Participants were recruited from the Urbana-Champaign area, were scanned under the UIUC IRB 10144 protocol, gave written informed consent, and were monetarily compensated. Subjects belonged to one of two groups: middle-aged adults with bilateral high-frequency sensorineural HL (*n* = 12), or their age- and gender-matched controls with NH (*n* = 15). Three NH subjects were excluded due to excessive motion artifact, and data from only 12 NH participants were included in the final analysis. During recruitment, we excluded subjects that presented with tinnitus or with asymmetric HL at the time of their audiological examination. We defined asymmetric HL to be more than a 15 dB HL difference between the right and left ear at one or more frequencies, or if the right and left ear differed by 10dB HL at two consecutive frequencies. The Beck Depression Inventory (BDI-II) and the Beck Anxiety Inventory (BAI) were used to assess depression and anxiety levels (Beck and Steer, 1984; Steer et al., 1993, 1999). All subjects scored in the minimal depression range and minimal anxiety range for the BDI-II and BAI, respectively. See **Table 1** for information about subject demographics.

#### **AUDIOMETRIC EVALUATION**

A comprehensive audiometric evaluation was performed for each subject. Audiological testing took place inside a soundattenuating booth and included pure tone testing, word recognition testing, and bone conduction testing. Additional tests included distortion product otoacoustic emissions and tympanometry measurements to eliminate any confounding peripheral hearing pathologies. For pure tone testing, the test frequencies included 0.25, 0.5, 1, 2, 4, 6, and 8 kHz. For all of the test frequencies, each subject in the NH group had pure-tone thresholds of 25dB HL or lower. Participants in the HL group had a puretone threshold of 30 dB HL or lower for the test frequencies 0.25–2 kHz, with the exception of two HL subjects who had a slightly elevated threshold of 35 dB HL at 1 kHz. For the test frequencies 4–8 kHz, the HL subjects had pure-tone thresholds between 30 and 70 dB HL, ranging from mild to moderatelysevere HL. **Table 1** contains information about the average hearing at testing frequencies for each subject group. None of the HL participants relied on aided hearing.

#### **DATA ACQUISITION**

A 3Tesla Siemens Magnetom Allegra head-only scanner was used to acquire all MRI images. A series of two anatomical and two functional images were acquired- the first functional scan was of the emotional task and the second acquired data on resting

*Pure tone audiogram information at each frequency is averaged across both ears and all subjects. BDI, Beck Depression Inventory; BAI, Beck Anxiety Inventory.*

state; order of acquisition varied across the subjects. Thirty-two low-resolution T2-weighted structural transversal slices (*TR* = 7260 ms, *TE* = 98 ms) were collected for each volume with a 4.0 mm slice thickness and a 0.9 × 0*.*9 × 4*.*0 mm3 voxel size [matrix size (per slice), 256 × 256; flip angle, 150◦]. We obtained 160 high resolution magnetization-prepared rapid-acquisition with gradient echo (MPRAGE) sagittal slices for each volume that were 1.2 mm in thickness with a 1.0 × 1*.*0 × 1.2 mm3 voxel size [*TR* = 2300 ms; *TE* = 2*.*83 ms; matrix size (per slice), 256 × 256; flip angle, 9◦]. The functional images were acquired using the following parameters: slice thickness, 4 mm; inter-slice gap, 0.4 mm; 32 axial or transverse slices, distance factor, 10%; voxel size, 3.4 × 3*.*4 × 4.0 mm3; field of view (FoV) read, 220 mm; TR, 9000 ms with 2000 ms acquisition time; TE, 30 ms; matrix size (per slice), 64 × 64; flip angle, 90◦. Functional images were acquired separately for (a) an emotional task and (b) a resting state study.

#### *(a) Emotion task*

To mitigate the loud noise of the radio frequency gradients generated during image acquisition from interfering with the perception of the stimuli, we used clustered echo-planar imaging (EPI) (Hall et al., 1999; Gaab et al., 2003; Zaehle et al., 2004). Clustered EPI, or sparse sampling, was chosen particularly to improve the listening environment for the subjects with HL. To reduce scanner noise interference with sound perception, we collected one image volume (2 s acquisition time) post stimulus presentation rather than using continuous image acquisition during a period of "relative quiet" when the radio-frequency gradients were switched off and the only source of ambient noise was the scanner pump. The repetition time was 9 s, and within each trial a 6 s stimulus was presented during a 7 s interval of reduced scanner noise. To optimize the scanning procedure to detect neural response from regions within the limbic system, prior to data acquisition, a custom MATLAB (http://www*.* mathworks*.*com/products/matlab/) toolbox was used in order to fine-tune the timing of stimulus presentation relative to image acquisition (Dolcos and McCarthy, 2006). Stimuli were selected from the International Affective Digital Sounds (IADS) database and had normative scores for valence and arousal; sounds were rated on a scale of 1–9 (9 very pleasant, 1 very unpleasant for valence and 9 very arousing, 1 not at all arousing for arousal) (Bradley and Lang, 2007). Normative scores were as follows: pleasant (valence: 6*.*83 ± 0*.*54, arousal: 6*.*46 ± 0*.*56), unpleasant (valence: 2*.*78 ± 0*.*58, arousal: 6*.*9 ± 0*.*31) and neutral (valence: 4*.*81 ± 0*.*43, arousal: 4*.*85 ± 0*.*57). The normative valence ratings for P and U sounds are statistically different at *p <* 0*.*00001, but did not differ in arousal scores. **Supplementary Table 1** contains a complete list of the affective sounds used in the present study. We presented the sounds in the scanner at a maximum comfort level, as indicated by each participant, during the relatively quiet intervals of image acquisition. Prior to data collection, subjects were given both written and verbal instructions. Additionally, subjects were trained using sounds from the IADS database, different from the stimuli chosen in the experiment, to familiarize the participants with the task. During the final experiment, Presentation 14.7 software (www*.*neurobs*.*com) on a Windows XP computer in the fMRI control room was used to deliver sounds and instructions via pneumonic headphones (Resonance Technology, Inc., Northridge, CA.). To complete the task, subjects listened to 90 affective sounds [30 pleasant (P), 30 neutral (N), 30 unpleasant (U)], each 6 s in duration and were instructed to rate the sound as P, N, or U as soon as they felt confident in their rating.

#### *(b) Resting state*

To acquire information about the resting state, continuous scanning instead of clustered image acquisition was employed. During the resting scan, which was continuous and lasted approximately 5 min, subjects were instructed to lie still and to look at a fixation cross for the scan duration. One hundred and fifty volumes were collected for each subject. The first four images were discarded prior to preprocessing, leaving 146 volumes for analysis.

#### **PREPROCESSING**

Pre-processing was similar for both types of functional scans. Statistical Parametric Mapping 8 (SPM, Welcome Trust Centre for Neuroimaging, http://www*.*fil*.*ion*.*ucl*.*ac*.*uk/spm/software/ spm8/) software was used to analyze the functional imaging data. The images were first realigned using a rigid body transformation to control for head motion. Next, the low resolution Axial T2 (AxT2) image was registered to the mean fMRI image generated during the first step. The high resolution MPRAGE image was then registered to the AxT2 image. To normalize the functional images to MNI space, the MPRAGE was normalized to match a standard T1 MNI template. The normalized images were then smoothed using a Gaussian kernel of 8 × 8 × 8 mm3 full width at half-maximum. To account for artifacts created by head motion, data from 3 NH subjects who displayed excessive motion (defined as at or above ±1.5 mm translation and ± 1.5◦ rotation in any direction) were not included for further analysis. We also included regressors of motion as covariates of no-interest in the general linear models created in the different statistical analyses, in order to (partially) remove motion-related artifacts. Further, *t*-tests of root-mean-square estimates of both rotational and translational movement showed that there was no statistical significant difference between the two groups (translational motion mean ± standard deviation): NH (0*.*63 ± 0*.*28); HL (0*.*67 ± 0*.*23); rotational motion: NH (0*.*01 ± 0*.*005), HL (0*.*01 ± 0*.*004).

#### **DATA ANALYSIS**

#### *(a) Emotion task*

Behavioral data were obtained in the scanner during fMRI data acquisition. We collected individual subject ratings of each sound as P, N, or U along with reaction times. Subject ratings and reaction times were analyzed using separate Two-Way ANOVA tests in SPSS ver. 20 software (statistical package for social sciences, IBM, http://www-01*.*ibm*.*com/software/analytics/spss/). Group (NH, HL) and condition (P, N, U) were set as independent fixed factors in a general linear model within SPSS, and significance was set at *p <* 0*.*05.

For data analysis, trials were coded based upon each individual's subjective rating of the affective sounds. We chose to employ the individual ratings to classify trials as "P," "N," or "U," rather than the norms reported in IADS. Either using the IADS classification or individual rating to analyze data are valid methods of classifying the individual trials for further analysis. The trend in affective neuroscience literature is to move away from the normative classification to the individual classification, particularly when examining special populations where it is expected that emotional responses are altered (e.g., older adults or patient population) (St Jacques et al., 2010). It should be noted that the ratings reported with the IADS were obtained from younger adults with NH (Bradley and Lang, 2007). In the present study, the subject population was older with mean age of 51*.*4 ± 9*.*9 for NH adults and 58*.*2 ± 9*.*5 for those with HL. Therefore, individual ratings were used rather than the norms for classifying the trials obtained during fMRI scanning. First level fixed effects analysis was performed on each subject's smoothed images to generate P *>* N and U *>* N contrast images. Motion parameters were included in the first level model as covariates of no-interest. The contrast images were then included in the flexible factorial analysis and *post-hoc* two-sample *t*-tests at the second level. The P + U *>* N contrast was computed by performing a *t*-test on the condition vectors for each group separately in the flexible factorial model. The three factor design included group, subject and condition. Group was assumed to be independent and have unequal variance, subject was assumed to be independent and possess equal variance, and condition was assumed to be a dependent factor and to have equal variance. To directly compare the NH and HL groups, we conducted *post-hoc* two-sample *t-*tests within the flexible factorial model. Additionally, we performed a region-ofinterest (ROI) analysis using the Wake Forest University (WFU) pickatlas toolbox (http://www*.*fmri*.*wfubmc*.*edu) within SPM8, with regions defined anatomically based on the human MNI atlas within the toolbox. Based on our *a priori* hypothesis about the involvement of auditory and limbic regions in affective sound processing, we created a single anatomically-defined mask via selecting regions including the amygdala, insula, parahippocampus, nucleus accumbens, ventral medial prefrontal cortex, inferior colliculus, medial geniculate body and primary auditory cortex (Brodmann areas 42, 41, 22). ROI analysis was performed on the NH (P + U *>* N), HL (P + U *>* N), and between group contrasts, and small volume correction (SVC) was employed. All clusters identified in the results were reported using a significance level set at *p <* 0*.*025 FWE at either the voxel or cluster level (threshold halved from the standard *p <* 0*.*05 to account for a two-tailed *t*-test).

#### *(b) Resting state*

Preprocessing of the resting state data began with slice time correction for the interleaved, ascending data collection. Following that, the same preprocessing steps used for the emotion task were applied. The Functional Connectivity Toolbox (Conn) (Whitfield-Gabrieli and Nieto-Castanon, 2012) for MATLAB was used for data analysis. The smoothed fMRI data was band-pass filtered from 0.008 to 0.08 kHz. The average BOLD timeseries of the segmented white matter and cerebrospinal fluid as well as the realignment/motion parameters generated during preprocessing were regressed out of the data. Then, seed-to-voxel analysis was performed to analyze the auditory resting state network, the DMN, and the DAN. Connectivity was assessed using pairs of seed regions for both the DMN and auditory networks; correlations between each seed and the whole brain were measured and averaged across seed pairs. Seed locations are listed in **Table 2**. For the auditory network, seeds were located in the bilateral primary auditory cortices. For the DMN, they were located in the medial prefrontal cortex and the posterior cingulate cortex. The DAN was examined using four seeds in the left and right posterior intraparietal sulci and the left and right frontal eye fields (Burton et al., 2012). All seeds were created using Marsbar (Brett et al., 2002) and were 5 mm in radius. Coordinates of seed regions were the same as those used in (Schmidt et al., 2013). The resting state data used in the present study were partially described in the Schmidt et al. (2013) study, but have been re-analyzed for the purpose of this study. Correlation maps of the whole brain were created for each seed and then averaged over all seeds for a specific network for each subject. These correlations were then z-transformed, group averages were computed, and across-group comparisons were made via two-sample *t*-tests in the Conn toolbox (Whitfield-Gabrieli and Nieto-Castanon, 2012). Results were then exported to SPM8 for display purposes. After whole brain analysis at *p <* 0*.*001 uncorrected threshold, clusters that were significant at *p <* 0*.*025 FWE either at voxel or cluster level were selected to account for both tails of the *t*-test, with cluster extent set at 25 voxels.

