Time is a very important variable in hearing because all sounds vary in frequency and amplitude over time. Complex natural sound signals such as speech and music can be decomposed into slowly varying “envelope” and rapidly oscillating “fine structure.” Out of the two, envelope plays a more important role in speech comprehension (1). Auditory temporal resolution is the ability to detect temporal changes of sound stimuli and to correctly recognize the envelope of sound signals. The temporal resolution of the auditory system is often assessed by the gap detection test. This test uses sound stimuli with silent gaps to estimate the minimum perceivable gap length (2, 3). It should be noted that this method requires the participant to take an action in response to the sound signal whenever they perceived the gap. As such, this method is not objective since the participant's personality, varying levels of concentration and dexterity necessary for the physical action of pressing the button may affect the results to some extent. Therefore, many studies currently aim to find objective measures of the temporal resolution using neuroimaging techniques.

Previous studies have shown that gaps embedded in continuous tones elicit various types of event related potentials. In an experiment by Uther et al. (4), continuous 2,000 Hz pure tones inserted with silent gaps of 3, 5, and 7 ms evoked mismatch negativity (MMN) and the MMN amplitude was proportional to the gap length. Electrophysiological studies have shown that the main sources of MMN are located close to the primary and secondary auditory cortex (5). Furthermore, Werner et al. (6) found that the behavioral gap detection threshold is similar to the neurophysiological gap detection threshold which was estimated by the sensitivity of the V wave of the auditory brainstem response elicited by silent gaps embedded within a broadband noise. The neural generator of V wave is thought to be the inferior colliculus (7). It remains elusive whether it is the cortex or the brainstem that plays a more important role in auditory gap detection.

In our previous study (8), we have obtained clear 40 Hz auditory steady state responses (ASSRs) elicited by the silent gaps of 3.125, 6.25, and 12.5 ms embedded within a broadband noise. The ASSR is one of the objective hearing tests used in clinical practice. Its neural sources and sensitivity depend on the modulation frequency. The 40 Hz ASSR appears to originate in the region spanning from the primary auditory cortex (9). It is considered to have a higher signal-to-noise ratio while the subject is awake (10). On the other hand, the 80 Hz ASSR appears to originate primarily from subcortical sources (11), and sleep has little effect on the response (12).

In the previous study, we demonstrated that gaps embedded within a broadband noise elicited clear 40 Hz ASSR; however, there has been no report that investigated the ASSR elicited by gap stimulus presented at the 80 Hz rate and compared the gap-evoked ASSR and the behavioral gap detection thresholds. The 80 Hz ASSR has the advantage of being an objective measure used in clinical practice because it can be measured reliably even during sleep (13). In this study, we conducted an experiment to verify whether the ASSR can be elicited by 80 Hz silent gaps of different lengths that are below and above the behavioral threshold. The results would contribute to developing a non-invasive objective measure of auditory temporal resolution in humans.

After the measurements were obtained from all participants, the collected data were analyzed. The average of the behavioral data of single gap GDT was 2.76 and the 95% confidence interval obtained by boot-strap resampling tests (iteration = 100,000) was 2.60–2.92. The average of the behavioral 80 Hz gap GDT was 0.994 and the 95% confidence interval obtained by boot-strap resampling tests (iteration = 100,000) was 0.902–1.101. For the EEG analysis, the mean rejection rate of the obtained epochs was 6.6%. Figure 3 shows grand averaged FFT waveforms under each condition. No clear response at 80 Hz was observed under GAP_0, GAP_0.4, and GAP_0.8 conditions in contrast to GAP_1.6, GAP_3.1, and GAP_6.3 conditions, where prominent responses at 80 Hz were observed. Figure 4 shows grand averaged EEG waveforms under each condition. Similar to the FFT results, no significant ASSR was evoked in the GAP_0, GAP_0.4, and GAP_0.8 conditions. In the GAP_1.6, GAP_3.1, and GAP_6.3 conditions, clear EEG was evoked and the amplitude increased as the gap length increased.

