Department of Otology and Laryngology, Harvard Medical School, Harvard-MIT Division of Health Sciences and Technology and Eaton-Peabody Laboratory, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts 02114
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ABSTRACT |
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Brown, M. C.. Response Adaptation of Medial Olivocochlear Neurons Is Minimal. J. Neurophysiol. 86: 2381-2392, 2001. Response adaptation is a general characteristic of neurons. A number of studies have investigated the adaptation characteristics of auditory-nerve fibers, which send information to the brain about sound stimuli. However, there have been no previous adaptation studies of olivocochlear neurons, which provide efferent fibers to hair cells and auditory nerve dendrites in the auditory periphery. To study adaptation in efferent fibers, responses of single olivocochlear neurons were recorded to characteristic-frequency tones and noise, using anesthetized guinea pigs. To measure short-term adaptation, stimuli of 500 ms duration were presented, and the responses were displayed as peristimulus time histograms. These histograms showed regular peaks, indicating a "chopping" pattern of response. The rate during each chopping period as well as the general trend of the histogram could be well fit by an equation that expresses the firing rate as a sum of 1) a short-term adaptive rate that decays exponentially with time and 2) a constant steady-state rate. For the adaptation in medial olivocochlear (MOC) neurons, the average exponential time constant was 47 ms, which is roughly similar to that for short-term adaptation in auditory-nerve fibers. The amount of adaptation (expressed as a percentage decrease of onset firing rate), however, was substantially less in MOC neurons (average 31%) than in auditory-nerve fibers (average 63%). To test for adaptation over longer periods, we used noise and tones of 10 s duration. After the short-term adaptation, the responses of MOC neurons were almost completely sustained (average long-term adaptation 3%). However, in the same preparations, significant long-term adaptation was present in auditory-nerve fibers. These results indicate that the MOC response adaptation is minimal compared with that of auditory-nerve fibers. Such sustained responses may enable the MOC system to produce sustained effects in the periphery, supporting a role for this efferent system during ongoing stimuli of long duration.
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INTRODUCTION |
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Most sensory neurons adapt
their response to constant stimuli. Presumably, this adaptation allows
the neurons to produce large responses to transient stimuli and smaller
responses to ongoing stimuli. In the auditory nerve, firing rates of
fibers adapt in response to a tone burst (Chimento and Schreiner
1991; Delgutte 1980
; Javel 1996
;
Kiang et al. 1965
; Müller and Robertson
1991
; Nomoto et al. 1964
; Rhode and Smith
1985
; Smith 1979
; Smith and Zwislocki
1975
; Westerman and Smith 1984
; Yates et
al. 1985
; Young and Sachs 1973
). Adaptation in
the auditory nerve follows a multi-component time course: 1)
rapid adaptation over the first few milliseconds (Chimento and
Schreiner 1991
; Westerman and Smith 1984
),
2) short-term adaptation over about 50-100 ms
(Chimento and Schreiner 1991
; Müller and
Robertson 1991
; Rhode and Smith 1985
;
Westerman and Smith 1984
), 3) long-term
adaptation over several seconds (Javel 1996
), and
4) very long-term adaptation over several minutes
(Javel 1996
; Kiang et al. 1965
). It is
likely that rapid and short-term adaptation arise from processes at the
synapse between the hair cell and auditory-nerve fiber, because
although the fiber's discharge adapts, receptor potentials of inner
hair cells are sustained over tens of milliseconds (Goodman et
al. 1982
; Russell and Sellick 1978
). Long-term
adaptation in nerve fibers may also result from synaptic processes
(Javel 1996
). Such processes probably involve depletion
of neurotransmitter at the hair cell/nerve fiber synapse (Furukawa et al. 1978
; Norris et al.
1977
), although their exact nature remains to be determined.
Olivocochlear (OC) neurons are efferent neurons that arise in the brain
stem and project to the cochlea. In the cochlea, medial (M) OC neurons
innervate outer hair cells (reviewed by Warr 1992). Their action on outer hair cells probably controls the dynamic range of
the cochlea, reduces the effects of noise masking, and protects the
cochlea from damage due to acoustic overstimulation (Geisler
1974
; Kawase et al. 1993
; Meric and
Collet 1994
; Rajan 1995
; Wiederhold and
Kiang 1970
; Winslow and Sachs 1988
; reviewed by
Guinan 1996
). MOC neurons can perform these functions
because they respond to sound as part of a reflex. In response to
sound, typical latencies of MOC neurons are 5-50 ms (Brown
1989
; Liberman and Brown 1986
; Robertson
and Gummer 1985
), and typical effects at the outer hair cells
are seen about 50-100 ms later (Wiederhold and Kiang
1970
). Because of these relatively long times, it seems unlikely that the MOC system could alter the response of the cochlea to
brief sounds with durations less than about 100 ms. For example, MOC
protection from overstimulation is not likely for short-duration sounds
like a gunshot. Rather, MOC protection has been demonstrated for sound
exposure durations of minutes (Rajan 1995
; Reiter
and Liberman 1995
) to hours (Kujawa and Liberman
1997
; Zheng et al. 1997a
,b
).