A seeding analysis designed to examine resting state network connectivity was performed using ROIs determined from published studies as stated earlier, as well as using ROIs identified from the task-based study. The latter ROIs included the left amygdala, left inferior parietal lobule, left superior frontal gyrus, right middle temporal gyrus, and the right superior parietal lobule (**Table 2**). The left amygdala seed was created based on the NH *>* HL (P + U *>* N) contrast from the task ROI analysis. The right

**Table 2 | Seed regions for the resting state functional connectivity analysis, consisting of seeds for canonical resting state networks and for networks based on local axima from the results of the emotion task study.**

*Coordinates are listed in Montreal Neurological Institute (MNI) coordinates.*

superior parietal lobule and left inferior parietal lobule were both based on the HL *>* NH (P + U *>* N) contrast, and the right middle temporal gyrus and left superior frontal gyrus seeds were based on the NH *>* HL (P + U *>* N) contrast from the emotion task results. All of these ROIs were determined from group-level contrasts. Peak maxima were used as the centers for the spherical ROIs, and the mean BOLD timeseries of the voxels in the ROI was generated. Seed specification, data generation and statistical analyses were performed in the manner described earlier for the standard seeds.

#### **RESULTS**

#### **BEHAVIORAL DATA**

#### *(a) Emotion task*

Ratings were obtained in the scanner simultaneous with fMRI data acquisition. There was a statistically significant main effect of group [*F(*1*,* 23*)* = 69*.*53, *p <* 0*.*000001], main effect of condition [*F(*1*,* 23*)* = 17*.*59, *p <* 0*.*000001] and interaction between group and condition [*F(*1*,* 23*)* = 7*.*79, *p <* 0*.*000423] for the reaction time data. The NH group responded significantly slower to the neutral sounds compared to the pleasant and unpleasant sounds (**Figure 1A**). The HL group's reaction time for the three types of sounds did not significantly differ (**Figure 1A**). For between group comparisons, the HL group was significantly slower for both pleasant and unpleasant sounds compared to the NH group (**Figure 1A**). Concerning the type of responses, there was a significant main effect of condition [*F(*1*,* 23*)* = 7*.*162, *p <* 0*.*01]; however, the main effect of group [*F(*1*,* 23*)* = 0*.*118, *p* = 0*.*733] and the interactions [*F(*1*,* 23*)* = 0*.*109, *p* = 0*.*897] did not reach significance. Both groups rated significantly more stimuli as unpleasant compared to pleasant and neutral (determined using *post-hoc* within-group *t*-tests) (**Figure 1B**). Note that the experimental design used an equal number of sounds classified as P, N, and U according to the normative IADS scores (Bradley and Lang, 2007). Due to the observed variation from the normative ratings, we chose to code the trials during analysis according to each individual's rating.

#### *(b) Resting state*

No behavioral data were obtained for the resting state study.

## **fMRI DATA**

#### *(a) Emotion task*

Within group comparisons: In the NH group, areas of increased activation for the contrast P + U *>* N were observed in the bilateral middle temporal gyri, right transverse temporal gyrus, left superior temporal gyrus, left post central gyrus, right medial frontal gyrus, left superior frontal gyrus, left middle frontal gyrus, left anterior cingulate and the left insula (**Figure 2**, **Table 3**). With respect to the HL group, increased response was obtained in the bilateral superior temporal gyri, bilateral transverse temporal gyri, right middle temporal gyrus, right superior frontal gyrus, left middle frontal gyrus, right medial frontal gyrus, right precuneus, left inferior parietal lobule, left precentral gyrus, left lentiform nucleus and corpus callosum for affective sounds compared to neutral sounds (**Figure 2**, **Table 3**). ROI analysis revealed increased response in bilateral transverse temporal gyri, bilateral

**(A)** Mean reaction time data. For within group comparison, the HL group did not statistically differ between the P, N, and U reaction times. However, the NH group responded significantly slower to the N sounds compared to the P

patterns from both groups (MNI coordinate *z* = +14). The maps are displayed at *p <* 0*.*001 uncorrected level for better visualization, but the clusters in the circles are corrected for multiple comparisons (*p <* 0*.*05 FWE). (1) bilateral middle temporal gyrus, (2) medial frontal gyrus, (3) bilateral middle temporal gyrus, (4) medial frontal gyrus.

superior temporal gyri, bilateral medial frontal gyrus, left insula, right middle temporal gyrus and right parahippocampus for the NH (P + U *>* N) contrast (**Table 3**). For the HL (P + U *>* N) comparison, increased response was observed in the bilateral transverse temporal gyri, bilateral superior temporal gyri, bilateral superior frontal gyri, right medial frontal gyrus and left insula (**Table 3**).

Between-group comparisons: For the NH *>* HL (P + U *>* N) comparison, we observed heightened response in the left superior frontal gyrus, right middle temporal gyrus, left superior temporal gyrus, and left superior occipital gyrus (**Figure 3**, **Table 4**). Concerning the HL *>* NH (P + U *>* N) comparison, elevated response was observed in the right superior parietal lobule, right precuneus, and left inferior parietal lobule (**Figure 3**, **Table 4**). significantly slower for P and U sounds. **(B)** Mean number of responses. The NH and HL groups responded U significantly more than N and P. Statistical significance level *p <* 0*.*05 indicated by ∗.

For the ROI analysis, no suprathreshold voxels were obtained for the HL *>* NH (P + U *>* N) contrast. However, for the NH *>* HL (P + U *>* N) comparison, increased response was observed in the left amygdala and left parahippocampus (**Figure 3**, **Table 4**).

#### *(b) Resting state*

No significant differences were found between groups in the auditory resting state network. With respect to the DMN, a significant difference in the left middle frontal/precentral gyrus was observed in the HL *>* NH comparison. Analysis of the DAN revealed a significant difference in the left postcentral/precentral gyrus and left insula. These results of the typical intrinsic networks are listed in **Table 5** and displayed in **Figure 4**. For the connectivity analysis with task-based ROIs, no significant differences in connectivity with the left amygdala were seen. Placing a seed in the left inferior parietal lobule also did not reveal significant differences. With the seed in the left superior frontal gyrus, the NH *>* HL contrast showed significant differences in connectivity with the left middle occipital lobe. The right middle temporal seed showed a stronger correlation with the right precentral gyrus in the HL group compared to the NH group. Finally, no connectivity differences were seen with the seed in the right superior parietal lobule. The results of this analysis are also shown in **Table 5** and **Figure 4**.

#### **DISCUSSION**

We used a combined task- and rest-based fMRI study to identify the influence of HL on auditory and emotion processing. Results of the responses collected in the scanner revealed similar ratings for the unpleasant, pleasant, and neutral sounds—both groups tended to rate more sounds as unpleasant relative to the other types of sounds. However, the HL group differed from

## **Table 3 | Local maxima for the P + U** *>* **N contrasts from the emotion task.**

*(Continued)*

#### **Table 3 | Continued**

*The loci are listed for each group separately for the whole-brain analysis and the region-of-interest (ROI) analysis. Reported regions are listed in Montreal Neurological Institute (MNI) coordinates and in terms of Brodmann areas (before determining the Brodmann areas the MNI coordinates were converted to Talairach coordinates). The ROI mask comprised of bilateral regions in the primary auditory cortex, medial geniculate body, inferior colliculus, amygdala, insula, parrahippocampus, nucleus accumbens, and ventral medial prefrontal cortex, defined anatomically. Statistical threshold was set at p < 0.05 FWE corrected for multiple comparisons; all clusters noted here were significant at both voxel and cluster level. L, left; R, right.*

the NH group in their response times, which were significantly slower for the affective sounds. Overlapping patterns of fMRI activation were observed in both groups when processing affective sounds compared to neutral sounds. The main finding for the emotion task was increased activation in the left amygdala/parahippocampal gyrus complex for the NH *>* HL (P + U *>* N) comparison, via targeted ROI analysis. The reverse contrast, HL *>* NH (P + U *>* N), did not show increased activation within the limbic system, but rather revealed heightened responses in the right superior parietal lobule, right precuneus, and left inferior parietal lobule. Resting-state functional connectivity in the same group of participants focused on the canonical intrinsic networks and on seed regions obtained from the task-based activation patterns. Among the typical intrinsic networks, the default mode and DAN, but not the auditory network, revealed differences between the groups. Using seeds from the local maxima noted in the task-based analysis, decreased connectivity between the frontal cortex and other brain regions was noted, with the exception of stronger connectivity between the middle temporal gyrus and the right precentral gyrus in the HL group compared to the NH group. Our results suggest that HL may alter the emotional processing networks and lead to slower reaction times to affective stimuli.

## **EMOTION TASK**

HL may reduce the engagement of the emotional processing system, either because of disordered processing of acoustic or of valence features. Complex anatomical and functional connections exist between the auditory cortex and the limbic system, primarily with the amygdala (Amaral and Price, 1984; Blood and Zatorre, 2001; Koelsch et al., 2006; Tschacher et al., 2010; Kumar et al., 2012). Forward projections from the auditory cortex to the amygdala have been shown to be modulated by acoustic features, but backward projections appear to be modulated by the valence of sound (Kumar et al., 2012). The forward and backward projections work in concert to interpret incoming affective stimuli (Kumar et al., 2012). Sound deprivation may reduce the amount of acoustic or valence information available for this network. Reduced information may result in a dampened emotional response to affective stimuli, because individuals with HL may not receive all of the acoustic or valence information necessary to cause a robust emotional response. We investigated this hypothesis via whole brain analysis and a targeted ROI analysis of the auditory and limbic areas.

In our study, both positively and negatively valent sounds exhibited greater engagement of the limbic system and faster response times, compared to neutral sounds, in NH individuals. However, this pattern differed in the HL group, with a decreased response in the temporal and frontal cortices (whole brain analysis) and in the amygdala and parahippocampus (ROI analysis), and an increased response in the parietal cortices and precuneus compared to the NH group when processing affective sounds (**Table 4**). Similarly, in behavioral responses, the reaction times for the P and U sounds were significantly slower in the HL group (**Figure 1A**). The behavioral data suggest that the advantageous faster processing of affective sound found in the NH group does not occur in the HL group. The slower responses to pleasant and unpleasant sounds in the HL group may be due to lack of faster, bottom-up engagement of the amygdala and other limbic structures during auditory processing. However, HL does not appear

**Table 4 | Local maxima for the P + U** *>* **N contrasts from the emotion task for the group comparisons.**

*The loci are listed for each group separately for the whole-brain analysis and the region-of-interest (ROI) analysis. Reported regions are listed in Montreal Neurological Institute (MNI) coordinates and in terms of Brodmann areas (before determining the Brodmann areas the MNI coordinates were converted to Talairach coordinates). The ROI mask comprised of bilateral regions in the primary auditory cortex, medial geniculate body, inferior colliculus, amygdala, insula, parrahippocampus, nucleus accumbens, and ventral medial prefrontal cortex, defined anatomically. Statistical threshold was set at p < 0.025 FWE corrected for multiple comparisons (to account for two-tailed t-test); all clusters noted here were significant at both voxel and cluster level, unless noted with an \* which indicates significance only at cluster level. L, left; R, right.*

to affect the response to all sounds, with the reaction times for neutral sounds being nearly identical in the two groups. Instead, the highly valent sounds were most affected, indicating that it is identification of valence information rather than acoustic information that is impacted by HL. Another possible explanation of the differential processing of affective sounds by the HL group could be that there is more energy or information in the high frequency regions in the affective sounds and less so in the neutral sounds. Difficulty of processing high-frequency information in the affective sounds by the HL group led to longer reaction times. In order to maintain ecological validity, we chose not to low-pass filter the sounds to compensate for the HL of the HL group. In a previous study with mild-to-moderate HL (Husain et al., 2011b), we filtered sounds such that there was no energy in frequencies greater than 2 kHz. We found no difference in accuracy or reaction times for a discrimination task between HL and NH groups, nevertheless, the fMRI activation patterns were different (Husain et al., 2011b). In sum, regardless of the actual mechanism, our results suggest that HL may reduce engagement of the amygdala and result in slower reaction to positively and negatively valent sounds.

Another interesting aspect of the behavioral results was the finding that an increased number of sounds were classified as unpleasant, which differed from the published normative data (Bradley and Lang, 2007). There are at least two possible explanations for this finding. First, the normative scores were obtained in a young, NH population; therefore it is not surprising that both sets of older middle-aged participants in our study differed from these ratings, which points to an effect of age. Another possible explanation is that possible discomfort in the scanner may have influenced our participants to be more negative in their ratings. In order to tease apart these explanations and better understand the effect of aging on emotional processing, we intend to conduct a follow-up study with both young and older participants using stimuli from the IADS database.

#### **RESTING STATE DATA**

Resting state functional connectivity demonstrated alterations in the frontal cortex in HL patients. Increased connectivity between frontal regions and seed regions for the DAN and DMN was seen in HL patients compared to NH controls. A decreased correlation was seen between the left superior frontal cortex and the left middle occipital gyrus in HL subjects compared to controls. The relationship seen between HL and alterations in frontal connectivity may not just be important at baseline, as engagement of frontal regions was also apparent during the emotion task. The cause of these network alterations is not clear. The alterations could be purely attentional in nature, but they may also be a factor of interactions between emotional and attentional systems. A study including an attentional task without an emotional component may help to clarify this.

