Leibniz Institute for Neurobiology, D-39118 Magdeburg, Germany
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ABSTRACT |
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Biermann, Silke and Peter Heil. Parallels Between Timing of Onset Responses of Single Neurons in Cat and of Evoked Magnetic Fields in Human Auditory Cortex. J. Neurophysiol. 84: 2426-2439, 2000. Sound onsets constitute particularly salient transients and evoke strong responses from neurons of the auditory system, but in the past, such onset responses have often been analyzed with respect to steady-state features of sounds, like the sound pressure level. Recent electrophysiological studies of single neurons from the auditory cortex of anesthetized cats have revealed that the timing and strength of onset responses are shaped by dynamic stimulus properties at their very onsets. Here we demonstrate with magnetoencephalography that stimulus-response relationships very similar to those of the single neurons are observed in two onset components, N100m and P50m, of auditory evoked magnetic fields (AEFs) from the auditory cortex of awake humans. In response to tones shaped with cosine-squared rise functions, N100m and P50m peak latencies vary systematically with tone level and rise time but form a rather invariant function of the acceleration of the envelope at tone onset. Hence N100m and P50m peak latencies, as well as peak amplitudes, are determined by dynamic properties of the stimuli within the first few milliseconds, though not necessarily by acceleration. The changes of N100m and P50m peak latencies with rise time and level are incompatible with a fixed-amplitude threshold model. The direct comparison of the neuromagnetic and single-neuron data shows that, on average, the variance of the neuromagnetic data is larger by one to two orders of magnitude but that favorable measurements can yield variances as low as those derived from neurons with mediocre precision of response timing. The striking parallels between the response timing of single cortical neurons and of AEFs provides a stronger link between single neuron and population activity.
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INTRODUCTION |
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Transients, i.e., rapid changes
in spectral composition or amplitude of sounds, constitute important
information-bearing elements in audition. Neurons of the auditory
system, including auditory cortex, respond preferentially to acoustic
transients, such as the onsets of sounds. Somewhat paradoxically,
however, onset responses have usually been analyzed with respect to
measures of the steady state of a stimulus, such as the sound pressure
level (SPL) [for a review of studies using magnetoencephalography
(MEG) or electroencephalography, see Näätänen
and Picton 1987]. And consequently changes in neuronal onset
responses observed with changes in level have been interpreted to
be significant for intensity coding (for electrophysiology, see e.g.,
Brugge and Merzenich 1973
; Heil et al.
1994
; Phillips et al. 1995
; Schreiner et
al. 1992
; Suga 1977
; for MEG, see Pantev et al. 1989
). However, with variation of level, dynamic
features of the stimulus are inevitably covaried, such as the time
course of the envelope (peak pressure) at sound onset and the
derivatives of that time course (see Fig.
1, top). Hence, such standard
experimental paradigms are inherently ambiguous with respect to the
stimulus variable(s) critical for shaping the ubiquitous onset
responses.
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Recently, Heil and Irvine (Heil 1997a,b
; Heil
and Irvine 1996
, 1998
) have addressed some of these ambiguities
by studying the responses of auditory cortical neurons to tone onsets
with parametric variation of level and rise time. With this
two-dimensional design (Fig. 1), they could show that these onset
responses are shaped by dynamic properties of such stimuli at their
very onsets and not by steady-state level. Furthermore these data
suggest a way in which such responses might track and encode the
time-varying envelopes. The details of temporal envelopes provide
critical information, e.g., in speech (Drullman et al.
1994
; Shannon et al. 1995
), and have perceptual
correlates, such as timbre (e.g., Gray and Gordon 1978
;
Krumhansl and Iverson 1992
; Pitt and Crowder 1992
).
If the data of Heil and Irvine, which were collected in deeply
anesthetized animals, and their interpretations have any significance for signal processing in the awake human brain, then similar
stimulus-response relationships should be demonstrable in onset
responses from the human auditory cortex. The present study examines
this possibility in detail using MEG, a noninvasive technique now
widely employed to study macroscopically cortical neuronal activity
with high temporal resolution (Hari 1990, 1996
;
Hyde 1997
; Jacobson 1994
; Lounasmaa et al. 1996
; Sams and Hari
1991
). Auditory evoked magnetic fields (AEFs), which can be
measured outside the head, are produced by intracellular currents
flowing tangentially to the skull and simultaneously in thousands of
pyramidal cells of the auditory cortex on the supratemporal gyrus
(Hämäläinen et al. 1993
; Papanicolaou 1995
; Sato et al. 1991
). We
focus on two prominent components of AEFs, specifically the N100m and
the P50m, as they are triggered by stimulus onset despite their long
latencies of about 100 and 50 ms, respectively. This paper presents a
direct comparison of the properties of these onset components of AEFs with those of single auditory cortical neurons. This comparison provides an estimate to what extent temporal properties of single neurons are reflected in the global magnetic responses of neuronal populations as recorded with MEG.
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METHODS |
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Subjects
Eight subjects (7 males and 1 female), aged between 21 and 39 yr, contributed data to this study. All subjects had normal hearing in
both ears as determined by standard clinical audiometry and were
right-handed as established with a modified version of the handedness
questionnaire by Annett (1967). Some subjects had previous experience with MEG. All subjects gave written informed consent.
Acoustic stimuli, experimental protocol, and apparatus
All stimuli used were tone bursts shaped with cosine-squared rise and fall functions. Fall times were held constant at 2 ms, whereas rise time, plateau amplitude, plateau duration, and carrier frequency were variables as described in the following text.