If MOC effects are most functionally significant for long-duration
sounds, it is important to consider response adaptation of MOC neurons.
For instance, MOC effects are likely to be lessened if the firing rates
of MOC neurons adapt over time. This logic follows from studies where
the MOC neurons are stimulated electrically to elicit protection; in
such studies decreasing the stimulation rate from 100 to 50 shocks/s
approximately doubles the damage, as measured by the threshold shift 30 min after an acoustic overstimulation (Rajan and Johnstone
1988). Numerous other data indicate that decreasing MOC
stimulation rates from the optimal 200-400 shocks/s can greatly
decrease other effects of MOC neurons on peripheral responses to sound
(Brown and Nuttall 1984
; Desmedt 1962
;
Gifford and Guinan 1987
; Konishi and Slepian
1971
). However, our knowledge of whether sound-evoked MOC
firing decreases over time is limited, since studies of MOC adaptation
characteristics have not been made. Knowledge of such characteristics
would be useful; for example, to predict the exposure durations for
which MOC-mediated protection is most robust. Similar knowledge would
also help predict the sound durations for which the MOC system plays a
role in adjusting the dynamic range of the cochlea and in reducing the
effects of noise masking.
In this study, we measured the adaptation characteristics of single MOC
neurons in response to sounds of 500 ms or 10 s duration. We
compare these characteristics to those of auditory-nerve fibers recorded in the same preparations. These nerve fibers provide input to
the brain and form the initial portion of the MOC reflex. Differences
between nerve fiber and MOC adaptation reflect the central changes in
response within the MOC reflex pathway. Our results demonstrate that
MOC response can be relatively constant with time, far more constant
than responses of the auditory nerve, and more constant than many
neurons in the cochlear nucleus and superior olivary complex
(Boettcher et al. 1990; Finlayson and Adam
1997
; Palombi et al. 1994
; Shore
1995
). A preliminary version of the results has been presented
(Brown and Duca 1998
).
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METHODS |
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Experimental preparation
A total of 24 albino guinea pigs, of either sex, were used as
experimental animals. The methods for surgery and recordings are
described elsewhere (Brown 1989). All surgical
procedures were in accordance with the National Institutes of Health
guidelines for the care and use of laboratory animals. For
physiological recordings, guinea pigs were anesthetized with a
combination of pentobarbital sodium (Nembutal; 15 mg/kg ip) and
Innovar-Vet (0.5 ml/kg im, each ml of Innovar-Vet contains 0.4 mg of fentanyl and 20 mg of droperidol) or with a combination of
urethan (1,500 mg/kg ip), fentanyl (0.2 mg/kg im), and droperidol (10 mg/kg im). Additional doses of anesthetics were administered as needed
to eliminate the toe-pinch reflex. Sound stimuli were produced by
half-inch condenser microphones coupled to the ear canal in a closed
speculum assembly similar to that of Evans (1979)
. Sound
pressure was measured with additional microphones fitted with probe
tubes that were advanced within 1 mm of the tympanic membrane. The
sensitivity of hearing was measured by recording compound action
potentials (CAPs) at the round window in response to 5-ms tone pips
(0.5-ms rise time, cos2 shaping) at 15 logarithmically spaced frequencies from 1.9 to 25.4 kHz. Sound pressure
level was varied to determine the level (to within 1 dB) required to
produce a CAP of 10 µV (peak-to-peak amplitude). Sound pressure was
measured with a probe-tube microphone. The spiral ganglion, which
contains the cell bodies of the auditory-nerve fibers, and which is
located in the central core or modiolus of the cochlea, was exposed
through scala tympani of the cochlear basal turn as described
previously (Robertson and Gummer 1985
; Sellick
and Russell 1979
). This surgery was accomplished with minimal
alterations in CAP sensitivity (<5-10 dB) across the frequency range
tested, otherwise the animal was excluded from this study.