Except for activation of the left temporal pole, we did not find evidence to support our expectation that the response of the auditory cortex would differ between the two groups when processing affective sounds. A separate resting-state functional connectivity analysis with seeds in the primary auditory cortices also failed to find significant connectivity differences at rest between the two groups. The lack of significant findings may relate to mild-tomoderate severity of the HL in our study. To date, there have been no studies of resting state functional connectivity in patients with this level of HL. Deafness, however, has been investigated in this context. Intrinsic connectivity has been shown to be impacted by deafness, both within and outside of the temporal cortex (Li et al., 2013). The findings of the (Li et al., 2013) study are similar to those in our own study. Deaf patients showed increased negative correlations between the middle superior temporal sulcus and

**Table 5 | Local maxima for results of the resting state functional connectivity analysis.**

*The seeds are listed for the canonical resting state networks first, followed by seeds from the task-based study. Reported regions are listed in Montreal Neurological Institute (MNI) coordinates and in terms of Brodmann areas (before determining the Brodmann areas the MNI coordinates were converted to Talairach coordinates). Statistical threshold was set at p < 0.025 FWE corrected for multiple comparisons (to account for two-tailed t-test). All regions were significant at the cluster level. Abbreviations: L, left; R, right; amyg, amygdala; inf pariet, inferior parietal; sup front, superior frontal; mid temp, middle temporal; sup pariet; superior parietal.*

the left middle occipital and right precentral gyri when compared to NH controls. In our study, the left middle occipital gyrus was shown to be less correlated to the left superior frontal gyrus in the HL group, whereas the right precentral gyrus was more correlated with the right middle temporal gyrus. The presence of altered connectivity in similar regions in both deaf and our mild-tomoderate HL patients warrants further resting state connectivity studies examining varying degrees of HL.

It is important to note that inferences about the directionality of the connections cannot be made from the present functional connectivity analysis. An effective connectivity analysis, perhaps implemented as a structural equation modeling or dynamic causal modeling, would be needed to examine directionality (Horwitz, 2003). Our results suggest only a general alteration in connectivity between two related regions; differences in correlation may not be specifically due to coupling between the seed and a particular region, but may also arise due to the influence of a third region, or changes in noise, etc. (Friston, 1994).

HL is positively correlated with age (Yueh et al., 2003); age is therefore a potential confound in our research. A study examining connectivity in resting state networks (Onoda et al., 2012) noted a significantly decreased correlation between the auditory resting state network and the salience network (which includes the insula, ventrolateral prefrontal cortex (VLPFC), thalamus and cerebellum) with age. In addition, connections between regions of the salience network also weakened with age. The regions of the salience network relate to the processing of emotional stimuli. It is possible that HL within the older population in this study is partially responsible for the observed results. The decreased correlation between regions of the DAN and the insula seen in our study fit well with the results of the aging study. In our own study, however, all participants were matched for age; therefore, we are unable to parse out the effects of age from those of HL. More fMRI studies specifically addressing the effects of HL of varying severity, in different age groups, should be performed to clarify its impact on intrinsic and task-based functional networks. Subject motion in the scanner is another notable confound with the resting state data being particularly sensitive to its influence. Although we excluded data from participants who exhibited excessive head motion, included motion parameters as covariates of no-interest in our statistical models and statistical tests did not show any significant differences between

the groups, it is possible that motion-related artifacts continue to affect our results, as shown by recent publications (Kundu et al., 2012; Van Dijk et al., 2012; Kundu et al., 2013; Power et al., 2014). Future studies with more stringent data acquisition considerations and more advanced analytical methods will need to be conducted to fully account for the possibility of motion artifacts.

sulci and the bilateral frontal eye fields to examine connectivity in the

### **CONCLUSION**

Our results suggest that HL may affect emotional processing by decreasing amygdalar recruitment, resulting in slower reaction times to highly valent sounds. Although the HL group demonstrated slower response times to affective sounds, there was no difference in sound ratings between the HL and the NH group. Altered engagement of the frontal regions was also demonstrated in both emotion task-based subtraction and resting state functional connectivity analyses. HL is the third most common chronic condition in older adults and is highly comorbid with another hearing disorder, tinnitus. Our results in those with unaided, mild-to-moderately severe HL have implications for auditory rehabilitation for hearing impairment, reducing social isolation in the elderly, and management strategies for tinnitus.

#### **ACKNOWLEDGMENTS**

We wish to acknowledge the support of Tinnitus Research Consortium to Fatima T. Husain and of the NeuroEngineering NSF IGERT (Integrative Graduate Education and Research Traineeship) to Jake R. Carpenter-Thompson and Sara A. Schmidt. We are grateful to Kwaku Akrofi and Jaclyn Utz for their assistance in data acquisition.

#### **SUPPLEMENTARY MATERIAL**

frontal gyrus; R MTG, right middle temporal gyrus.

The Supplementary Material for this article can be found online at: http://www.frontiersin.org/journal/10.3389/fnsys. 2014.00010/abstract

**Supplementary Table 1 | Sounds included in the study.** Separated by column are 30 pleasant, 30 unpleasant, and 30 neutral sounds chosen from the IADS database to be included in the study.

## **REFERENCES**

Brett, M., Anton, J. L., Valabregue, R., and Poline, J. B. (2002). Region of interest analysis using an SPM toolbox. *NeuroImage* 16, 1140–1141.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 15 August 2013; accepted: 15 January 2014; published online: 04 February 2014.*

*Citation: Husain FT, Carpenter-Thompson JR and Schmidt SA (2014) The effect of mild-to-moderate hearing loss on auditory and emotion processing networks. Front. Syst. Neurosci. 8:10. doi: 10.3389/fnsys.2014.00010*

*This article was submitted to the journal Frontiers in Systems Neuroscience. Copyright © 2014 Husain, Carpenter-Thompson and Schmidt. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided*

*the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## Left temporal alpha-band activity reflects single word intelligibility

#### **Robert Becker1† , Maria Pefkou2† , Christoph M. Michel 1 and Alexis G. Hervais-Adelman2\***

1 Functional Brain Mapping Lab, Department of Fundamental Neuroscience, University of Geneva, Geneva, Switzerland 2 Brain and Language Lab, Department of Clinical Neuroscience, University of Geneva, Geneva, Switzerland

#### **Edited by:**

Jonathan E. Peelle, Washington University in St. Louis, USA

#### **Reviewed by:**

Philipp Ruhnau, Università degli Studi di Trento, Italy Björn Herrmann, Max Planck Institute for Human Cognitive and Brain Sciences, Germany

#### **\*Correspondence:**

Alexis G. Hervais-Adelman, Brain and Language Lab, Department of Clinical Neuroscience, University of Geneva, 1 Rue Michel-Servet, CH – 1211, Geneva, Switzerland e-mail<EMAIL_ADDRESS>†These authors have contributed equally to this work.

The electroencephalographic (EEG) correlates of degraded speech perception have been explored in a number of recent studies. However, such investigations have often been inconclusive as to whether observed differences in brain responses between conditions result from different acoustic properties of more or less intelligible stimuli or whether they relate to cognitive processes implicated in comprehending challenging stimuli. In this study we used noise vocoding to spectrally degrade monosyllabic words in order to manipulate their intelligibility. We used spectral rotation to generate incomprehensible control conditions matched in terms of spectral detail. We recorded EEG from 14 volunteers who listened to a series of noise vocoded (NV) and noise-vocoded spectrallyrotated (rNV) words, while they carried out a detection task. We specifically sought components of the EEG response that showed an interaction between spectral rotation and spectral degradation. This reflects those aspects of the brain electrical response that are related to the intelligibility of acoustically degraded monosyllabic words, while controlling for spectral detail. An interaction between spectral complexity and rotation was apparent in both evoked and induced activity. Analyses of event-related potentials showed an interaction effect for a P300-like component at several centro-parietal electrodes. Timefrequency analysis of the EEG signal in the alpha-band revealed a monotonic increase in event-related desynchronization (ERD) for the NV but not the rNV stimuli in the alpha band at a left temporo-central electrode cluster from 420–560 ms reflecting a direct relationship between the strength of alpha-band ERD and intelligibility. By matching NV words with their incomprehensible rNV homologues, we reveal the spatiotemporal pattern of evoked and induced processes involved in degraded speech perception, largely uncontaminated by purely acoustic effects.

**Keywords: speech intelligibility, degraded speech, noise-vocoding, alpha oscillations, left inferior temporal cortex**

## **INTRODUCTION**

Despite the great acoustic variability of the speech signal, normally-hearing listeners are able to understand speech seemingly effortlessly in day-to-day situations. In order for this to be possible, the human speech perception system must be robust in the face of both natural variability and acoustic degradations. Natural variability can arise from the different realization of speech sounds by different speakers, who can be understood even though they vary in terms of their size and sex (Peterson and Barney, 1952; Smith et al., 2005), accent (Clarke and Garrett, 2004; Clopper and Pisoni, 2004; Evans and Iverson, 2004), and speech rate (Miller and Liberman, 1979), or may suffer from articulation disorders such as dysarthria (Darley et al., 1969; Kent et al., 1989). Beyond signal variations related to the speaker, external factors also have an impact on the quality of speech that is heard, such as transmission through telecommunications systems, in which case it is heavily filtered, the presence of background noise masking part of the speech signal, the presence of echoes, or the presence of multiple talkers simultaneously.

In order to investigate the mechanisms of speech perception, researchers are increasingly turning to paradigms employing artificial acoustic degradations of speech, which allow fine-grained manipulation of the level of intelligibility of a speech signal, rendering the behavioral or neural correlates of speech intelligibility amenable to investigation.  We chose to employ the latter of these, as it has been the subject of intensive research both behaviorally (e.g., Davis et al., 2005; Stacey and Summerfield, 2007; Hervais-Adelman et al., 2008, 2011; Dahan and Mead, 2010) and increasingly in neuroimaging experiments (e.g., Scott et al., 2000; Davis and Johnsrude, 2003; Scott et al., 2006; McGettigan et al., 2011; Obleser and Kotz, 2011; Hervais-Adelman et al., 2012; Evans et al., 2013).

An increasing amount of evidence regarding the neural correlates of degraded speech perception is becoming available. Functional magnetic resonance imaging (fMRI) has been extensively used to detect brain regions active while listening to degraded speech (e.g., Scott et al., 2000; Davis and Johnsrude, 2003; Giraud et al., 2004; Davis and Johnsrude, 2007; McGettigan et al., 2011; Hervais-Adelman et al., 2012; Wild et al., 2012a), while electroencephalography (EEG) and magneto-encephalography (MEG) have been employed to study the timing of degraded speech processing through measures such as evoked response potentials and synchronized brain oscillations (e.g., Obleser and Kotz, 2011; Sohoglu et al., 2012; Peelle et al., 2013). Such experiments also provide insights into the cerebral processes that support speech perception in hearing-impaired listeners.

Neuronal oscillations are an important tool in the study of different brain functions, such as speech perception, as they address the issue of synchronization of neuronal networks within a given brain region as well as between regions, based on data obtained non-invasively and at the temporal scale neuronal activity occurs. Recent studies (reviewed by Weisz and Obleser, 2014) have focused on brain oscillations in auditory processing and speech perception. Among the different "brain rhythms", which differ from each other on the basis of their characteristic frequency (e.g., delta oscillations are of low frequency, 1–3 Hz, while gamma oscillations have a high frequency of 30–80 Hz) and which seem to play a role in speech processing, a major stream of research has focused on the alpha rhythm (the range of 8–13 Hz). Traditionally, alpha rhythm has been considered as being highly related to visual spatial attention (Thut et al., 2006), beginning with the reports of Hans Berger in the late 1920s (Berger, 1929). Berger (1929) observed that opening the eyes lead to clear attenuation of previously visible oscillations in the 10 Hz range over occipital electrodes. Since then, numerous studies have reported findings about the close relationship of alpha rhythm activity and the visual system (e.g., Makeig et al., 2002; Becker et al., 2008; Busch et al., 2009; Mathewson et al., 2009) as well as working memory tasks (Jokisch and Jensen, 2007) to attentional and other cognitive paradigms (for a review, see Klimesch, 1999).

Despite this strong interest in alpha rhythm, much less is known about the relationship of alpha activity and auditory processing. This may be in part owed to the fact that resting alpha is much more prominent in occipital electrodes, likely detecting visual-cortical activity rather than in temporal electrodes. This might be related to the larger volume of the visual cortex compared to the auditory cortex and to the fact that it is impossible to completely deprive a healthy, hearing individual from auditory input but easy to close one's eyes and thereby block visual input (for a review on auditory alpha see Weisz et al., 2011). The first reports of auditory-cortical oscillations in the alpha-band originate from MEG studies, in which the rhythm was initially named "tau" (Tiihonen et al., 1991; Lehtela et al., 1997). Lehtela et al. (1997) demonstrated that the presentation of short (500 ms) noise bursts was followed by decreases in alpha power, localized in the superior temporal lobes, suggesting a role for alpha as a marker of auditory processing similar to that shown in the visual system. Regarding alpha oscillations and speech perception, Krause et al. (1997) reported stronger alpha-band suppression for listening to a comprehensible text passage as when compared to listening to the same passage being played backwards (Krause et al., 1997). Despite their scarcity, such studies point to a functional role of alpha oscillations in processing speech.

