Multiple effects of childhood deafness on cortical activity in children receiving bilateral cochlear implants simultaneously

Clinical Neurophysiology 122 (2011) 823–833

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Multiple effects of childhood deafness on cortical activity in children receiving bilateral cochlear implants simultaneously q K.A. Gordon a,b,⇑, S. Tanaka a,d, D.D.E. Wong a,e, T. Stockley c, J.D. Ramsden f, T. Brown g, S. Jewell a, B.C. Papsin a,b a

Archie’s Cochlear Implant Laboratory, The Hospital for Sick Children, Toronto, Canada Department of Otolaryngology-Head and Neck Surgery, University of Toronto, Canada Department of Pediatric Laboratory Medicine, The Hospital for Sick Children, Toronto, Canada d Department of Physiology, University of Toronto, Canada e Institute of Biomaterials and Biomedical Engineering, University of Toronto, Canada f ENT Department, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom g Auditory Science Laboratory, The Hospital for Sick Children, Toronto, Canada b c

a r t i c l e

i n f o

Article history: Accepted 26 October 2010 Available online 19 November 2010 Keywords: Deafness/hearing loss Congenital/child Auditory cortex/brain Electrophysiology/evoked potentials GJB-2 mutation Connexin 26 Simultaneous bilateral cochlear implants Electrical stimulation

h i g h l i g h t s  Cortical responses evoked with newly provided cochlear implants in children captured a measure of function in the deaf and immature brain.  These responses were highly variable reflecting heterogeneous effects of deafness on brain development.  Cortical function was more uniform when the aetiology of deafness was known to be associated with bilallelic GJB-2 mutations.

a b s t r a c t Objective: Auditory development is disrupted without normal hearing but might proceed to some extent depending on the type and onset of deafness. We therefore hypothesized that activity in the auditory cortex would be highly variable in children who are deaf. Methods: To answer this, activity in the deaf brain was evoked by electrical pulses from newly provided bilateral cochlear implants (CIs) in 72 children (n = 144 responses). Results: Responses were categorized by visual inspection into 3 main types which were validated by principal component cluster analyses; 49% had a negative amplitude wave similar to that previously reported in pre-term infants, 26% were dominated by a positive peak typical of responses in young normal hearing children and experienced paediatric CI users, 25% were novel multi-peaked responses. No significant demographic differences, including duration and onset of deafness, were found between response types. However, children with severe biallelic mutations of GJB-2 showed predominately negative peak type responses (79%) as compared with their peers without these mutations who had a more equal distribution between cortical response types. Conclusion: Cortical development in children who are deaf is heterogeneous but can be better predicted when the genotype is known to be a GJB-2 mutation. Significance: Remediation of childhood deafness seeks to restore normal development and function of central auditory functions and thus may need to be tailored to account for effects specific to the aetiology of deafness. Ó 2010 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

1. Introduction q

All work was completed at the Hospital for Sick Children, Toronto, ON, Canada

⇑ Corresponding author at: Rm. 6D08, The Hospital for Sick Children, 555 University Avenue, Toronto, ON, Canada M5G 1X8. Tel.: +1 416 813 7259, +1 416 813 6683; fax: +1 416 813 5036. E-mail address: [email protected] (K.A. Gordon).

As many as 6/1000 children have permanent hearing loss. Although these are most commonly due to deficits in the cochleae (inner ears), the site of lesion and/or mechanism leading to abnormal function remains unclear. This means that there could be

1388-2457/$36.00 Ó 2010 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.clinph.2010.10.037