Single-unit recordings and response measures
Single-fiber recordings of MOC neurons were made from the spiral
ganglion in the basal turn (Brown 1989), where MOC axons travel in the intraganglionic spiral bundle. MOC neurons were identified by their regular interspike intervals, long latencies (>5
ms) in response to search stimuli (binaural noise bursts), and the fact
that some responded only to monaural contralateral stimulation. Units
with these types of responses have been confirmed to be MOC neurons
that innervate outer hair cells (Brown 1989
; Robertson and Gummer 1985
), whereas axons of lateral
olivocochlear neurons are probably too thin to be recorded. In
contrast, auditory-nerve fiber recordings were identified by their
irregular firing patterns, their short latencies, and their response
exclusively to ipsilateral stimulation. Auditory-nerve fibers recorded
from the spiral ganglion all had high characteristic frequencies (CFs,
12-17 kHz) because the recording site was located in the lower basal
turn. Thus to obtain other fibers with lower CFs, some recordings were
made from the auditory nerve as accessed through an opening in the modiolus (Alder and Johnstone 1978
). In contrast, MOC
neurons recorded from the spiral ganglion had a variety of CFs because MOC axons spiral through the recording site en route to their terminations at diverse cochlear locations.
Before adaptation was measured, it was necessary to characterize the
MOC neurons based on their response properties. First, noise bursts
were presented monaurally to determine each neuron's response type
(Ipsi units respond to stimuli in the ipsilateral ear, Contra units
respond to stimuli in the contralateral ear, and Either Ear units
respond to stimuli in either ear). A tuning curve was then determined
using an automated procedure (Kiang et al. 1970). The CF
and threshold at CF were determined from the tuning curve. To measure
response over time, peristimulus time (PST) histograms were obtained in
response to CF tone bursts or noise bursts of 500 ms or 10 s
duration (50% duty cycle). Histograms for 500 ms were obtained with
50-100 burst presentations, and those for 10 s were obtained with
5-15 burst presentations. Histogram binwidths were 5 or 100 ms,
respectively (each histogram contained 200 bins, and these bins
encompassed a total time of 1,000 ms or 20 s). Sound burst
rise-fall time was always 2.5 ms. Noise and tone levels are specified
in dB SPL. For level functions, PSTs were taken at increasing levels
with steps of 10 dB. Spontaneous rate (SR) was estimated by averaging
the last 10 bins of the PST histogram (at the end of the "off time"
when there was no sound stimulus). In a few neurons,
"afterdischarge" activity appeared in the off-time in response to
moderate and high level bursts (Brown 1989
;
Liberman and Brown 1986
). This activity was not used for
the curve fits nor was it counted as SR; rather, SR for these neurons
was measured during a 10-s period of silence.
Curve fits to rate equations
For studies of short-term adaptation, the PST data for 500-ms
bursts were fit by an equation that expresses the rate as the sum of a
declining exponential function plus a steady-state rate
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(1) |
The starting bin for each fit was determined individually for each PST
because MOC responses can have significant and variable latencies
(Brown 1989; Liberman and Brown 1986
).
For each PST, several fits with different starting bins were tried, and
the rates predicted by the fits were compared with the actual
peak-by-peak average rate (
, Fig. 1).
The fit that most closely approximated the average rate was chosen
(e.g., for the PST of Fig. 1 the fit began with the 4th bin); this fit
usually began with the first bin that had more spikes than spontaneous
activity. The fit was computed for all the data beginning with this bin
and including a total of 100 bins, so that a "response window" of
500 ms was included. For auditory-nerve fibers, which have much shorter
latencies, the fit began with the first bin of the histogram. A
curve-fitting program (Kaleidagraph) was used to fit the PST data to
the equation by a least-squares procedure so that errors for the
parameters were minimized. To compare adaptation in MOC neurons and
auditory-nerve fibers, we define
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(2) |
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For studies of long-term adaptation, the PST data for 10-s bursts were
not fit to equations, since for MOC neurons there was an almost steady
rate that was ill-fit with an exponential function. Instead, we define
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(3) |
Our data set included a total of 27 MOC neurons and 20 auditory-nerve fibers from 15 guinea pigs that were studied with bursts of 500 ms duration, and a total of 19 MOC neurons and 14 auditory-nerve fibers from 14 guinea pigs studied with bursts of 10 s duration. Statistical tests were t-tests performed at the 5% level of significance.
