Temporal gap and amplitude modulation detection evaluated using the electrically evoked auditory change complex in patients with auditory brainstem implants

Previous studies, using modulation stimuli, on the relative effects of frequency resolution and time resolution on CI users’ speech perception failed to reach a consistent conclusion. In this study, frequency change detection and temporal gap detection were used to investigate the frequency resolution and time resolution of CI users, respectively. Psychophysical and neurophysiological methods were used to simultaneously investigate the effects of frequency and time resolution on speech perception in post-lingual cochlear implant (CI) users. We investigated the effects of psychophysical results [frequency change detection threshold (FCDT), gap detection threshold (GDT)], and acoustic change complex (ACC) responses (evoked threshold, latency, or amplitude of ACC induced by frequency change or temporal gap) on speech perception [recognition rate of monosyllabic words, disyllabic words, sentences in quiet, and sentence recognition threshold (SRT) in noise]. Thirty-one adult post-lingual CI users of Mandarin Chinese were enrolled in the study. The stimuli used to induce ACCs to frequency changes were 800-ms pure tones (fundamental frequency was 1,000 Hz); the frequency change occurred at the midpoint of the tones, with six percentages of frequency changes (0, 2, 5, 10, 20, and 50%). Temporal silences with different durations (0, 5, 10, 20, 50, and 100 ms) were inserted in the middle of the 800-ms white noise to induce ACCs evoked by temporal gaps. The FCDT and GDT were obtained by two 2-alternative forced-choice procedures. The results showed no significant correlation between the CI hearing threshold and speech perception in the study participants. In the multiple regression analysis of the influence of simultaneous psychophysical measures and ACC responses on speech perception, GDT significantly predicted every speech perception index, and the ACC amplitude evoked by the temporal gap significantly predicted the recognition of disyllabic words in quiet and SRT in noise. We conclude that when the ability to detect frequency changes and the temporal gap is considered simultaneously, the ability to detect frequency changes may have no significant effect on speech perception, but the ability to detect temporal gaps could significantly predict speech perception.

Temporal gap detection was measured as a function of absolute signal bandwidth at a low-, a mid-, and a high-frequency region in six listeners with normal hearing sensitivity. Gap detection threshold decreased monotonically with increasing stimulus bandwidth at each of the three frequency regions. Given conditions of equivalent absolute bandwidth, gap detection thresholds were not significantly different for upper cutoff frequencies ranging from 600 to 4400 Hz. A second experiment investigated gap detection thresholds at two pressure-spectrum levels, conditions typically resulting in substantially different estimates of frequency selectivity. Estimates of frequency selectivity were collected at the two levels using a notched-noise masker technique. The gap threshold-signal bandwidth functions were almost identical at pressure-spectrum levels of 70 dB and 40 dB for the two subjects in experiment II, while estimates of frequency selectivity showed poorer frequency selectivity at the 70-dB level than at 40 dB. Data from both experiments indicated that gap detection in bandlimited noise was inversely related to signal bandwidth and that gap detection did not vary significantly with changes in signal frequency over the range of 600 to 4400 Hz. Over the range of frequencies investigated, the results indicated no clear relation between gap detection for noise stimuli and peripheral auditory filtering.

Temporal gap detection thresholds were measured in narrow-band noise-burst markers having acoustic characteristics representative of isolated steady-state second-formant (F2) properties for/p,t,k/paired separately with/i,ae,u,o/. The results revealed that gap detection threshold increased systematically as the difference was increased between the simulated stop and vowel F2 frequencies. A strong positive correlation (r = 0.87) between gap detection threshold and linear marker center frequency difference was highly significant (p < 0.001). Differences in other stimulus features had little influence on gap detection performance. Implications for speech perception are discussed.

Formby and Forrest [J. Acoust. Soc. Am. 89, 830–837 (1991)] measured temporal gap detection (TGD) thresholds as function of frequency separation between two sinusoids marking a silent temporal gap. Their results were fit well with a roex-filter model. Filter estimates (1) were based on TGD thresholds for post-gap marker frequencies F2≥ pre-gap marker frequencies F1 and (2) assumed symmetry of the roex-filter function for F2≤F1. In a related study, TGD thresholds were measured with narrow-band markers [Formby etal., J. Acoust. Soc. Am. 93, 1023–1027 (1993)]. The resulting thresholds raised doubts about the earlier assumption of filter symmetry. To resolve this issue, TGD thresholds were measured for F2 frequencies presented above and below F1=500 and 4000 Hz for a range of marker and masker presentation levels. Roex functions fitted for conditions of F2≥F1 were typically shallower than those fitted for conditions of F2≤F1. The same qualitative patterns of filter asymmetry have also been found and will be reported for TGD thresholds simulated by single- and multi-channel auditory models [Forrest and Formby; Heinz etal., companion papers at this meeting]. [Research supported by NIH.]

