J Neurophysiol
88: 1433–1450, 2002; 10.1152/jn.00156.2002.
Sound Repetition Rate in the Human Auditory Pathway:
Representations in the Waveshape and Amplitude of fMRI Activation
MICHAEL P. HARMS1,2 AND JENNIFER R. MELCHER1,2,3
Eaton-Peabody Laboratory, Massachusetts Eye and Ear Infirmary, Boston 02114; 2Harvard–Massachusetts Institute of
Technology Division of Health Sciences and Technology, Speech and Hearing Bioscience and Technology Program,
Cambridge 02139; and 3Department of Otology and Laryngology, Harvard Medical School, Boston, Massachusetts 02115
1
Received 5 March 2002; accepted in final form 14 May 2002
INTRODUCTION
It is well known from human psychophysical experiments
that the perception of a succession of sounds depends strongly
on the rate of sound presentation. For instance, when bursts of
noise are presented repeatedly at a low rate (e.g., ⬍10/s), each
burst can be separately resolved (Miller and Taylor 1948;
Address for reprint requests: M. P. Harms, Eaton Peabody Laboratory, 243
Charles St., Boston, MA 02114 (E-mail: [email protected]).
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Symmes et al. 1955). In contrast, bursts presented at a higher
rate fuse to form a single, modulated percept. In experiments
where multiple series of sounds are presented simultaneously
(e.g., a series of high and a series of low frequency tone bursts),
the rate of sound presentation influences whether the series are
perceived as single or separate streams, as well as the perceived temporal pattern within each stream (Bregman 1990;
Royer and Robin 1986). The dependencies on rate observed in
controlled psychophysical experiments such as these suggest
that rate plays an important role in the perception of the more
complex acoustic conditions encountered in everyday life.
Since repetition rate plays so basic a role in determining how
sounds are heard, it is not surprising that there have been
numerous neurophysiological studies of rate in animals. Broad
trends concerning the coding of rate in the auditory pathway
have emerged from this work. For instance, the highest repetition rates at which neurons respond faithfully to each successive sound in a train (or each successive cycle of amplitude
modulated stimuli) tends to decrease from brain stem to thalamus to cortex (e.g., Creutzfeldt et al. 1980; Langner 1992;
Schreiner and Langner 1988). In cortex, the neural coding of
low and high rates may be accomplished by different populations of neurons, one coding low-rate stimuli through stimulussynchronized activity and the other coding high rates in the
overall amount of discharge activity (Lu and Wang 2000; Lu et
al. 2001). While the animal work has shed light on the neural
representations of repetition rate, the degree to which the
animal findings extend to humans remains uncertain because of
interspecies differences, anesthesia differences, and a paucity
of data in humans that can serve as a link to the animal work.
In the end, direct neurophysiological data in human listeners is
important if we are to understand how repetition rate is represented in the activity patterns of the human brain.
Most previous neurophysiological studies of repetition rate
in humans have used noninvasive techniques for probing brain
function, such as evoked potential and evoked magnetic field
measurements. The evoked response work has examined averaged responses at short, middle, and long latencies to various
types of brief stimuli (e.g., clicks, tone and noise bursts)
presented at different rates (Näätänen and Picton 1987; Picton
et al. 1974; Thornton and Coleman 1975). A particular strength
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0022-3077/02 $5.00 Copyright © 2002 The American Physiological Society
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Harms, Michael P. and Jennifer R. Melcher. Sound repetition rate
in the human auditory pathway: representations in the waveshape and
amplitude of fMRI activation. J Neurophysiol 88: 1433–1450, 2002;
10.1152/jn.00156.2002. Sound repetition rate plays an important role
in stream segregation, temporal pattern recognition, and the perception of successive sounds as either distinct or fused. This study was
aimed at elucidating the neural coding of repetition rate and its
perceptual correlates. We investigated the representations of rate in
the auditory pathway of human listeners using functional magnetic
resonance imaging (fMRI), an indicator of population neural activity.
Stimuli were trains of noise bursts presented at rates ranging from low
(1–2/s; each burst is perceptually distinct) to high (35/s; individual
bursts are not distinguishable). There was a systematic change in the
form of fMRI response rate-dependencies from midbrain to thalamus
to cortex. In the inferior colliculus, response amplitude increased with
increasing rate while response waveshape remained unchanged and
sustained. In the medial geniculate body, increasing rate produced an
increase in amplitude and a moderate change in waveshape at higher
rates (from sustained to one showing a moderate peak just after train
onset). In auditory cortex (Heschl’s gyrus and the superior temporal
gyrus), amplitude changed somewhat with rate, but a far more striking
change occurred in response waveshape—low rates elicited a sustained response, whereas high rates elicited an unusual phasic response that included prominent peaks just after train onset and offset.
The shift in cortical response waveshape from sustained to phasic with
increasing rate corresponds to a perceptual shift from individually
resolved bursts to fused bursts forming a continuous (but modulated)
percept. Thus at high rates, a train forms a single perceptual “event,”
the onset and offset of which are delimited by the on and off peaks of
phasic cortical responses. While auditory cortex showed a clear,
qualitative correlation between perception and response waveshape,
the medial geniculate body showed less correlation (since there was
less change in waveshape with rate), and the inferior colliculus
showed no correlation at all. Overall, our results suggest a population
neural representation of the beginning and the end of distinct perceptual events that is weak or absent in the inferior colliculus, begins to
emerge in the medial geniculate body, and is robust in auditory cortex.
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M. HARMS AND J. MELCHER
J Neurophysiol • VOL
METHODS
Four series of experiments were conducted. The first two examined
the effect of repetition rate on the response to a noise burst train in the
inferior colliculus (IC), Heschl’s gyrus (HG), and the superior temporal gyrus (STG; exp. I), or the IC and medial geniculate body
(MGB; exp. II). The remaining experiments (exps. III and IV) were
aimed at understanding one of the findings from exp. I, namely an
unusual form of temporal response in the cortex to trains with a high
repetition rate.
A total of 12 subjects participated in these experiments. They
ranged in age from 19 to 35 yr (mean ⫽ 25 yr). Ten of the subjects
were male. Nine were right-handed. Subjects had no known audiological or neurological disorders.
This study was approved by the institutional committees on the use
of human subjects at the Massachusetts Institute of Technology,
Massachusetts Eye and Ear and Infirmary, and Massachusetts General
Hospital. All subjects gave their written informed consent.
