Behavioural performance

We first examined the behavioural performance of NE versus age-matched control rats by using temporal rate discrimination tasks (Fig. 1a). This behavioural evaluation of possible consequences of structured noise exposure consisted of a procedural-learning phase followed by a perceptual-testing phase. In that first experimental phase, rats were trained to discriminate between pulse trains presented at 6.3 pulses per second (p.p.s.) (the 'non-target') and 20 p.p.s. (the 'target'). The detection of this large difference in temporal rate for presented pulse trains (that is, 6.3 p.p.s. versus 20 p.p.s.) was perceptually unchallenging; both NE and control rats learned the task after several days of training. As illustrated in Fig. 1b, which shows the average performance scores across training blocks at the seventh day (that is, the last day of the procedural-learning phase), both rat groups quickly reached a steady performance score on the third training block. However, the average performance score recorded for NE rats (n=9) was lower than for control rats (n=12) at every block (P<0.05–0.001, t-test).

Figure 1: Behavioural performance on the temporal rate discrimination task. (a) Experimental timeline. Pw, postnatal weeks. (b) Discrimination performance of NEK (n=9) and control (n=12) rats across training blocks at the seventh training day (that is, the last day of the procedural-learning phase), when the animals were trained to discriminate a pulse train of 6.3 pulses per second (p.p.s.; that is, non-target) from that of 20 p.p.s. (target). Error bars represent s.e.m. *P<0.05, +P<0.001, t-test. (c) Average psychometric curves obtained from NE rats at the eighth training day (that is, 1 week after the end of noise exposure) and from age-matched control rats. Inset shows examples of the psychometric curves. PRR, pulse repetition rate. *P<0.0001, t-test. (d) Comparison of discrimination thresholds for NE and control rats. *P<0.0001, t-test. (e) Average slopes of the psychometric curves for NE and control rats. The slope of each psychometric curve was calculated at its steepest portion (difference in performance score for changing the PRR difference from 5.7 to 11.7 p.p.s.). *P<0.005, t-test. (f) The false positive ratio as a function of PRR difference for NE and control rats. *P<0.001, t-test. Full size image

On the eighth day, all animals with identical procedural-learning histories underwent a second perceptual-testing phase in which the non-target at each trial was randomly chosen from pulse trains of various repetition rates (6.3, 8.3, 10, 12.5, or 14.3 p.p.s.). A psychometric curve was then constructed to define the temporal rate discrimination ability of each animal. As shown in Fig. 1c, whereas the performance scores for rats of both groups increased as the rate difference between target and non-target increased, values at rate differences larger than 5.7 p.p.s. were significantly lower for NE than for control rats (all P<0.0001, t-test). That difference was supported by an analysis of the discrimination threshold, defined as the rate difference corresponding to a 50% performance score on the psychometric curve. That threshold was significantly higher in NE rats than in control rats (Fig. 1d; P<0.0001, t-test). The slope of psychometric curve for NE rats, however, was smaller than for control rats (Fig. 1e; P<0.005, t-test). Additionally, the false positive ratios for both NE and control rats decreased as the rate difference between target and non-target increased (Fig. 1f). Those changes were less for NE than for control rats (all P<0.001, t-test). The results showed that a 2-month-long exposure to moderate-level structured noises significantly degraded these adult animals' abilities to discriminate between sound stimulus rates. These recorded post-exposure effects persisted for at least 6 weeks after the end of noise exposure (Supplementary Fig. S1).

ABR thresholds and cortical responses

Auditory brainstem response (ABR) measurement and cortical recording were conducted on NE rats 1 week after they were returned to a normal sound environment. Data were then compared with those recorded in age-matched control rats (Fig. 2a).

