Учебники / Hearing - From Sensory Processing to Perception Kollmeier 2007

References

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Comment by Carr

Your paper discusses the advantages of a code without labeled lines for ITD. It is not clear to me why there need be a dichotomy between the labeled-line code strategy of an ITD map and a rate code. Many sensory variables are encoded by activity in populations of neurons with bell-shaped tuning curves. Models of information coding from these populations appear consistent with

the behavioral resolution of ITD (Skottun et al. 2001). Takahashi et al. (2003), however, have argued that rate coding and place coding are not mutually exclusive. In the barn owl, they have shown that changes in the firing rate of space-specific neurons can serve as the basis of a spatial discrimination task. The place code, which is based on the position of active neurons in the space map of the barn owl IC, appears to be used to direct orientation toward a sound source, such as the motor task you use in your example.

References

Skottun BC, Shackleton TM, Arnott RH, Palmer AR (2001) The ability of inferior colliculus neurons to signal differences in interaural delay. Proc Natl Acad Sci 98:14050–14054

Takahashi TT, Bala AD, Spitzer MW, Euston DR, Spezio ML, Keller CH (2003) The synthesis and use of the owl’s auditory space map. Biol Cybern 89:378–387

Reply

As you describe, Takahashi et al. argue that the two codes might coexist, but are applicable to different tasks. They suggest a population rate code for the general task of discriminating two stimuli, and a place code for the more specific task of sound localization. What I tried to show is that the population rate code (with four channels) can also account for sound localization, that is, it can apply to both kinds of tasks, at least under the stimulus conditions considered. The larger issue, of course, is whether or not mammals use the same strategy as barn owls (and chickens?) to localize sound based on ITD. That, I believe, is still an open and interesting question.

Fig. 1 Representation of ITD information in the form of a cross-correlogram. Grey-scale density illustrates central weighting function of ITD detectors used in central weighting models. Curved dashed lines indicate the “π-limit”. See text for full description

increased, however, the intracranial auditory image shifts from the right to the left side until, for a bandwidth of 400 Hz (grey vertical bar to left of correlogram in Fig. 1), the image is lateralised fully to the left side corresponding to that of the “true” ITD. As for a tone, multiple peaks of activation appear in the plot, but the peaks at the true ITD (in this case, −1500 µs) are aligned across frequency channels, a pattern referred to as “straightness” (Trahiotis and Stern 1989). As such, it has been proposed that a second layer of coincidence detectors exists, potentially in the midbrain, multiplying acrossfrequency straightness (Stern and Trahiotis 1997) and thereby giving weight to the true ITD which, under real-world listening conditions, would be consistent across frequency for a single sound emanating from a single source. This straightness-weighting therefore accounts for the ‘true’ lateralized image of broadband sounds with high ITD on the basis of midbrain activity on the opposite side to the lateralised sound image (grey curve at top of Fig. 1).

However, straightness-weighting is unsatisfactory because not only would a full representation of ITDs have to exist in each frequency band, including ITDs that can never be experienced under natural listening conditions, but also a second level of coincidence detectors specifically looking for across-frequency straightness at these ITDs. The existence or otherwise of neural mechanisms specialized to detect such long ITDs is disputed.

Recent investigations indicate only a restricted range of ITD detectors in the mammalian brain (McAlpine et al. 2001; Hancock and Delgutte 2004), with no neurons showing tuning for ITDs beyond approximately 1/2 a cycle of the centre frequency of each auditory filter. We refer to this as the π-limit (denoted by the curved dashed lines running down Fig. 1). Note that with the π-limit the 500-Hz frequency band possesses ITD detectors up to a maximum of 1000 µs, being 1/2 the period of a 500-Hz centre frequency. The π-limit must therefore account for the ‘true’ lateralized image of broadband sounds with high ITD on the basis of midbrain activity on the same side as the lateralised sound image. Note that the models make entirely opposite predictions to each other for ITDs of 1500 µs: the straightness model predicts greater activity in the brain hemisphere contralateral to the lateralised sound image, while the π-limit predicts greater activity ipsilateral. This is in contrast to ITDs of ±500 µs, for which both models predict greater activation in the brain hemisphere contralateral to the lateralized sound image.

Here, we examine the representation of ITD in the human midbrain and cortex using functional magnetic resonancing imaging (fMRI), the mismatch negativity potential (MMN), and headphone presented sounds with variable ITD.