Exps. I and II: noise burst trains with different burst
repetition rates
Nine subjects participated in a total of 11 imaging sessions for exps.
I and II (exp. I: 5 sessions, subjects 1–5; exp. II: 6 sessions, subjects
2, 5, and 6 –9).
The stimuli were bursts of uniformly distributed white noise. Individual noise bursts in all four experiments were 25 ms in duration
(full-width half-maximum), with a rise/fall time of 2.5 ms. The bursts
were presented at repetition rates of 1, 2, 10, and 35/s (exp. I) or 2, 10,
20, and 35/s (exp. II). The 1/s rate was used in only three of the five
sessions of exp. I. The spectrum of the noise stimulus at the subjects’
ears was low-pass (6-kHz cutoff), reflecting the frequency response of
the acoustic system.
Noise bursts were presented in 30-s trains alternated with 30-s “off”
periods, during which no auditory stimulus was presented (Fig. 1,
top). Four alternations between “train on” and “off” periods constituted a single scanning “run” (total duration 240 s). For all but two
sessions (in exp. I), each of the four rates was presented once during
each run, and their order was varied across runs. Within a train, the
repeated noise bursts were identical (i.e., “frozen”), but the noise
bursts differed across trains and runs. For the other two sessions, the
same rate was presented throughout a run, and this rate was varied
across runs. For these two sessions, the noise burst was frozen within
a run, but differed across runs. In each session, the total number of
train presentations at each rate was between 8 –13.
Exp. III: small numbers of noise bursts
To investigate how the initial bursts of a train contribute to cortical
responses to the onset of a train, we examined the responses to a single
noise burst and short clusters of noise bursts. Responses were collected in three imaging sessions with three subjects (exp. III; subjects
2, 5, and 10).
Either one noise burst or a cluster of noise bursts (2 or 5) was
presented once every 18 s, constituting a single “trial” (Fig. 1, bottom). For the clusters of five noise bursts, the interstimulus interval
(ISI, onset-to-onset) between noise bursts was 28.6 ms, equivalent to
the ISI for a rate of 35/s. For clusters of two noise bursts, two different
ISIs were used: 500 ms (2/s rate) and 28.6 ms (35/s rate). For two
sessions, the same stimulus was used in all of the trials for a given run
(12 runs total; 270 s per run; 45 total repetitions per trial type). In the
third session (subject 10), the stimulus was randomized across trials (7
runs; 288 s per run; 28 repetitions per trial type).
Exp. IV: noise burst trains with different durations
The effect of train duration was examined in two imaging sessions
with two subjects (exp. IV; subjects 11 and 12). Trains of four
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of evoked potential and magnetic field measurements is that they
can be used to examine responses to individual stimuli within a
train up to much higher rates than with other noninvasive brain
imaging techniques (see following paragraph). A limitation, however, is that the sites of response generation cannot always be
reliably localized. Evoked magnetic field examinations of repetition rate are further limited in that they provide information
mainly concerning cortical areas because of inherent limitations in
probing subcortical function (Erné and Hoke 1990).
Positron emission tomography (PET) and functional magnetic resonance imaging (fMRI), two techniques for spatially
mapping brain activity, have also been used to examine the
dependence of human brain activation on repetition rate. Compared with evoked potential and magnetic field measurement,
fMRI lacks the temporal resolution needed to separately resolve the responses produced by individual stimuli in a train
(except at extremely low rates, e.g., approximately 0.1/s), and
the temporal resolution of PET is even less. An important
advantage, however, is that both PET and fMRI enable activation to be directly localized to brain stem, thalamic, and
cortical structures of the auditory pathway (Griffiths et al.
2001; Guimaraes et al. 1998; Lockwood et al. 1999; Melcher et
al. 1999). The localization provided by fMRI is particularly
precise because of the technique’s high spatial resolution and
direct mapping to anatomy. Despite the fact that fMRI and PET
can show activation at different stages of the auditory pathway,
previous PET and fMRI studies varying rate have focused
largely on cortical areas (Binder et al. 1994; Dhankhar et al.
1997; Frith and Friston 1996; Giraud et al. 2000; Price et al.
1992; Rees et al. 1997; Tanaka et al. 2000). Additionally, most
of the studies focused on the low “rates” characteristic of
speech (e.g., ⬍2.5 words or syllables/s) because they were
directed at understanding speech processing. Overall, there is
limited PET or fMRI data concerning the representations of
rate within the human auditory pathway. Specifically, there is
little information concerning the transformation of rate representations from structure to structure within the pathway for a
wide range of psychophysically relevant rates.
The present fMRI study compared the representation of
repetition rate across cortical and subcortical structures of the
human auditory pathway using a wide range of rates. Stimuli
were trains of repeated noise bursts with repetition rates ranging from low (where each burst could be resolved individually)
to high (where individual bursts were not distinguishable and
the train was perceived as a continuous, but modulated, sound).
Noise bursts were chosen as the elemental stimulus based on
the assumption that broadband sound would elicit robust responses by activating neurons across a wide range of characteristic frequencies. fMRI was selected for its high spatial
resolution, its localizing capabilities, and its higher temporal
resolution (approximately 2 s) compared with PET (⬎10 s).
The latter feature proved important because one of the most
striking differences in rate representation across structures
occurred in the temporal dynamics of the fMRI response.
Portions of this work were presented at the annual meeting
of the Society for Neuroscience (1997), the 21st annual meeting of the Association for Research in Otolaryngology (1998),
the 4th and 5th International Conferences on Functional Mapping of the Human Brain (1998 and 1999), and in M. P. Harms’
doctoral thesis (Massachusetts Institute of Technology, 2002).
RATE REPRESENTATION IN HUMAN AUDITORY PATHWAY
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Task
Subjects were instructed to listen to the noise burst stimuli. At the
end of each scanning run, subjects reported their alertness on a
qualitative scale ranging from 1 (fell asleep during run) to 5 (highly
alert). Alertness ratings were almost always in the 3–5 range, and
were never 1.1
For exp. II, subjects performed the following task to further ensure
that they remained attentive: they indicated whenever they detected an
occasional increment or decrement in intensity (of 6 dB, lasting 1 s)
by raising or lowering their index finger. Each subject identified more
than 90% of the intensity changes.
Imaging
different durations (15, 30, 45, and 60 s) were presented with an “off”
period of 40 s following each train. Noise burst repetition rate within
each train was always 35/s. Each train duration was presented once
per run (8 –9 runs; 310 s per run) with the order of durations randomized across runs. Supplementary information concerning the effects of
train duration was obtained in two additional experiments that used a
single, long-train duration (60 s) and 35/s noise bursts.