During the rise time, TR, the peak
pressure, PP(t), (or envelope) increases according to the
function
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(1) |
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(2) |
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(3) |
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(4) |
Tones started with a positive zero crossing of the carrier. Within
a given experimental session, all tones had the same frequency (200, 500, 1,000, or 2,000 Hz). Five different SPLs at 12-dB intervals, and
thus covering a 48-dB range, and four to seven different rise times,
differing by a factor of two or four and varying between 2 and 128 ms,
were used. Most of the tone bursts had a plateau duration of 35 ms.
This duration was chosen because it had been reported that the N100m as
well as the N1-P2 amplitude of auditory evoked potentials (AEPs)
summate over ~30 ms of a stimulus (Joutsiniemi et al.
1989; Onishi and Davis 1968
), while, on the
other hand, responses to stimulus onset decrease and those to offset
increase with increasing stimulus duration and for a fixed
interstimulus interval (ISI) or stimulus repetition rate
(Hillyard and Picton 1978
; Pantev et al.
1996
). Because of the fixed plateau duration, the total
stimulus duration and stimulus energy covaried with varying rise time.
To test for the potential influence of these parameters on the
responses, we included, in several experimental sessions, additional
stimuli with 16-ms rise time but with a plateau duration of 135 ms.
These stimuli were presented at the same five SPLs as the other stimuli
in those sessions. Either 99 or 200 repetitions of each stimulus were
presented, in three blocks of 33 stimuli or in randomized order,
respectively, with an average ISI of 1 s (range 950-1,050 ms). In
the block design, each block was followed by a block with a new
stimulus until all different stimuli had been presented. The sequence
was then repeated another two times. Stimulus presentation was either
binaural or monaural to the right ear. Overall 27 experimental
sessions, each lasting ~60-70 min, were run.
Neuromagnetic data were recorded continuously (at sampling rates of 508 or 1,017 Hz and with passbands of DC to 100 or to 400 Hz) using a
148-channel whole-head biomagnetometer (Magnes 2500, Biomagnetic
Technologies, San Diego, CA). Measurements were carried out in a
magnetically shielded, illuminated, and ventilated room. The
homogeneous background magnetic field was suppressed by an on-line
noise-reduction system, consisting of three reference sensors, oriented
perpendicularly to each other and located ~10 cm from the measuring
sensor array. For each of these 148 sensors, weighting factors were
determined prior to an experiment such that the weighted sum of the
magnetic field vectors in the reference system minus the field in the
measuring sensor was minimized. The magnetically silent delivery of the
acoustic stimuli required a system with speakers outside the
magnetically shielded room. Acoustic stimuli were conducted to each ear
via two plastic tubes of ~6 m length and of 16 mm ID connected to
silicon ear pieces. This system produced acoustic delays of ~25 ms
and attenuation of 20 dB, depending on frequency. The frequency
response of this system was flat (±5 dB) within the range of
200-2,000 Hz, as checked by a microphone (Sennheiser KE4-211-2)
sealed into the ear piece and connected to an oscilloscope.
Prior to each experiment, the subjects' sensation levels (SLs) were determined for each ear with the 2-ms rise time tones under the proper experimental conditions. The level of the softest tone was then set to 16-35 dB SL, depending on subject and frequency, such that each subject could tolerate the strongest stimulus, whose level was 48 dB above that of the softest one, without discomfort or pain. Actual SPLs were estimated from the SLs by taking each subject's audiogram into consideration. The lowest estimated SPLs varied between 22 and 47 dB. During the experimental sessions, subjects lay comfortably in a deck chair. They were instructed to stay awake, to listen to the stimuli, and to avoid head movements as far as possible. The position of a subject's head relative to the sensor array was measured with five coils fixed at widely spaced positions on the subject's head. Changes in head position were quantified as the maximum difference in the location of these coils prior to and after data acquisition. A monitoring video camera offered additional control of head movements. The maximum differences between pre- and postacquisition locations ranged from 0.09 to 0.92 cm with a mean of 0.30 (18) cm.
Data analysis
Epochs of 1,300 ms, including 300-ms prestimulus intervals, were extracted from the continuously recorded neuromagnetic signals. Off-line noise reduction was carried out. Epochs with a signal deviation of 10 pT within 100 ms were considered to be contaminated by artifacts and rejected. The averaged data were offset-corrected and digitally filtered using a 0.1- to 40-Hz passband. Because of the large number of different stimuli required in every experimental session (viz., 20-40) and because the total duration of each session of ~1 h was approaching the limit that subjects could endure without moving, falling asleep, or feeling uncomfortable, the number of repetitions of each stimulus was limited (to 99 or 200; see preceding text). Hence for many stimuli, the signal-to-noise ratios were suboptimal. To improve those ratios, we selected from the 148 channels those four neighboring channels over each hemisphere which yielded the strongest signals of the same polarity (see Fig. 2) for averaging. The averaged signal amplitudes were then offset-corrected using a 100-ms prestimulus baseline. For a given subject, the same eight channels were selected for data collected in different experimental sessions.