Figure 5 shows the mean normalized ASSR amplitude in each GAP condition together with the corresponding 95% confidence intervals obtained by boot-strap resampling tests (iteration = 100 000). A one-way ANOVA applied to the normalized ASSR amplitude revealed a significant main effect for gap length [F_{(5, 120)} = 299.66, p < 0.0001]. As shown in Table 1, the post-hoc multi-comparison revealed significant differences between GAP_0 and GAP_3.1 (p < 0.0001), GAP_0 and GAP_6.25 (p < 0.0001), GAP_0.4 and GAP_1.6 (p = 0.048), GAP_0.4 and GAP_3.1 (p < 0.0001), GAP_0.4 and GAP_6.25 (p < 0.0001), GAP_0.8 and GAP_3.1 (p < 0.0001), GAP_0.8 and GAP_6.25 (p < 0.0001), GAP_1.6 and GAP_3.1 (p < 0.0001), GAP_1.6 and GAP_6.25 (p < 0.0001), and GAP_3.1 and GAP_6.25 (p < 0.0001). The ASSR amplitudes gradually increased with an increase in the gap duration.

Figure 6 shows the mean CSMs calculated from EEG in each GAP condition together with the corresponding 95% confidence intervals obtained by boot-strap resampling tests (iteration = 100 000). For the results in GAP_1.6, GAP_3.1, and GAP_6.25, the mean CSM gradually increased as GAP length increased. A one-way ANOVA applied to the CSM revealed a significant main effect for gap length [F_{(5, 120)} = 41.33, p < 0.0001]. As shown in Table 1, the post-hoc multiple comparison revealed significant differences between GAP_0 and GAP_1.6 (p = 0.011), GAP_0 and GAP_3.1 (p < 0.0001), GAP_0 and GAP_6.25 (p < 0.0001), GAP_0.4 and GAP_3.1 (p < 0.0001), GAP_0.4 and GAP_6.25 (p < 0.0001), GAP_0.8 and GAP_3.1 (p < 0.0001), GAP_0.8 and GAP_6.25 (p < 0.0001), GAP_1.6 and GAP_3.1 (p = 0.011), GAP_1.6 and GAP_6.25 (p < 0.0001), and GAP_3.1 and GAP_6.25 (p < 0.0001).

There was no significant correlation between GDT estimated from the CSM values and single gap behavioral GDT (r = 0.1566, p = 0.4978), between GDT estimated from the CSM values and 80 Hz gap behavioral GDT (r = 0.1699, p = 0.4617), nor between the behavioral single gap GDT and the behavioral 80 Hz gap GDT (r = 0.3994, p = 0.0728).

The results of the present study demonstrated that 80 Hz ASSR can be elicited by silent gaps embedded within a broadband noise in people with normal hearing. Significant ASSRs were elicited only by test sound stimuli with silent intervals longer than 1 ms, which was the gap detection threshold derived from behavioral data. The ASSRs elicited by GAP_0.4 and GAP_0.8, which were below the behavioral threshold, were similar to those elicited in the GAP_0 condition, in which a continuous broadband noise was used as a sound stimulus. These results suggest that significant ASSRs were elicited only when the length of the silent gaps exceeded the behavioral threshold. This implies that auditory temporal resolution may be objectively measured using the 80 Hz gap-evoked ASSR.

It is known that stimuli differing in modulation frequencies elicit ASSR originating in different brain areas. Previous studies compared cortical and subcortical neural activity with 40 and 80 Hz amplitude modulated sound stimulations. Herdman et al. (11) measured the ASSR elicited by amplitude modulated tones with three modulation frequencies, 12, 39, and 88 Hz. They estimated the neural sources using multi dipole model, revealing that 88 and 39 Hz amplitude modulated tones mainly elicited subcortical and cortical neural activity, respectively. Additionally, Farahani et al. (18) measured the ASSR elicited by amplitude modulated white noise with the modulation frequencies of 3.91, 19.53, 40.04, and 80.08 Hz and estimated the neural source using a minimum-norm imaging technique. They reached the same conclusions as Herdman et al.—subcortical activity dominant at 80 Hz and cortical activity dominant at 40 Hz. Following these results, it has been widely accepted that the 80 Hz ASSR is elicited mainly in the subcortical regions.