Acoustic stimulation
Separately for each ear, the subject’s threshold of hearing to 10/s
noise bursts was determined in the scanner room. For all experiments,
the stimuli were presented binaurally at 55 dB above this threshold.
During both threshold determination and functional imaging, there
was an on-going low-frequency background noise produced primarily
by the pump for the liquid helium (used to supercool the magnet coils)
(Ravicz et al. 2000). This sound reaches levels of ⬃80 dB SPL in the
frequency range of 50 –300 Hz. Additionally during functional imaging, each image acquisition generated a “beep” of approximately 115
dB SPL at 1.0 kHz (1.5 T scanner) or ⬃130 dB SPL at 1.4 kHz (3 T
scanner, see Imaging). The stimuli for all experiments were clearly
audible during functional imaging. For the low-rate trains, an individual burst was occasionally masked by a coinciding imaging
“beep.” However, because the imaging was cardiac gated (see Imaging), this coincidence of a noise burst with image acquisition occurred
only infrequently throughout the low-rate trains.
Noise bursts were delivered through a headphone assembly that
provided approximately 30 dB of attenuation at the primary frequency
of the scanner-generated sounds (1.0 or 1.4 kHz; Ravicz and Melcher
2001). Specifically, the noise bursts were produced by a digital-toanalog board (running under LabView), amplified, and fed to a pair of
audio transducers housed in a shielded box adjacent to the scanner.
The output of the transducers reached the subject’s ears via air-filled
tubes that were incorporated into sound attenuating earmuffs.
J Neurophysiol • VOL
1
The subjects for the first two sessions of exp. III did report difficulties in
maintaining a high level of alertness due to the sparseness and uniformity of
the stimulus trials. Therefore to help maintain alertness, the subject (subject
10) for the third session was instructed to count the number of trials per run.
2
While there are also approaches for reducing the impact of the background
acoustic noise on auditory activation during multislice imaging (Edmister et al.
1999; Hall et al. 1999), these sacrifice temporal resolution or data-taking
efficiency (e.g., Melcher et al. 1999).
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FIG. 1. Schematic of the stimulus paradigm for exps. I-III. In exps. I and II,
trains of noise bursts at a given repetition rate were presented for 30 s, followed
by a 30 s “off” period. This alteration was repeated 4 times for each imaging
“run,” typically using a different repetition rate for each “train on” period. Tick
marks represent an image acquisition (approximately every 2 s). The expanded
view uses a smaller time scale to illustrate the stimulus—in this case a portion
of a prolonged 10/s noise burst train. In exp. III, “trials” consisting of either 1,
2, or 5 noise bursts were presented once every 18 s (15–16 trials per run). The
interstimulus interval for the 2 noise bursts was either 500 ms or 28.6 ms (i.e.,
2 NBs@2/s and 2 NBs@35/s; in this case the expanded view shows the
complete stimulus for a given trial). The trials with 5 noise bursts used an
interstimulus interval of 28.6 ms.
Subjects were imaged using a 1.5 or 3 Tesla whole-body scanner
(General Electric) and a head coil (transmit/receive). The scanners
were retrofitted for high-speed imaging (i.e., single-shot echo-planar
imaging; Advanced NMR Systems, Inc.). Exps. I and II were conducted at 1.5 T. Exps. III and IV were conducted at 3 T (except for one
of the supplementary sessions of exp. IV). Subjects rested supine in
the scanner. To reduce head motion, a bite bar was custom-molded to
the subject’s teeth and mounted to the head coil, or pillow and foam
were packed snugly around the head. Each imaging session lasted
approximately 2 h and included the following procedures:
1) Contiguous sagittal images of the whole head were acquired.
2) An automated, echo-planar– based shimming procedure was
performed to increase magnetic field homogeneity within the brain
regions to be functionally imaged (Reese et al. 1995).
3) The brain slice to be functionally imaged was selected using the
sagittal images as a reference. For exps. I, III, and IV, the selected
(near-coronal) slice intersected the IC and the posterior aspect of HG
and STG in both hemispheres (Fig. 2, left and middle). When multiple
transverse temporal gyri were present, the anterior one was intersected. Based on these criteria, we expect that a portion of primary
auditory cortex was intersected in both hemispheres of all subjects for
exps. I, III, and IV (Rademacher et al. 1993, 2001). For exp. II, the
slice intersected the IC and MGB (located just ventral and lateral to
the cerebral aqueduct; Fig. 2, right). A single slice, rather than
multiple slices, was imaged in all experiments to reduce the impact of
scanner-generated acoustic noise on auditory activation.2
4) A T1-weighted, high-resolution anatomical image was acquired
of the selected brain slice for subsequent overlay of the functional data
[thickness ⫽ 7 mm; in-plane resolution ⫽ 1.6 ⫻ 1.6 mm; TR ⫽ 10 s,
TI ⫽ 1200 ms, TE ⫽ 40 ms (exps. I and II) or 57 ms (exps. III and
IV)]. A second high-resolution anatomical image was acquired at the
end of the session after functional imaging. A comparison of the initial
and final T1 images allowed for a gross check of subject movement
over the session.
5) Functional images of the selected slice were acquired using a
blood oxygenation level– dependent (BOLD) sequence (sessions at
1.5 T: asymmetric spin echo, TE ⫽ 70 ms, ␶ offset ⫽ ⫺25 ms, flip ⫽
90°; sessions at 3 T: gradient echo, TE ⫽ 40 or 30 ms, flip ⫽ 90 or
60°). Slice thickness was 7 mm. In-plane resolution was 3.1 ⫻ 3.1
mm. The beginning of each scanning “run” included four discarded
images to ensure that image signal level had approached a steady
state. During the remainder of the run, functional images of the
selected slice were acquired repeatedly (Fig. 1).
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M. HARMS AND J. MELCHER
Functional imaging was performed using a cardiac gating method
that increases the detectability of activation in the inferior colliculus
(Guimaraes et al. 1998) [except for one session of exp. III that used a
fixed interimage interval (TR) of 2 s]. Image acquisitions were synchronized to every other QRS complex in the subject’s electrocardiogram, and the interimage interval (TR) was recorded. The average TR
across all sessions was 2,035 ms (the average within a session varied
from 1,521 to 2,650 ms). Fluctuations in heart rate lead to variations
in TR that result in image-to-image variations in image signal strength
(i.e., T1 effects). Using the measured TR values, image signal was
corrected to account for these variations (Guimaraes et al. 1998).