In a more recent study, Obleser and Weisz (2012) investigated the role of alpha suppression in speech comprehension using degraded speech. They employed noise-vocoding, a form of artificial degradation which reduces the spectral information in the auditory signal (Shannon et al., 1995), in a graded manner, permitting the generation of stimuli on a continuum from unintelligible to intelligible, by increasing the spectral fidelity of the vocoding. The method is described in Section Stimuli below. Obleser and Weisz (2012) used words at different levels of intelligibility and hypothesized that alpha suppression should be enhanced with intelligibility, reflecting less need for functional inhibition and less effortful speech processing. This idea is compatible with the conception of alpha oscillations as a mechanism of gating or suppressing task-irrelevant high frequency oscillations (Osipova et al., 2008; Jensen and Mazaheri, 2010). In other words, a decrease in alpha power in a task-relevant region would accompany the disinhibition of gamma oscillations in the same region, which are thought to reflect active processing. In general, alpha suppression is accompanied by an increase of oscillations in the gamma band, which reflects engagement/processing as it has been shown by intracranial studies in animals and humans (e.g., Paller et al., 1987; Fries, 2005; Kaiser and Lutzenberger, 2005; Crone et al., 2006; Womelsdorf et al., 2007).

Obleser and Weisz (2012) showed a linear increase in alpha suppression as a function of word intelligibility. They interpret this finding as reflecting the listeners' effort or ability to comprehend degraded speech. The alpha suppression was localized in superior parietal, prefrontal and anterior temporal areas. This may indicate that these brain regions are more active during the task, assuming that a local decrease in alpha power is a marker of activation of the region in question. This idea is backed by early findings of changes in resting-state alpha power as well as by numerous more recent EEG-fMRI studies which reported an inverse relationship between alpha-power and BOLD signal, under rest as well as during stimulation (Laufs et al., 2003; Moosmann et al., 2003; Goncalves et al., 2006; de Munck et al., 2007; Becker et al., 2011). Furthermore, a more recent study showed an even more direct inverse relationship between alpha-power and neuronal firing rate in monkeys (Haegens et al., 2011). The localization of the alpha-suppresion effect reported by Obleser and Weisz (2012) is partially in agreement with previous fMRI studies seeking to identify the brain regions implicated in the processing of degraded but potentially comprehensible speech (Scott et al., 2000; Narain et al., 2003; Giraud et al., 2004; Eisner et al., 2010; Wild et al., 2012a,b), revealing recruitment of brain areas involved in speech processing in general, such as bilateral superior temporal gyrus (STG) and superior temporal sulcus (STS), together with a more left-lateralized inferior-frontal and motor network.

In the present study, our aims were twofold: (1) we aimed to dissociate the EEG signatures of intelligibility and spectral complexity while listening to degraded speech, using a paradigm similar to the one published by Obleser and Weisz (2012). To this end, we asked participants to listen carefully to noise-vocoded (NV) words with different levels of degradation (therefore different levels of spectral complexity), and importantly, to the spectrally rotated version of the same stimuli. Spectrally rotated speech (Blesser, 1972) retains the same amount of spectral complexity as its non-rotated equivalent but it is incomprehensible to the naïve listener [although listeners may adapt to it after extensive training (Green et al., 2013)]. In this way, we had a spectrally-matched control condition for each level of intelligibility in order to rule out the possibility that alpha suppression is purely driven by low-level acoustic properties of the signal. Importantly, we used monosyllabic words, which are very challenging for naïve listeners to understand when degraded, in order to increase task difficulty and limit the use of top-down processes. As lexical and prosodic information facilitate comprehension and perceptual learning of degraded speech (e.g., Davis et al., 2005), by using monosyllabic words our listeners would have to rely almost exclusively on the acoustic information they could extract from the degraded input. The nature of this acoustic input was therefore essential for comprehension, given the lack of other sources of information in the stimuli and (2) by using the approach described above, we wanted to examine both induced and evoked correlates for degraded speech processing, their timing, their topography as well as the putative cerebral sources underlying the observed effects. Although the work presented here employs artificial acoustic degradation to examine the neural underpinnings of degraded speech perception, we hope that the findings will also be relevant to the cerebral processes that help to compensate for degraded auditory input in hearing impaired listeners.

## **MATERIALS AND METHODS**

#### **PARTICIPANTS**

Fourteen right-handed native French speakers (two male) participated in the study. Participants self-reported as being righthanded and having no history of hearing or visual impairment. They all gave written informed consent to participate in the study, which was approved by the local ethics committee of the University Hospital of Geneva, and received financial compensation of 40 CHF.

#### **STIMULI**

Stimuli were 360 monosyllabic French nouns, among which 36 animal names, selected from the "Lexique 3" database for French words.1 The animal names were used as targets in the detection task we employed. All words, apart from the animal names, were divided into three groups, matched for spoken wordform frequency (*F*2,321 = 0.205, *p* = 0.82; Group 1 mean = 31.94/million, Group 2 mean = 27.43/million and Group 3 mean = 28.37/million). The animal names used had mean frequency = 15.41/million. Stimulus matching was achieved using the Match program.2

The words were recorded in a soundproof chamber by a male native French speaker and digitized at a 44.1 kHz sampling rate. Recordings were denoised using the algorithm implemented in Audacity software3 and trimmed at zero-crossings preceding and succeeding the words. Root mean square (RMS) amplitude of each of the extracted stimuli was set to a fixed level, and the stimuli were then bandpass filtered between 50 and 5000 Hz using a sixth order butterworth filter. Stimuli were then NV using 1, 4, 8 and 16 frequency bands following the procedure described by Shannon et al. (1995). Stimuli were filtered into quasi-logarithmically spaced frequency bands intended to mimic the tonotopic organization of the cochlea (Greenwood, 1990) between 50 and 5000 Hz, using second order butterworth filters and full-wave rectified, producing the amplitude envelope of each band. Each envelope was convolved with noise band-pass filtered into the same frequency range as the source band. The amplitude-modulated carriers were then recombined to produce NV words. The number of frequency bands has been shown to be a crucial factor for the intelligibility of vocoded words, with higher numbers of bands producing more intelligible stimuli (Shannon et al., 1995). The four NV versions of each word were created, producing a continuum from completely unintelligible (1-band noise-vocoded, NV1) to relatively easily comprehensible (16-band noise-vocoded, NV16).

Bandpass-filtered and NV words were also spectrally rotated, in order to create a control condition preserving the same amount of spectral information but rendering the input incomprehensible. Spectral rotation was achieved based on a procedure defined by Blesser (1972) and implemented following the procedure described by Scott et al. (2000). The speech signal was rotated around the midpoint of the frequency range of the bandpass filtered input (i.e., 2.5 kHz) and then pre-emphasized using a finite response (FIR) filter with the same long-term spectral distribution as the original input. Finally, signal was modulated by a sinusoid at 5 kHz. NV1 words were not spectrally rotated, since their spectral profile is that of white noise, i.e., flat, and would not be altered by rotation. Example NV and spectrally-rotated NV stimuli are illustrated in **Figure 1**.

These manipulations yielded four levels of spectral detail, crossed with the factor spectral rotation: band-pass filtered words (BP), NV16 words, 8-band noise-vocoded words (NV8) and 4 band noise-vocoded words (NV4) and their spectrally rotated homologues (rBP, rNV16, rNV8, rNV4), as well as a 1-band NV (NV1, equivalent to signal-correlated noise) control condition.

The experiment was divided into three blocks (see Section Procedure, below), for which stimulus selection proceeded as follows. Words were randomly selected from the pool of 108 non-target stimuli of each stimulus group, and allocated without replacement to one of the four non-rotated conditions (NV4, NV8, NV16, BP). Each selected word was also allocated to the spectrally rotated condition at the same level of spectral detail. Twenty seven words were allocated to each level of degradation per block. Due to the limited number of target stimuli (the 36 animal names) these were randomly allocated without replacement to each of the four levels of degradation, with a new randomization applied for each of the three blocks, thus each target stimulus appeared three times in a non-rotated condition and three times in a rotated

1http://www.lexique.org, New et al. (2001)

2http://www.mrccbu.cam.ac.uk/∼maarten/match.htm, Van Casteren and Davis (2007)

3http://audacity.sourceforge.net/

condition over the course of the experiment. For each block, 27 words were randomly selected without replacement from the whole pool of stimuli and allocated to the NV1 condition. There were therefore 81 non-target stimuli per condition and 27 target stimuli in each condition barring NV1, for a total of 279 trials in each of the three blocks.

#### **PROCEDURE**

Participants were seated in front of a computer screen, at a distance of approximately 1 m, in a sound-insulated Faraday cage. The overhead light in the booth was turned off while EEG was recorded. Stimuli were delivered through ER4 noise-isolating in-ear headphones (Etymotic Research Inc., Elk Grove Village, Illinois) at a comfortable sound level, adjusted at the beginning of the experiment.

The experiment was divided into three blocks of 12.5 min and 279 trials each. Participants were instructed to listen carefully to the words and respond each time they heard an animal name by pressing a button on a response-box placed on a table in front of them with their right index finger. Brain electrical activity was recorded during the whole period of each block. Short breaks took place after the end of the first two blocks in order to check, and if necessary reduce, the impedances of the electrodes.

Each trial began with the appearance of a fixation cross at the center of the screen. The stimulus was presented after a silent interval whose duration was randomly jittered on a uniform distribution between 400 and 600 ms after the onset of the

fixation cross. The fixation cross remained on screen during the presentation of the word so as to encourage fixation and minimize eye movements. 1000 ms after the offset of the stimulus a symbol appeared on-screen for 1000 ms, indicating to participants that they could blink if they wished. By cueing blinks we aimed to ensure that any blinking-related electrical artefacts would not contaminate the recording of EEG responses to the stimuli. The structure of a single trial is illustrated in **Figure 2**.

## **EEG RECORDING**

Brain electrical responses were recorded with a 256-electrode Electrical Geodesics HyrdoCel system (Electrical Geodesics Inc., Eugene, Oregon). Signal was recorded continuously and digitized at a sampling rate of 1000 Hz. By default, the recording system sampled at 20 kHz before applying an analog hardware antialiasing filter with a cut-off frequency of 4 kHz and downsampling the signal to 1000 Hz, and applying a software low-pass Butterworth filter with a cutoff of 400 Hz. The reference electrode was the Cz, situated at the vertex. Electrode impedances were checked at the beginning of the session and after the first and second recording blocks and were under 30 k at the beginning of each block.

## **DATA ANALYSIS**

#### **Behavioral data**

We computed and analyzed accuracy scores and response times for correct trials. We also analyzed the number of false alarms per condition.

## **EEG data preprocessing and artifact removal**

EEG data were initially analyzed using custom Matlab (The Mathworks, Natick, MA) scripts and the freely available EEGLAB toolbox (Delorme and Makeig, 2004). Data were re-referenced to average reference and downsampled to 200 Hz (after lowpass filtering to avoid aliasing). For further analyses a set of 204 channels was analyzed (channels covering the cheeks were excluded). The three blocks per subject were concatenated and an Independent Components Analysis (ICA) was computed on the whole dataset using the Infomax routine from the Matlab-based EEGLAB toolbox (Delorme and Makeig, 2004) in order to remove blink artefacts and 50 Hz line-noise. The resulting data were filtered between 0.75 and 40 Hz using a fifth order Butterworth bandpass filter and separated into epochs starting 800 ms before the sound onset and finishing 1000 ms after. A baseline correction was applied by subtracting from each epoch the mean signal computed over 200 ms preceding the onset of the stimulus. The filtered and epoched data were scanned to detect epochs in which the amplitude between 300 ms pre-stimulus onset and the end of the epoch was higher than 75 µV or lower than −75 µV. These epochs were considered outliers and were excluded from further analysis.

Furthermore, only trials with non-target words and no button presses (i.e., false alarms) were kept for further EEG analyses.

#### **Analysis of evoked activity—ERP analysis**

Single-trial evoked responses were averaged for each condition separately to examine the component of neural activity being phase-locked to the stimulation event, i.e., to obtain conditionspecific event-related potentials (ERPs). Data were analyzed in a time window from −100 to 1000 ms about stimulus onset.

#### **Analysis of induced activity—time-frequency analysis**

Using the EEGLAB toolbox, we chose a hanning-tapered shortterm fast-fourier-transformation (stFFT) to compute timefrequency representations of our data in the range of the alpha band (8–13 Hz). These time-frequency power fluctuations (referred to as *TF*α, in µ*V* 2 ) were then converted to *dB* logarithmic scale using the following equation:

$$dB\_{\alpha} = 10 \times \log\_{10}(TF\_{\alpha}) \tag{1}$$

and finally averaged across the defined frequency band. As for the ERPs, data were analyzed in a time window from −100 to 1000 ms.

#### **ANOVA**

The ERPs as well as the stFFT data were submitted to a repeatedmeasures ANOVA, including two factors: spectral detail (four levels, 4-bands, 8-bands, 16-bands and band-pass filtered), and spectral rotation (two levels, non-rotated and rotated), and including subject as a random factor. We were specifically interested in the interaction of spectral detail and spectral rotation to identify correlates of degraded word processing, which are not confounded with the factor of spectral detail. ANOVA was performed for each time point and electrode.