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multiple forms of deafness in childhood, each with its own onset and developmental time course. If so, there may be multiple effects of deafness on development of the auditory pathways. Any changes to the immature auditory pathways could affect how auditory prostheses, including cochlear implants, evoke auditory activity and promote hearing. In the present study, we explored the effects of deafness on the developing auditory cortex. To do this, we recorded cortical responses evoked by acute electrical stimulation of the auditory nerve in 72 children with severe to profound deafness. Electrophysiological recordings were chosen for use because other imaging techniques cannot be used in cochlear implant users; magnetic resonance imaging is contra-indicated and magnetoencephalography is severely compromised by the device. Permanent hearing impairments in children can be present at birth or acquired post-natally. Infectious diseases such as cytomegalovirus or meningitis can damage the cochleae as can ototoxic medications including many aminoglycosides. The cause of congenital deafness is often unclear but likely due to genetic abnormalities in many cases. Major advances have been made in this area with the identification of over 30 genes linked to syndromes which include hearing loss as well as over 50 genes which are linked to non-syndromic hearing loss (http://webhost.ua.ac.be/ hhh/). The most common mutation appears to be to the GJB-2 gene which explains the origin of 15–50% of non-syndromic childhood deafness (Propst et al., 2006b; Bonyadi et al., 2009; Tamayo et al., 2009; Yuan et al., 2009). Current imaging techniques can locate gross abnormalities in the temporal bone but do not have sufficient resolution to detect subtle intracochlear abnormalities nor can provide an indication of onset of deafness. It is also important to recognize that there are many children who have no known genetic or anatomical markers and nothing in their medical history which can explain their deafness. The heterogeneity of childhood deafness means that there can be no single animal model which predicts changes from normal in all affected children. However, these models provide important information about effects of deprivation on the immature auditory system. The deaf white cat, born with hearing loss due to malformations of the cochleae (Ryugo et al., 2003), has been proposed as a better reflection of childhood deafness than experimentally induced lesions (Heid et al., 1998). Unlike children, who might have heard pre-natally, the onset of hearing in cats occurs post-natally and thus, it is known that deaf white cats never had any access to sound (Ryugo et al., 2003). The lack of auditory activity in deaf white cats causes both anatomical and physiological changes along the auditory pathways. Structural changes found in the synapse between the terminal ends of primary auditory neurons (endbulbs of Held) and the spherical bushy cells of the cochlear nucleus (Ryugo et al., 1998) are thought to reflect compensatory mechanisms in response to diminished activity through this synapse (Ryugo et al., 1997, 1998). Physiological changes include the reduction in spontaneous activity in the auditory nerve (Ryugo et al., 1998) and abnormalities in cortical activity evoked by electrical pulses delivered by an array of electrodes in the cochlea (Kral et al., 2009; Tillein et al., 2010). Perhaps the most concerning change associated with deafness in early development is the vulnerability of association areas of the auditory cortex to be ‘‘taken over’’ by non-auditory inputs. Recent reports by Lomber et al. (2010) from deaf white cats suggest that specific association areas of the auditory cortex reorganize, becoming abnormally responsive to visual and/or somatosensory inputs. This process has been referred to as ‘‘cross-modal plasticity’’ (Lee et al., 2001) and has been shown in human adults who are deaf and use sign language to communicate (Fine et al., 2005). In the latter study, visual stimuli (either moving dots or dots peripheral to the normal visual field) evoked activity in areas of the

temporal lobe normally associated with the auditory cortex (Fine et al., 2005). Importantly, this change did not occur in fluent users of sign language if they also had normal hearing which highlights the importance of auditory input for normal development of the auditory cortex. In the present study, we ask whether some children who have profound hearing loss are more vulnerable to abnormal cortical development than others because of differences in aetiology and onset of deafness. Our aim is to reverse any effects of hearing loss on the auditory system in children and to promote hearing development through auditory prostheses. Cochlear implants help children who are severe to profoundly deaf to hear by bypassing the dysfunctional cochlea and directly stimulating the auditory nerve with electrical pulses. The device converts acoustic sound into pulses which are delivered by an array of electrodes surgically placed into the scala tympani of the cochlea. This primary nerve activity is carried through the auditory pathways to the cortex allowing a sensation of hearing for the cochlear implant user. The cochlear implant is typically programmed and activated 3–4 weeks after surgery and this moment may represent the first significant auditory input for a child with severe to profound congenital deafness. The children who participated in the present study received bilateral cochlear implants in the same surgery and both devices were activated on the same day. We recorded cortical responses at this initial stage of cochlear implant use in order to assess any effects of deafness during early development on cortical function evoked by auditory input. We questioned whether the heterogeneity in aetiology, onset, and duration of deafness in children receiving cochlear implants would be reflected in the evoked activity of the cortex prior to consistent stimulation of the auditory system by the cochlear implant. Results indicate that childhood deafness has several distinct effects on cortical function and that the duration of deafness, within a fairly restricted period, does not predict these differences. On the other hand, there is a relationship between cortical responses and aetiology of deafness. A more uniform phenotype of cortical activity was found in children with severe biallelic mutations of GJB-2 than in their peers without these mutations. 2. Methods and materials 2.1. Participants A total of 72 children receiving bilateral cochlear implants (CIs) in the same surgery participated in the present study. The majority (n = 65) of these children were less than 4 years of age when the two CIs were activated (mean ± sd = 1.6 ± 0.8 years). The other 7 children ranged in age from 4.1 to 15.8 years (8.8 ± 4.8 years). Most of the children (n = 61, 85%) had severe to profound deafness bilaterally with no evidence of better hearing in the post-natal period. Only 3 of these 61 children were able to detect sounds of 40 dB HL or quieter with their hearing aids. Because normal conversational speech occurs between 40–50 dB HL, they had better access to spoken language than the other children prior to cochlear implantation. In 2 of these 3 children, the duration of time spent with usable hearing (as defined by detection of sounds with or without hearing aids at 640 dB HL) was limited (0.5 and 1.5 years) and they were implanted at young ages (2.6 and 2.5 years), whereas the third child heard through hearing aids for longer (14.8 years) and was implanted at an older age (15.5 years). The other 11 children studied showed progression of hearing loss from moderate to more profound degrees during early childhood with all having severe to profound deafness by age 3 years. The time during which these 11 children had greater access to auditory input than their peers with early onset deafness (as defined by hearing thresholds with or without hearing aids of 40 dB HL) was 1.3 ± 1.2 years.