Figure 2: ABR thresholds. (a) Experimental timeline. Pw, postnatal weeks. (b) ABR thresholds obtained from NE rats (n=8) relative to normative ABR thresholds of control rats (n=12). Positive values indicate higher ABR thresholds compared with controls. Dashed lines, range of normative ABR thresholds (±2 s.d.). Full size image

As shown in Fig. 2b, all ABR data recorded from NE rats were within the normal±2 s.d. boundaries. Statistical analysis showed no significant threshold differences between NE (n=8) and control (n=12) rats at any frequency determined (all P>0.39, t-test).

Cortical responses were also recorded from 322 sites in 7 NE rats and from 369 sites in 8 age-matched control rats (see Fig. 2a for the experimental line). Response thresholds and latencies recorded at cortical sites in NE rats (22.1±0.7 dB SPL and 11.5±0.1 ms; mean±s.e.m.) did not differ from those recorded in control rats (20.6±0.7 dB SPL and 11.4±0.1 ms; both P>0.1, t-test). The frequency selectivity was first evaluated for each cortical site by constructing the tuning curve using tone pips with random frequencies and intensities (Fig. 3a). The tuning curve bandwidths measured 20 dB above threshold (BW20 s) were larger for NE than for control rats (Fig. 3b; P<0.01–0.00001, t-test), indicating that the frequency response selectivity was significantly and systematically degraded by noise exposure. We further examined the similarity of the tuning curves recorded from neighbouring cortical neurons (that is, overlap index) to evaluate the extent of spatial activation overlap in the A1 (Fig. 3c). As expected, the average overlap index systematically changed as a function of distance between recording sites for both rat groups. The index of NE rats, however, was significantly larger than that of control rats for distances smaller than 1.2 mm (P<0.001–0.00001, t-test).

Figure 3: Cortical frequency selectivity. (a) Representative examples of tuning curves obtained from NE and control rats. (b), Average receptive field bandwidths at 20 dB above threshold (BW20s) for all recording sites in NE (recording sites=322) and control (recording sites=369) rats, for each of four CF ranges. Bin size=1.2 octave. Error bars represent s.e.m. *P<0.01, +P<0.00001, t-test. (c) Average tuning curve overlap index as a function of distance between recording sites for NE and control rats. *P<0.001, +P<0.00001, t-test. Full size image

When tested with characteristic frequency (CF) tone pulses delivered at variable rates, most cortical neurons in control rats followed repeated identical stimuli at and below rates of 10 p.p.s., with each brief tone evoking about the same number of spikes as did the first tone in a stimulus train. Numbers of responses per tone then fell off rapidly at still higher repetition rates. By contrast, most neurons in NE rats followed stimuli at or below repetition rates of only about 7 p.p.s. (Fig. 4a, left versus right). These effects were further documented by deriving repetition rate transfer functions (RRTFs) recorded for neurons at each cortical site (Fig. 4a, insets). Although the average RRTFs took a low-pass form in both rat groups, values at high temporal rates (>7 p.p.s.) for NE rats were significantly lower than in control rats (Fig. 4b; recording sites=316 for NE rats and 368 for control rats; P<0.05–0.0005, t-test).

Figure 4: Cortical temporal responses. (a) Dot-raster plot examples of cortical responses to pulse trains of different repetition rates recorded from NE and control rats. Red lines indicate pulse durations. Inset shows the RRTF for each raster plot example. Unfilled circle and dashed line show f h1/2 and 50% of the maximal normalized response for each RRTF, respectively. (b) Average RRTFs for all recordings obtained from NE (recording sites=316) and control (recording sites=368) rats. Error bars represent s.e.m. *P<0.05, +P<0.0005, t-test. (c) Representative auditory cortical f h1/2 maps for NE and control rats. The colour of each polygon indicates the f h1/2 recorded at that site. D, dorsal; A, anterior. (d) Cumulative frequency histograms (left) showing a significant leftward shift of the f h1/2 distribution for NE rats compared with control rats (Kolmogorov–Smirnov test, P<0.0001), and average f h1/2 s (right) for all recording sites in both rat groups, for each of four CF ranges. Bin size=1.2 octaves. *P<0.0005, t-test. (e) Average asynchronous response rates measured at different pulse repetition rates for NE and control rats. *P<0.00001, t-test. (f) Average vector strengths measured at different pulse repetition rates for NE and control rats. *P<0.0005, t-test. (g) Average MR obtained using the Van Rossum spike train distance metric for all combination of repetition rates, for NE and control rats. (h) Results of t-test for comparisons of MR between NE and control rats. (i) Percentage of synchronized spontaneous discharge as a function of distance between two cortical recording sites. *P<0.008, t-test. Full size image