Analysis
The images for each scanning run were
corrected for any movements of the head that may have occurred over
the course of the imaging session. Each functional image of a session
was translated and rotated to fit the first image of the first functional
run using standard software (SPM95; without spin history correction;
Friston et al. 1995, 1996). Because only one functional slice was
acquired, these corrections for motion were necessarily limited to
adjustments within the imaging plane. In most cases, the motion
correction algorithm was well-behaved and resulted in an improvement in image alignment. However, for one session, the algorithm
introduced some clearly artifactual movement, so the premotion corrected data were utilized. Additionally, we did not include the MGB
of one subject in the analysis, because the image translations calculated by the motion-correction algorithm were smaller than the movement evident at the location of the MGB in the T1 anatomical images
acquired pre- and post- functional imaging. A similar discrepancy did
not occur for the IC of this subject, so the IC data were included. The
images for each run were further processed in two ways to enhance
the likelihood of detecting activation. 1) Image signal versus time for
each voxel was corrected for linear or quadratic drifts in signal
strength over each run (i.e., drift-corrected). 2) Image signal versus
time for each voxel and run was normalized such that the timeaverage signal had the same (arbitrary) value for all voxels and runs.
(Specifically, the signal vs. time data were ratio normalized based on
the intercept of a least-square quadratic fit to the data). This normalization was done to eliminate artificial discontinuities in the signal
level between runs in the subsequently concatenated data. All subse-
IMAGE PREPROCESSING.
J Neurophysiol • VOL
quent analyses were performed on the drift-corrected, normalized
images.
Maps of activation to noise burst
trains (exps. I, II, and IV) were derived as follows. First, each image
was assigned to either a “train on” or “off” period. Stimulus-evoked
changes in image signal typically have a delay of 4 – 6 s (Bandettini et
al. 1993; Buckner et al. 1996; Kwong et al. 1992). To account for this
(hemodynamic) delay, the first three images taken after the onset of a
noise burst train were assigned to the preceding “off” period, and the
first three images after the train offset were assigned to the preceding
“train on” period. For each rate, the images assigned to each “train on”
period and its following “off” period were concatenated into a single
file. Image signal strength during train on versus off periods was then
compared for each voxel using an unpaired t-test (Press et al. 1992).
The P value result of this statistical test was plotted for each voxel
with P ⱕ 0.01 to yield a spatial map of activation. P values were not
corrected to account for the correlated nature of fMRI time-series
(Purdon and Weisskoff 1998), nor were they adjusted for the repeated
application (voxel-by-voxel) of a statistical test (Friston et al. 1994).
GENERATING ACTIVATION MAPS.
Responses were analyzed quantitatively within four anatomically defined regions of interest (ROIs):
the IC, MGB, HG, and STG. These ROIs, which were defined
separately for each hemisphere, were first identified in the highresolution anatomical images (in-plane resolution: 1.6 ⫻ 1.6 mm) of
the functional imaging plane. These “high resolution” ROIs were
down-sampled to the same resolution as the functional images (3.1 ⫻
3.1 mm), to yield the ROIs used for all subsequent analyses. The ROI
borders were defined as follows:
IC. In exps. I, III, and IV, the IC were readily identified as distinct
anatomical circular areas (e.g., Fig. 3). For exp. II, only the caudal
edge of the IC were distinguishable (e.g., Fig. 6), so the area of each
IC ROI was defined as a circle sized to fit this visible edge. The IC
ROIs were defined liberally to include voxels at the edge of the IC.
MGB. The ambient cistern defined the caudal border of the MGB
ROI. The distance from this caudal border to the rostral edge of the
MGB ROI was determined from atlases of histologically prepared
brains, as was the distance between the midline and medial edge of the
MGB ROI. These distances were computed by first normalizing atlas
measurements to maximum brain width, and then multiplying the
normalized atlas measurements by the maximum width of the imaged
brain slice for each subject. The lateral edge of the ambient cistern
DEFINING REGIONS OF INTEREST.
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FIG. 2. Functional imaging planes superimposed on sagittal, anatomical images. In exps. I, III, and IV, the plane (thick white
line) passed through the inferior colliculi (left) and Heschl’s gyri (middle). In exp. II, the plane passed through the inferior colliculi
(located just lateral to the brachium of the inferior colliculi) and the medial geniculate bodies of the thalamus (located just ventral
and lateral to the cerebral aqueduct; right).
RATE REPRESENTATION IN HUMAN AUDITORY PATHWAY
defined the lateral edge of the MGB ROI. The lateral extent of the ROI
was liberal in that it probably included a portion of the lateral
geniculate body in some subjects. Correspondingly, activation generally did not reach the lateral-most edge of the MGB ROI.
HG. When HG was visible as a “mushroom” protruding from the
surface of the superior temporal plane, the lateral edge of this mushroom defined the lateral edge of the HG ROI. The medial edge of the
ROI was the medial-most aspect of the Sylvian fissure. When a
distinct mushroom was not present, the HG ROI covered approximately the medial third of the superior temporal plane (extending
from the medial-most aspect of the Sylvian fissure). In the inferiorsuperior dimension, the HG ROI extended superiorly to the edge of
the overlying parietal lobe and inferiorly so as to entirely encompass
any activation centered on HG.
STG. The STG ROI was defined as the superior temporal cortex
lateral to the HG ROI. The definition of the inferior and superior
borders was the same as for the HG ROI.
The resulting average number of total voxels (3.1 ⫻ 3.1 ⫻ 7 mm)
per hemisphere in the ROIs was 5 (IC), 10 (MGB), 29 (HG), or 36
(STG).
CALCULATING RESPONSE TIME COURSES. The time course of response was computed for specific voxels within each ROI. For exps.