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evidence of having cytomegalovirus which could have caused their deafness and another 3 children had developmental delays (none of these 7 children had GJB-2 mutations). Many children (n = 26) had had some neonatal complications (stay in a neonatal intensive care unit, prematurity, jaundiced, poor APGAR score) but this was not correlated with the genetic mutation (spearman q = 0.12 p > 0.05), age at implantation, duration of usable residual hearing, or age at onset of deafness (spearman q < 0.035, p > 0.05). 2.2. Evoked potential response recordings

Fig. 1. Of the 64 children tested for GJB-2 mutations, 14 had a family history of hearing loss. GJB-2 mutations were found in 20 children (14 with no family history and 8 with a family history of hearing loss). Severe mutations found were 35delG, 167delT, and 235delC. Other mutations were associated with only mild hearing loss or were of unknown clinical significance. Details are provided in Table 1.

Fig. 1 indicates that genetic testing of the GJB-2 gene was completed in 64 of the 72 children who participated in the study. The children are divided into those with and without a family history of childhood hearing loss. Of the 64 children tested, no mutations were found in 42 children (66%) even though 6 of these children had family members with hearing loss in childhood. GJB-2 mutations were found in the other 20 children (31%). Table 1 provides the details of all mutations found on at least one GJB-2 allele. The most common mutation was 35delG (12 children). Interestingly, exactly half of the children with 35delG mutations (6/12) had no family history of childhood hearing loss. One of these 12 children had a 35delG mutation on only one allele (no family history of hearing loss) and thus the deafness may not be clearly ascribed to GJB-2 mutation. Only 1 child had homozygous 167delT mutations (with a family history of childhood hearing loss) and 2 children had 235delC mutations (no family history). Five children had a variety of other mutations which have been associated with only mild hearing loss or were of unknown clinical significance. None of these 5 children had a known history of childhood hearing loss in their families. None of the 20 children with mutations of GJB-2 had abnormalities of either cochlea as imaged using computed tomography (CT) scans and magnetic resonance imaging (MRI). Abnormalities of the vestibular acqueduct were found in the 1 child with a monoallelic 35delG mutation. In children with no genetic changes to GJB-2, 4 had cochlear abnormalities, 6 had enlarged vestibular acqueducts, and 1 had a narrow internal auditory canal. There were 4 children who had radiological

Table 1 Mutations of GJB-2 gene identified by clinical testing. Mutations associated with severe to profound hearing loss

Mutations associated with mild hearing loss or of unknown clinical significance

35delG/35delG (n = 8)

298C>T(p.His100Tyr)/269T>C (p.Lys90Pro) (n = 1) C109G>A (p.Val37Ile) (n = 1) 380G>A(p.Arg127His) on 1 allele (n = 1) Monoallelic 35delG/no mutation on other allele (n = 1) Monoallelic Exon2:c.79G>A/no mutation on other allele (n = 1) c.598G>A/c.598G>A (n = 1)

35delG/167delT(n = 1) 35delG/313_326del14 (n = 1) 167delT/167delT (n = 1) 235delC/235delC (n = 1) 235delC/299_300del (n = 1)