The two relatively prominent deflections of opposite polarity that
occurred in most data sets ~50 and 100 ms after stimulus onset (see
Fig. 3) are termed here P50m and N100m, in analogy to the corresponding
components of AEPs. The peak latency and peak amplitude of these two
deflections were measured for each stimulus and hemisphere whenever
they could be identified. Because the channels had been selected to
yield favorable N100m signals and because the sources of P50m are
slightly more anterior than those of N100m (e.g., Pellizone et
al. 1987), the signal-to-noise ratios for P50m were likely even
smaller than they could have been had the channel selection been
optimized for this deflection. Analysis of peak values seemed to be
more reliable than those of the center of gravity or, for latency, the
initial zero crossing. The 108 data sets (27 sessions by 2 hemispheres
by 2 deflections) yielded 2,300 measures of response latency and
amplitude each, i.e., ~80% of the maximum number possible. Averaging
over a large number of channels would have reduced signal amplitudes
and broadened the signals in the time domain, because peak latencies
vary across different sensors, due to moving sources or multiple
sources with different response time courses (e.g., Lü et
al. 1992
; McEvoy et al. 1997
; Sams et al.
1993
). All measures of latency described in the following text
are corrected for acoustic delays of the sound-delivery system.
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RESULTS |
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Waveforms
Figure 2 illustrates neuromagnetic
signals recorded simultaneously at 148 sensor positions over both
hemispheres from a single subject. The strongest deflections of the
signals, each of which is the average of ~200 epochs, occur at ~100
ms after stimulus onset (N100m) in the lateral sensors over each
hemisphere. The reversals of signal polarity from more anterior to more
posterior sensors correspond to pronounced dipolar magnetic field
distributions that are consistent with a source in the auditory cortex
on the superior temporal plane in each hemisphere (e.g., Hari
1990; Lütkenhöner and Steinsträter
1998
). The details of the spatial distribution and time course
of AEFs depend on subject, hemisphere, and stimulus.
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Figure 3 shows, for two selected subjects
(PeHa and SeLi), the details of the time courses
of the signals averaged across four neighboring channels over the left
hemisphere. Signals were evoked by 500-Hz tones of different level
(A and C) and rise time (B and
D), presented binaurally for PeHa and monaurally
to the right ear for SeLi. Small P50m deflections (~50-ms
latency) and larger N100m deflections (~100-ms latency) with opposite
polarity are seen in both subjects. However, the waveforms,
particularly for N100m, show considerable interindividual variability.
For all stimulus conditions shown, the N100m of PeHa
(A and B) display single sharp peaks with
well-defined maxima, while those of SeLi (C and
D) display one or two shoulders before the global maxima are
reached, consistent with the contribution of multiple, temporally overlapping sources to that deflection, as argued by others (e.g., Lü et al. 1992; McEvoy et al. 1997
;
Sams et al. 1993
; Zouridakis et al.
1998
).
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Effects of stimulus level, rise time, and acceleration of pressure on N100m and P50m latency
We have reported previously that the first-spike latency of single
neurons from the primary (AI) and posterior fields of the auditory
cortex of barbiturate-anesthetized cats varies systematically with rise
time and with level. However, for cosine-squared rise functions,
latency forms a rather invariant function of the acceleration of peak
pressure at tone onset (APPmax) which covaries
with rise time and level (Fig. 1) (Heil 1997a;
Heil and Irvine 1996
-1998
). Data from one AI neuron
(95-98-11) are shown in Fig.
4, left. Figure 4A
shows that latency increases monotonically with rise time for any given
level and that the increase is most pronounced for low levels. It is
also obvious that the increase in latency with rise time is
compressively nonlinear. In Fig. 4B, the data are replotted
as functions of level with rise time as the parameter. For any given
rise time, latency decreases monotonically and nonlinearly with
increasing level. Latencies are longest and the decreases are steepest
for long rise times and low levels. In Fig. 4C, the data of
B are plotted against APPmax. It is
obvious that all latency functions are now in very close register.
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Very similar observations are made here for N100m peak latency. Figure 3 shows that for a given rise time (32 ms in A; 4 ms in C), N100m peak latency decreases with increasing level and, for a given level increases with increasing rise time (Fig. 3, B and D). P50m peak latency also varies with level, and rise time, though not as systematically as N100m peak latency. The systematic nature of the dependencies of N100m latency on level and rise time are shown for a complete data set in Fig. 4, D and E (subject PeHa). Figure 4D shows that, apart from some irregularities, N100m peak latency increases monotonically with rise time for a given level and that the increase is most pronounced for low levels. Also the increase of latency with rise time seems to be compressively nonlinear at least at low tone levels. In Fig. 4E, where the data are replotted as functions of level with rise time as the parameter, latency consequently decreases monotonically and nonlinearly with increasing level for a given rise time. Latencies are longest and the decreases are steepest for long rise times and low levels. Also note that latency functions obtained with tones of 16-ms rise time and plateaus of short versus long duration (ld16 ms) are very similar. Thus variation in total duration and stimulus energy with variation in rise time and level has minor, if any, effects on N100m peak latency (see also following text). In Fig. 4F, where the data of E are plotted against APPmax, it can be seen that all latency functions are now in close register.
Figure 5 illustrates four additional examples from another four subjects, for four different frequencies, both hemispheres, monaural and binaural stimulation, and for N100m as well as for P50m. In the left column, latency is plotted against level with rise time as the parameter. In the right column, the same data are plotted against APPmax. Note that in each case the different latency functions tend to be more aligned and be in closer register when plotted against APPmax as compared with level.