Regarding the neural center for auditory temporal processing enabling auditory gap detection, previous studies have suggested that auditory cortex rather than brainstem plays a key role (19–22). Ison et al. (19) measured gap detection thresholds in rats by acoustic startle reflex using a white noise with silent gaps. KCl injections caused cortical disruptions in rats, inducing prolonged gap detection thresholds, whereas the disruption of auditory brainstem had little effect on gap detection. The results suggested that cortex plays a major role in auditory temporal processing. Syka et al. (21) also demonstrated that neural activity related to auditory temporal processing was delayed after surgical removal of the rat auditory cortex. Similarly, human studies on patients with damage to cerebral hemispheres reported impaired auditory temporal processing regarding the auditory stimuli presented to the ear contralateral to the damaged hemisphere. Jafari et al. (23) performed Gaps-In-Noise test (GIN test, a form of gap detection test) in patients with right hemisphere infarction, patients with left hemisphere infarction and normal subjects. All participants had normal pure tone audiograms; however, the results indicated that auditory temporal processing was impaired when the GIN test was performed on the ear contralateral to the infarction site. Lavasani et al. (24) found that the GDT became longer in patients with temporal epilepsy with normal pure tone audiograms, indicating that auditory temporal processing was impaired in those people. Moreover, research done by Bamiou et al. (25) suggested that insula plays an important role in auditory temporal processing, since patients with insular hemispheric infarction showed prolonged GDT.

In the present study, we used gap sounds that are thought to be processed in the auditory cortex, yet we obtained clear 80 Hz ASSR which are mostly associated with the brainstem. The results obtained could be interpreted as follows. First, while the 80 Hz ASSR is generally believed to originate primarily from the brainstem, the signal appears to be contaminated with the neural activity in the auditory cortex (18, 26). Therefore, the results obtained in the present study might reflect a portion of 80 Hz ASSR derived from the auditory cortex. Alternatively, the present results could imply that the auditory temporal processing involved in the gap detection occurs at least partially in the brainstem. Galambos et al. (10) first reported that the ASSR amplitude became maximal at the modulation rate of 40 Hz. The 40 Hz ASSR was found to become smaller with sleep, sedation, and anesthesia (27). On the other hand, a higher modulation frequency (70–110 Hz) provided a stable ASSR even during sleep or under sedation (13, 28). The ASSRs elicited by 80 Hz amplitude modulated tones are clinically used as an objective audiometry and are often measured during sleep since the signal-to-noise ratio of 80 Hz ASSR improves during sleep (29). In the present study, the participants were awake during the EEG recording; however, the 80 Hz gap-evoked ASSR may become more prominent during sleep. Eighty hertz ASSR recording during sleep would be especially useful for infants and children who could not stay still during the EEG recording.