Recordings were completed within the first week of bilateral cochlear implant use. Children wore an electrocap, created by Compumedics Neuroscan (El Paso, Texas) and watched a video or were kept entertained and occupied by a second tester. Responses were recorded from an electrode placed on the mid-cephalic location (Cz) and reference electrodes were placed on each earlobe in separate recording channels. Recorded signals passed through an analog low pass filter with a frequency cutoff of 32 kHz to minimize interference from the CI transmitting coil which sends information from the external to internal CI components. Responses were recorded by Neuroscan 4.3 software and a Synamps I amplifier using a sampling rate of 500 Hz. Signals greater than ±100 lV were rejected from averaging. Accepted signals were filtered using 0.1 Hz high pass and 100 Hz low pass digital filters on-line and 30 Hz low pass off line. Baseline corrections based on the pre-stimulus interval were performed online ( 50 to 0 ms). At least 50 sweeps were accepted for each average and at least 2 averages were collected. Additional averages were obtained if there was poor agreement as determined visually between the first and second averages. The rejection percentage varied between recordings, but in general, about 10–20% of the recorded sweeps were rejected which meant that at least 120–150 sweeps were recorded for each stimulus. 2.3. Stimuli evoking cortical responses Biphasic electrical pulses were delivered using the SPEAR 3 software and processor made by CRC-HEAR, Melbourne (in collaboration with Richard van Hoesel). Trains of electrical pulses were delivered at 250 pulses per second for 36 ms (9 pulses per train) from a single CI electrode in the apical end of the array (typically #20). Pulse widths of these stimuli were 25 ls/phase for all children. The stimulation mode was MP1+2, which was the same as the child’s regular implant settings. Current level was determined by measuring amplitude growth of the auditory brainstem responses evoked by single electrical pulses delivered at 11 pulses per second. Brainstem response amplitudes were maximized within the comfortable listening range of the child and differences between right and left CI evoked response amplitudes were minimized. Once these criteria were met, pulse trains for cortical recordings were reduced by 10 current units to account for any growth of loudness due to the increased rate of pulse presentation. Separate recordings were evoked by each cochlear implant in the 72 participants providing 144 responses to assess. 2.4. Categorization of CAEP responses Identification of specific wave peaks could not be done without first categorizing the multiple types of waveforms collected. Categorization was accomplished using visual inspection and this method was compared with categorization through fuzzy clustering analyses. Visual categories were based on previous reports from paediatric cochlear implant users which describe responses dominated by a positive peak (Ponton and Eggermont, 2001; Sharma et al., 2005) as well as responses with a clear negative peak

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Fig. 2. Three types of cortical waveforms were identified visually. Examples of each from individual children are shown.

which preceded a positive peak (Gordon et al., 2008). A third category was added to describe waveforms with multiple positive peaks. Thus, visual categories, as shown in the examples from individual children in Fig. 2, were: (1) Positive peak responses, (2) Negative responses, and (3) Multi-peak responses. All decisions were made by a single marker who was blinded to demographic details or ear of stimulation. Fuzzy clustering was used to test the reliability of the visual inspection. A principal component analysis (PCA) was completed on all 144 responses. All responses were first normalized by dividing amplitude at each time point by the root of the sum of squares of amplitudes (of the same response) for all time points. This was done so that all responses were weighted equally in overall amplitude and could then be compared for variability in number and polarity of amplitude peaks across time. Then, the cortical responses were transformed onto one or more orthogonal basis vectors which are referred to as principal components. The first principal component (PC) accounts for the maximum possible variability in the data and each successive component accounts for as much of the remaining residual variability as possible. PC scores represent the strength of the relationship between the original data (i.e. each cortical response) and the principal components with ±1 indicating perfect correspondence and 0 indicating no relationship. Using the first 3 PCA scores as coordinates (representing >80% of the variance in the overall data), each response was represented as a point in 3 dimensional space. A clustering algorithm was used to group the points based on Euclidean distance. Due to large spread in the PCA points and the resulting difficulty in visually identifying clusters, a fuzzy C-means clustering algorithm (Bezdek, 1981) (fuzziness index m = 2) was used rather than hard-threshold methods such as K-means (MacQueen, 1967) or hierarchical clustering (Ward, 1963). Doing so allowed us to define cluster centers while permitting points (responses) to belong to each cluster, but to varying degrees (i.e. membership values). The points were then grouped using K-means fuzzy clustering. It should be noted that each point/response belongs to all clusters but to a varying degree. In this analysis, a response is assigned to the cluster to which it is most similar as defined by the first 3 principal components. This was done by assigning it to the cluster to which it had the greatest membership value. The optimal number of cluster groups was determined by computing the Xie-Beni index (Xie and Beni, 1991) for between 2 and 9 clusters.

peak in the same latency range (70/144 responses, 49%), and the third had multiple peaks with two positive peaks often separated by a negative peak (36/144 responses, 25%). These visual categorizations were compared with the results of PCA clustering analyses. PCA results indicated that 3 components, shown in Fig. 4, were able to account for 87% of the variability in the 144 waveforms recorded. Optimal clustering indicated by the highest Xie-Beni index was gained by including 7 clusters. The weighting of each principal component (PC Score) for each response is shown in 3 dimensions by the scatterplots on the bottom panel of Fig. 4. Each plot includes 7 symbols indicating the grouping of responses into 7 clusters. As detailed in the methods, there was too much variability to visually group the clusters and thus a fuzzy C-means clustering algorithm (Bezdek, 1981) (fuzziness index m = 2) was used. All 144 responses are shown in Fig. 5 by cluster group. Responses are plotted by normalized amplitude over time in light

3. Results We recorded 144 cortical responses in 72 children with severe to profound deafness who were provided with bilateral cochlear implants simultaneously but who had not used these devices for longer than a week. Fig. 3 demonstrates the high degree of variability across the recorded responses. Three types of waveforms were identified through visual inspection. As shown in examples from individual children in Fig. 2, the first was dominated by a large positive peak at 80– 150 ms (38/144 responses, 26%), the second had a large negative

Fig. 3. 144 cortical responses (thin grey lines) evoked by electrical pulses delivered by the left and right cochlear implants in 72 children with severe to profound deafness. There is a large degree of variability around the mean (thick black line).