We quantified the cortical capacity for processing high-rate stimuli by determining the highest modulation rate at which RRTF was at half-maximum (that is, f h1/2 ; Fig. 4a, insets). As shown in Fig. 4c, where we illustrate f h1/2 maps for representative NE and control rats, the f h1/2 s obtained at most cortical sites in NE rats were lower than in control rats. A quantitative comparison of the distribution for all f h1/2 s obtained from both rat groups is shown in Fig. 4d (left). A significant leftward shift of the f h1/2 distribution for NE rats compared with control rats (P<0.0001, Kolmogorov–Smirnov test) confirmed a degraded rate-following ability induced by this chronic noise exposure. Interestingly, this degradation was recorded for neurons extending across all CF ranges (Fig. 4d, right; all P<0.0005, t-test).

Earlier studies have shown that the rate-following ability of cortical neurons is proportionally related to the degree of post-stimulus suppression25. Here we quantified the asynchronous response, defined as the mean firing rate outside the phase-locking windows minus the spontaneous firing rate for both NE and control rats, to evaluate the post-stimulus suppression (Fig. 4e). Asynchronous responses were weaker at low temporal rates in NE rats, indicating stronger and/or longer post-stimulus suppression compared with control rats (all P<0.00001, t-test).

To characterize the temporal fidelity of cortical responses, we calculated vector strengths, which quantify the degree of phase locking of neural responses to repetitive stimuli. As shown in Fig. 4f, average vector strengths as a function of temporal rates shifted leftward and peaked at lower rates in NE compared with control rats (peak at 7 p.p.s. in NE rats versus 10 p.p.s. in control rats). In addition, vector strengths of neurons in NE rats were smaller at high repetition rates (that is, 10–20 p.p.s.) but greater at low rates (that is, 2–7 p.p.s.; all P<0.0005, t-test).

We further examined the reliability of cortical responses to repetitive stimuli by calculating the misclassification rate (MR) for every possible combination of pulse trains used to construct the RRTFs (Fig. 4g). That measurement, obtained using the Van Rossum spike train distance metric26, quantifies the similarity between spike trains recorded, using different pulse trains, or the difference between spike trains recorded, using identical pulse trains. Larger MR values indicate more confusable and unreliable spike trains representing temporal structure in acoustic inputs. We found that the average MRs for combinations of dissimilar high pulse rates (10–20 p.p.s.) were significantly larger in NE versus control rats (Fig. 4g and h). The average numbers of 'misrepresentations' of identical stimuli were greater in NE rats, markedly at lower pulse rates (that is, 2 p.p.s. versus 2 p.p.s., or 4 p.p.s. versus 4 p.p.s.; Fig. 4h).

To assess horizontal cortical network connectivity, we calculated correlation coefficients for neuron pairs separated by variable distances by simultaneously recording their spike discharges during spontaneous activity periods. Correlation coefficients quantify the degree of cortical horizontal connectivity, with higher values representing stronger horizontal connections. We considered all spikes that occurred in two recording channels, within 10 ms of one another, to be synchronized events. The average correlation coefficient between −10 and 10 ms lags was 31% larger for NE than for control rats (P<0.00001, t-test). The degree of synchronization for simultaneously recorded spontaneous discharges, expressed as a percentage of synchronized events, significantly decreased as a function of inter-electrode distances in both rat groups (Fig. 4i; both P<0.0001, ANOVA). However, values were higher at electrode separations less than 1.3 mm in NE than in control rats (P<0.008 at electrode separations of 0.3, 0.5 and 0.7 mm; t-test).