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The closer alignment of the N100m and P50m peak latency functions, when
plotted against APPmax rather than level, was not always as obvious as for the examples shown in Figs. 4 and 5 or as
obvious as for the single-neuron data from cat auditory cortex (cf.
Fig. 4, C with F). To quantify the improvement in
alignment of latency functions by the transformation from level to
APPmax, we first fitted the following simple
power function to each data set
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(5) |
We also calculated the variance of each fit. The improvement in alignment of latency functions by the transformation from SPL to APPmax was then quantified as the ratio of the variances of fits of latency against SPL and against APPmax. Ratios >1 thus identify data sets that do bring about an improvement in alignment of latency measures by this transformation. And, the larger that ratio, the greater the improvement. Figure 6 shows a scatterplot of these improvement ratios against the variance of latency fits against APPmax, separately for N100m and P50m. For N100m, 47 of 53 ratios were >1 with ratios ranging from ~0.5 to 14 and with a geometric mean of 2.1. For P50m, 33 of 43 ratios were >1 with ratios ranging from ~0.5 to 7 and with a geometric mean of 1.3. The higher improvement ratios for N100m are probably due to the larger signal-to-noise ratios for N100m than for P50m. For each of the neuromagnetic signals and for the two combined, the improvement effect was highly significant (i.e., the null-hypothesis that improvement ratios are equal to 1 has a probability P < 0.0004; Wilcoxon). For comparison, the single-neuron data from cat AI are also shown in Fig. 6. For these data, all 90 ratios were >1, ranging from 2.7 to 690, with a geometric mean of 46.
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Figure 6 also shows that the improvement ratios are the higher, the
lower the variance of the fit of the latency against
APPmax function, even for the neuromagnetic data
alone. Because of error along both axes, the Bartlett-procedure
(Sachs 1997) was used to quantify the slope of the
log-transformed data. The negative estimate of
0.26
(n = 98; r2 = 0.151)
for the neuromagnetic data thus confirms the impression that the better
the quality of the data, the clearer is the improvement effect. Hence
it is likely that the lack of improvement in the minority of cases (6 of 53 for N100m and 10 of 43 for P50m) is caused by too large a
variance. The ratios for the single neurons seem to constitute a
continuation of those for N100m and P50m, i.e., the variance of the fit
of latency against APPmax is much smaller and the
improvement larger than for the neuromagnetic signals. However,
differences in the number and range of rise times tested for a given
neuron and differences in extent of latency functions (see Heil
1997a
; Heil and Irvine 1997
) also contributed to
the variation in improvement ratios. It is likely that the larger
variance of the neuromagnetic data is due to the fact that a large
number of neurons with different properties contribute to the signal
(see following text). Also note in Fig. 6 that favorable neuromagnetic
measurements can yield variances as low as, and improvement ratios as
high as, those derived from some single neurons. This is reflected in
the histograms in Fig. 6 as overlap between neuromagnetic and
single-neuron data.
It can also be tested for each individual data set whether there is a significant improvement effect, for example, by a pairwise comparison of the squared deviations of fits of latency against level versus those of fits of latency against APPmax. For the examples shown in Figs. 4 and 5, there was a significant effect (Wilcoxon, Fig. 4, E and F: P < 0.001; Fig. 5, A-D: P < 0.01; Fig. 5, E-H: P < 0.05). Similar results were obtained with the absolute deviations.
In summary, these analyses reveal that, much like the first-spike latencies of single auditory cortical neurons, the peak latencies of both N100m and P50m are better described as functions of the acceleration of peak pressure at stimulus onset than of level, i.e., plateau peak pressure, even though the neuromagnetic data are noisier than the single-neuron data.
Shape of latency versus acceleration functions
It was observed previously that the latency versus acceleration
functions of different neurons were of very similar shape (Heil
1997a; Heil and Irvine 1997
, 1998
). Much the
same applies to the latency versus acceleration functions derived from
the neuromagnetic signals. N100m and P50m peak latency versus
acceleration functions obtained in the same and in different
experimental sessions, under different protocols (binaural and
monaural), with different frequencies, and from different subjects,
appeared to be of similar shape. This qualitative impression was
quantified by the results of the fits of Eq. 5 to the data,
with X and X0 representing
APPmax and the reference
APPmax(0), respectively. Specifically the values of the exponent c are informative because that parameter
quantifies the degree of curvature of the latency versus acceleration
function, while Lmin and
APPmax(0) characterize the position of the
function along the ordinate and abscissa, respectively. Figure
7A illustrates the
distribution of the exponent c for the single-neuron data. That distribution is very narrow with a mean of 0.40(08). Figure 7B shows, stacked on top of each other, the distributions
for the 53 and 43 sets of N100m and P50m peak latency data,
respectively, which could be fitted with Eq. 5. The
histograms show the relative frequencies of the exponent c,
after each estimate had been weighted with the root number of latency
measures having contributed to the fit and with the reciprocal of the
variance of the fit. In this way, estimates of c derived
from only few latency measures or from noisy data are weighted less.
The distributions for N100m and for P50m are both relatively narrow and
not different from one another: for N100m the mean c equals
0.13(18) and for P50m 0.14(22). Therefore the two distributions were
combined and yielded a mean of 0.14(16). As this exponent is smaller
than that of the single-neuron data, the latency versus acceleration
functions of single neurons from the cat auditory cortex are more
steeply curved that those of N100m and P50m.