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Fig. 4. Three principal components (PC) of the 144 responses were identified, explaining 87% of the response variability. The PC score was determined for each response. The three dimensional relationship between each PC Score for all 144 components is represented by the two dimensional scatterplots. Seven different symbols represent the 7 clusters into which responses were grouped.

grey lines and the cluster means are shown by the thicker black lines. The clusters reflected 2 of the 3 categories of waveforms identified visually. Specifically, 2 clusters (Clusters 1 and 5) appear to have a dominant positive peak in the cluster mean response. The other 4 clusters (Clusters 2, 3, 6, 7) show a mean which contains a negative peak which, in 2 clusters, precedes a positive peak. Cluster 4 is multi-peaked. Comparisons between the visual categories and cluster analyses of response type confirmed a strong agreement between the 2 analyses for positive and negative peak responses. Specifically, of the 34 responses visually categorized as Positive Peak responses, 78% were independently grouped into positive peak clusters (#1 or #5) through the PCA analysis. Of the 58 responses visually categorized as Negative Peak responses, 83% were grouped into negative peak clusters (#2, #3, #6, #7). The responses categorized visually as being multi-peaked, were almost equally divided in the clustering analyses between positive and negative peaked clusters with a wide distribution of visually defined multi-peaked responses across the 7 clusters. Fig. 6 shows the mean of response amplitude over time (grandmean) (±1 SD) for all waveforms with a positive peak and those with a negative peak as defined independently by visual categorization and by cluster analyses. Excluded responses were those which were visually categorized as multipeaked and Cluster 4 because it had multiple peaks. Fig. 6 shows that visual groupings yielded very similar mean amplitude waveforms compared to clustered groups.

Once the waveforms were categorized, we could identify and measure individual wave peaks. Mean latency and amplitude data for each of the 3 types of waveforms as categorized visually are detailed in Table 2. The positive peak type wave P1ci occurred at a similar latency and amplitude as the N1ci of the negative peak type. The N1ci of the positive peak response occurred at a similar latency and amplitude to the P2ci of the negative peak type. Multi-peaked responses showed wave peaks which were at somewhat different latencies than the main peaks in the other 2 types but which were of similar mean amplitudes. We asked whether there were any differences in responses evoked by stimulation from the left versus right CI. Responses to right CI stimulation were visually categorized and compared to left responses in the same children. Fig. 7 displays the grandmean (±1 SD) responses evoked from both sides as grouped in this way and shows that, on average, the responses evoked by the right ear were similar to those evoked by the left. Indeed, there were only 10 children for whom left ear evoked responses were of a different visual type than the right. Most of these children (n = 8) had a multipeaked response evoked by one side and either a positive or negative peak response on the other. We also asked whether there were any predictors of response type. No significant differences of age at implantation, age at onset of bilateral deafness, duration of bilateral deafness (F(2, 141) 6 2.1, p > 0.05) or duration of residual hearing (v2 (df = 2) = 2.2, p > 0.05) were found between children with positive, negative or multi-

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Fig. 5. All 144 responses are plotted by cluster group. Individual response amplitudes were normalized and are shown in light grey. The cluster mean is shown by a thicker black line. Cluster means on the left had one positive peak with the exception of cluster 4 which is multi-peaked with a small normalized mean reflecting the large variability between responses in this cluster. Cluster means on the right had a negative peak which in 2 cases preceded a positive peak.

peaked cortical responses. Analyses also showed no significant effect of the presence or absence of cytomegalovirus, neonatal complications, or family history of hearing loss (v2 (df = 2) < 4.0, p > 0.05). Temporal lobe abnormalities did not reach significance (v2 (df = 2) = 4.0, p > 0.05). However, no positive peaked responses were found amongst the 6 responses from children with cochlear abnormalities which were negative peaked (n = 4) or multi-peaked (n = 2). In comparison, positive peaks tended to be more prevalent in the 16 responses from children with normal cochleae but an enlarged vestibular acqueduct: 8 (50%) positive peak types, 4 (25%) negative peaked, and 4 (25%) multi-peaked responses. There were 4 responses from children with narrow internal auditory canals; 2 were positive peaked and 2 were multi-peaked.