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RESULTS |
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Short-term adaptation for stimuli of 500 ms duration
MOC neurons showed a small amount of adaptation in response to
constant stimuli of 500 ms duration. An example PST histogram from one
neuron is shown in Fig. 1. The histogram has peaks that reflect the
regular firing, or chopping, pattern of these neurons. One method of
measuring the firing rate over time is to count the number of spikes in
each chopping interval and divide by the time from the beginning to the
end of the interval, thus obtaining an average rate for each chopping
interval (dots on Fig. 1). For the first chopping interval, this rate
was 11.8 spikes/s, and for the last interval it was 8.4 spikes/s,
indicating a decrease in firing to 71% of the original rate, or
conversely, an adaptation of 29%. An inspection of the PST (Fig. 1)
indicates that most of this adaptation takes place within approximately
125 ms from stimulus onset. The decrease in firing will therefore be
called short-term adaptation because it occurs on a time scale similar to the short-term adaptation of auditory-nerve fibers (Chimento and Schreiner 1991; Westerman and Smith 1984
),
and to distinguish it from long-term adaptation over the time course of
10 s that will be discussed later.
The PST data were fit by an equation that expresses the rate as a sum of two subcomponents, a short-term adaptation that decays exponentially with time and a steady-state rate that is constant with time (Eq. 1 in METHODS and on Fig. 1). The best-fit curve is illustrated by the heavy line on the figure, and the parameters are given at the top of the figure. This fit accurately captures the firing rate measured during each chopping interval (dots) as well as the general outline of the PST. The steady-state rate, RSS, can be thought of as a combination of a driven steady-state rate, RDSS, plus a spontaneous rate, SR, or mathematically, RSS = RDSS + SR. Like most MOC neurons, the SR for the neuron shown in Fig. 1 was zero, so RDSS was equal to RSS. Decaying exponential fits are also shown for four other MOC neurons in the top row (A-D) of Fig. 2; only one of these neurons (D) had spontaneous activity. For curve fits for all 27 MOC neurons in our sample, the subcomponent of the rate due to short-term adaptation, RA, averaged 15.0 ± 2.5 (SE) spikes/s, and the average RDSS was 31.6 ± 2.4 spikes/s. The sound levels used to collect these data were bursts between 65 and 95 dB SPL; the effects of level are considered below.
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The curve fits can be used to calculate the amount of short-term adaptation on a percentage basis, which is defined as the decline in rate from the initial rate to the steady-state rate, normalized by the initial rate (Eq. 2 in METHODS). For the neuron illustrated in Fig. 1, the fit predicted a short-term adaptation of 32%, very close to the 29% adaptation obtained from measuring the rates during the first and last chopping intervals. Calculation of adaptation using the fit produces a slightly higher percent since a higher initial rate (numerically RA + RDSS) is used in the calculation, whereas calculation of adaptation by measuring the rate during the first chopping interval (about 25 ms for Fig. 1) uses an average rate over the first chopping interval. It could be argued that a high initial rate predicted by the fit does not represent the true rate in the first histogram bins (Fig. 1); although true, this argument does not greatly change the percent adaptation. For the neurons illustrated in Fig. 2, the percent short-term adaptation from the fits are indicated on each panel; these range from 23 to 31%. For all MOC neurons, the average percent adaptation was 30.9 ± 2.1.
Decaying exponential equations were also fit to PST data from
auditory-nerve fibers (Fig. 2, E-H). For the 20 auditory-nerve fibers of our sample, the average percent adaptation was
62.6 ± 2.9. This adaptation was significantly greater than the
average for MOC fibers (30.9%), using a t-test
(P = 0.001). For nerve fibers, alternate methods for
computing percent adaptation did not change the values much. The
alternate method of measuring the percent change from the onset rate
(the 1st bin) to the steady-state rate (the average of the last 10 bins
in the response window) yielded an average of 65.6%, a very similar
value to that obtained using the curve fits (62.6%). To explore
whether the higher adaptation in auditory-nerve fibers could be made
artificially as low as MOC neurons by constraining the measurement bins
to intervals as long as the MOC chopping intervals, auditory-nerve
firing was artificially binned into long intervals (20 ms) and
adaptation was measured again. This test only changed the average
nerve-fiber adaptation by 3.2%, having most influence on fibers with
very short time constants of adaptation (e.g., Fig. 2G). All
of these methods are likely to underestimate the true amount of
adaptation in auditory-nerve fibers because relatively coarse binwidths
(5 ms in our data) do not capture the true onset rate that, unlike MOC
neurons, is typical of nerve fibers (Chimento and Schreiner 1991; Müller and Robertson 1991
;
Westerman and Smith 1984
). Indeed, percent adaptations
for nerve fibers average 84% when calculated from published data
obtained with finer binwidths and with equations that two exponential
components of decay. Conversely, MOC data taken with finer binwidths do
not show high onset firing (Brown 1989
; Liberman
and Brown 1986
; Robertson and Gummer 1985
),
making it very likely that our methods have accurately estimated the amount of MOC adaptation.