The ability of subjects to detect temporal gaps between bursts of sinusoids or bursts of bandlimited noise was measured to evaluate the phenomenon of tactile "sensory persistence" in older persons. Vibratory stimuli were delivered to the right thenar eminence of 27 subjects ranging in age from 8-75 years. The subjects' task was to detect the presence of a silent interval or "gap" between flanking 350-ms vibrotactile stimuli. The gap-detection threshold, expressed as the amplitude of vibration relative to the absolute detection threshold, decreased as the gap duration increased and was higher for gaps in noise than for gaps in sinusoids. The threshold for detecting short gaps increased with age for noise stimuli, but not for sinusoidal stimuli. Furthermore, the gap-detection threshold recovered more rapidly in older subjects for noise stimuli, but less rapidly in older subjects for sinusoidal stimuli. Because of these differences, it appears that the effects of age on gap detection cannot be due to a simple increase in sensory persistence, but may be due to multiple processes.

The impact of amplitude modulation frequency in harmonic motion imaging on inclusion characterization.

The goal of this paper is to show the impact of the tracer size and the temporal gap between images in atmospheric motion vector (AMV) extraction schemes. A test has been performed using NWC SAF/High Resolutions Winds AMV software for different configurations with a tracer size varying between 8 × 8 and 40 × 40 pixels and a temporal gap between images varying between 5 and 90 min. AMVs have been extracted for four different MSG/SEVIRI channels (HRVIS, VIS0.8, WV6.2, and IR10.8) over the European and Mediterranean area for a 6-month period (January–June 2010). The AMV performances have been tested against radiosonde winds and ECMWF model analysis winds. The results show a small impact of the tracer size on the number of valid AMVs, which is, however, more significant for clear air AMVs, and a significant impact of the temporal gap between images. The largest number of valid AMVs has been found in general for a temporal gap of 5 min for the 1-km pixel scale and for a temporal gap of 10 min for the 3-km pixel scale. Results also show a decrease of the mean AMV speed and the normalized BIAS (NBIAS) with larger tracer sizes, and a relatively small impact of the temporal gap on these parameters. Finally, the results show minimum values of the normalized root-mean-square vector difference (NRMSVD) for intermediate temporal gaps between 15 and 30 min with a relatively small impact of the tracer size on this parameter.

Auditory gap detection is a measure of temporal acuity. The paradigm comes in two forms, distinguished by whether the sounds bounding the silent period are the same (within channel [WC]) or different (between channel [BC]). The purpose of this study was to test normal children and children referred for auditory processing disorder (APD) assessment, with both gap detection paradigms. Best gap durations (i.e., shortest reliably detected gaps) were measured in a two-interval, two-alternative forced-choice design embedded within a modified method of limits, for both WC and BC paradigms, with stimuli presented at 55 dB HL. Sixteen control children and 20 children referred for APD assessment participated in the study. Of the 20 referred children, 9 were diagnostically positive for APD (APD+), and 11 were negative (APD-). The mean age of children in all three groups was 10-11 yr. Data collected were best gap durations for each paradigm, for each child. Group differences were assessed using Kruskal-Wallis analyses of variance. WC best gap durations were very similar across the three participant groups. BC best gap durations varied significantly between listener groups, with the greatest difference being between controls and APD+ samples. BC best gap durations differed among the listener groups while WC ones did not. This suggests that the relative timing perceptual operations required by the BC task are more susceptible to the perceptual disturbances in APD than is the simple event detection required by the WC task.

