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.

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.

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-of-interest (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).

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 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.

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.

No behavioral data were obtained for the resting state study.

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 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). 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).

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.