrials contaminated by artifact exceeding 6150 mV were rejected.Averaged waveform analysis was processed with baseline correction from –50 to 0 ms, and data were digitally filtered off-line witha 1-30 Hz, zero phase shift band-pass filter (12 dB down).Automated artefact rejection using higher-order statistics [30] wasthen applied using default settings in EEGLAB.b)c)To document developmental trajectories for auditory ERPsin children aged 7 to 11 years. We predicted that, incontrast to previous studies, change in the auditory ERPmight be detectable across this age range, given therelatively long SOA and more powerful longitudinal designthat we adopted.To compare developmental trends at temporal vs. frontocentral electrode sites. In line with previous studies wepredicted that the signals from these electrode sites havedifferent underlying sources, which would show differentdevelopmental trajectories.To consider how well the auditory ERP predicted a child’schronological age. We predicted that inclusion of informationfrom time-frequency analysis might give better predictionthan reliance on waveform shape alone.Materials and MethodsEthics statementThe paper reports data from human subjects, and ethicalapproval was obtained from the University of Western AustraliaHuman Research Ethics Committee. Written informed consentwas obtained and the rights of the participants were protected.Analytic approachResults were compared for the two age groups (Younger andOlder) at session 1 (2007–2008) and session 2 (2009–2010). Bothgroup and session comparisons are sensitive to changes between 7and 11 years, but the group comparison is between subjects,whereas the session comparison is within subjects. An interactionbetween group and session would indicate differing amounts ofchange from 7 to 9 years than from 9 to 11 years.ParticipantsChildren participated in a two-day research program investigating the cognitive, emotional, and social development ofchildren. The program is designed as a child-friendly holidayactivity program to enhance task engagement. Children aged 7 or9 years were recruited during July 2007 and 2008 (initialassessment), and were retested for session 2 during July 2009and 2010 respectively (follow-up). ERP data were excluded fromindividuals who were not available for retesting, where a history ofneurological disorders or hearing impairment was reported, orwhere reliable auditory evoked responses were not elicited to thetones (see Fox et al.[27]). The final sample included 62 youngerchildren (31 girls, 31 boys; mean age at initial testing 7.48 yr,SD 0.27) and 43 older children (17 girls, 26 boys; mean age atinitial testing 9.49 yr, SD 0.37).Analysis of mean amplitude of ERP componentsQuantitative analyses were conducted on the fronto-central andtemporal electrodes (Fz, F3, F4, Cz, C3, C4, T7, T8 and Pz),where the auditory ERP is maximal. Mean amplitude wasmeasured from time windows corresponding to P1 and Ta/N1bregions, as identified previously [27]. The first window, from 58–98 ms corresponds to P1, the second, from 102–146 ms to Ta/N1b. Mean amplitudes were computed for each of these intervals,for each group, session and electrode, and entered into a 3-wayANOVA, with session and electrode as repeated measures, andgroup as between-subjects factor. Bonferroni adjustment was usedto take into account the fact that ANOVAs were run for twointervals, and so a p-value of .025 was regarded as significant.Greenhouse-Geisser correction was applied to correct forviolations of sphericity.Tone stimuliAuditory stimuli were 1000 Hz sinusoidal tones of 50 msduration with 2 ms rise and fall times. Sound intensity wascalibrated using a 1-second continuous 80 dB SPL tone measuredwith a Bruel and Kjaer sound level meter.Time-frequency analysisProcedureTime-frequency analysis was then conducted on the specifiedchannels to measure inter-trial coherence (ITC) and event-relatedspectral perturbation (ERSP). For this analysis, a baseline of200 ms was used, with frequency extraction using a fast Fouriertransform, and a pad ratio of 2. This provides measures of ITCand ERSP in frequency bands with centres at 3.9 Hz, 7.8 Hz,11.7 Hz, 15.6 Hz and 19.5 Hz. The first band was designateddelta, the second of these bands was designated theta, and thethird as alpha, the fourth as lower-beta, and fifth as upper-beta. Toquantify these results, the mean ITC and mean ERSP werecomputed over the interval from 100 to 300 ms post-onset. Notethat there is a trade-off between time and frequency resolutionAn electrode cap was fitted and participants were presentedwith auditory stimuli while they silently read or played electronicgames. They were instructed to ignore the tone sequences, but toremain quiet and still throughout the recording session. Stimuliwere equiprobable single tones or tone pairs with varying intertone interval (25, 50, 100, 200, 400 or 600 ms), presented atrandom. The interval between trial onsets was 1.5 s and the onsetof the first tone was randomly jittered between 0 and 200 ms. Forthe current analysis, only responses to single tones or to the firsttone from pairs with inter-tone interval of 600 ms were studied;these are

developmental trajectory is for the auditory ERP, but also how much variation there is at a given age. One goal of our study was to examine how well one could predict a child’s chronological age from a knowledge of the auditory ERP. The previous study by Auditory Development between 7 and 11 Years

Related Documents:

Test of Auditory Comprehension of Language-3. Austin, TX: PRO-ED. Test of Auditory Processing Skills, 3rd Edition (TAPS-3): This test measures what the person does with what is heard, and can be used for ages 4-18. There are numerous sub scores, and three cluster scores including basic auditory skills, auditory memory, and auditory cohesion.

and auditory perceptual parallel processing effect in the brain. On this account, auditory sentence materials were presented that varied in syntax (syntactically correct vs. syntactically incorrect) and auditory space (standard vs. infrequent ITD change). For the source analysis, a distributed source model was used without any priors regarding the

auditory imperative stimulus cued by a visual stimulus, the auditory cortex can be activated already during the later anticipation period, within 0.5 s before the auditory stimulus sound [23]. The aim of the current study was to determine whether anticipation of emotional vs. neutral sounds would modulate the

Review Autocorrelation Spectrum White Bandwidth Bandstop Shape Summary Outline 1 Review: Power Spectrum and Autocorrelation 2 Autocorrelation of Filtered Noise 3 Power Spectrum of Filtered Noise 4 Auditory-Filtered White Noise 5 What is the Bandwidth of the Auditory Filters? 6 Auditory-Filtered Other Noises 7 What is the Shape of the Auditory Filters? 8 Summary

Auditory processing is crucial because our learning is heavily reliant on auditory system--- think of how teachers teach from early age-talking, singing, etc. Auditory processing issues can be inherited, or acquired (e.g. by problems at birth, or ear infections when young). 3

Auditory processing disorder is a controversial issue in the educational setting. As a result of widespread concern, a national conference was held in April 2000 with the intent of reaching a consensus on problems related to the diagnosis of auditory processing disorders in children. One outcome was a change in terminology from central auditory .File Size: 290KBPage Count: 74

different auditory features given the same acoustic mixture of two simultaneous spoken . different cortical regions for selection based on source location and source pitch 1,2,3*, . study involving shifting of attention between auditory and visual stimuli, Shomstein and Yantis (2006) observed distributed acti- .

Tourism is a sector where connectivity and the internet have been discussed as having the potential to have significant impact. However there has been little research done on how the internet has impacted low-income country tourism destinations like Rwanda. This research drew on 59 in-depth interviews to examine internet and ICT use in this context. Inputs Connectivity can support inputs (that .