I and II, the voxels (3.1 ⫻ 3.1 ⫻ 7 mm) were chosen based on the
activation maps for a particular “reference rate” (i.e., the rate that
typically produced the strongest activation in the maps): 35/s for IC,
20/s for MGB, 10/s for HG, and 2/s for STG. For each IC and MGB
ROI, we used the single voxel with the lowest P value at the reference
rate. For each HG and STG ROI, we averaged the responses of the
four voxels (not necessarily contiguous) with the lowest P values at
J Neurophysiol • VOL
the reference rate.3 Note that for a given structure, session, and
hemisphere, the same voxels were used in computing the response
time course at each rate. For exps. III and IV, which focused on
auditory cortex, the same number of lowest P value voxels (four in
each hemisphere) were selected for analysis within the HG and STG
ROIs. However, the activation map for selecting voxels was based on
a single run of music (4 repetitions of the first 30 s of the fourth
movement in Beethoven’s Symphony No. 7). Music was used because
1) it typically evokes larger magnitude cortical responses than noise
burst trains, so robust cortical activation maps could be obtained with
a single run, thereby allowing more time for collecting responses to
the primary stimuli of interest in exps. III and IV, and 2) the dominant
amplitude modulation frequencies of the music stimulus (⬍5 Hz)
were comparable with the “reference rates” for auditory cortex.4
For exps. I and II, response time courses were computed as follows.
Because cardiac gating results in an irregular temporal sampling, the
time series for each imaging “run” and voxel was linearly interpolated
to a consistent 2-s interval between images, using recorded interimage
intervals to reconstruct when each image occurred. These data were
then temporally smoothed using a three-point, zero-phase filter (with
coefficients 0.25, 0.5, 0.25). A response “block” was defined to
include the 10 s prior to a noise burst train, the period coinciding with
the train, and the off period following the train. These response blocks
were averaged according to rate to give an average signal versus time
waveform for each rate, session, and hemisphere. The signal at each
time point was then converted to a percent change in signal relative to
a baseline. The baseline was defined as the average signal from t ⫽
⫺6 to 0 s, with time t ⫽ 0 s corresponding to the onset of the noise
burst train. Finally, for each rate, the percent change time courses
were averaged across sessions and hemispheres.5 For exp. IV, response time courses were calculated the same way, except response
blocks were averaged according to train duration.
For exp. III, time courses for each stimulus were computed as
described above, with the following exceptions: 1) no temporal
smoothing was applied (to avoid disproportionally altering the responses, which were expected to be brief in duration) and 2) the
baseline signal level for converting time courses to percent change
was based on the average of just two time points, t ⫽ ⫺2 to 0 s. [This
was done because the “off” period between stimuli (18 s) was less for
this experiment than for the others (30 or 40 s) and we wanted to avoid
including time points where the response may have not yet returned to
baseline from the preceding stimulus.]
For exps. I and II, response
magnitude in each auditory structure was quantified using two measures computed from the percent change time courses. “Time-average” percent change, a measure of the overall response strength during
a noise burst train, was computed as the mean percent change from
QUANTIFYING RESPONSE MAGNITUDE.
3
In a supplementary analysis, we determined that response waveshape was
not sensitive to our voxel selection criteria. For comparison, time courses were
computed using all voxels with P ⬍ 0.01 at the reference rate (instead of just
the voxels showing the strongest activation). As expected, the resulting responses were reduced in magnitude. However, their waveshape was unaffected.
4
It is unlikely that the results for exps. III and IV were affected by the use
of a music stimulus, rather than noise burst trains, to select particular voxels
within an ROI. In particular, in several sessions (data not shown), in which the
stimuli included both music and trains of 2/s and 35/s noise bursts, we obtained
similar responses for the noise burst trains in HG and STG, irrespective of
whether the reference voxels were chosen using activation maps based on
music or 2/s noise bursts. Typically, at least two of the four reference voxels
were in common between the two activation maps. Importantly, the cortical
response time courses for music and 2/s noise burst trains are similar in shape.
5
In a supplementary analysis, we found that the waveshape of the average
responses (Fig. 4) was unaffected when the responses for individual sessions
and hemispheres were first normalized (by dividing by their maximum value)
and then averaged. This result indicates that average response waveshape was
not unduly influenced by just a small subset of individual responses.
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FIG. 3. Activation maps for the inferior colliculus (IC) (2 subjects, exp. I).
Stimuli were noise burst trains with repetition rates of 2, 10, or 35/s. Each
panel shows a T1-weighted anatomic image (grayscale) and superimposed
activation map (color). Rectangle superimposed on the diagrammatic image
(bottom, right) indicates the area shown in each panel. For the activation maps,
regions are colored according to the result of a t-test comparison of image
signal strength during “train on” and “off” periods. In this and all subsequent
figures, blue and yellow correspond to the lowest (P ⫽ 0.01) and highest (P ⫽
2 ⫻ 10⫺9) significance levels, respectively. (Areas with P ⬎ 0.01 are not
colored). The activation maps (based on functional images with an in-plane
resolution of 3.1 ⫻ 3.1 mm) and anatomic images (1.6 ⫻ 1.6 mm) have been
interpolated. Images are displayed in radiological convention, so the subject’s
right is displayed on the left. R, right; L, left.
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M. HARMS AND J. MELCHER
t ⫽ 4 to 30 s. “Onset” percent change, a measure of the response
amplitude near the beginning of a noise burst train, was computed as
the maximum percent change from t ⫽ 4 to 10 s. Since “time-average”
and “onset” percent change were calculated from the percent change
time courses, they indicate image signal deviations relative to a 6-s
baseline immediately preceding the stimulus (i.e., the baseline period
used in calculating the time courses). For exp. III, peak percent change
was defined as the maximum value in the percent change time courses.
RESULTS
Response to noise burst trains: effect of burst repetition rate
FIG. 4. Response time courses averaged across sessions and hemispheres [solid lines; IC, n ⫽ 22 for 2, 10,
and 35/s, n ⫽ 12 for 20/s; medial geniculate body (MGB),
n ⫽ 10 for all rates; HG and STG, n ⫽ 10 for 2, 10, and
35/s, n ⫽ 6 for 1/s]. Dashed lines give the mean ⫾ SE at
each time point. Note that the vertical scale for the IC and
MGB responses differs slightly from the scale for Heschl’s gyrus (HG) and superior temporal gyrus (STG).
J Neurophysiol • VOL
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INFERIOR COLLICULUS. Activation maps for the IC showed an
increase in activation with increasing burst repetition rate.
Figure 3 demonstrates this increase for two sessions from
exp. I. The maps show activation that is greatest at 35/s, less at
10/s, and absent at 2/s. (Activation strength is reflected in the
maps as a lower P value from the statistical comparison of
image signal level during train “on” and “off” periods). Greater
IC activation at higher rates is also demonstrated by the maps
in Fig. 6, which correspond to two sessions from exp. II.