In accordance with behavioural data, cortical changes in temporal processing, induced by noises, endured for at least 6 weeks after the end of noise exposure (Supplementary Fig. S2).

Passive sound exposure-driven plasticity in A1

To determine whether or not structured noise exposure restores passive sound exposure-driven plasticity in the frequency representation domain in mature cortices, a subset of NE rats were exposed to pulsed 7-kHz tones for 1 week, beginning at the end of their noise exposure epoch (these rats were defined as NE plus tone-exposed rats (TE), that is, NE+TE rats in Fig. 5a). As an additional control, another group of age-matched control rats were also exposed to pulsed 7-kHz tones over the same epoch (these rats were defined as tone-exposed rats, that is, TE rats in Fig. 5a). To quantitatively characterize the effects of pulsed tone exposure on A1-frequency representation, percentages of A1 areas representing different frequency ranges were averaged within the same experimental group and differences between exposed and control animals plotted. As shown in Fig. 5b, the percentages of A1 areas representing each frequency range for NE+TE (n=6) and TE (n=4) rats were comparable to that in control (n=8) rats (all P>0.3, ANOVA). This result, in contrast to our recent report that applied continuous unstructured noises23, showed that structured noise exposure did not result in the re-opening of an epoch of critical period-like plasticity.

Figure 5: Structured noise exposure does not restore the passive sound exposure-driven plasticity. (a) The experimental timeline for NE plus tone-exposed (NE+TE), tone-exposed (TE), and control rats. Pw, postnatal weeks. (b) Differences in percentages of A1 areas tuned to different frequency ranges for NE+TE (n=6) or TE (n=4) versus control (n=8) rats. Full size image

Effects of exposure to noises presented at different temporal rates

All data described above were recorded from NE rats that were exposed to pulse trains with repetition rates that varied over a significant frequency range (Fig. 6a, top). To investigate how temporal rates of environmental noises might direct exposure-induced cortical changes, additional groups of adult rats were exposed to 1-s pulse trains delivered at fixed repetition rates of either 5 p.p.s. (Fig. 6a, middle) or 15 p.p.s. (Fig. 6a, bottom), again for 2 months (24 h per day). Cortical RRTFs were then constructed and compared with those obtained from rats exposed to noises of mixed rates as described earlier, and again, with those from age-matched control rats. Statistical analysis showed that RRTFs and f h1/2 s recorded from different NE groups were comparable (Fig. 6b and c; P>0.05 for all comparisons, ANOVA with post-hoc Student–Newman–Keuls test) but were significantly different from that of control rats (P<0.05–0.001 for all comparisons, except for RRTF values at 7 p.p.s., and for NE-15 p.p.s. and NE versus control rats at 20 p.p.s., ANOVA with post-hoc Student-Newman-Keuls test). These results showed, to our surprise, that post-exposure cortical changes were independent of the modulatory rates of structured noises that animals were exposed to. As expected, cortical response thresholds and latencies recorded from different rat groups did not significantly differ (Fig. 6d and e; both P>0.16, ANOVA).

Figure 6: Effects of temporal rates of noises on cortical responses. (a), Schematic of the stimuli used in the different noise-exposure conditions. For the first condition, 1-s pulse train with repetition rates of 3, 4, 8, 10, 12, 15 or 18 p.p.s. was randomly presented (top). For the other two conditions, the repetition rate of 1-s pulse train was either set at 5 p.p.s. (middle) or 15 p.p.s. (bottom). These pulse trains were presented once every 3, 1.5, or 4.5 s such that the total acoustic energy of noise bursts received by rats of all three groups was approximately equal during the 2-month's exposure. (b,c), Average RRTFs (b) and f h1/2 s (c) obtained from different NE groups (recording sites=180 for NE-5 p.p.s. rats, 230 for NE-15 p.p.s. rats, and 316 for NE rats, respectively), illustrated with that of age-matched control rats (recording sites=368). Error bars represent s.e.m. *P<0.05, +P<0.001 compared with controls, ANOVA with post-hoc Student-Newman-Keuls test. (d,e), Response thresholds (d) and latencies (e) recorded from different NE groups, illustrated with control rats. Full size image