#### **Inverse solution modeling**

For the ERPs as well as the stFFT results we used the weighted minimum norm (WMN) approach (Lin et al., 2006), which belongs to the family of distributed inverse solution methods. We computed inverse solutions by applying the WMN inverse matrix to the data (5018 solution points, no regularization). The lead field model we employed was a LSMAC model (locally spherical model with anatomical constraints, for details about this approach see Brunet et al., 2011) which then in turn is applied to the MNI brain template (ICBM152) coregistered to EEG electrodes. Standard spatial electrode positions were used in all subjects, co-registered to the template MRI by adjusting the position of the nasion, inion, Cz and pre-auricular landmarks.

For the ERP data, condition and subject specific averages were inverted using the method described above and then subject to the same ANOVA procedure as the surface EEG data. To compute the inverse equivalent of the stFFT surface data in the alpha-band, we filtered the single EEG trials for each condition and subject in the alpha frequency band, inverted them and then averaged the normed (i.e., scalar) values of all single trials (similar to the approach used by de Pasquale et al., 2013). This results in a temporally resolved representation of alpha-band power distributed across 5018 solution points in the inverse space. Afterwards, these WMN inverted averages were subject to the ANOVA procedure as stated above. For visualization, results were projected onto the coregistered MNI brain template using the Cartool toolbox (Brunet et al., 2011).

## **RESULTS**

#### **BEHAVIORAL DATA**

Mean accuracy values for the non-rotated potentially intelligible conditions were 26, 66, 80 and 91% for the 4-bands, 8-bands, 16-bands and bandpass-filtered stimuli respectively. Accuracy values entered in a one-way ANOVA with spectral detail as the within-subject factor (four levels: 4-bands, 8-bands, 16-bands and bandpass-filtered), which revealed a significant main effect of spectral detail (*F*(3,39) = 339.931, *p* < 10−6 , partial-η 2 = 0.96). The mean accuracy value per condition is displayed in **Figure 3A**.

Response times for the intelligible correct trials were also computed and entered in a one-way repeated-measures ANOVA with spectral detail as the within-subjects factor (four levels, as described above). There was a significant main effect of spectral detail (*F*(3,39) = 26.531, *p* < 0.001, partial-η 2 = 0.67) with response latencies being faster for intelligible trials and slower for more degraded words. Response time results are displayed in **Figure 3B**. We did not compute accuracy values or response times for the rotated trials, as no response could be categorized as "correct" in the face of incomprehensible stimuli. Those instances where participants did respond must be considered false alarms.

We also analyzed the number of false alarms as we expected more false alarms in the unintelligible or potentially intelligible but difficult conditions. The 1-band NV words were not included in this analysis. Number of false alarms was entered a repeatedmeasures ANOVA with spectral detail (four-levels, as described above) and spectral rotation (two levels: spectrally rotated and non-rotated) as within-subjects factors. There was a significant main effect of spectral complexity (*F*(3,19) = 5.037, *p* = 0.021, partial-η 2 = 0.279) and a significant spectral complexity by rotation interaction (*F*(3,19) = 8.534, *p* = 0.001, partial-η 2 = 0.396). The mean false alarms per condition is displayed in **Figure 3C**.

#### **EVOKED ACTIVITY—ERP ANALYSIS**

Average ERPs were entered into a two-way, repeated measures analysis of variance with number of frequency bands (i.e., spectral detail) and rotation as within-subjects factors. ANOVA was performed for each channel and sample point. To correct for multiple comparisons, we employed the approach of controlling the false discovery rate, FDR (Benjamini and Yekutieli, 2001) using a threshold of *p*(FDR) < 0.05. In order to ensure we report robust results, we additionally disregard results involving fewer

than two electrodes or having a duration of less than three consecutive sample points (15 ms). After FDR correction, we found the following main effects, at *p*(FDR) < 0.05, (uncorrected *p*-values are provided here): There was a significant main effect of rotation, temporally centered around 305 ms, involving up to eight suprathreshold electrodes. At the centroparietal peak electrode 132, *F*(1,13) = 57.59, *p* < 3.97\*10−6 , the effect ranged from 295 to 325 ms. A main effect of spectral detail was found in a set of seven left superior-parietal and right temporal electrodes, in a timewindow ranging from 185 to 215 ms post stimulus-onset. At the centroparietal peak electrode 81, *F*(3,39) = 16.5, *p* < 4.45\*10−7 .

The focus of our analysis was the interaction between the number of frequency bands and spectral rotation which was intended to specifically reveal the EEG signatures of listening to degraded words, while controlling for the impact of spectral complexity. We observed a significant spectral detail by rotation interaction located over a set of spatially coherent central parieto-frontal electrodes, in one distinct post-stimulus time-window, from 305– 390 ms, peaking at 345 ms (comprising a cluster of up to five supra-threshold electrodes around electrode 186, with maximum *F*(3,39) = 21.96, *p* < 1.72\*10−8 , **Figures 4A, B**). It appears that the effect in this time-window is driven specifically by a significantly greater positivity in response to the bandpass-filtered words in comparison to all other categories of stimuli (see **Figure 4D**). In order to confirm this hypothesis, a post-hoc analysis was carried out, comparing the clear condition with all others, pooled, in a univariate ANOVA, including subject as a random factor. This showed that the clear condition was indeed significantly different from all the others *F*(1,13) = 45.303, *p* < 0.001, partial-η 2 = 0.777.

#### **INDUCED ACTIVITY—TIME-FREQUENCY ANALYSIS**

ANOVA and correction for multiple comparisons was performed as for the ERP analysis reported above. After FDR correction, we found no significant main effects. We found a significant interaction in the alpha-band, at electrodes situated over the left temporal lobe, from 462–633 ms, with the peak of this effect found at 533 ms (comprising a spatially coherent cluster of eight supra-threshold electrodes around left-temporal electrode 75 with maximum *F*(3,39) = 12.12, *p* < 9.23\*10−6 see **Figures 5A, B**). This spatiotemporally coherent effect was manifest as a decrease in alpha-band power (i.e., alpha suppression) as a function of stimulus clarity for the NV but not rNV conditions (see **Figures 5D, E**). In contrast to the earlier effect revealed in the ERP analysis, this effect showed a graded response, suggesting sensitivity to the difficulty of comprehending words at different levels of degradation. Sidak-corrected post-hoc pairwise comparisons comparing the alpha-band power averaged over the 5 electrodes showing the greatest interaction effect in the period from 462–633 ms for the four potentially-comprehensible conditions partly support this: NV4 > NV16 (*p* = 0.021), NV4 > BP (*p* = 0.011), NV8 shows marginally less alpha power than BP (*p* = 0.063), other comparisons not significant. Although not all pairwise differences between the levels are significant, this is suggestive of increasing alpha-suppression with increasing intelligibility.

#### **INVERSE SOLUTIONS**

For visualization of results in the inverse space, we performed identical statistics as those on the surface data, confined to the two temporal windows showing FDR-corrected effects in both the ERP and the induced activity. The results are presented in **Figures 4C**, **5C** showing the resulting *F*-values averaged over the time-window of interest.

For effects on evoked activity, the source of the corresponding effect (i.e., the interaction between spectral complexity and rotation) in the time window from 300–400 ms with a peak at approximately −13, −27, 63 (MNI co-ordinates) principally located in medial precentral gyrus (BA6, extending posteriorly into BA4). This effect is shown in **Figure 4C**.

The source corresponding to the interaction between spectral complexity and rotation in the alpha band (time window 450– 650 ms) was localized to the left superior temporal lobe/inferior

parietal lobe. The maximum effect was located at −46, −27, 20 (MNI co-ordinates), and spanned the posterior reaches of the left supramarginal gyrus, and covered the superior posterior temporal gyrus. The effect is presented in **Figure 5C**.

## **DISCUSSION**

Behavioral data demonstrate that the rNV conditions were unintelligible to the participants in this study, while the intelligibility of the NV conditions increased as a function of the number of frequency bands used, as previously demonstrated (e.g., Shannon et al., 1995; Davis and Johnsrude, 2003). Although we did not obtain responses for every trial, we are confident that the d' measures established for the target-detection task reflect the mean level of intelligibility of each of the conditions employed.

Our analysis of the EEG data revealed two time-periods in which the brain response is significantly affected by the potential intelligibility of a stimulus. In the earlier one of these, we show an effect in evoked activity that appears to relate to the early identification of clear stimuli as a special category. Its timing and its topography suggest that we may be detecting modulation of the P300 component (for a review of the P300, see Polich, 2007). The P300 is usually apparent as a positivity in the midline parietofrontal electrodes and is observed in tasks, such as the animal name detection task used here, that require stimulus discrimination. Its appearance has been suggested to be an index of the occurrence of stimulus events sufficiently important to inhibit concurrent processes, and has therefore been associated with attention and memory processes (Polich, 2007). In the present case, it could reflect the fact that deciding whether the stimulus is a target or not is substantially easier when the word is more easily intelligible. Another possibility is that because the clear stimuli are categorically different from all others, this relatively early effect results from an oddball effect (Polich and Margala, 1997). This is also supported by the fact that the probability of a clear stimulus being presented was relatively low, at only 13%. The fact that this evoked effect seems to be rather central than left-lateralized could also be seen to support the view of this component being rather a general cognitive component than one specific to language processing.

We did not find effects in the evoked responses that we can more specifically ascribe to comprehension processes. This may be the result of the inherent difficulty of time-locking EEG responses to specific events during auditory word processing. Although we used the acoustic onset of the stimuli as our marker for the beginning of each epoch, given that we are interested in comprehension processes rather than auditory processes, this may not be the best point. Ideally, we might have used the recognition point determined using (for example) gating (Grosjean, 1980). This issue, of course, applies to all studies of spoken word perception that use measures with a high temporal resolution.

During a second time-window, we showed a significant interaction between spectral rotation and spectral complexity on induced activity—specifically the alpha band, reflecting increased alpha suppression as a function of increasing spectral complexity in the NV (i.e., potentially comprehensible) and not the rNV (always unintelligible) stimuli. This finding is superficially

similar to that reported by Obleser and Weisz (2012), who demonstrated a connection between alpha-band suppression and intelligibility of stimuli. However, by including the spectrally rotated stimuli as an unintelligible control condition we extended this finding by confirming that the relationship between alphasuppression and intelligibility is almost certainly the result of brain mechanisms that extract meaning from words, rather than the result of purely acoustic analysis of more vs. less complex auditory input. Furthermore, the effects we find occur earlier than those reported by Obleser and Weisz (2012). This may be the result of our having employed exclusively monosyllabic words rather than the mixture mono-, bi- and trisyllabic words they employed.

As discussed in Section Introduction, the functional role of auditory alpha is unclear, although it is increasingly becoming a focus of attention in the field of speech perception. At present, the relevant evidence seems to indicate that alpha-power suppression is related to comprehension, and it has been suggested that this could relate to the role of alpha-band activity in suppressing higher-frequency oscillations. It may be the case that alpha-band activity reveals an inhibitory process that suppresses processes whose occurrence is indicated by the presence of higher-frequency oscillations (Osipova et al., 2008). Thus, alpha-suppression may reflect the release of inhibition of processing. In the present case, where alpha-suppression reflects the intelligibility of auditory input, it is highly likely that the process indexed by this effect is related to word identification and comprehension. This effect is present in the time range of the N400 complex, which is related to semantic processing (Kutas and Hillyard, 1984).

Increased alpha-suppression for more easily comprehensible words during this time window is consistent with the idea that this effect reveals the on-average greater semantic processing in the easier compared to the harder conditions. However, a less prosaic explanation of this effect would be that the relatively higher alpha power in the more challenging conditions reflects increased functional inhibition related to the demands placed on attention and working memory by the challenge of comprehending acoustically degraded speech. Indeed, several studies have shown that the ability to comprehend degraded speech is influenced by working memory capacity (Ronnberg et al., 2008, 2010; Zekveld et al., 2009, 2011, 2012; Rudner et al., 2011) and selective attention (Shinn-Cunningham and Best, 2008; Wild et al., 2012b). It is possible that this effect is the manifestation of the inhibitory networks that are required to maintain selective attention and working memory processes to support the comprehension of degraded speech. A possibility raised by Obleser and Weisz (2012) is that the increase in alpha-suppression observed in response to less degraded stimuli is not necessarily increased suppression as a function of increased intelligibility, but increasing alpha-power as stimulus intelligibility falls. If this is the case, then the effect may be explained if alpha is considered to be a gating mechanism that inhibits other cognitive processes locally while challenging speech comprehension takes place.

Our data indicate that—in comparison to baseline—there is indeed an increase in alpha-power in response to the most challenging NV condition, indeed suggesting that the role of temporal alpha may be to actively regulate the engagement of downstream cognitive processes (confirmed by one-tailed posthoc *t*-test, *p* = 0.017). In the present case, it is possible that suppressing the final stages of word-identification processes (i.e., lexical access or semantic access) is a way of improving word-identification accuracy by enabling additional time for processing of the acoustic input. The possibility that the identification processes are delayed as a function of degradation can be investigated in future studies.

The effect at this time was localized to a region incorporating the left supramarginal gyrus (SMG), left inferior parietal lobule, and the posterior left superior temporal gyrus. This approximate localization situates the effect in a region that has been considered by some authors to be part of Wernicke's region (Geschwind, 1972; Bogen and Bogen, 1976) and has also been associated with sensorimotor integration during speech comprehension (Hickok and Poeppel, 2007). A role for sensorimotor integration during degraded speech comprehension would be consistent with the notion that, when confronted with degraded speech, the speech comprehension system falls back on higher-level (topdown) mechanisms to help resolve the degraded acoustic input. In this case, since very little linguistic information is present in the degraded monosyllabic stimuli, such information could potentially be articulatory in nature (e.g., as suggested by Hervais-Adelman et al., 2012). Nevertheless, beyond implicating this region in degraded speech processing, evidence for its exact role is scant.