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The narrow distributions of the exponent c confirm quantitatively the visual impression of rather similar shapes of latency versus acceleration functions across neurons and stimulus conditions for the single-neuron data on the one hand and across subjects and stimulus conditions for the neuromagnetic data on the other. Next all data sets were fitted once again, now with the exponent c fixed at the mean of 0.40 of the single-neuron data and at 0.14, the mean of the combined distributions, for the neuromagnetic data. In this way, a function with a fixed shape is fitted to each single-neuron and neuromagnetic data set, and estimates of Lmin and APPmax(0), i.e., of the function's position within the coordinate system, can be compared across data sets. This procedure yielded physiologically plausible estimates of the transmission delay, i.e., Lmin > 0 ms, for 49 of 53 data sets for N100m and for 42 of 43 data sets for P50m.
It was observed previously for the single-neuron data that the
transmission delay decreased with increasing stimulus frequency (Heil 1997a; Heil and Irvine 1997
). This
is shown in Fig. 8A where Lmin, obtained from fits of Eq. 5 with c = 0.4, is plotted against stimulus
frequency. For all data points shown that frequency is identical to the frequency to which each neuron is most sensitive, i.e., its characteristic frequency. While there is considerable scatter
of Lmin at a given frequency, an
overall trend for Lmin to decrease
with increasing frequency is indicated. Such a decrease is consistent
with the frequency-dependent delays in the cochlea. Figure
8B shows the transmission delays, estimated from fits of Eq. 5 with c = 0.14, for the neuromagnetic
data, separately for N100m and for P50m. Medians are also shown and
connected by lines. Despite considerable scatter of points for both
deflections and at all frequencies, medians of
Lmin for N100m and P50m are ~40-45 ms apart, independent of frequency. However, there is no clear indication for Lmin to decrease with
increasing frequency. This may be due to the fact that partly, or even
largely, overlapping neuronal populations would contribute to the
neuromagnetic signals recorded with different stimulus frequencies.
Note that the estimates of transmission delays for the neuromagnetic
signals, particularly for N100m, are much longer than those for the
vast majority of single neurons, recorded mostly from middle layers of
cat AI. However, a few single AI neurons have minimum latencies of
30-40 ms, i.e., near the estimates of transmission delays for P50m. Hence, these data are compatible with the suggestion that P50m originates in the human primary auditory cortex, though likely not in
the input layers, and N100m in the planum temporale (Hashimoto et al. 1995
; Pantev et al. 1990
;
Pellizone et al. 1987
).
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Figure 8C shows for the single-neuron data a plot of
APPmax(0), resulting from the same fits,
against stimulus/characteristic frequency. As explained in the
preceding text, this parameter can be thought of as an inverse measure
of the neuron's sensitivity to the tones' onsets and will therefore
be referred to as transient sensitivity. As noted earlier (Heil
1997a), the transient sensitivity varies with frequency in a
manner somewhat similar to the cat's audiogram. A corresponding
plot for the neuromagnetic data is shown in Fig. 8D. In
addition, medians are shown and connected by lines. For N100m,
APPmax(0) ranged
from 101 to
1014 Pa/s2 and for P50m
from 10
3 to
1012 Pa/s2. There is
no uniform dependence of APPmax(0) on
frequency, although for both N100m and P50m, medians are lowest, i.e.,
sensitivity is highest, at 1 kHz, where humans have the lowest
threshold when tested with ear phones, which bypass the
frequency-dependent amplification of the pinna (Han and Poulsen
1998
).
The fits of latency versus acceleration functions with Eq. 5
and a common exponent c allow superimposition of all fitted
functions, which in turn allows examination of the residuals more
systematically. Figure 9A
shows, for the single neurons, a plot of (L Lmin) against
APPmax/APPmax(0), i.e., of
the difference between measured first-spike latency and estimated
transmission delay against the stimulus APPmax
normalized for each neuron's estimate of transient sensitivity
APPmax(0). In this way, the functions fitted to
each data set (with c fixed at 0.4) are all superimposed.
The 2,288 data points in this plot form a very narrow band (Fig.
9A), emphasizing once again the similarity in the shape of
latency versus acceleration functions of different neurons and stimulus
conditions (Heil 1997a
). A renewed fit of these data
with Eq. 5 (Fig. 9A,
) yielded the low variance
of 1.9 ms2. Figure 9B shows the
residuals of this renewed fit. Note that the residuals scatter
unsystematically and relatively closely around the horizontal zero
line. Most data points fall within a band of 1 or 2 ms from that line
of best fit. An error estimation for c, provided by the
functions that would fit 70% of measured points, reveals that
0.35 < c < 0.48 is compatible with variance.
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Corresponding plots for N100m and P50m peak latency are shown in Fig.
9, D and E. The 2,187 data points from the 93 data sets (51 for N100m and 42 for P50m) are in rather close register,
confirming the similarity in the shape of latency versus acceleration
functions derived from the neuromagnetic data. A renewed fit yielded a
variance of 39 ms2, ~20 times that of the
single-neuron data. This is compatible with the suggestion that neurons
with different transient sensitivities and transmission delays (Fig. 8,
A and C) contribute to the neuromagnetic signals
(see also Fig. 3 in Heil 1997a). The larger variance is also reflected in a larger scatter of the neuromagnetic residuals in
Fig. 9E (cf. with Fig. 9B). Nevertheless, the
residuals scatter unsystematically, emphasizing the quality of the fit.