For all neurons recorded, the percent short-term adaptation and the
time constant of adaptation, computed from the curve fits, are plotted
against the fiber CF in Fig. 3. MOC
neurons clearly have the lowest percent adaptations throughout the CF
range (A), although the adaptation of some low-SR nerve
fibers can be equally low (squares on Fig. 3A, PSTs of Fig.
2, F and H) as previously reported
(Müller and Robertson 1991; Rhode and Smith
1985
). In contrast to the percent adaptation, time constants
(Fig. 3B) are not significantly different: the average MOC
time constant was 46.7 ± 5.1 ms and for the auditory-nerve fibers
it was 44.4 ± 7.3 ms. There was little CF dependence of percent
adaptation or time constants, for either MOC neurons or auditory-nerve
fibers. There was also little difference in these measures between the MOC response types, which in our sample are Ipsi units (
, Fig. 3)
and Contra units (
, Fig. 3). Adaptation also did not obviously depend on whether tones or noise were used as the stimulus. In the data
of Fig. 3, 17 of the neurons were stimulated with noise, and the other
10 points were stimulated with tones. The average percent adaptation
measured with noise (29.9 ± 2.9, n = 17) was not
significantly different from that measured with tones (32.6 ± 2.8, n = 10). In addition, one neuron was studied with
both tones and noise at a variety of levels; the percent adaptations and time constants were overlapping for the two stimuli. Finally, present data (not shown) do not reveal any dependence of short-term adaptation on MOC neuron threshold at CF (average 40, range 16-75 dB
SPL) or on MOC neuron spontaneous rate (average 1.9, range 0-24.6
spikes/s).
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After the tone burst ends, auditory-nerve fiber spontaneous activity is
depressed transiently but returns with a time course similar to that of
short-term adaptation (Fig. 2, E and G)
(Harris and Dallos 1979; Kiang et al.
1965
). We observed a similar depression and return of
spontaneous activity in the five MOC neurons with significant
spontaneous activity; one example is shown in Fig. 2D.
Spontaneous activity in MOC neurons usually returned in peaks synchronized to the stimulus offset (Fig. 2D).
Level dependence of short-term adaptation
The level dependence of short-term adaptation and other measures
of rate are shown for four MOC neurons in Fig.
4. The measured average rate () is a
count of all the spikes in the "response window" of 500 ms and is
the value usually plotted in level functions. This measured rate is a
driven rate because spontaneous rate has been subtracted.
Calculated average rates from the exponential fits (
, Fig. 4)
closely match the measured average rate, indicating that the fits
closely reflect the PST data. Also plotted on Fig. 4 are the two terms
from the fit, the short-term adaptation
(RA) subcomponent of the rate and the
steady-state rate (RDSS) subcomponent, and their sum, the onset rate. RA is
smaller than the other components, and it increases with level in three
of the four neurons shown (A, C, and D).
RDSS also increases with level,
closely tracking the average rate and having the same large dynamic
range that is typical of the measured average rate for MOC neurons
(Brown 1989
; Brown et al. 1998
;
Liberman and Brown 1986
; Robertson and Gummer
1985
). Similar to data from auditory nerve fibers
(Westerman and Smith 1984
), in MOC neurons
RA increases fairly linearly with RDSS (data not shown). Thus when
short-term adaptation is computed on a percentage basis
(RA divided by
RA + RDSS), there is little dependence on
level (Fig. 5A). The time
constants also show little overall dependence on level (Fig.
5B), although there is somewhat more variability from level
to level in these data.
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Adaptation for stimuli of 10 s duration
The adaptation characteristics of MOC neurons were also
measured in response to much longer stimuli (10 s duration). For these long bursts, example PST histograms from MOC neurons and auditory-nerve fibers are shown in Fig. 6. On the time
scale illustrated, the MOC neuron's latency to response and its
short-term adaptation take place within the first two bins of the
histograms (1st 0.2 s). For MOC neurons, there were almost no
further rate changes. To compare MOC behavior with that of
auditory-nerve fibers, a simple measure of long-term adaptation was
defined using two measurement windows that are shown by brackets on
Fig. 6A. Long-term adaptation (Eq. 3 in
METHODS) was defined as the normalized decline in rate from
an initial rate, Ri, which is measured
over a 1-s period after short-term adaptation had decayed, to a final
rate, Rf, which is measured over the
final 1-s period of the burst on time. For the histograms shown in Fig.