The localization of the source of this alpha-suppression effect using a weighted minimum norm estimate was rather different to that shown by Obleser and Weisz (2012) using a beamformer approach. While we show relatively localized sources, they showed more distributed sources incorporating anterior temporal regions, parietal lobes, right dorsolateral prefrontal cortex and left inferior frontal regions.

Another potentially fruitful line of enquiry concerns the link between alpha-suppression and the pulvinar nucleus of the thalamus. Although we do not directly observe any effects in the pulvinar in this study, an investigation by Erb et al. (2012) implicated this structure as being linked to individuals' ability to learn to comprehend NV speech. Activity in the pulvinar nucleus has been suggested as a driver of alpha suppression (Lopes da Silva et al., 1980; Liu et al., 2012), and is also linked to attentional control in vision (e.g., Kastner et al., 2004; Smith et al., 2009) and audition (Wester et al., 2001), while attention has been found to affect the cerebral processing of NV speech (Wild et al., 2012b). Although at this stage this potential link is highly speculative, we would suggest that the thalamo-cortical networks implicated in alpha-band activity are worthwhile targets for investigations of speech comprehension.

Interestingly, we do not find regions in which alphasuppression shows the opposite response to intelligibility (i.e., alpha is more suppressed when stimuli are harder to understand). This is inconsistent with the fMRI literature, in which many studies have demonstrated the recruitment of additional brain areas when speech perception is challenging due to acoustic degradation compared to when it is clear. For example, several studies have demonstrated that left inferior frontal gyrus regions are engaged when acoustically degraded speech is presented (Davis and Johnsrude, 2003; Giraud et al., 2004; Obleser et al., 2007; Hervais-Adelman et al., 2012; Wild et al., 2012b), others have shown increased recruitment of anterior and posterior temporal lobe structures in the face of acoustic degradation (Scott et al., 2000; Narain et al., 2003; Evans et al., 2013). We would have expected to see such effects reflected in the EEG signature of degraded speech processing. That we do not find effects in this direction in these data is puzzling and demands further investigation. However, generation of alpha oscillations can differ considerably from site to site, even within the visual system. This has been reported for example with regard to originating layers of alpha and even relationship of the measured alpha oscillations to behavioral performance (Bollimunta et al., 2008).

Another interesting detail is that the sites of our observed major effect in the alpha-band and the sites of highest spectral power in the alpha-band do not spatially coincide. As is commonly known, occipital electrodes exhibit much higher resting-state alpha power than the remaining electrodes. The lefttemporal cluster identified in our study shows only moderate prestimulus alpha-amplitude. Yet its modulation is highly significant. This demonstrates that while such alpha modulation might be more difficult to identify, it can nonetheless be extracted.

In general, the link between alpha rhythm and language/word processing is relatively sparsely described. While there seems to be some consensus about auditory alpha/tau (see Lehtela et al., 1997), the idea of higher-level language-related alpha oscillations is still quite recent (e.g., Obleser and Weisz, 2012) and highly exciting. Whether this points to an even more universal role of alpha oscillations than previously envisioned remains to be seen and will require further investigations in more diverse experimental settings.

## **CONCLUSION**

Taken together, the data presented above suggest that the auditory system can rapidly differentiate easily-comprehensible speech (approximately 300 ms after stimulus onset), and that potentiallycomprehensible degraded speech can begin to be processed differently to incomprehensible speech as early as 480 ms after stimulus onset. We demonstrate that alpha-band power in the left temporal lobe is modulated by stimulus intelligibility. Spatial and temporal features of this effect seem to suggest this effect indexes the ease of access to word meaning. Our paradigm ensures that the alpha-band desynchronization we observe is not simply related to increasing spectral detail in the stimuli, and thus confirms that alpha-band desynchronization at left temporal sites reflects word intelligibility. These findings provide insights into the neural mechanisms of degraded speech perception, and may be taken to suggest that one strategy in compensating for acousticallydegraded input may be to suppress recognition processes that ordinarily take place rapidly and automatically, in order to permit additional processing to be carried out on the degraded signal.

## **ACKNOWLEDGMENTS**

This work was supported by a Marie Curie fellowship to Robert Becker and received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 267171. Christoph M. Michel is supported by the Swiss National Science Foundation (Grant No. 310030\_132952). Alexis Hervais-Adelman and Maria Pefkou were supported by the Swiss National Science Foundation (Grant No. 320030\_122085 awarded to Professor Narly Golestani). The EEG equipment and Cartool software are supported by the Center for Biomedical Imaging (CIBM), Geneva and Lausanne, Switzerland. We would like to thank the reviewers for their careful reading of the manuscript and their helpful comments.

#### **REFERENCES**

bias and predicts visual target detection. *J. Neurosci.* 26, 9494–9502. doi: 10. 1523/jneurosci.0875-06.2006

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 15 September 2013; accepted: 10 December 2013; published online: 27 December 2013*.

*Citation: Becker R, Pefkou M, Michel CM and Hervais-Adelman AG (2013) Left temporal alpha-band activity reflects single word intelligibility. Front. Syst. Neurosci. 7:121. doi: 10.3389/fnsys.2013.00121*

*This article was submitted to the journal Frontiers in Systems Neuroscience*.

*Copyright © 2013 Becker, Pefkou, Michel and Hervais-Adelman. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms*.

## Corrigendum: Left temporal alpha-band activity reflects single word intelligibility

#### *Robert Becker 1, Maria Pefkou2, Christoph M. Michel 1 and Alexis G. Hervais-Adelman2 \**

*1 Functional Brain Mapping Lab, Department of Fundamental Neuroscience, University of Geneva, University Medical School, Geneva, Switzerland*

*2 Brain and Languge Lab, Department of Clinical Neuroscience, University of Geneva, University Medical School, Geneva, Switzerland \*Correspondence<EMAIL_ADDRESS>
#### *Edited and reviewed by:*

*Jonathan E. Peelle, Washington University in St. Louis, USA*

**Keywords: speech intelligibility, degraded speech, noise-vocoding, alpha oscillations, left inferior temporal cortex**

#### **A commentary on**

#### **Left temporal alpha-band activity reflects single word intelligibility**

*by Becker, R., Pefkou, M., Michel, C. M., and Hervais-Adelman, A. G. (2013). Front. Syst. Neurosci. 7:121. doi: 10.3389/fnsys.2013.00121*

Figure 5 of the article by Becker et al. (2013) contained a minor error, which we hereby rectify. In the original figure at the bottom left of panel C the indication of the sagittal section used for display of the inverse solution is incorrect. We therefore re-submit **Figure 5** with the correct crosssection for subpanel C.

## **REFERENCES**

*Received: 27 February 2014; accepted: 14 March 2014; published online: 01 April 2014.*

*Citation: Becker R, Pefkou M, Michel CM and Hervais-Adelman AG (2014) Corrigendum: Left temporal alpha-band activity reflects single word intelligibility. Front. Syst. Neurosci. 8:47. doi: 10.3389/fnsys. 2014.00047*

*This article was submitted to the journal Frontiers in Systems Neuroscience.*

*Copyright © 2014 Becker, Pefkou, Michel and Hervais-Adelman. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

#### **FIGURE 5 | Results of ANOVA of induced activity in the alpha-band. (A)** Electrode-by-time plot of the *p*-values for the interaction of rotation × spectral detail with corresponding *F*- and *p*-values, thresholded at *p* = 0*.*001, revealing the time-window of interest (462–633 ms). The color bar indicates the corresponding *F*- and *p*-values, the threshold for *p*(FDR) *<* 0*.*05 is indicated. **(B)** Topography of this effect, using the same color scale as in **(A)** at the peak of the effect (533 ms), indicating a contribution of left-temporal sources. **(C)** Localization of this effect in the inverse space, the main source being in the left supramarginal gyrus extending into left inferior

parietal and superior temporal structures, showing the average F-statistic over the time-window of interest. **(D)** Average time-course of this effect in a cluster of five contributing electrodes across NV conditions, demonstrating enhanced alpha-band suppression for more intelligible conditions. **(E)** Corresponding time-courses for the spectrally rotated conditions, where the effect of spectral detail is absent. **(F)** Alpha-band activity for each condition in the significant time-window, error bars represent standard error of the mean corrected to be appropriate for repeated-measures comparisons, as described in Loftus and Masson (1994).

## Compensatory changes in cortical resource allocation in adults with hearing loss

## *Julia Campbell 1 and Anu Sharma1,2\**

*1 Department of Speech, Language and Hearing Sciences, University of Colorado at Boulder, Boulder, CO, USA 2 Institute of Cognitive Science, University of Colorado at Boulder, Boulder, CO, USA*

#### *Edited by:*

*Arthur Wingfield, Brandeis University, USA*

#### *Reviewed by:*

*Preston E. Garraghty, Indiana University, USA Teresa Mitchell, University of Massachusetts Medical School, USA*

#### *\*Correspondence:*

*Anu Sharma, Department of Speech, Language and Hearing Sciences, University of Colorado at Boulder, 2501 Kittredge Loop Road, Boulder CO 80309, USA e-mail<EMAIL_ADDRESS>
Hearing loss has been linked to many types of cognitive decline in adults, including an association between hearing loss severity and dementia. However, it remains unclear whether cortical re-organization associated with hearing loss occurs in early stages of hearing decline and in early stages of auditory processing. In this study, we examined compensatory plasticity in adults with mild-moderate hearing loss using obligatory, passively-elicited, cortical auditory evoked potentials (CAEP). High-density EEG elicited by speech stimuli was recorded in adults with hearing loss and age-matched normal hearing controls. Latency, amplitude and source localization of the P1, N1, P2 components of the CAEP were analyzed. Adults with mild-moderate hearing loss showed increases in latency and amplitude of the P2 CAEP relative to control subjects. Current density reconstructions revealed decreased activation in temporal cortex and increased activation in frontal cortical areas for hearing-impaired listeners relative to normal hearing listeners. Participants' behavioral performance on a clinical test of speech perception in noise was significantly correlated with the increases in P2 latency. Our results indicate that changes in cortical resource allocation are apparent in early stages of adult hearing loss, and that these passively-elicited cortical changes are related to behavioral speech perception outcome.

**Keywords: adult, sensorineural hearing loss, cortical auditory evoked potential, cortical resource allocation, source localization**

## **INTRODUCTION**

Adults with hearing impairment have been shown to exhibit concomitant deficiencies in cognitive performance (see Craik, 2007; Tun et al., 2012, for a review). A possible reason for this interaction between hearing loss (HL) and cognition may be due to an increase in cognitive load as greater attention is devoted to auditory signals in hearing impairment. For instance, when hearing-impaired adults allocate cognitive processing strategies to understand a degraded incoming auditory signal, the increased load at a basic processing level may detract from later cognitive performance downstream (Pichora-Fuller et al., 1995; Pichora-Fuller and Singh, 2006). As a result, cognitive processes such as memory and executive function are adversely affected in hearing impairment (Arlinger et al., 2009; Lunner et al., 2009; Rönnberg et al., 2010, 2011a,b; Lin, 2011; Rudner et al., 2012; Lin, 2013).

Studies using functional neuroimaging, neural models, and behavioral measures have demonstrated a strong relationship between auditory cortical integrity and the processing of challenging auditory information, such as degraded signals and complex speech in individuals with HL (Wingfield et al., 2006; Harris et al., 2009; Miller and Wingfield, 2010; Peelle et al., 2010a,b, 2011; Wong et al., 2010).

Recent research has shown a compelling correlation between degree of HL severity and all-cause dementia (including Alzheimer's disease), suggesting that increases in auditory deprivation may subsequently influence overall cognitive decline (Lin, 2011, 2012, 2013; Lin et al., 2011a,b). Lin et al. (2011a; Lin, 2013) discuss the decrease in cognitive reserve accompanying HL as a possible mechanism for the link between HL and dementia. Cognitive or neural reserve reflects the ability of the brain to compensate for the deleterious effects of sensory deprivation through the recruitment of alternative or additional brain networks to perform a specific task (Boyle et al., 2008). Sensory deprivation, as in HL, appears to tax the brain by altering normal resource allocation, thereby affecting neural reserve and cognitive performance. Given the relationship between degree of HL and cognitive decline, there appears to be a clear need for systematically examining changes in cortical resource allocation as HL progresses in severity from mild to profound, and to determine whether these changes are apparent at early stages of cortical auditory processing. Electroencephalography (EEG) is a useful measure to examine cortical changes associated with HL due to its non-invasive nature, widespread use in clinical settings and high temporal resolution important in measures of auditory processing.

In this study, we examined cortical re-organization resulting from HL in adult listeners with mild-moderate sensorineural hearing impairment using high-density EEG. We evaluated obligatory, passively-elicited P1, N1, and P2 components of the cortical auditory evoked potential (CAEP) using source localization. We correlated CAEP changes with performance on a clinical test of speech perception in noise to better understand the impact of cortical changes in early stages of hearing decline.