Interestingly, the detection threshold of 80 Hz gap inserted sound stimuli was significantly shorter than the normal single gap GDT. While the single gap GDT (mean: 2.76 ms) in the present study was similar to those obtained in the previous studies (30, 31), the regular insertion of gaps at 80 Hz appears to shorten the GDT. Bacon et al. (32) measured detection thresholds of sinusoidal amplitude modulation on a broadband noise and found that normal hearing participants could detect up to 1,024 Hz modulation frequency. The results indicated that the participants could detect the envelope fluctuations of about 1 ms. Moreover, Ross and Pantev (33) measured behavioral GDT and the auditory evoked fields elicited by gaps embedded within 500 Hz tones with 40 Hz amplitude modulation. Normally, the gap detection of pure tones with frequencies between 400 and 1,000 Hz was estimated to be between 6 and 8 ms (34); however, Ross and Pantev's (33) results showed that the detection threshold for the gap embedded within the 40 Hz AM tone was 3 ms and the gap duration of 3 ms elicited significant auditory evoked fields. Similar to the above results, the present study also demonstrated that the detection thresholds for gaps embedded within sounds with repetitive envelope fluctuations became shorter than those embedded within continuous sounds with no repetitive fluctuation (35). One might argue that the insertion of periodic gaps added spectral components corresponding to the stimulation rate (80 Hz) and its harmonics, and consequently the participants might have detected the corresponding spectral components. In the present study, we used white noise segments longer than and equal to the half (6.25 ms) of one 80 Hz cycle and thus the 80 Hz gap inserted sound stimuli had spectral components similar to the control white noise as shown in Figure 2. Another possibility is that the participants might have detected the spectral splatter caused by the steep onset and offset sound envelopes of the gap. However, we used white noise as sound stimuli and the onset and offset of white noise did not give a spectral cue since the spectral splatter was masked by the white noise. Therefore, it is less likely that the participants in the present study detected the spectral changes of test sound stimuli instead of temporal ones.

We found no significant relationship between the GDT estimated from the CSM and the behavioral GDT. All the participants in the present study were young adults and had normal hearing. This could explain why there was little variance in the behavioral GDTs among the participants. Moreover, alertness and motivation of the participants appears to have a stronger impact on the behavioral results than the inter-individual difference. It is necessary to conduct similar studies on people with suspected deterioration of auditory temporal resolution, such as auditory neuropathy patients (36, 37) and the elderly (38).

Recent studies focused their attention on people who have normal pure tone audiogram but struggle to listen to speech signals especially in noisy environments (39, 40). This symptom may derive from impaired temporal processing in the auditory system (41). It remains elusive whether speech perception is significantly correlated with the within-channel gap detection threshold (42). According to Tyler et al. (43) and Snell et al. (44) who used noise bursts as stimulus sounds, GDT and speech perception under noise are significantly correlated, while Strouse et al. (45) and Snell et al. (46) found no significant correlation. Although most of those results were obtained in cross-sectional studies, a longitudinal study by Babkoff and Fostick (38) showed a significant relationship between temporal processing and speech perception even after corrections for auditory level and cognitive ability.

Therefore, the gap-evoked ASSR obtained in the present study may reflect the auditory temporal processing and speech comprehension ability. Many ailments thought to be related to impaired auditory temporal processing, such as hidden hearing loss, auditory processing disorder, and other listening difficulties, are currently diagnosed mainly on the basis of the patients' subjective symptoms. The gap-evoked ASSR can be introduced as an objective diagnostic measure for such cases. Moreover, objective measure of auditory temporal processing can be used as a screening test for children with language and speech developmental delays, allowing early detection and early therapeutic intervention for them. Additionally, in cases of elderly patients with communication problems, measurement of gap-evoked ASSR could potentially help discriminate between patients suffering from dementia and those with impaired auditory temporal processing.

Significant ASSRs and CSMs were elicited only by test stimuli with gap lengths above the behavioral threshold. The test stimuli with gap lengths below the behavioral threshold elicited ASSRs similar to those elicited by the continuous broadband noise. Eighty Hertz gap-evoked ASSR may provide insights into the neural mechanisms of auditory temporal processing and may be applied to objectively and non-invasively measure auditory temporal resolution in humans.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The studies involving human participants were reviewed and approved by Ethics Committee of the International University of Health and Welfare, School of Medicine. The patients/participants provided their written informed consent to participate in this study.

HO conceived and designed research. SK, TM, and HO performed the experiments. SK and HO analyzed the data. SK, TM, MP, SM, and HO interpreted the results of experiments. SK prepared the figures and drafted the manuscript. All authors edited and revised the manuscript and approved the final version of the manuscript.