Figure 4 (left) shows the time course of the responses in the
IC averaged across all sessions. At all rates, the response was
“sustained” in that image signal increased when the noise burst
train was turned on, remained elevated while the train was on,
and decreased once the train was turned off. The amplitude of
the sustained response during the “train on” period increased
with increasing rate.
The increase in response amplitude was quantified using two
measures: maximum percent signal change near the beginning
of the “train on” period (“onset” percent change), and percent
signal change time-averaged over the on period (“time-average” percent change; defined in METHODS). On average, both
measures increased with increasing rate (Fig. 5, top left). Onset
and time-average percent change showed a significant increase
from 2/s to 10/s (P ⫽ 0.01, onset; P ⫽ 0.05, average; paired
t-test), and from 10/s to 35/s (P ⫽ 0.02, onset; P ⫽ 0.006,
average). Plots of percent change versus rate for individual IC
also showed an overall trend of increasing percent change with
increasing rate (Fig. 5, top middle and right). For 19 of 22 IC,
the response at 35/s was greater than the response at 2/s (for
both measures).
For the rates that overlapped between exps. I and II (2, 10,
and 35/s) there was no significant difference between the
percent signal change values (P ⬎ 0.1, t-test), suggesting that
the two main differences between these experiments (intensity
detection task and imaging plane) did not have a strong effect
on inferior colliculus responses. There was no significant difference between the values obtained from the left and right IC
(P ⬎ 0.3, paired t-test, collapsing the data across all rates).
In summary, the IC showed a sustained response to noise
RATE REPRESENTATION IN HUMAN AUDITORY PATHWAY
1439
FIG. 5. Response magnitude vs. repetition rate in the IC, MGB, HG, and
STG. Left: time-average and onset percent change averaged across sessions
and hemispheres. Bars indicate the SE (see Fig. 4 legend for the number of
sessions and hemispheres represented by each data point). Middle and right:
Time-average and onset percent change for each session and hemisphere vs.
rate. To facilitate comparison of the trends across rate, each curve has been
displaced vertically by adding a constant (specific to each curve), such that the
resulting mean of the values for 2, 10, and 35/s is equal to the population mean
for these rates (left). In all plots, the repetition rate axis uses a categorical scale.
Note that there are no data at 20/s for all of the HG and STG curves and for
10 of the IC curves.
burst trains. The amplitude of this response increased with
increasing burst repetition rate.
MEDIAL GENICULATE BODY. In contrast with the IC, activation
maps for the MGB usually showed a nonmonotonic change in
activation with rate. The trend for the MGB is illustrated by the
maps for two sessions in Fig. 6. The maps show an increase in
MGB activation with increasing rate in the 2/s–20/s range, but
a decrease from 20/s to 35/s.
The trend in the activation maps parallels the rate dependence of time-average percent signal change in the MGB, but
not onset percent change. The close correspondence between
time-average percent change and activation maps is to be
expected since the maps are based on a comparison of timeaverage signal levels during “train on” and “off” periods.
Time-average percent change increased significantly from 2/s
to 20/s (P ⫽ 0.005, paired t-test), and decreased from 20/s to
35/s (P ⫽ 0.03; Fig. 5, left). Onset percent signal change
showed the same trend from 2/s to 20/s (i.e., a significant
increase; P ⬍ 0.001), but not at high rates in that there was no
difference between 20/s and 35/s (P ⫽ 0.9). The different rate
dependence for onset versus time-average percent change is
also apparent overall in the plots for individual MGBs (Fig. 5,
J Neurophysiol • VOL
FIG. 6. Activation maps for the MGB and IC (2 subjects, exp. II). Stimuli
were noise burst trains with repetition rates of 2, 10, 20, or 35/s. See Fig. 3
legend.
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middle and right), despite the intersession variability in the
precise trends between the rates. Neither onset nor time-average percent change differed significantly between the left and
right MGBs (P ⬎ 0.15, paired t-test, collapsing the data across
all rates).
The different rate-dependencies for onset and time-average percent change indicate that the time course of the MGB
response varies with rate. This variation is illustrated in Fig.
4 (2nd column). On average, responses to a 35/s train peaked
just after train onset and then declined by approximately
50% during the remainder of the train. This moderate decrease in the response differs from the largely sustained
responses at the lower rates of 2/s, 10/s, and 20/s. A quantitative comparison of onset percent change to the percent
change at the end of the train (i.e., at 30 s) confirmed the
response difference at the highest rate. For the 35/s train,
response amplitude was significantly less at the end of the
train (P ⫽ 0.04, paired t-test), consistent with a response
decrease. In contrast, there was no significant difference at
the lower rates (P ⬎ 0.1), consistent with a sustained
response. Thus MGB responses varied over the course of
high, but not lower rate trains.
The rate dependencies seen in time-average percent
change and the activation maps can be explained in terms of
the time course and onset amplitude of MGB responses.
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M. HARMS AND J. MELCHER
Between 2/s and 20/s, the increase in time-average percent
change (and activation in the maps) is largely attributable to
the increase in sustained response amplitude, which is simultaneously reflected as an increase in onset percent
change. Given that 20/s and 35/s evoke equal onset responses, the decrease in time-average percent change (and
in the maps) between these two rates can be primarily
attributed to a change in the response to the latter portion of
the train (i.e., the change from a sustained response to one
with a moderate decrease following onset).
In summary, between 2/s and 20/s, onset percent change
increased with increasing rate in MGB, while response time
courses remained primarily sustained. Between 20/s and 35/s,
there was no change in onset amplitude, but the response
waveshape changed from sustained to moderately decreasing
following the train onset.
HESCHL’S GYRUS AND SUPERIOR TEMPORAL GYRUS. A nonmonotonic relationship between rate and activation was apparent in the activation maps for HG and STG (Fig. 7). The maps
showed an activation increase from 1/s to 2/s, and a decrease
from 10/s to 35/s.
As expected, the trends in the activation maps paralleled
the rate dependence of time-average percent signal change.