In contrast to the other demographic factors assessed, significant effects of GJB-2 mutations were found on cortical response type (v2 (df = 2) = 10.8, p = 0.005). Fig. 8A shows that the distribution of response types in children with no evidence of GJB-2 mutations associated with severe hearing loss were 44% negative peak, 29% positive peak responses and 27% multi-peaked responses. In comparison, the vast majority of the 28 responses in children with severe GJB-2 mutations were of the negative peak type (79%) with only 14% being multi-peaked and 7% being positive peaked. Fig. 8B shows the distribution of response types according to specific GJB2 mutations. The most common GJB-2 mutation, 35delG, was strongly associated with negative peak responses whereas responses in children with either no GJB-2 mutations or mutations

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Fig. 6. Grandmean and ±1 SD of positive and negative peak waveforms are shown as grouped by visual and cluster analyses and have good agreement. Stimulus artifact can be seen in 3 of 4 plots and is contained to the first 40 ms of the recording window.

Table 2 Latencies and amplitudes of cortical response peaks by response type. P1ci (Mean ± SD)

N1ci (Mean ± SD)

P2ci (Mean ± SD)

Latency (MS) Positive peak response Negative peak response Multi-peak response

135.7 ± 28.8 – 116.8 ± 39.2

258.0 ± 47.8 127.6 ± 33.4 188.5 ± 49.9

– 238.9 ± 52.1 285.0 ± 49.7

Amplitude (lV) Postive peak response Negative peak response Multi-peak response

5.3 ± 4.5 – 3.6 ± 3.5

4.4 ± 3.7 6.2 ± 5.4 4.9 ± 6.1

– 5.2 ± 5.8 5.0 ± 4.6

with mild or no known clinical significance were more equally distributed across types. There were 8 responses from children with global developmental delays. As a group, they had more positive peak responses (62.5%) than negative peak (12.5%) and multi-peaked (25%) responses whereas the 136 responses in children who were typically developing were most often negative peak type (51%) with fewer positive peaked responses (25%) and multi-peaked responses (24%) (v2 (df = 2) = 6.2, p < 0.05). 4. Discussion In the present study, we asked whether cortical activity evoked by electrical pulses from bilateral cochlear implants reflects the heterogeneity in cause and onset of deafness in children. Recordings were completed prior to any consistent cochlear implant use

thus reflecting activity in a deprived auditory cortex. Our results confirmed our hypothesis as, although replicable responses were recorded, there was large variability in the number and polarity of identifiable wave peaks which meant that single wave peaks could not be easily identified across the group. The degree of variability in auditory evoked cortical responses, shown in Fig. 3, has not been previously reported in other populations of children to our knowledge; we thus aimed to explain why such differences in cortical function exist with intent to better understand the effects of deafness on the immature auditory cortex. Careful analysis and categorization of waveforms provided an explanation for much of the variability in responses. Visual categorization of responses shown in Fig. 2 was confirmed using PCA analyses and clustering as detailed in Fig. 4 and 5. Fig. 6 demonstrates the consistency between the two methods for positive and negative peak responses. As shown in Fig. 7, we found that the response evoked by the cochlear implant in one ear was usually similar in type to that evoked in the other ear suggesting that, despite the many effects of deafness on the developing auditory cortex, these effects were usually symmetrical between the ears. No significant effects of duration of deafness or age at cochlear implantation on response type were found but there was evidence that the variability in cortical response type could be explained to some extent by the aetiology of deafness. As detailed in Fig. 8, severe biallelic mutations of the GJB-2 gene were associated with a predominance of cortical responses characterized by a negative amplitude peak. We suggest that this reflects a more uniform stage of early cortical development in children whose deafness is associated with severe GJB-2 mutations whereas variable cortical development in children

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Fig. 7. Responses recorded in each child were grouped according to the wave type evoked by the right cochlear implant. Amplitude mean (thick black line) and ±1 SD are shown for each type. The corresponding responses as evoked by the left ear in the same children are shown to the left; mean amplitude is shown by the thick black line with ±1 SD. Mean responses from the left CI are similar to the right. Stimulus artifact was clear in some cases and is contained to within the first 40 ms of the recording window.