6, the long-term adaptation ranged from 1 to 6% for MOC neurons
(A-D). A small negative value of adaptation,
1% seen in
D, indicates that the rate shows a small increase
during the 10 s burst. In Fig. 6, auditory-nerve fibers (E-H) have obvious positive adaptations over the 10-s
burst. Their values for percent long-term adaptations range from 16 to
180%. The one fiber with an adaptation of over 100% reflects the fact that the rate during the stimulus has fallen below the spontaneous rate. Some fibers with similarly high adaptations were reported by
Javel (1996)
. Our definition of long-term adaptation
uses a broad time window (1 s) for comparing rate at the beginning
versus the end of the burst. An inspection of Fig. 6 indicates that
using an earlier subinterval for the initial rate (e.g., the period between 0.2 to 0.7 s rather than between 0.2 and 1.2 s) would probably not change the measure of adaptation for MOC neurons but would
increase it for auditory-nerve fibers.
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Although there was little long-term adaptation in MOC neurons, there was sometimes a suppression and a slow recovery of spontaneous activity after the burst ended. This recovery occurred over a time course of several seconds (Fig. 6B), a pattern seen in three of the six MOC neurons with spontaneous activity that were tested with 10-s bursts. The spontaneous activity in two of the six neurons returned within a half-second of the offset. In the final neuron, there was no obvious decline in spontaneous activity (Fig. 6D), and for its response to 500-ms bursts, the spontaneous activity was synchronized in peaks locked to the offset of the burst (shown for another neuron in Fig. 2D).
The percent long-term adaptation is shown for MOC neurons and auditory nerve fibers in Fig. 7. MOC neuron long-term adaptation is almost always less than that of auditory-nerve fibers and is near zero in the majority of MOC neurons. The average percent long-term adaptation for MOC neurons was 2.9 ± 1.9% (for 19 neurons), whereas the average for auditory nerve fibers was 35.4 ± 11.6% (for 14 fibers), a difference that was statistically significant. Similar to the data for short-term adaptation, MOC neuron long-term adaptation had little CF dependence nor did it differ between Ipsi and Contra units (Fig. 7). For the eight neurons tested with both noise and tones, there was also no significant difference in long-term adaptation for the two stimuli (data not shown). For auditory-nerve fibers, there is little CF dependence, but there is a tendency for high-SR rates to adapt the most (although there is considerable overlap for fibers of different SRs).
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DISCUSSION |
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Comparison of MOC and auditory-nerve adaptation
Adaptation characteristics of MOC neurons and auditory-nerve
fibers differ in several ways. For short-term adaptation that is seen
over about 100 ms after stimulus onset, the amount of adaptation seen
in MOC neurons is approximately half that seen in nerve fibers. For the
firing pattern over several seconds, there is almost no long-term
adaptation of MOC response, even though auditory-nerve fibers in the
same preparations showed substantial long-term adaptation. The other
types of adaptation seen in the auditory nerve were not studied: rapid
adaptation (Chimento and Schreiner 1991;
Westerman and Smith 1984
) would be difficult to study in
MOC neurons because it would take place within the first peak of the
PST histogram. Very long-term adaptation has yet to be systematically
investigated, although minimal declines in rate in response to
continuous tones have been reported for a few MOC neurons
(Liberman and Brown 1986
).
Present data indicate that short-term adaptation in MOC neurons
and auditory-nerve fibers show a difference in adaptation magnitude,
yet the level dependence and time constants are similar. Our knowledge
of adaptation in nerve fibers is based on a number of earlier studies
(Chimento and Schreiner 1991; Delgutte
1980
; Kiang et al. 1965
; Müller and
Robertson 1991
; Smith 1979
; Smith and
Zwislocki 1975
; Westerman and Smith 1984
;
Yates et al. 1985
; Young and Sachs 1973
).
In MOC neurons, both the short-term adaptation subcomponent and
steady-state subcomponent increase with level (Fig. 4), and such
increases are also reported for nerve fibers (Westerman and
Smith 1984
). Many MOC neurons have large dynamic ranges,
however, which are not typical of auditory-nerve fibers. Present data
show that the dynamic range is usually large both for the small
adaptive and larger steady-state components of the rate. Yet overall in
both auditory-nerve fibers (Westerman and Smith 1984
)
and in MOC neurons, the percent short-term adaptation and its time
constant do not generally depend on level (Fig. 5). Also like earlier
studies of nerve fibers (Westerman and Smith 1984
), we
found little dependence of short-term adaptation on CF for MOC neurons,
and in addition we found little adaptation difference between Ipsi and
Contra MOC neurons. In a previous study of nerve-fiber adaptation
(Yates et al. 1985
), short-term adaptation was reported
to have a linear rather than exponential decay. Our fit to nerve fiber
data used a single exponential; this method is simplified but
appropriate given the coarse binwidth of the PST histograms. The decay
is better fit by two exponentials that reflect rapid as well as
short-term adaptation (Chimento and Schreiner 1991
;
Javel 1996
; Westerman and Smith 1984
).