In HG, time-average percent change increased from 2/s to
10/s (P ⫽ 0.05, paired t-test), but decreased markedly from
10/s to 35/s (P ⬍ 0.001; Fig. 5, left). These trends were
observed consistently in individual HG (Fig. 5, middle). In
STG, the rate of greatest time-average percent change (2/s)
was less than in HG (10/s; Fig. 5, left). For the six STG with
J Neurophysiol • VOL
6
The time courses in Fig. 4 are based on responses averaged over the
entirety of each experimental session, so they do not address whether response
waveshape might have changed over the repeated train presentations within a
session. To test for such changes in cortex, we computed response time courses
for each session based on the three initial and three final presentations of the
2/s and 35/s trains. For each rate, the waveshapes of these “initial” and “final”
time courses, averaged across sessions, were not substantially different in
either HG or STG, indicating that systematic, within-session waveshape
changes did not occur (see Harms 2002, p. 32 for details).
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FIG. 7. Activation maps for HG and STG (2 subjects, exp. I). Stimuli were
noise burst trains with repetition rates of 1, 2, 10, or 35/s. See Fig. 3 legend.
1/s data, time-average percent change at 2/s was greater than
at 1/s (P ⫽ 0.003, paired t-test). Time-average percent
change in STG tended to decrease from 2/s to 10/s and from
10/s to 35/s, so that the overall decrease from 2/s to 35/s was
significant (P ⫽ 0.002; Fig. 5).
Onset percent change again showed differences compared
with time-average percent change. In HG, the difference
was primarily one of degree. Onset percent change at 10/s
was significantly greater than both 2/s and 35/s (P ⫽ 0.01,
paired t-test; Fig. 5, left and right), but the decrease from
10/s to 35/s averaged only 20% for onset percent change
compared with 50% for time-average percent change. In
STG, onset and time-average percent change had overall
different trends. Whereas time-average percent change decreased from 2/s to 35/s (P ⫽ 0.002), onset percent change
was unchanged over this range (P ⫽ 0.4; Fig. 5, left and
right).
A dramatic rate-dependent change in response waveshape
accounts for the differences between onset and time-average
percent change in HG and STG. At low rates, responses
were sustained, whereas at high rates they were not (Fig. 4,
3rd and 4th columns).6 At the highest rate of 35/s, image
signal increased to a peak occurring 6 s following train onset
(“on-peak”), declined substantially over the next 8 s, increased slightly over the remainder of the on period, and
peaked again 6 s following train offset (“off-peak”). The
most prominent features of this “phasic” response are the
peaks just after train onset and offset. In HG, the reduction
in time-average percent change between 10/s and 35/s was
partly because of 1) the decrease in onset percent change,
and 2) the more dramatic signal decline during the on period
for the 35/s train. In STG, onset percent change did not vary
significantly with rate, so the decline in time-average percent change at high rates was primarily due to the change in
response waveshape.
While the rate-dependencies of the HG and STG responses
paralleled each other, there were also clear differences between
the two structures. In both HG and STG, the signal decline
during the on period became increasingly pronounced with
increasing repetition rate, so responses had an increasingly
phasic appearance. However, at any given rate, the magnitude
of the signal decline was greater in STG. In STG, the magnitude of the decline (measured as a percentage decrease from
the onset peak to the value at 14 s in the average response time
courses; Fig. 4) was 22, 25, 58, and 93% for 1/s, 2/s, 10/s, and
35/s respectively. In HG, the corresponding values were all
less: 15, 13, 32, and 78%.
In light of the phasic response for high-rate trains, it is not
surprising that the activation maps frequently showed little
evidence of activity at the 35/s rate (e.g., Fig. 7, top left). The
activation maps were based on the difference in time-average
signal between “train on” and “off” periods. It is clear that for
RATE REPRESENTATION IN HUMAN AUDITORY PATHWAY
1441
However, the amplitude of the on-peak did not vary significantly with rate.
Response to small numbers of noise bursts
the response to the 35/s train (Fig. 4), the difference between
the time-average of these two periods will be close to zero,
even though cortex is responding robustly (albeit transiently).
Thus for cortex, the activation maps, calculated using a standard method, provide only a partial picture of cortical rate
dependencies.
In HG and STG there were right–left differences in response
magnitude. In HG, both time-average and onset percent
changes were greater on the right for 16 of 18 possible comparisons (P ⬍ 0.001, paired t-test, collapsing the data across all
rates). In STG, the same trend was apparent, but was weaker
(right greater in 12 of 18 cases; P ⬍ 0.02). These right–left
differences may reflect a functional difference between right
and left auditory cortex. Alternatively, they may reflect a
functional difference in auditory cortex in the anterior–posterior dimension. For example, since HG tends to be shifted more
anteriorly on the right compared with the left hemisphere
(Leonard et al. 1998; Penhune et al. 1996; Rademacher et al.
2001), the imaged slice may have sampled different regions of
primary cortex in the two hemispheres (Morosan et al. 2001).
In summary, responses in HG were sustained at low rates,
but became phasic at high rates. The most prominent features
of this phasic response are signal peaks just after train onset
and offset. The amplitude of the on-peak (onset percent
change) was greatest at 10/s. Responses in STG also showed a
progression from sustained to phasic with increasing rate.
J Neurophysiol • VOL
7
In making these comparisons, we took into account the two main differences between exps. I and III. First, because the responses in exp. III were
computed without temporal smoothing, we recomputed the onset percent
change values from exp. I without temporal smoothing. The resulting mean
onset percent change values for a 35/s train increased by 20% for both HG and
STG. Second, we took into account the difference in imaging parameters (exp.
I: 1.5 T scanner, ASE sequence; exp. III: 3 T scanner, GE sequence). The effect
of this difference was estimated empirically using responses to music obtained
under the same conditions as exp. I (11 sessions) and exp. III (11 sessions).
Time-average percent change was, on average, about 25% greater for the exp.
III conditions, so the onset amplitude from exp. I (calculated without temporal
smoothing) was revised up by this percentage before comparison with the
single and multiple noise burst responses.
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FIG. 8. Top: average response time courses in HG and STG to either a
single noise burst, or a cluster of 2 or 5 noise bursts. Each trace is an average
across both hemispheres of 3 subjects. Bottom: normalized peak response for
each subject and hemisphere. Dashed line indicates the prediction from a
model in which each successive noise burst evokes a response identical to the
1 NB response and the responses to each burst add.
To investigate how the initial bursts of a train contribute to
cortical responses at train onset, we examined the responses to
a single noise burst, and clusters of two or five noise bursts
with a burst-to-burst ISI of 28.6 ms (35/s rate) or 500 ms (2/s
rate). Both single and clustered noise bursts elicited measurable responses in HG and STG. The responses, averaged across
subjects and hemispheres, peaked 4 – 6 s after the stimulus, and
then returned to baseline by 8 –10 s (Fig. 8, top). After 8 –10 s,
the average response dipped below baseline. However, this
response feature, unlike the others, was dominated by the data
for only one of the three subjects (subject 2).