The goal of this study was to examine the degree to which age-related differences in early or automatic levels of auditory processing and attention-related processes explain age-related differences in auditory temporal processing. We hypothesized that age-related differences in attention and cognition compound age-related differences at automatic levels of processing, contributing to the robust age effects observed during challenging listening tasks. We examined age-related and individual differences in cortical event-related potential (ERP) amplitudes and latencies, processing speed, and gap detection from 25 younger and 25 older adults with normal hearing. ERPs were elicited by brief silent periods (gaps) in an otherwise continuous broadband noise and were measured under two listening conditions, passive and active. During passive listening, participants ignored the stimulus and read quietly. During active listening, participants button pressed each time they detected a gap. Gap detection (percent detected) was calculated for each gap duration during active listening (3, 6, 9, 12, and 15 msec). Processing speed was assessed using the Purdue Pegboard Test and the Connections Test. Repeated measures analyses of variance assessed effects of age on gap detection, processing speed, and ERP amplitudes and latencies. An "attention modulation" construct was created using linear regression to examine the effects of attention while controlling for age-related differences in auditory processing. Pearson correlation analyses assessed the extent to which attention modulation, ERPs, and processing speed predicted behavioral gap detection. Older adults had significantly poorer gap detection and slower processing speed than younger adults. Even after adjusting for poorer gap detection, the neurophysiological response to gap onset was atypical in older adults with reduced P2 amplitudes and virtually absent N2 responses. Moreover, individual differences in attention modulation of P2 response latencies and N2 amplitudes predicted gap detection and processing speed in older adults. That is, older adults with P2 latencies that decreased and N2 amplitudes that increased with active listening had faster processing speed and better gap detection than those older adults whose P2 latencies increased and N2 amplitudes decreased with attention. The results from the present study are broadly consistent with previous findings that older adults exhibit significantly poorer gap detection than younger adults in challenging tasks. Even after adjusting for poorer gap detection, older and younger adults showed robust differences in their electrophysiological responses to sound offset. Furthermore, the degree to which attention modulated the ERP was associated with individual variation in measures of processing speed and gap detection. Taken together, these results suggest an age-related deficit in early or automatic levels of auditory temporal processing and that some older adults may be less able to compensate for declines in processing by attending to the stimulus. These results extend our previous findings and support the hypothesis that age-related differences in cognitive or attention-related processing, including processing speed, contribute to an age-related decrease in gap detection.

Experiments with the Rashbass ‘step-ramp’ paradigm have revealed that the initial catchup saccade that occurs near pursuit onset uses target velocity as well as position information in its programming. Information about both position and motion also influences smooth pursuit. To investigate the timing of velocity sampling near the initiation of saccades and smooth pursuit, we analyzed the eye movements made in nine ‘step-ramp’ conditions, produced by combining –2, 0 and +2 deg steps with –8, 0 and +8 deg/s ramps. Each trial had either no temporal gap or a 50-ms gap during which the laser target was extinguished, beginning 25, 50, 75 or 100 ms after the step. Six subjects repeated each of the resulting 45 conditions 25 times. With no temporal gap, saccades were larger in the step-ramp-away’ than the ‘step-only’ condition, confirming that saccade programming incorporates ramp velocity information. A temporal gap had no effect on the accuracy of saccades on ‘step-only’ trials, but often caused undershoots in ‘step-ramp’ trials. A 50-ms gap within the first 100 ms also increased the latency of the initial saccade. Although initial pursuit velocity was unaffected by a temporal gap, a gap that started at 25 ms reliably delayed pursuit onset for ramp motion of the target toward the fovea. Later gaps had a minimal effect on initial pursuit latency. The similar timing of the temporal gaps in target motion information that affect the initiation of saccades and pursuit provides further behavioral evidence that the two types of eye movements share pre-motor neural mechanisms.

Differences in sensory function between young (n = 42, 18-31 years old) and older (n = 137, 60-88 years old) adults were examined for auditory, visual, and tactile measures of threshold sensitivity and temporal acuity (gap-detection threshold). For all but one of the psychophysical measures (visual gap detection), multiple measures were obtained at different stimulus frequencies for each modality and task. This resulted in a total of 14 dependent measures, each based on four to six adaptive psychophysical estimates of 75% correct performance. In addition, all participants completed the Wechsler Adult Intelligence Scale (Wechsler, 1997). Mean data confirmed previously observed differences in performance between young and older adults for 13 of the 14 dependent measures (all but visual threshold at a flicker frequency of 4 Hz). Correlational and principal-components factor analyses performed on the data from the 137 older adults were generally consistent with task and modality independence of the psychophysical measures.