MOC neuron adaptation, however, was well fit by a single exponential process.
Although we have described the adaptation occurring within a
stimulus; a related issue is the effect of repeating a series of
bursts. This question has been investigated for repeated bursts of 50 ms duration with a 50% duty cycle, which, due to the slow chopping in
MOC neurons, evoke only one or two spikes per burst. For repeated
bursts, there is little habituation of spike rate over a time course of
seconds; that is, the response to the 10th burst is the same as the
response to the 1st burst (Brown et al. 1998;
Liberman and Brown 1986
). Apparently, short-term
adaptation is so small and so quick to recover that responses to later
bursts are not different from that to the initial burst. It is likely, however, that anesthetic state plays some role in the pattern of MOC
response. Thus some neurons in deeply anesthetized cats "build up"
their response; that is, they only begin to respond to repeated tone
bursts after several tone bursts have been presented (Liberman
and Brown 1986
).
Long-term adaptation was almost nonexistent in MOC fibers; this
finding is also evident from an inspection of a few figures of early
MOC recordings in decerebrate cats (Fex 1962,
1965
). Results of the present study show that in the
same preparations where MOC response is constant, long-term adaptation
in auditory-nerve fibers (Javel 1996
; Kiang et
al. 1965
) can be prominent. Long-term adaptation in nerve
fibers can be fit with a decaying exponential function with a time
constant of about 3.6 s (Javel 1996
). Average amounts of long-term adaptation for high-SR nerve fibers were about
40%, which is similar to that found in the present study. In the
present study, there was a tendency for the rates of high-SR fibers to
adapt the most (although there is considerable overlap for fibers of
different SRs). Javel's study also showed considerable overlap, but
there was a tendency for low-SR fibers to adapt more than those with
medium and high spontaneous rates. The reason for this difference in
tendencies is not known.
Very long-term adaptation is also present in auditory-nerve fibers with
time constants of minutes. Although not studied here, an earlier report
indicates that for the few MOC neurons studied, there was a small
adaptation of roughly 10% over about 10 min (Liberman and Brown
1986). This percentage is also much smaller than very long-term
adaptation in nerve fibers (Javel 1996
). Overall, compared with auditory-nerve response, MOC response is much more sustained over a period of seconds to minutes after stimulus onset.
How is adaptation minimized in MOC neurons?
This study has demonstrated large differences in the amount of adaptation in MOC neurons and auditory-nerve fibers, for both short- and long-term adaptation. This difference is found for units within the same preparation, demonstrating that it is unlikely to be due to factors such as recording methods, differences in the state of the experimental preparation or species differences. The difference in adaptation is interesting because the auditory-nerve fibers provide the input to the MOC reflex. Thus the decline in input provided by the nerve fibers must be compensated for by elements within the MOC reflex at more central locations. The location of these elements could be within the interneurons of the pathway leading to the MOC neurons or possibly within the MOC neurons themselves.
The identities of the interneurons in the MOC reflex pathway are not
precisely known. These interneurons must include neurons of the
cochlear nucleus, because this nucleus is an obligatory synaptic site
for the auditory nerve. The interneurons are likely located in the
ventral cochlear nucleus (VCN), either in the posterior subdivision of
the VCN (de Venecia et al. 2001; Thompson and
Thompson 1991
; Warr 1982
) and/or in the anterior
subdivision (Robertson and Winter 1988
; Ye et al.
2000
). Some VCN neurons show little adaptation. In particular,
little short-term adaptation is seen in "Sustained" chopper units
(Blackburn and Sachs 1989
), and in low-intensity Chopper
units, which are units reported to have a chopping pattern only at low
sound levels (Shore 1995
). Certainly, those VCN neurons
with relatively sustained rates are candidates to be the MOC reflex
interneurons. Some of this sustained cochlear nucleus firing might be
generated by convergence of low-SR auditory-nerve fibers, which have
been demonstrated to have fairly sustained responses
(Müller and Robertson 1991
; Rhode and Smith
1985
). Indeed, preferential input to the MOC reflex by low-SR
auditory-nerve fibers has been previously suggested on the basis of
correlation of thresholds at CF in cat data (Liberman
1988
), although similar data in guinea pig suggest input from
high-SR fibers (Brown 1989
). From the cochlear nucleus,
the interneurons may directly project to the MOC neurons, as
suggested by the short group delays and the relatively peaked shape of
the modulation transfer functions of MOC neurons (Gummer et al.