#### **METHODS**

#### **PARTICIPANTS**

Adults between the ages of 37 to 68 years participated in this study (*n* = 17). Subjects were recruited using fliers and recruitment letters. Consent was obtained through documentation approved by the University of Colorado at Boulder Institutional Review Board. Hearing acuity was measured using standard clinical audiometric procedures. Normal hearing (NH) thresholds [below 25 dB Hearing Level (HL)] for frequencies ranging from 0.25–8 kHz were observed for eight of the participants (*M* = 50*.*5 years, *SD* = ±6*.*2 years), while the remaining nine demonstrated HL (*M* = 56*.*9 years, *SD* = ±8*.*9 years). The HL group showed, on average, NH from 0.25 through 1 kHz and a mild-to-moderate sensorineural HL bilaterally from 2 to 8 kHz. Mean threshold audiograms for the two groups are shown in **Figure 1**. Participants in the HL group had received no clinical intervention, and many were unaware of their HL at the time of enrollment, consistent with the mild nature of their HL, and suggesting that their HL might have been fairly recent. Participants reported no history of neurological impairment. The NH group and HL group showed no significant difference in age between groups [*t(*15*)* = −1*.*69, *p* = 0*.*537].

#### **SPEECH PERCEPTION IN NOISE**

The QuickSIN™, a clinical measure of auditory threshold for sentences in background noise, was used to determine acuity of speech perception in background noise (Killion et al., 2004). Stimuli were presented via a speaker placed at 0◦ azimuth. Standard clinical testing procedures were used: Listeners were instructed to repeat two sentence lists, consisting of six sentences each, presented at 65 dB HL. Background noise was increased for each consecutive sentence in 5 dB increments, so that the signalto-noise ratio (SNR) began at 25 dB and ended at 0 dB for the last sentence. The SNR score from the two lists was averaged for each

**FIGURE 1 | Average pure tone thresholds across clinical test frequencies (X-axis) for right and left ears, respectively.** Intensity of frequency presentation level is shown on the Y-axis. The normal hearing group (NH) thresholds are depicted in solid black, and the hearing loss (HL) group thresholds in dashed red. Vertical black bars indicate standard deviation. The solid black line illustrates the criterion for normal hearing, at 25 dB HL.

listener, providing the level necessary for each individual to correctly repeat 50% of the key words in each sentence. The lower the SNR score, the greater the level of background noise that could be tolerated by the listener, and the better the performance.

#### **EEG AUDITORY STIMULI**

Participants were presented with a nonsense speech syllable, /ba/, at a level of at 65 dB HL, via two speakers placed at 45◦ angles in relation to the subject (Sharma et al., 2005). Stimuli were presented at a similar intensity level to all subjects consistent with previous studies examining cortical functioning in HL listeners (e.g., Harkrider et al., 2009; Bertoli et al., 2011; Peelle et al., 2011). Subjects were asked to ignore the stimulus while watching a movie, with the sound off and subtitles on, to ensure that participants remained awake (Sharma et al., 2005). Each /ba/ stimulus was 90 ms in duration and was presented at an inter-stimulus interval of 610 ms. One block of 1200 sweeps was collected per subject.

#### **EEG RECORDING AND ANALYSES**

Participants were fit with a 128-channel electrode net (Electrical Geodesic, Inc.) and seated in a reclining chair in an electromagnetically shielded sound booth. Auditory stimuli were presented via stimulus software E-Prime 2.0. The recording sampling rate was 1 kHz, with a band-pass filter of 0.1–200 Hz.

EEG topographic map analysis was completed offline using Net Station 4 (Electrical Geodesic, Inc.). A two-dimensional voltage map was generated for each group grand average waveform for each of the three obligatory CAEP peak components (P1, N1, P2). Regions of interest (ROI) were identified based on the greatest group differences for each CAEP component. Four ROIs were determined to be present: the frontal region, central region, the left frontal hemisphere (LH), and the right frontal hemisphere (RH). Individual EEG data was then exported from Net Station and imported into the EEGLAB toolbox (Delorme and Makeig, 2004) supported by MatLab (The MathWorks®, Inc., 2010). Epoched data was baseline corrected to the pre-stimulus interval of 100 ms and initial artifact rejection performed at ±100µV. The sampling rate was down-sampled from 1 kHz to 250 Hz in order to decrease processing time, resulting in a change of the poststimulus time to 592 ms. Concatenated EEG sweeps were then pruned using an independent component analysis (ICA) statistical procedure (Debener et al., 2006, 2008). Additional artifact such as ocular and other extraneous muscle movement identified as separate components were removed from the data. CAEP waveform peak components were visually identified and averaged after this step. For each subject, three electrodes were then grand averaged in each ROI, except for the central ROI where we averaged across four electrodes. Latency and amplitude values were determined for each participant CAEP waveform. All peak component amplitudes (P1, N1, P2) were measured from baseline to peak, or the midpoint of broad peaks. Latencies were chosen at the highest amplitude of the peak, or the midpoint of broad, flat peaks. Planned statistical comparisons were performed on the CAEP latency and amplitude components averaged within each ROI to determine significant differences between groups.

#### **CURRENT DENSITY RECONSTRUCTIONS**

ICA on concatenated EEG sweeps was performed to remove artifact, increase signal to noise ratio, and identify underlying components to be sourced. ICA results in multiple temporally independent components that underlie the evoked potential and are fixed in the spatial domain (Makeig et al., 1997; Delorme et al., 2012). These components allow for precise generator localization when used in cortical source modeling (Makeig et al., 2004; Hine and Debener, 2007; Debener et al., 2008). Concatenated EEG sweeps were pruned, as previously described, using ICA in order to remove noise artifact (Debener et al., 2006, 2008). This first pruning was followed by a second pruning to identify major components making up each CAEP peak component. Only independent components that accounted for the greatest percent variance underlying a CAEP peak of interest (P1, N1, P2) were retained for source localization analysis, or current density reconstruction (CDR). The individually pruned waveforms were grandaveraged for the NH and HL groups and exported into CURRY® Scan 7 Neuroimaging Suite (Compumedics Neuroscan™) for CDR. In CURRY®, another ICA was performed on each group average, and only components showing a SNR of at least 2.0 accepted.

CDR was performed separately for each CAEP peak component using sLORETA. Standardized low-resolution brain electromagnetic tomography (sLORETA) is a statistical procedure that estimates a focal CDR with zero localization error using actual source and measurement variance (Pascual-Marqui, 2002; Grech et al., 2008). The selected head model utilized for source modeling consisted of the standardized boundary element method (BEM) (Fuchs et al., 2002). A color scale corresponding to the intensity of cortical activation, as estimated by sLORETA, illustrates the CDR on an average magnetic resonance image (MRI) consisting of 100 people.

### **RESULTS**

#### **AUDITORY EVOKED POTENTIALS**

Based on the two-dimensional voltage maps for both groups and group differences between the waveforms, four ROIs were determined in the frontal, central, left frontal hemispheric (LH), and right frontal hemispheric regions (RH). Three obligatory CAEP components elicited by the speech sound were evaluated: the P1 (occurring at approximately 70 ms), N1 (at approximately 100 ms), and P2 (at approximately 180 ms). Group differences for the amplitude and latency of each component were analyzed using a One-Way ANOVA, and planned *post-hoc* comparisons were made between the groups at each ROI.

P2 amplitude was found to be significantly larger in the HL group (relative to the NH group) for the frontal ROI [*F(*1*,* 60*)* = 8*.*7, *p* = 0*.*005], the central ROI [*F(*1*,* 60*)* = 14*.*97, *p* = 0*.*000], and the LH ROI [*F(*1*,* 60*)* = 8*.*856, *p* = 0*.*004], but not at the RH ROI [*F(*1*,* 60*)* = 3*.*621, *p* = 0*.*062]. P2 latency was found to be significantly longer for the HL group in the frontal ROI [*F(*1*,* 60*)* = 5*.*34, *p* = 0*.*024], but not the central [*F(*1*,* 60*)* = 0*.*783, *p* = 0*.*380], LH [*F(*1*,* 60*)* = 1*.*054, *p* = 0*.*309], or RH [*F(*1*,* 60*)* = 3*.*832, *p* = 0*.*055] ROIs. P1 amplitude did not differ significantly between groups in any ROI [frontal: *F(*1*,* 60*)* = 2*.*149, *p* = 0*.*148; central: *F(*1*,* 60*)* = 3*.*715, *p* = 0*.*059; LH: *F(*1*,* 60*)* = 2*.*446, *p* = 0*.*123; RH: *F(*1*,* 60*)* = 1*.*661, *p* = 0*.*202]. P1 latency showed no significant difference [frontal: *F(*1*,* 60*)* = 1*.*163, *p* = 0*.*285; central: *F(*1*,* 60*)* = 0*.*234, *p* = 0*.*630; LH: *F(*1*,* 60*)* = 0*.*295, *p* = 0*.*589; RH: *F(*1*,* 60*)* = 0*.*251, *p* = 0*.*618]. Similarly, the N1 component did not differ significantly between groups in amplitude [frontal: *F(*1*,* 60*)* = 3*.*685, *p* = 0*.*060; central: *F(*1*,* 60*)* = 0*.*362, *p* = 0*.*549; LH: *F(*1*,* 60*)* = 3*.*322, *p* = 0*.*073; RH: *F(*1*,* 60*)* = 0*.*042, *p* = 0*.*838], or latency [frontal: *F(*1*,* 60*)* = 2*.*409, *p* = 0*.*126; central: *F(*1*,* 60*)* = 0*.*020, *p* = 0*.*887; LH: *F(*1*,* 60*)* = 1*.*625, *p* = 0*.*207; RH: *F(*1*,* 60*)* = 0*.*851, *p* = 0*.*360]. **Figure 2** shows the grand average waveforms from the frontal ROI, with mean amplitude bar graphs depicting the significantly larger P2 amplitude and longer P2 latency for the HL group compared to the NH group.

It should be noted that we presented the auditory stimuli at a comfortably loud conversational level for our participants. The /ba/ stimulus is comprised of spectral energy occurring mainly in the low-mid frequency region (0.5–2 kHz) (Sharma et al., 2002), and the HL listeners demonstrated average thresholds that were within the normal range at these frequencies. There was an average difference of approximately 10 dB HL between thresholds for the HL and NH group in the 0.5–2 kHz range, therefore, some HL listeners may have heard the stimuli at a sensation level (SL) that was, on average, 10 dB lower than for NH subjects. However, it is important to note that it is a well-established finding that CAEP amplitude decreases with lower intensity level for both NH and HL listeners (Bertoli et al., 2011), while the results of this study show increased P2 amplitude for the HL listeners. That is, if results were influenced by the decreased SL for HL listeners, we would have expected to observe a corresponding decrease in P2 amplitude for HL compared with NH listeners rather than a larger P2 amplitude for HL listeners (**Figure 2**). Furthermore, our results are consistent with those of Bertoli et al. (2011) and Harkrider et al. (2009), who reported larger P2 amplitudes for adults with mild-moderate HL compared with those for control subjects.

#### **CURRENT DENSITY RECONSTRUCTIONS**

Cortical source localization, or CDR, was conducted using the sLORETA algorithm provided by CURRY Scan 7 Neuroimaging Suite for the three CAEP peak components (**Figure 3A**). The activations were superimposed on an average MRI (axial slice view) and the MNI co-ordinates are shown beneath each slice. The scale of the F distribution, indicating the strength of the activations, is also shown. **Figure 3A** shows axial views of the CDR. For NH listeners, as seen in **Figure 3A**, the P1, N1, and P2 CAEP components activated temporal cortical regions including superior temporal gyrus (STG) and inferior temporal gyrus (ITG). Responses for the P1 and P2 components were relegated to the left hemisphere (LH), likely due to our use of a speech syllable (Stefanatos et al., 2008). See **Figure 3B** for a table describing the main activated regions. Cortical activation by speech stimuli in regions of temporal cortex is consistent with fMRI neuroimaging and intracranial electrocorticographic studies using speech stimuli (Stefanatos et al., 2008; Pasley et al., 2012). In contrast, for the HL group, clearly decreased activation of auditory areas such as STG and MTG within temporal cortex was apparent (see **Figure 3A**).

**Figure 4** shows sagittal views for the CDR. Consistent with the axial views shown in **Figure 3A**, as seen in **Figure 4A**, NH listeners showed activation of temporal cortical areas including STG and ITG. Conversely, for HL listeners, cortical responses to speech stimuli were localized to frontal cortex, in medial frontal gyrus (MFG), inferior frontal gyrus (IFG), and Brodmann Area 11 (BA 11). See **Figure 4B** for a table describing the main areas of activation. Frontal cortical activation was clearly the largest for the P1 and P2 CAEP components (**Figure 4A**).

#### **SPEECH PERCEPTION IN NOISE**

Behavioral testing of speech perception in noise acuity was measured for both groups using the QuickSIN™ clinical test (Killion et al., 2004). The higher the SNR score, the louder the signal has to be in order for the listener to perceive speech. As shown in **Figure 5A**, the HL group required the signal to be, on average, almost four decibels higher than the background noise for correct

**FIGURE 3 | (A)** Current density reconstructions (CDR) showing cortical activation at the P1, N1, and P2 CAEP peak components on axial MRI slices for the normal hearing (NH) and hearing loss (HL) groups. The scale of the F Distribution is shown in the upper right corner ranging from red to yellow

(yellow is highest level of activation), and the Montreal Neurological Institute (MNI) coordinates are listed below each MRI slice. **(B)** A table describing activated anatomical cortical areas for the CAEP components for each group, listed in approximate order of highest level of activation.