An error analysis analogous to those for the single-neuron data reveals a range of 0 < c < 0.33. These results confirm
that the curvature of the latency versus acceleration functions for
N100m or P50m is less steep than that of the functions for the single neurons.
Effects of stimulus duration on shape of latency versus acceleration functions
Only data points from stimuli with a plateau duration of 35 ms
contributed to the parameter fitting. Because, in several experimental sessions, control stimuli with a plateau duration of 135 ms were also
presented (see METHODS), the 140 latency measures obtained from these stimuli could post hoc be compared with the global fit. In a
plot of the residuals of (L Lmin) against
APPmax/APPmax(0) of the
global fit with c = 0.14, data points also scattered
randomly around the horizontal zero line (not shown). Hence the changes in latency with rise time, described in the preceding text, are not due
to the concomitant changes in total stimulus duration and energy (see
also Figs. 4, E and F, and 5, G and
H).
Test of the fixed-amplitude threshold model
It has been proposed that the changes in response latency
associated with changes in stimulus level or rise time can be explained by changes in the time needed for the stimulus to reach some
fixed-amplitude threshold (e.g., Ruhm and Jansen 1969).
Our data allow us to test this model directly. It follows from
Eq. 1 that the time tthr needed to reach some fixed threshold peak pressure
PPthr is given by
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(6) |
Effects of stimulus level and rise time on N100m amplitude
The peak amplitude of the neuromagnetic deflections tended to
increase with increasing tone level for a given rise time and to
decrease with increasing rise time for a given level (see Fig. 3).
However, most individual amplitude data sets, even of N100m, were very
noisy, in agreement with previous reports (Hyde 1997; Rogers et al. 1990
). Thus to obtain a clearer picture,
at least for N100m, data sets were averaged. However, two facts needed to be considered. First, there was considerable variability in absolute
N100m amplitudes across subjects (see Fig. 3). Second, for binaural
stimulation, N100m amplitudes, averaged across all stimuli presented in
a given session, were similar in the left and right hemisphere
(n = 18; Wilcoxon, P = 0.468 > 0.3), but for monaural stimulation were considerably larger in the left hemisphere, contralateral to the stimulated ear (n = 9;
Wilcoxon, P < 0.01; not shown). Hence to highlight the
average changes in N100m amplitude with tone level and rise time, each
data set was first normalized with respect to the N100m peak amplitude
averaged across the five levels of the tones of 500 Hz and 2-ms rise
time and recorded in a given subject and hemisphere. Then these
normalized amplitudes from different subjects and hemispheres were averaged.
Figure 10 shows these average normalized N100m amplitudes plotted against tone level (in dB SL) with rise time as the parameter, and separately for the four different tone frequencies tested. While these data are still rather noisy, some trends emerge: overall, N100m amplitudes increased with tone level. The increases tended to be most pronounced for tones of shorter rise times so that at high levels, N100m amplitudes tended to decrease most steeply with increasing rise time. It is also obvious, at least for short-rise-time tones, that the increase of N100m amplitude with tone level is steepest at 500 Hz, intermediate at 1,000 Hz, and shallowest at 2,000 Hz.
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The systematic effects of level and rise time on N100m peak amplitude,
described in the preceding text, are qualitatively similar to those
seen in the spike count versus tone level functions of many cortical
neurons, viz. those where such functions are monotonic (Heil
1997b). Here threshold level and dynamic range increase
systematically with increasing rise time. As shown previously (Heil 1997b
; Heil and Irvine 1998
), the
spike count versus tone level functions obtained from a given neuron
with tones of different rise time can be brought into close register.
This is achieved by plotting the responses as a function of the
stimulus amplitude at the instant of response generation, instead of
level, a measure of the steady-state or plateau amplitude reached at
the end of the rise time. The instant of response generation is given
by the difference between the measured latency and the estimated transmission delay Lmin. Hence
response magnitude can be considered as a function of the integral of
rate of change of amplitude from tone onset to that instant. The
correspondences of the average effects of level and rise time on N100m
peak amplitude with those on the spike counts of single neurons
therefore suggests that not only the peak latency but also the peak
amplitude, at least of N100m, are determined by dynamic stimulus
properties at their very onsets.
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DISCUSSION |
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Comparison with previous studies
Several previous studies have demonstrated systematic influences
of rise time, or rise time and level, on latencies and amplitudes of
various components of AEPs (Barth and Burkard 1993;
Brinkmann and Scherg 1979
; Folsom and Aurich
1987
; Hecox and Deegan 1983
; Hecox et al.
1976
; Kodera et al. 1979
; Milner
1969
; Onishi and Davis 1968
; Pardo et al.
1999
; Ruhm and Jansen 1969
; Suzuki and Horiuchi 1981
; but for contrasting results, see
Liegeois-Chauvel et al. 1994
). A few studies have also
looked at the effects of these parameters on brain stem evoked
potentials in anesthetized (e.g., Burkard 1991
;
Starr and Farley 1983
) and awake animals (Phillips and Burkard 1999
). While there is some
disagreement with respect to details, the major findings of these
studies are consistent with ours in that peak latencies decrease with
increasing level and increase with increasing rise time and peak
amplitudes increase with level and decrease with rise time (for review,
see Hyde 1997
). Furthermore consistent with previous
results, we found that tone duration (plateaus of 35 vs. 135 ms) has no
effect on N100m latency (Joutsiniemi et al. 1989
), that
monaural stimulation elicits larger N100m peak amplitudes in the
contralateral than in the ipsilateral hemisphere
(Mäkelä et al. 1993
; Pantev et al.