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Fig. 8. (A) The proportion of negative peak responses was significantly greater in children with severe biallelic GJB-2 mutations (35delG, 167delT, and 235delC) than in children without these mutations. (B) The distribution of responses by specific mutation is shown. Other mutations were monoallelic, associated with mild hearing loss, or of no known clinical significance.

without these mutations reflects a myriad of deficits causing deafness which have yet to be determined. These changes do not reflect a global developmental delay in these children. Indeed, children who were developing more slowly than expected for their age did not show the same predominance of negative peak responses. Two of the 3 response types identified in this study have been reported in normal hearing children and in children using cochlear implants. The positive peak type of response most closely resembles cortical responses evoked by auditory stimuli presented at rates of approximately 1 Hz in normal hearing children. When recorded at the midline of the head, these responses consist of a broad positive peak at 200–250 ms, followed by a negative peak at about 300–700 ms (Kurtzberg et al., 1984; Sharma et al., 1997; Gilley et al., 2005; Picton and Taylor, 2007; Sussman et al., 2008; Lippe et al., 2009). Only after about 5–10 years age do the responses gain the mature morphology consisting of a well described complex of positive and negative peaks (P1–N1–P2–N2) (Ponton et al., 1996; Ponton and Eggermont, 2001; Sussman et al., 2008; Lippe et al., 2009). This positive peak response type appears to be the most common type of cortical response in children who have used a unilateral cochlear implant for approximately 3–6 months or longer (Ponton et al., 1996; Ponton and Eggermont, 2001; Sharma et al., 2005; Sharma and Dorman, 2006). In a previous study, we found that the positive peak response was associated with good perception of spoken words in children who, in many cases, had several years of experience listening with their cochlear implants (Gordon et al., 2008). As shown in Fig. 6, this response was also found in 38 of 144 cortical responses (26%) prior to consistent cochlear implant use in children suggesting that, although these children had had severe to profound hearing loss from young ages and no cochlear implant experience, they were already showing a similar stage of cortical development. If this development is activity

dependent, it implies that these children had some access to stimulation of the auditory system prior to cochlear implantation. No significant effects of onset or duration of hearing loss were found between response types suggesting that current measures of hearing may not be sensitive to particular types of activity in the auditory system and/or that some degree of hearing was present prior to the child’s first hearing assessment. Alternately, cortical development could have been promoted by activity independent mechanisms. If so, perhaps there is a specific genotype which gives rise to this. The second response type was characterized by a dominant negative peak which in some cases preceded a positive peak at a later latency. A comparable response was reported in pre-term infants who showed a broad negative peak in their cortical responses at approximately 200 ms (Weitzman et al., 1967; Rotteveel et al., 1987). Slightly older infants (1 month of age) have responses containing a small negativity around 80 ms which precedes the broad positive peak. This early negative peak could be an immature form of the MLR (Lippe et al., 2009) and is unlikely to be equivalent to the adult N1 given that the N1 is not evoked by sound presented at stimulation rates of 1 Hz in children younger than approximately 9 years of age (Sharma et al., 1997; Gilley et al., 2005). One other group has reported cortical responses prior to chronic cochlear implant use in children with severe to profound deafness (Sharma et al., 2005). They reported a very similar response in the group mean data to the negative peak response shown in the present study but focused on the later positive peak rather than the negative wave. We suggest that this negative peak should not be discounted from analysis because it was consistently identified through PCA cluster analyses in the present group of children at early stages of cochlear implant use and may persist in some children even after more significant cochlear implant experience. In a