1988
).
The sustained pattern of MOC firing, however, could also result
from changes at the level of the MOC neurons themselves. The MOC firing
patterns reflect the number and position of inputs as well as the
membrane properties of the neuron. For instance, even with adapting
inputs like auditory-nerve fibers, modeling studies of the cochlear
nucleus show that relatively sustained postsynaptic firing can be
generated by positioning inputs on the distal dendrites, since
dendritic filtering exerts a low-pass characteristic at the soma
(Banks and Sachs 1991). Likewise, MOC neurons have long
dendrites (Brown 1993
) and receive most of their sparse
input onto these dendrites (Adams 1996
; Spangler
et al. 1986
). Such anatomy could transform an adapting input
into a more sustained response, assuming a long membrane time constant
for the dendrites of the MOC neuron. In fact, long time constants could
explain the observation that MOC units do not typically fire for brief
stimuli like clicks or short-duration (<10 ms) tone bursts, indicating
that a long summation of inputs is necessary for a response
(Liberman and Brown 1986
). Long time constants are also
consistent with the unusually long latency of response of MOC neurons
at low sound levels, which, at levels near threshold, can be as long as
60 ms (Brown 1989
; Liberman and Brown
1986
; Robertson and Gummer 1985
). Such latencies
suggest that a long summation of inputs may be necessary to generate a
response. A time constant of tens of milliseconds could account for the
minimal short-term adaptation of MOC neurons, but is unlikely to
account for the almost complete lack of long-term adaptation since
membrane time constants would not be expected to be on the order of
seconds. Rather, an additional element has to make up for the input
lost by long-term nerve-fiber adaptation. Finally, although recordings from the superior olivary complex indicate that neurons have large response adaptation (Finlayson and Adam 1997
), these
neurons were probably located in the two main nuclei of the complex,
the medial and lateral superior olives, rather than the peri-olivary
regions where MOC neurons are located.
Relevance of minimal adaptation for MOC function
MOC function during long-duration sounds would be effective, given
relatively nonadapting responses and relatively nonadapting peripheral
effects. Minimal adaptation has been demonstrated in the present study.
Peripheral effects have been shown to be sustained in earlier studies.
One peripheral effect of MOC neurons, reduction in firing of
auditory-nerve fibers, can be relatively constant when MOC firing is
produced by direct electrical stimulation of the OC bundle in the brain
stem (Wiederhold and Kiang 1970). This reduction in
nerve-fiber firing can be observed over a period of tens of seconds,
although there is some decrease in such effects for high-CF fibers in
the first few seconds of stimulation. These results, coupled with the
lack of long-term adaptation observed in the present study, suggest
relatively constant effects on the MOC targets when this efferent
system is activated by sound. Another peripheral effect of MOC neurons,
protection from acoustic overstimulation, is seen in paradigms where
the sound exposures are hours in duration (Kujawa and Liberman
1997
; Zheng et al. 1997a
,b
), suggesting a sustained MOC response over even longer intervals. The minimal adaptation of MOC neurons and their sustained peripheral effects are
likely to be important in the functional role of the MOC system. Experimental results suggesting roles for the MOC system in dynamic range control, anti-masking and protection implicitly assume that the
system functions for ongoing sounds, because of long latencies for
responses and long lag times for peripheral effects. This assumption is
consistent with the present results, demonstrating that there is
minimal response adaptation for sounds of duration as long as 10 s. Whatever the precise role of the MOC system, sound-evoked MOC
effects are expected to be stable for long-duration sounds.
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ACKNOWLEDGMENTS |
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We thank M. L. Duca for technical assistance and Drs. John Guinan, Jr., M. Charles Liberman, and Ronald K. de Venecia for comments on an earlier version of the manuscript.
This work was supported by National Institute on Deafness and Other Communication Disorders Grant DC-01089.
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FOOTNOTES |
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Address for reprint requests: Eaton-Peabody Laboratory, Massachusetts Eye and Ear Infirmary, 243 Charles St., Boston, MA 02114 (E-mail: mcb{at}epl.meei.harvard.edu).
Received 21 May 2001; accepted in final form 23 July 2001.
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