1998
; Reite et al. 1981
), and that the average
growth of N100m amplitude with stimulus level is steeper for lower than
for higher frequencies, a fact attributed to deeper source locations
for higher frequencies (tonotopic organization) (Antinoro et al.
1969
; Pantev et al. 1995
; for review, see
Hyde 1997
).
We have used our data to show that for tones shaped with cosine-squared rise functions, N100m and P50m peak latency form a rather invariant function of the acceleration of peak pressure at tone onset, a parameter that covaries with level and rise time. This is not to say that latency is mechanistically determined by that acceleration because stimuli which share a common acceleration at onset also share common initial time courses of the rate of change and of the peak pressure itself (Fig. 1). Latency may therefore also be determined by these stimulus parameters or a combination thereof. Nevertheless it can be safely concluded that N100m and P50m peak latencies are largely determined by dynamic stimulus properties at their very onsets. This must consequently also apply to peak amplitudes. Indeed, as shown here, the N100m peak amplitude varies systematically with stimulus level but also with rise time.
Hari and Mäkelä (1986, 1988
) have
conjectured that the synchrony of postsynaptic potentials, evoked by
afferent input to the cortex, would depend on the speed of modulation
(of frequency or amplitude) and would so affect N100m latency. Our data
from auditory-nerve fibers (Heil and Irvine 1997
) and
auditory cortical neurons (Heil 1997a
;
Heil and Irvine 1998
) show that the first-spike latencies of individual fibers/neurons decrease with increasing acceleration. Because of their curved nature, functions from different fibers/neurons will converge with increasing acceleration, thus leading
to higher synchronization of first spikes, and probably also of
postsynaptic potentials, across the neuronal population. Thus increased
synchrony is a consequence of the increased rapidity of the transient
but is likely not the cause, or at least not the sole cause, of
decreasing N100m latency.
Latency versus acceleration functions obtained under different stimulus
conditions and from different subjects are of rather similar shape as
reflected in a similar exponent of the functions fitted to the latency
versus acceleration functions. The functions are, however, displaced
along the latency axis, reflecting differences in transmission delay
Lmin, and displaced along the
acceleration axis, reflecting differences in transient sensitivity
APPmax(0). The transient sensitivity was highest
at 1 kHz, both for P50m and for N100m, i.e., latency versus
acceleration functions obtained with that frequency are in a most
leftward position, while those obtained with lower and higher
frequencies would be in more rightward positions within the coordinate
system. Thus for tones of any given acceleration (i.e., of fixed level
and rise time), latency would be shortest for 1 kHz and would increase
for lower and higher frequencies. In addition, because latency versus
acceleration functions are curved and not straight, different functions
converge with increasing acceleration. Therefore those latency
increases with frequency distance from 1 kHz would be the steeper the
lower the acceleration, i.e., the lower the level when rise time is fixed. Exactly such results have recently been observed for N100m (Roberts and Poeppel 1996; Stufflebeam et
al. 1998
).
Rejection of the fixed threshold model and functional implications
Because signals of cosine-squared rise function have practically
identical initial time courses of the peak pressure when they share the
same acceleration at their onsets (Fig. 1), one may argue that latency
could be determined by the instant at which tones of identical
acceleration at onset reach some low but common and fixed-amplitude
threshold. Hence changes in latency with changes in level or rise time
would then be brought about by changes in the time needed to reach that
fixed-amplitude threshold. This fixed-amplitude threshold model has
been proposed directly (e.g., Ruhm and Jansen 1969) or
is implied indirectly (e.g., Brinkmann and Scherg 1979
;
Ross et al. 1999
), although Onishi and Davies (1968)
have pointed out one observation incompatible with it. Our data clearly show that this model is inadequate. The deviations of
the measured N100m and P50m peak latencies from those predicted by the
model are quite pronounced and systematic (Fig. 9F). In other words, the stimulus amplitude at which the N100m or P50m peak
amplitudes are triggered is not constant, even for a given frequency,
but varies systematically with the dynamics of the increase in stimulus
amplitude, i.e., with rise time, with level, and likely with rise
function. Furthermore the deviations of the measured latencies from the
fixed threshold model are unlike those that would be expected if
accommodation would operate. Accommodation would effectively raise the
trigger amplitude when the pressure increases slowly so that latency
versus rise time functions should be curved upward. Instead they are
compressively nonlinear (Fig. 4, A and D). Hence
the faster the increase in stimulus amplitude (peak pressure), the
higher the trigger amplitude.
These phenomena are not restricted to cosine-squared rise functions or
AEFs. A re-analysis (Heil, unpublished) of published data from suitable
studies (Barth and Burkard 1993; Folsom and Aurich 1987
; Hecox et al. 1976
; Milner
1969
; Onishi and Davis 1968
; Ruhm and
Jansen 1969
; Suzuki and Horiuchi 1981
) shows
that for linear rise functions, latency of AEPs (Jewett's wave V and N100) is an invariant function of the rate of change of peak pressure. Hence for these stimuli, latency is also determined by stimulus events
at their very onset (see also Onishi and Davis 1968
;
Suzuki and Horiuchi 1981
). And second, the deviations of
the shape of the latency functions from the shape predicted by a
fixed-amplitude threshold model are much the same as those described in
the preceding text for cosine-squared rise functions.