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previous study, we showed that a negative peak in the cortical response was found in children who still had difficulty recognizing spoken words after using unilateral cochlear implants for several years (Gordon et al., 2008). We suggested that this response type reflected either abnormal cortical development or persistent immaturity despite chronic electrical stimulation from a cochlear implant. Given that a similar response was found in normal hearing infants at early developmental stages, that it has been previously reported at early stages of cochlear implant use in children, and that it has been associated with poor cochlear implant performance in children, we suggest that the negative peak response type evoked by acute cochlear implant stimulation reflects relatively immature or abnormal cortical function compared to the positive response type evoked in the same way. We asked whether this could reflect different effects on cortical development by various etiologies of deafness in children. Although a number of factors were assessed, the only clear finding, as shown in Fig. 8, was that the presence of severe GJB-2 mutations was associated with a larger proportion of negative peak responses than when only mild or no mutations of this gene were present. Autosomal recessive GJB-2 mutations have been reported to explain 15–50% of childhood deafness (Propst et al., 2006b; Bonyadi et al., 2009; Tamayo et al., 2009; Yuan et al., 2009). In a previous study, we found that 20% of the children followed in our cochlear implant program had homozygous and severe mutations of GJB-2 (Propst et al., 2006b). The incidence in the population studied here (22%) was consistent with this. GJB-2 normally codes for the Connexin 26 protein which plays an important role in establishing gap junctions between the supporting cells in the cochlea and in separating endolymphatic fluid, which is high in K+, from perilymph. The sensory hair cells of the cochlea sit in endolymph in the scala media and are normally depolarized when sound causes them to be displaced along the basilar membrane thus mechanically opening K+ ion channels. Depolarization leads to release of glutamate in the hair cell–nerve synapse and excitatory potentials in the postsynaptic neuron. The lack of Connexin 26 is thought to disrupt the ability of the cochlear gap junctions to maintain the normal chemical gradient between endolymph and perilymph thus rendering the sensory cells incapable of depolarization and of stimulating the primary auditory nerve (Zhang et al., 2005; Zhao, 2005; Chang et al., 2008, 2009), and restricting normal cochlear development (Inoshita et al., 2008; Wang et al., 2009). We suggest that this lack of stimulation has effects on the developing auditory pathways. In a previous study we suggested that these deficits should be present along the length of the cochlea and thus should have similar effects on the function of the auditory nerve regardless of cochlear place of stimulation. In support, we found that auditory nerve responses evoked by acute electrical stimulation in children with severe homozygous GJB-2 mutations were present but were not affected by the place of stimulation (Propst et al., 2006a). In comparison, children who were deaf but who did not have these mutations, showed larger amplitude responses from the auditory nerve when stimulated by cochlear implant electrodes at the apical rather than basal ends of the implanted array. The children with GJB-2 mutations also had poorer hearing sensitivity in the low frequencies (coded in the apical end of the cochlea) than children without GJB-2 mutations. In the present study, as shown in Fig. 8, we found that children with severe GJB-2 mutations showed a significantly larger proportion of cortical activity responses which are more typical of earlier stages of development (negative peak types) compared to their peers without these genetic changes. Taken together with the auditory nerve findings, we suggest that the auditory pathways in children with GJB-2 mutations remain in a very early stage of development perhaps because there is very limited stimulation available to the

auditory nerve. In contrast, children whose deafness is due to other factors may have received more significant auditory input. This stimulation could come from specific parts of the cochlea (i.e. more hearing at apical areas of the cochlea) or could reflect differences in onset of deafness. Indeed, the time course of deafness might vary considerably due to differences in how the ear either forms or degenerates during development. If the negative peak response type reflects an earlier stage of cortical development than the positive peak response, then we must ask whether the multi-peaked response can provide any further clues about cortical development. It is possible that this response is similar to the broad P1–N1–P2 complex responses evoked in normal hearing children using slow presentation rates of auditory stimuli and/or that it reflects an intermediary stage of development between the negative and positive peak responses. This might explain why multi-peaked responses were categorized fairly equally into positive and negative clusters in the PCA analysis. Perhaps this is a combination response in which components of negative and positive peak responses exist in different weights relative to one another. It is important to note that variability in overall amplitude of responses between children was not assessed in this study. In general, small amplitude responses would suggest that less synchronous activity was evoked by the cochlear implant or that the direction of the evoked electrical field was poorly detected by the recording electrodes (positioned at the midline of the head). Any decrease or absence of synchrony in the evoked cortical response could be due to effects on endogenous components by factors such as severe structural abnormalities in the cochlea and/or auditory nerve (no such issues were present in the study cohort) or other immaturity or abnormalities in the auditory system. Efforts were made to keep the children awake and alert during testing although the level of attention was not monitored and thus might also have affected endogenous components. Exogenous components could also be disrupted by the use of low stimulus levels, which could occur if the child was intolerant to cochlear implant stimulation at this initial stage of cochlear implant use. In addition, the electroencephalographic signal can be large in children who are active during the recording and this could limit normal methods used to isolate the auditory evoked response. It will therefore be important to monitor both intra- and inter-subject changes in response amplitude as children use their cochlear implants over time. The results from the present study demonstrate for the first time that cortical development in children receiving cochlear implants is highly variable. Moreover, this variability reflects the aetiology of deafness to some extent. We have confirmed that GJB-2 associated deafness has a unique effect on the immature auditory pathways perhaps because of a more restricted access to auditory input during development. It is necessary to further characterize and understand childhood deafness in order to improve outcomes of cochlear implantation or any other treatment for deafness in children. Our next steps toward this goal will be to use multi-channel recordings of electrically evoked cortical activity in children to localize the cortical source of each peak. This is needed to identify the generators of the peaks defined in the present study in space in order to assess the relationship between these generators and to ensure that the same generator is being tracked over time. Longitudinal analyses will further elucidate the multi-peaked response and help to determine the stage of cortical development reflected by it. Funding This work was supported by the Canadian Institutes of Health Research (#MOP-97924).

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Acknowledgements We gratefully acknowledge our collaboration with Dr. Richard van Hoesel and the support of Dr. Robert Harrison for help in the cluster analyses. We also thank the children and families who donated their time and effort to the collection of this data.

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