2Methods

For the functional magnetic resonance imaging (fMRI) experiment, stimuli were 400-Hz band-pass noises exemplars (fixed-amplitude, random-phase) centred at 500 Hz. Stimuli consisted of eight consecutive noise bursts of 1 s each from the same condition (8 s), or 8 s of silence, presented via stereo headphones. ITDs were ±1500 µs, ±500 µs and 0 µs plus a silent condition. Sounds with negative ITDs were always perceived on the left, and those with positive ITDs on the right. BOLD contrast images were acquired using T2* weighted EPI and sparse imaging. A total of 48 slices in ascending axial order were obtained with cardiac gating for 14 subjects, all of whom were right-handed, with no hearing impairment or history of neurological disorder. Subjects were asked to pay attention to the noises, and to press a button on their keypad at the end of each trial to maintain alertness. They were also asked to keep their eyes open and fixate on a central cross, in order to counteract any confound caused by correlated eye movements. Imaging data were analysed using the statistical parametric mapping algorithm implemented in SPM2. Structural and EPI images were co-registered and normalised to a standard grey-matter template (Montreal Neurological Institute), Data were thereby transformed into a standard stereotaxic space and subsampled with a voxel resolution of 2 × 2 × 2 mm (original voxel resolution was approximately 3 × 3 × 3 mm). Data were spatially smoothed with a Gaussian smoothing kernel of 5 mm. SPM2 was used to compute individual subject analyses according to the General Linear Model by

fitting the data time-series with the canonical Haemodynamic Response Function (cHRF) at the onset of each trial. Each condition was modeled as an individual regressor in the design matrix, and statistical parameter estimates were computed individually for each brain voxel.

The mismatch negativity (MMN), a component of the auditory evoked potential produced by context-dependent changes in the environment, is reported as being greater at electrodes contralateral to the perceived location of a sound. In each of four blocks, participants read self-selected material while seated in a soundproof booth, and were instructed to ignore the sounds. Stimuli were presented via headphones at 65 dB SPL in pseudo-random order with a SOA of 1 s. Each block was ten minutes long and consisted of 480 standard stimuli and 30 × 4 deviant stimuli (0.8 and 0.05 probability of occurrence, respectively). A total of 2400 (1920 standard and 480 deviant) 400 Hz wide, bandpass-filtered white noise bursts, centred on 500 Hz were generated in MATLAB using the Binaural Toolbox. Noises of 500 ms duration, including a 50-ms rise/fall time, were digitized at 20,000 Hz. There was no difference in onset time, and delays were applied to the right channel only. Standard stimuli had an ITD of 0 µs and a perceived location in the middle of the head. The electroencephalogram (EEG) was recorded using NUAMPS with Ag-AgCl electrodes from 32 channels positioned according to the extended 10–20 system and re-referenced offline to the average from the mastoids. Horizontal and vertical electroocculograms were recorded with electrodes placed above and below the left eye, and on the outer canthi. Blink removal, artefact rejection (threshold: ±100 µV) and lowpass-filtering below 20 Hz were performed offline. Data for each condition were averaged from 600-ms epochs (beginning 100 ms pre-stimulus). MMN was defined as the mean amplitude of the deviant-standard difference waveforms in a 40-ms window centred on the grand average negative peak at each electrode. Statistical analysis was performed at three left-right electrode pairs: F3-F4, FC3-FC4, C3-C4. One-sample t-tests were used to determine that MMN amplitudes differed from zero and three-way ANOVA delay (−1500, −500, +500 +1500)* hemisphere (left, right)* row (F, FC, C) confirmed no main effects and no interactions of peak MMN latencies.