Thresholds for the detection of gaps in sinusoidal signals were measured as a function of frequency (100-2000 Hz) and level (25-85 dB SPL) in 11 normally hearing subjects. The sinusoids were presented in a background noise intended to mask spectral splatter associated with the gap. In a separate experiment, auditory filter shapes and detection efficiency were estimated for the same 11 subjects using the notched-noise method, at center frequencies of 100, 200, 400, and 800 Hz. Gap thresholds varied only slightly with frequency over the range 400-2000 Hz, but increased markedly at 200 and 100 Hz. At all center frequencies, gap thresholds were almost invariant with level for levels above 55 dB SPL. Gap thresholds increased at low levels, reaching values about 50% greater than their asymptotic values at a sensation level of about 20 dB. The decrease in auditory filter bandwidth with decreasing center frequency does not seem sufficient to account for the increase in gap thresholds. Also, individual gap thresholds at a given center frequency were not significantly correlated with the bandwidth of the auditory filter at that center frequency, as would be expected if the auditory filter played a role in limiting gap detection. Detection efficiency decreased with decreasing center frequency. Individual differences in detection efficiency were significantly correlated with gap thresholds. However, changes in detection efficiency with frequency do not seem to be of the right form to account for the increase in gap thresholds at low frequencies. It seems likely that there is a central sliding temporal integrator which integrates over longer times at lower center frequencies.

Auditory steady-state response (ASSR) is a translational biomarker for several neurological and psychiatric disorders, such as hearing loss, schizophrenia, bipolar disorder, autism, etc. The ASSR is sinusoidal electroencephalography (EEG)/magnetoencephalography (MEG) responses induced by periodically presented auditory stimuli. Traditional frequency analysis assumes ASSR is a stationary response, which can be analyzed using linear analysis approaches, such as Fourier analysis or Wavelet. However, recent studies have reported that the human steady-state responses are dynamic and can be modulated by the subject’s attention, wakefulness state, mental load, and mental fatigue. The amplitude modulations on the measured oscillatory responses can result in the spectral broadening or frequency splitting on the Fourier spectrum, owing to the trigonometric product-to-sum formula. Accordingly, in this study, we analyzed the human ASSR by the combination of canonical correlation analysis (CCA) and Holo-Hilbert spectral analysis (HHSA). The CCA was used to extract ASSR-related signal features, and the HHSA was used to decompose the extracted ASSR responses into amplitude modulation (AM) components and frequency modulation (FM) components, in which the FM frequency represents the fast-changing intra-mode frequency and the AM frequency represents the slow-changing inter-mode frequency. In this paper, we aimed to study the AM and FM spectra of ASSR responses in a 37 Hz steady-state auditory stimulation. Twenty-five healthy subjects were recruited for this study, and each subject was requested to participate in two auditory stimulation sessions, including one right-ear and one left-ear monaural steady-state auditory stimulation. With the HHSA, both the 37 Hz (fundamental frequency) and the 74 Hz (first harmonic frequency) auditory responses were successfully extracted. Examining the AM spectra, the 37 Hz and the 74 Hz auditory responses were modulated by distinct AM spectra, each with at least three composite frequencies. In contrast to the results of traditional Fourier spectra, frequency splitting was seen at 37 Hz, and a spectral peak was obscured at 74 Hz in Fourier spectra. The proposed method effectively corrects the frequency splitting problem resulting from time-varying amplitude changes. Our results have validated the HHSA as a useful tool for steady-state response (SSR) studies so that the misleading or wrong interpretation caused by amplitude modulation in the traditional Fourier spectrum can be avoided.

Detection of silent temporal gaps is characterized by two prominent features when measured as a function of frequency separation between two sinusoids that mark the silent gap. First, over a range of about an octave separation, gap detection thresholds (GDTs) routinely increase as the frequency difference is increased between the two sinusoids. Second, GDTs become asymptotic for greater sinusoidal frequency separations. This characteristic GDT pattern probably reflects two different processes. The first process almost certainly reflects gap detection measured within a single auditory filter or channel. The nature of the second process is less certain, but may reflect across-channel processing of the silent gap stimulus in two or more independent frequency channels. To evaluate the idea that asymptotic GDTs can be explained with a simple two-channel model, GDTs were measured as a function of frequency separation between a pre-gap sinusoid presented to the left ear (channel 1) and a post-gap sinusoid presented to the right ear (channel 2). The resulting GDTs for standard pre-gap sinusoids from 250 to 2000 Hz correspond closely with asymptotic GDTs measured for three listeners for the same set of stimulus conditions presented monaurally. This correspondence of the data supports a two-channel hypothesis. [Research supported by NIH.]