Effects of intermittent noise exposure

To investigate the effects of intermittent noise exposure on cortical temporal responses, 5 additional adult rats were exposed to noise stimuli (as shown at the top of Fig. 6a) for 10 h per day followed by 14 h in the quiet, simulating a noisy-work/quiet-living environment. These passive exposure epochs again extended over a 2-month period beginning at 3 months of age. As shown in Fig. 7a, average RRTFs at higher temporal rates (>7 p.p.s.) were significantly smaller for these NE than for control rats (P<0.005–0.00001, t-test). And again, average f h1/2 s in NE rats were lower than that of control rats at every CF range (Fig. 7b; P<0.05–0.00001, t-test).

Figure 7: Effects of intermittent noise exposure on cortical temporal responses. (a) Average RRTFs for all recordings obtained from NE (recording sites=219) and control (recording sites=368) rats. NE rats were exposed to noise stimuli (as shown at the top of Fig. 6a) for 10 h per day followed by 14 h in the quiet, simulating a noisy-work/quiet-living environment. Error bars represent s.e.m. *P<0.005, +P<0.00001, t-test. (b), Average f h1/2 s for all recording sites in NE and control rats for each of four CF ranges. *P<0.05, +P<0.00001, t-test. Full size image

Physical indices of neurological changes

To begin to determine how physical changes in cortical networks might contribute to these noise-induced changes in cortical temporal processing, we first quantified the expression levels of parvalbumin (PV), by using immunohistochemical methods. We found significant decreases in the numbers of PV-immunostained (PV+) neurons across cortical layers II–VI in A1 in NE compared with control rats (Fig. 8a versus b, left; and Fig. 8c, all P<0.00001, t-test). Percentage decreases were 32.7, 43.3, 37.2 and 30.1% for layers II–III, IV, V, and VI, respectively. Reduced dendritic PV immunoreactivity was also noted in NE rats compared with control rats (Fig. 8d versus e, arrows).

Figure 8: Expression levels of cortical PV and BDNF. (a,b), Photomicrographs of PV-immunostained (PV+, left) and Nissl-stained (right) cortical sections for NE (a) and control (b) rats. NE rats were exposed to noise stimuli shown at the top of Fig. 6a for 2 months (24 h per day) beginning at 3 months of age. Cortical layers are indicated in Nissl-stained sections. Scale bar, 200 μm. (c), Average PV+ neuron counts for NE (12 hemispheres) and control (12 hemispheres) rats. Error bars represent s.e.m. *P<0.00001, t-test. (d,e), Higher magnification photomicrographs showing the reduced dendritic PV immunoreactivity in NE (d) compared with control (e) rats (arrows). Scale bar presents 100 μm. (f,g), Photomicrographs of BDNF-immunostained (BDNF+) cortical sections for NE (f) and control (g) rats. Scale bar, 200 μm. (h) Average BDNF+ neuron counts for NE (16 hemispheres) and control (16 hemispheres) rats. *P<0.0005, t-test. Full size image

We also applied the same approach to measure the expression levels of brain-derived neurotrophic factor (BDNF). BDNF is an enabler of cortical growth and plasticity, and is linked in its actions to the expression of inhibitory neurons23,27,28. We found that the numbers of BDNF-immunostained (BDNF+) neurons in layers II–V were significantly increased for NE compared with control rats (Fig. 8f versus g; and Fig. 8h; all P<0.0005, t-test). That increase of BDNF+ neurons was more pronounced in cortical layer IV (73.1%) than in layers II–III or V (27% and 23.9%, respectively).