There appear to be quite parallel phenomena in the visual system.
Jaskowski (1993) has presented stimuli shaped with
linear luminance increases of various rise times to human subjects
whose task was to press a response button as soon as they detected the stimulus. He observed that the reaction time increased with increasing rise time when the peak luminance was held constant, while reaction time was constant for stimuli with the same rate of change of luminance. Jaskowski also suggested a model where reaction time behaves
as onset latency, i.e., as the time interval from stimulus onset to the
moment that an internal response crosses a critical value and where the
internal response is directly proportional to luminance. However, the
data illustrated are too noisy and too few to clearly decide whether
that fixed-luminance threshold model is really appropriate.
The failure of a fixed-amplitude threshold model is noteworthy because
in audiology AEPs such as the N100 are used, or promoted, as tools to
estimate the pure-tone audiogram for a large and diverse target
population, including medicolegal and industrial hearing-loss compensation claimants (Hyde 1997; see also Ross
et al. 1999
). Hyde has suggested that rise function is
irrelevant and that rise times are important only in so far as they
affect spectral splatter. It is therefore important to stress that our
data show that the dynamics of the stimulus at tone onset, determined
by level but also by rise time and by rise function, are absolutely
critical for shaping N100m and P50m and likely other AEFs or AEPs. It
should be kept in mind that such components are triggered by the
increase in stimulus amplitude at tone onset, i.e., by a transient.
Hence such components may be more useful as estimates of sensitivity to
transients rather than of absolute threshold.
The potential integration times underlying P50m and N100m are given by
the difference between measured latency and transmission delay and can
be directly read from the ordinate of Fig. 9D. Depending on
the rapidity of the amplitude increase, here on the acceleration at
tone onset, integration times vary between near 0 ms and ~50-70 ms.
In our data, 92% were shorter than 30 ms and 48% still shorter than
10 ms. While for short-rise-time tones most integration times were
longer than the rise times, that percentage dropped to about half for
16-ms rise-time tones and to zero for 64- and 128-ms rise-time tones.
Overall the integration times were shorter than the rise times for 62%
of our combinations of rise times and levels. Therefore the peak
amplitudes of N100m and P50m were triggered before these tones reached
their steady-state levels. This raises doubts as to the validity of the
concept of an amplitopic organization of the human auditory cortex.
Such an organization has been suggested based on systematic changes in
the position of the N100m equivalent current dipole with the level of
tones shaped with 15-ms rise times (Pantev et al. 1989).
Comparison with single-neuron data and implications for envelope coding
As demonstrated in this paper, essentially all observations made
on N100m and P50m are very similar to those made previously on the
onset responses of single neurons from the auditory cortex of
barbiturate-anesthetized cats (Heil 1997a,b
; Heil
and Irvine 1997
, 1998
). As expected, the data obtained from
spikes of single neurons are much cleaner than those from AEFs: on
average, variances of single-neuron data are one to two orders of
magnitude smaller. Still, some favorable MEG measurements can yield
variances as low as those from single neurons with rather sloppy timing
precision (Fig. 6). The major difference between the human and the cat
data is that the latency versus acceleration functions of the former are less steeply curved. However, the fact that in MEG thousands of
neurons, many of which have characteristic frequencies different from
the frequency of the tonal stimulus, contribute to the signal is
unlikely to account for the difference in curvature. This is so because
latency versus acceleration functions obtained from a given neuron with
tones of different frequencies are displaced along the acceleration
axis but have the same curvature (Heil 1997a
). And a
compound latency versus acceleration function, obtained by averaging
such functions, would have the same curvature as that of each
individual function. Nevertheless it is conceivable that the difference
in curvature is due to differences in measuring methods and their
underlying signals (dendritic currents in large populations of neurons
vs. spikes of single neurons), or due to differences in state (awake
vs. anesthetized), or in species.
The correspondences suggest that the observed properties of single
neurons from the cat's auditory cortex per se are not artifacts of
anesthesia. As shown here, similar relationships exist in the human
auditory cortex and involve thousands of neurons simultaneously. As
detailed elsewhere (Heil 1997b; Heil and Irvine
1997
, 1998
), the single-neuron properties seem ideal not for
coding steady-state SPL, but for tracking, in real time and with high
fidelity, rapidly varying envelopes, such as, but not limited to, those
occurring at tone onset. The present study suggests that much the same
mechanisms operate in the auditory cortex of awake humans where they
might be most useful to encode the rapidly varying envelopes
characteristic of many sounds, including speech.
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ACKNOWLEDGMENTS |
---|
We are grateful to Prof. Henning Scheich for continuing support, to the staff in the Department of Otorhinolaryngology of the Otto-von-Guericke University Magdeburg for measuring the audiograms, to H. Neubauer and Dr. Birgit Gaschler-Markefski for statistical advice, and to Dr. Michael Brosch for critically reading an earlier version of the manuscript. P. Heil is also most grateful to Prof. Dexter R. F. Irvine in whose laboratory and with whose help the cat data were recorded.
This study was supported by the Bundesministerium für Bildung und Forschung and the state of Sachsen-Anhalt.
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FOOTNOTES |
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Address for reprint requests: P. Heil, Leibniz Institute for Neurobiology, Brenneckestr. 6, D-39118 Magdeburg, Germany (E-mail: peter.heil{at}ifn-magdeburg.de).
Received 6 April 2000; accepted in final form 2 August 2000.
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REFERENCES |
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