You have accessThe ASHA LeaderBottom Line1 May 2008Auditory Implants: Coding and Reimbursement Issues Steven C White Steven C White Google Scholar More articles by this author https://doi.org/10.1044/leader.BML.13062008.3 SectionsAbout ToolsAdd to favorites ShareFacebookTwitterLinked In The topic of cochlear implants, middle-ear implants, and brain-stem implants is an active one in the coding and reimbursement of audiology and speech-language pathology evaluation and treatment services. Specific Current Procedural Terminology (CPT) codes are related to testing and rehabilitation for prospective implant patients and patients who have received an implant. Robert Fifer and other members of ASHA’s Health Care Economics Committee have drafted and defended CPT proposals to the American Medical Association CPT Editorial Panel to ensure that appropriate codes are available to audiologists and speech-language pathologists to report auditory implant device-related procedures. These questions and answers focus on coverage and procedural coding of these services. Q: I am an audiologist and am seeing a Medicare beneficiary for auditory rehabilitation following a cochlear implant. Can I bill Medicare for CPT 92633 (auditory rehabilitation; postlingual hearing loss)? No. Medicare covers only diagnostic audiology services. The Medicare statute needs to be amended to permit audiologists to bill for rehabilitation services. ASHA is working to accomplish that goal. Q: I am an SLP and work in a hospital. Can the hospital bill Medicare for CPT 92633 (auditory rehabilitation; postlingual hearing loss) when I provide the procedure? No. Although SLPs can provide Medicare rehabilitation services, the Centers for Medicare and Medicaid Services (CMS) made the decision not to cover 92633—and also not to cover CPT 92630, auditory rehabilitation; prelingual hearing loss—to clarify that audiologists could not bill for the procedure. CMS instructs SLPs providing auditory rehabilitation to Medicare beneficiaries to use CPT 92507 (treatment of speech, language, voice, communication, and/or auditory processing). Q: Can the hospital, my employer, bill a private health plan (e.g., an employer-sponsored health plan) for CPT 92633 when I provide the service? I’m an SLP. The hospital definitely can bill a private health plan for CPT 92633. Only Medicare disallows CPT 92633. You may want to check the health plan’s Web site to ensure that 92633 is included in its medical policy. For example, Cigna has a Healthcare Coverage Position (0180) that specifically lists 92630 and 92633 as covered procedures when medically necessary (search “aural rehabilitation” on Cigna’s Web site). The summary of the Cigna coverage position states, “AR is indicated for the treatment of such impairment and is a medically necessary component of the management of cochlear device and auditory brainstem implantation.” Q: As an audiologist in private practice, can I bill a private health plan (e.g., an employer-sponsored health plan) for CPT 92633 when I provide the service? Yes, you can bill a private health plan for CPT 92633. Only Medicare disallows CPT 92633. As stated above, you may want to check the health plan’s Web site to ensure that 92633 is included in its medical policy. The Cigna policy is a good one for you to review to see how private health plans view AR. Q: I am a private-practice audiologist and have a Medicare provider number. What CPT code should I use when seeing a 15-year-old patient who has just had a cochlear implant? The code for this situation is CPT 92603 (diagnostic analysis of cochlear implant, age 7 years or older; with programming). For follow-up services you can use CPT 92604 (subsequent reprogramming). Q: I recently took a position as an audiologist in an auditory implant center. Is there a code for programming an auditory brainstem implant? I know Medicare covers the surgery. The correct code is CPT 92640 (diagnostic analysis with programming of auditory brainstem implant, per hour). Author Notes Steven C White, director of health care economics and advocacy, can be reached at [email protected]. Advertising Disclaimer | Advertise With Us Advertising Disclaimer | Advertise With Us Additional Resources FiguresSourcesRelatedDetails Volume 13Issue 6May 2008 Get Permissions Add to your Mendeley library History Published in print: May 1, 2008 Metrics Downloaded 443 times Topicsasha-topicsleader_do_tagleader-topicsasha-article-typesCopyright & Permissions© 2008 American Speech-Language-Hearing AssociationLoading ...