3Results

Initial analysis served to map functionally the IC for both hemispheres. To that end individual statistical maps were generated for the contrast All Sounds>Silence. Data from the resulting contrast maps for 14 subjects were entered into a second level analysis to allow population-level inferences to be drawn (random effects). The group analysis was thresholded at 0.05 FWE corrected to map the IC in both hemispheres as our a priori ROI. The one resulting ROI was divided in the midline (x = 0) in two ROIs representing

Fig. 3 a Grand mean difference waveforms (left panels) for each condition at the electrode pairs FC3-FC4. Note that negative amplitudes are plotted upwards. b Mean MMN amplitudes from electrode pairs in a. The ITDs of ±1500 s correspond to 3/4 of a cycle of the stimulus centre frequency and ±500 s 60 1/4 of the cycle. Ipsilateral and contralateral refer to brain hemisphere referenced to the ear at which the sound leads in time

A similar pattern to the IC responses was also observed at the cortical level in the analysis of MMN at each electrode pair. Figure 3a shows the difference waveforms at FC3 (left hemisphere) and FC4 (right hemisphere). Peak activation occurs between 100 and 200 ms post-stimulus onset and is greater at the electrode contralateral to the perceived location for the −500-µs condition (p<0.05). This conforms to a bias toward greater right hemisphere activation previously seen in a number of studies and presumably due to right hemispheric specialisation for spatial stimuli. Sounds on the left that are within the π-limit elicit stronger contralateral activity, whereas right-sided stimuli are represented more bilaterally; outside the π-limit there is no difference between left and right electrodes.

In order to examine the data without this bias, mean contralateral (e.g. [+500 µs at FC3] + [−500 µs at FC4]; [+1500 µs at FC3] + [−1500 µs at FC4]) and ipsilateral (e.g. [+500 µs at FC4] + [−500 µs at FC3]; [+1500 µs at FC4] + [−1500 µs at FC3]) MMN amplitudes were compared. Here, contralateral activation was greater (p<0.05) within, but not beyond the π-limit, as can be seen in Fig. 3b. These data are consistent with our fMRI results and with animal data, but not with the straightness model or a full-range of ITD representation.

4Discussion

Imaging data from the human brain are consistent with the notion that ITD is represented by a restricted range of detectors, such that ITDs beyond 1/2 a cycle of the stimulus centre frequency are not explicitly coded. For the stimuli used in the current study, ITDs equivalent to ±3/4 of the period of the stimulus centre frequency are therefore likely encoded by neurons whose response

maxima are evoked by ITDs equivalent to 1/4 of the period of the stimulus centre frequency, and of opposite sign (and opposite lateralised location). Thus, the notion that activation of auditory spatial detectors is determined by the side from which a sound source is heard to originate does not hold. Clearly, however, both the absolute brain activation as well as the relative activation across the two brain hemispheres, differs between the ITDs of ±500 and ±1500 µs. These differences likely stem from differences in interaural correlation between the two stimuli. How such differences might be interpreted as differences in lateral position and extent of an intracranial sound image remains to be determined, but the existence should be noted of successful models of binaural hearing, designed to account for psychophysical data, in which the notion of internal delays is completely absent.

The weighted image model of ITD processing (Stern et al. 1988), designed to account for human psychophysical performance, posits the existence of a second-level of coincidence detection explicitly to account for the switch in lateralised percept of noise with long (±1500 µs) delays as the bandwidth is increased. However, the current data call into question the existence of the required straightness detectors, at least up to the level of primary auditory cortex. Explicit anatomical and physiological manifestations such have been suggested for the barn-owl brain (Wagner et al. 1987) therefore might not be relevant to binaural hearing in mammals. Future use of binaural displays such as the cross-correlogram should take account of both physiological data when interpreting psychophysical findings. Although the straightness-weighting model is a simple and attractive notion, our data demonstrate that it does not hold for all ITDs, e.g. it cannot account for sensitivity to ITDs greater than 1/2 the stimulus period. Our data are entirely consistent with the π-limit model.

Changes in the perceived location of a sound have been reported to generate a MMN, which is reported to be maximal in the brain hemisphere contralateral to the perceived location. Ergo, there is presumed to be a switch in the laterality of brain activation to accompany the switch in lateral image for broadband stimuli with ITDs of opposite sign. The current study indicates this not to be the case. The MMN does not appear to accompany the perceptual shift, but rather accompanies the neural population most active, suggesting that its generation is related to detection of activity in a neural population, rather than the perceived location of a sound source. Therefore, the popularlyheld notion that the MMN is a marker for the perceptual processing of auditory cues, including cues for spatial hearing, may have to be revised.

References

Hancock KE, Delgutte B (2004) A physiologically based model of interaural time difference discrimination. J Neurosci 24:7110–7117

Jenkins WM, Merzenich MM (1984) Role of cat primary auditory cortex for sound-localization behavior. J Neurophysiol 52:819–847