**FIGURE 4 | (A)** Current density reconstructions (CDR) showing cortical activation at the P1, N1, and P2 CAEP peak components on sagittal MRI slices for the normal hearing (NH) and hearing loss (HL) groups. The scale of the F Distribution is shown in the upper right corner ranging from red to

yellow (yellow is highest level of activation), and the Montreal Neurological Institute (MNI) coordinates are listed below each MRI slice. **(B)** A table describing activated anatomical cortical areas for the CAEP components for each group, listed in approximate order of highest level of activation.

**FIGURE 5 | (A)** Mean QuickSIN™ scores for normal hearing (NH, in black) and hearing loss (HL, in red) groups. Standard deviations are shown as vertical bars. One asterisk reflects a significant difference at *p <* 0*.*05. **(B)** The correlation of the CAEP P2 component latency as a function of QuickSIN™ scores. The Spearman's rank order correlation coefficient value and significance level are indicated in the right upper corner.

perception. Due to the non-parametric distribution of individual QuickSIN™ scores, a Mann-Whitney U Test was calculated to determine statistical significance between the groups (*U* = 10*.*5, *Z* = −2*.*46, *p* = 0*.*014). This difference in performance has been found in similar studies with NH listeners and listeners with HL (Killion et al., 2004; Wilson et al., 2007).

QuickSIN scores were correlated with P2 latency and amplitude. All participants were included in the correlation, as HL can be considered a gradual decrease in threshold starting at 0 dB HL. Frontal ROI P2 latency showed a significant positive correlation with speech performance in background noise (*r* = 0*.*494, *p* = 0*.*022), suggesting that increases in P2 latency were associated with greater difficulty in perceiving speech in noise. We did not see a significant correlation between QuickSIN™ scores and P2 amplitude.

#### **DEGREE OF HEARING LOSS AND CAEP P2 AMPLITUDE**

Frontal P2 amplitude showed a significant positive correlation with high frequency Pure Tone Average (PTA), i.e., the degree of hearing impairment at 2–8 kHz for both ears (right ear: *r* = 0*.*538, *p* = 0*.*013, left ear: *r* = 0*.*474, *p* = 0*.*027). Thus, as HL increased across participants, there was a corresponding increase in P2 amplitude. No significant correlation was observed between P2 latency and high-frequency PTA.

#### **DISCUSSION**

We examined cortical changes secondary to mild-moderate HL in post-lingually hearing-impaired adults. When tested using speech-evoked EEG in a passive stimulation paradigm, adults with mild to moderate sensorineural HL showed the following distinct cortical changes relative to age-matched NH controls: (1) increased P2 CAEP amplitude and latency, (2) reduced activation in temporal auditory cortical regions, (3) activation of frontal cortical regions in response to auditory stimulation, (4) significantly poorer speech perception in background noise that correlated with increased P2 latency and (5) a significant correlation between increased P2 amplitude and hearing thresholds at high frequencies (2, 4, and 8 kHz). Thus, even in relatively early stages of HL and early stages of auditory processing, adult subjects appear to show significant alterations in cortical activation.

Our finding of increased P2 amplitude for HL listeners is consistent with previous studies, which documented increased P2 CAEP amplitude in older adults who were long-time hearing aid users (Bertoli et al., 2011), and in young adults with mild-moderate HL (Harkrider et al., 2009). Bertoli et al. (2011) reported larger P2 amplitudes for adults with mild-moderate HL who were long-term hearing aid users, and attributed the larger auditory cortical responses in HL adults to an increase in "effortful listening." It is of further interest to note that larger P2 CAEP amplitudes have been reported after auditory training, possibly indicating increased utilization of auditory memory and perceptual resources (Naatanen and Picton, 1987; Shahin et al., 2003; Ross and Tremblay, 2009; Tong et al., 2009). Along these lines, our finding of an increase in P2 latency is also consistent with previous studies in adults with HL, which suggest that the increased latency reflects inefficient cortical processing as the central auditory system is required to process a degraded and/or challenging signal (Harkrider et al., 2005, 2009; Ross et al., 2007).

Aging has also been reported as a factor in increased P2 amplitude and latency, possibly due to decreased central inhibition. However, we included age-matched, NH listeners, making it unlikely that aging solely accounts for the differences in P2 amplitude and latency seen for the HL group (Harkrider et al., 2006; Ceponiene et al., 2008). Furthermore, Harkrider et al. (2009) observed increased P2 amplitude and latency in young adults with mild-moderate HL in response to nonsense speech syllables, suggesting that higher-order auditory processing is affected by auditory deprivation and not age alone, though an interaction between age and HL is likely. In the case of older listeners with HL, reduced central inhibition via an interaction between aging and HL may result in increased P2 amplitude (Dustman et al., 1996; Syka, 2002). Our results also showed a significant increase in P2 amplitude for the HL group relative to the NH groups over the LH but not the right hemisphere (RH). Given our use of a speech stimulus, the larger P2 amplitude in the HL group over the LH may be due to more active role of the LH in processing of speech information combined with a lack of inhibition due to HL (Syka, 2002; Stefanatos et al., 2008).

A major finding in our study was that listeners with mild-moderate sensorineural HL showed significant cortical re-organization. Current density reconstructions via sLORETA revealed that HL listeners showed decreased activation of auditory cortical areas (STG and MTG) relative to NH listeners (**Figure 3A**) and showed activation of frontal cortical regions (e.g., IFG, MFG, SFG) in response to passive auditory stimulation (**Figure 4A**). This change in cortical activation from temporal regions to frontal regions indicates a possible re-allocation of cortical processing in response to auditory stimuli, likely as a compensatory effect of HL. The finding of a shift of the auditory response to frontal areas is consistent with the fMRI studies of Peelle et al. (2010a, 2011) and Wingfield and Grossman (2006), who showed lower amounts of gray matter volume in temporal cortices in adults with HL, as well as greater activation in frontal cortices in response to challenging listening conditions for older adults. This frontal and pre-frontal activation was associated with increased listening effort, as these regions have been traditionally associated with tasks involving working memory and executive function (Collette et al., 2006; Eckert et al., 2008; Liakakis et al., 2011). Overall, our results are consistent with neuroimaging research, which has demonstrated a reliance on frontal regions involved in the cognition and the processing of complex auditory stimuli in older adults (Sharp et al., 2006; Eckert et al., 2008; Tyler et al., 2010; Obleser et al., 2011). Thus, the present results of cortical re-organization in HL adults support recent hypotheses suggestive of an increased cognitive load in hearing impaired listeners, and may provide evidence for the taxation of the reserve of cognitive processes (Pichora-Fuller and Singh, 2006; Lin, 2011, 2012; Lin et al., 2011a,b).

It is surprising, however, that we observed that frontal cortical regions, typically associated with cognitive processing, were engaged in response to a passive auditory task that did not require the participants' attention. This finding suggests that compensatory processing may begin at early stages of central auditory processing in adult-onset HL (Harris et al., 2009; Anderson and Kraus, 2010). Indeed, another form of compensatory plasticity (i.e., recruitment of auditory cortical regions for visual processing) has been observed in adults with mild-moderate HL in whom passively viewed visual stimuli activated temporal cortical regions (Campbell and Sharma, in review). Recent studies have shown similar temporal cortical activation by visual stimuli in deaf adults fitted with cochlear implants (Doucet et al., 2006; Buckley and Tobey, 2011; Sandmann et al., 2012). Visual information becomes of greater importance in HL, especially in watching a speaker's face and lip movements for contextual cues (McCullough et al., 2005; Letourneau and Mitchell, 2011). These findings, taken together with the present results, suggest that increased frontal activation and reduced temporal activation to speech may occur in parallel with increased temporal activation to visual stimuli (likely due to reliance on faces and lipreading in everyday communication), even as early as in mild-moderate HL. Thus, cortical re-allocation during processing of auditory stimuli may result in increased cognitive load that usually occurs in higher-order processing, but that is now occurring for lower-level passive processing, resulting in degraded behavioral outcomes for challenging listening environments (Pichora-Fuller and Singh, 2006; Larsby et al., 2008). It is possible that various training paradigms using speech and music (possibly in conjunction with hearing aid rehabilitation) may allow for re-training of auditory cortices in HL listeners to re-activate normal neural networks during auditory processing (Petersen et al., 2009; Shahin, 2011; Turner et al., 2013).

Hearing loss is most consistently associated with poor outcomes in recognizing speech in background noise, a skill essential for everyday listening (Souza et al., 2007). Consistent with previous research in hearing-impaired adults, our results show that listeners with even mild-to-moderate HL demonstrate a significant deficit when listening to speech in background noise (Dubno, 1984; Vermiglio et al., 2012). HL listeners required a much larger SNR to accurately perceive sentences in noise (**Figure 5A**). Audibility does not appear to fully account for this decrease in performance (Hällgren et al., 2005; Souza et al., 2007; Léger et al., 2012; Vermiglio et al., 2012). In this study, speech perception in background noise was significantly correlated with increased P2 latency (**Figure 5B**). This increase in latency is consistent with previous studies suggesting that the increase in auditory processing time (as reflected by the P2 latency increase in the HL group) may be reflective of additional activated cognitive cortical regions, or compensatory cortical circuitry (Ross et al., 2007; Harkrider et al., 2009). In addition, larger P2 CAEP amplitudes were correlated with worse auditory pure tone thresholds at high frequencies (2, 4, and 8 kHz). Given that P2 amplitude has been associated with re-allocation of cognitive resources, (Tremblay et al., 2003; Harkrider et al., 2005, 2009; Tong et al., 2009), it would appear that the degree of cortical re-organization increases with the severity of the HL.

Taken together, the observed increase in P2 CAEP amplitude and latency, decreased activation in temporal areas with increased activation of frontal cortical regions during passive listening, and poorer behavioral outcomes in the HL group, provide evidence of compensatory cortical plasticity occurring in mild-moderate HL (i.e., in early stages of hearing decline). The nature of this plasticity is observed as a re-allocation of cortical resources from temporal auditory areas to frontal cognitive areas, which appear to be recruited to assist with processing of auditory stimuli even at the level of passive listening. Overall, our results are consistent with the hypothesis that HL appears to initiate a process of resource re-allocation, which results in increased cognitive load

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(Pichora-Fuller et al., 1995; Pichora-Fuller and Singh, 2006; Peelle et al., 2010a,b, 2011; Lin, 2011, 2012, 2013; Lin et al., 2011a,b). Finally, measures of cognitive resource re-allocation in HL, both objective and behavioral, may become increasingly relevant in the clinical setting in order to determine patients at risk for cognitive decline. It would be of interest to determine whether hearing aids, auditory training, or a combination might possibly alleviate this cognitive resource re-allocation as reflected by a possible decrease in frontal activation and return to normal levels of temporal cortical activation (Lunner et al., 2009; Parbery-Clark et al., 2011; Rudner et al., 2012).

## **SUMMARY**

Our results demonstrate auditory cortical re-organization in the form of decreased temporal activation and increased frontal activation in early stage HL of mild-moderate severity using passively elicited EEG responses. Furthermore, increased latency and amplitude of the P2 component were associated with decreases in speech perception performance and increase in hearing threshold, respectively. Due to the strong relationship between HL and cognitive deficits, such as dementia, that arise later in life, it is important that clinical evaluation of cognitive reserve in HL be included as part of intervention services. Future research should focus on better understanding the relationship between the severity of cognitive re-allocation in relation to severity of HL as well as reversibility of re-organization as a result of intervention with amplification.

#### **ACKNOWLEDGMENTS**

We would like to acknowledge the assistance of Lauren Durkee, B.A. This research was supported by NIH grants R01DC06257 and F31DC011970.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 16 August 2013; paper pending published: 11 September 2013; accepted: 07 October 2013; published online: 25 October 2013.*

*Citation: Campbell J and Sharma A (2013) Compensatory changes in cortical resource allocation in adults with hearing loss. Front. Syst. Neurosci. 7:71. doi: 10.3389/fnsys.2013.00071*

*This article was submitted to the journal Frontiers in Systems Neuroscience.*

*Copyright © 2013 Campbell and Sharma. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## Enhanced representation of spectral contrasts in the primary auditory cortex

## *Nicolas Catz and Arnaud J. Noreña\**

*Laboratory of Adaptive and Integrative Neurobiology, Fédération de recherche 3C, UMR CNRS 7260, Université Aix-Marseille, Marseille, France*

#### *Edited by:*

*Jonathan E. Peelle, Washington University in St. Louis, USA*

#### *Reviewed by:*

*Christo Pantev, University of Muenster, Germany Jean-Marc Edeline, Université de Paris, France*

#### *\*Correspondence:*

*Arnaud J. Noreña, Laboratory of Adaptive and Integrative Neurobiology, Fédération de recherche 3C, UMR CNRS 7260, Université Aix-Marseille, 3, Place Victor Hugo, 13331 Marseille, France e-mail: arnaud.norena@ univ-provence.fr*