Figure 8 (bottom) shows normalized peak response versus
number of noise bursts for each subject and hemisphere. These
normalized responses were quantified as the peak percent signal change in the response time course (which always occurred
at t ⫽ 4 or 6 s), divided by the peak percent change for a single
noise burst. The normalized peak response generally increased
with increasing number of noise bursts (Fig. 8, bottom). However, the response increase was always less than would be
predicted by a model in which each successive noise burst
evokes a response equivalent to the 1 NB response and the
responses to each burst add (i.e., linear growth). Similarly, for
every subject and hemisphere, the peak response to 5
NBs@35/s was ⬍2.5 times the response to 2 NBs@35/s. These
results are consistent with a model in which the responses to
noise bursts at the beginning of a train are greater than those
occurring later. The fact that the peak response for 2 NBs@2/s
was always greater than for 2 NBs@35/s indicates that any
decline in response from the first burst to the second was
greater at high, compared with low, rates.
We compared the mean peak percent change for single and
multiple noise bursts with the mean onset percent change for
35/s trains from exp. I to gain an appreciation for the proportion of the on-peak accounted for by the earliest noise bursts in
the train.7 In STG, we estimated that the mean peak percent
change for 1 NB and 5 NBs@35/s was approximately 40% and
65%, respectively, of the mean onset percent change. In HG,
the corresponding estimates were approximately 25% and
40%. These values indicate that the earliest noise bursts of a
high-rate train account for a substantial portion of the on-peak,
especially in STG.
1442
M. HARMS AND J. MELCHER
FIG. 9. Response time courses in HG (top)
and IC (bottom) to 35/s noise burst trains,
with durations of 15, 30, 45, and 60 s. Each
trace is an average across hemispheres for a
given subject. The off-peak in each HG response is indicated by an arrow.
By considering noise burst trains with different durations,
we tested whether the off-peak in cortical phasic responses is
specifically linked to train termination. Two subjects were
studied using 35/s noise burst trains with durations of 15, 30,
45, and 60 s. For both subjects and all durations, HG responses
showed a distinct off-peak after train offset (Fig. 9, top).
Regardless of train duration, the off-peak occurred approximately 6 s after train offset, indicating a strong coupling
between off-peak and train termination. A similarly strong
coupling between off-peak and train termination was also
found in STG for both subjects (not shown). One subject
(subject 11) was unusual in that the response in STG did not
show a clear off-peak for voxels selected by our standard
criteria. Nevertheless, there was a clear off-peak for other,
nearby voxels, and this off-peak always occurred approximately 6 s after train offset, regardless of train duration. In
contrast to cortex, IC responses were largely sustained for all
durations and showed no sign of an off-peak (Fig. 9, bottom).
Data for two additional subjects further support the strong
coupling between cortical off-peak and train termination.
These subjects, tested with a single train duration of 60 s,
showed off-peaks in both HG and STG that occurred approximately 6 s after train offset. All of the train duration data taken
together indicate that the cortical off-peak is specifically
evoked by the termination of high-rate noise burst trains.
DISCUSSION
fMRI responses to trains of noise bursts changed substantially with burst repetition rate in every studied structure,
although the nature of the changes was highly structure-dependent. In the IC, response amplitude following train onset increased with increasing rate while response waveshape remained unchanged (i.e., sustained). In the MGB, increasing
rate produced an increase in onset amplitude up to a point
where a further increase in rate instead produced a change in
waveshape (from a largely sustained response to one showing
a distinct peak just after train onset). In HG, the site of primary
auditory cortex, onset amplitude changed somewhat with rate,
but the most striking change occurred in response waveshape.
J Neurophysiol • VOL
At low rates the waveshape was sustained, while at high rates
it was strongly phasic in that there were prominent response
peaks just after train onset and offset. In STG, which includes
secondary auditory areas, onset amplitude showed no systematic dependence on rate, whereas response waveshape showed
a strong and dramatic rate dependence paralleling that in HG.
Overall, from midbrain to thalamus to cortex, there was a
systematic shift in the form of response rate-dependencies from
one of amplitude to one of waveshape.
Sustained response waveshapes (as seen in subcortical structures and for low rates in auditory cortex) are commonly
reported in the fMRI literature. In contrast, phasic responses
(as seen for higher rates in auditory cortex) are not, nor are
their individual signature features. One signature feature—the
prominent peak following stimulus onset— has been reported
for a few prolonged acoustic, odorant, and visual stimuli (Bandettini et al. 1997; Giraud et al. 2000; Jäncke et al. 1999; Sobel
et al. 2000) but is nevertheless a fairly uncommon feature for
responses in the fMRI literature. A second signature feature of
phasic responses—the peak following stimulus offset—is
highly unusual. To our knowledge, the only other reported
fMRI “off-response” occurred in a subregion of primary visual
cortex following the transition from steady white light to
darkness (Bandettini et al. 1997). The paucity of previous
reports of phasic fMRI responses may be partly an issue of
detection since phasic responses are poorly detected by some
of the most commonly-used analysis approaches (e.g., a t-test
comparison of stimulus “on” and “off” periods, or equivalently, correlation or analyses using the SPM software package
that assume a sustained response; Bandettini et al. 1993; Sobel
et al., 2000). It is also possible that phasic responses have not
been seen because they reflect neurophysiological mechanisms
that are only invoked in particular, largely unexplored stimulus
regimes.
It is widely assumed that different sound features (e.g.,
frequency, bandwidth, repetition rate) are represented in the
amplitude of fMRI activation or amplitude variations with
position (e.g., Giraud et al. 2000; Talavage et al. 2000; Wessinger et al. 2001; Yang et al. 2000). In contrast, the possibility
of representations in the temporal dimension is not generally
entertained, and this makes the wide variations in cortical
response waveshape of this study especially intriguing. A few
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Response to high-rate (35/s) noise burst trains:
effect of train duration
RATE REPRESENTATION IN HUMAN AUDITORY PATHWAY
other studies have also reported covariations between sound
characteristics and temporal fMRI activation patterns in the
auditory system. For instance, Gaschler-Markefski et al. (1997)
examined the degree of temporal stationarity of auditory cortical fMRI responses and reported regional variations depending on stimulus and task. In studying fMRI responses to
amplitude modulated noise, Giraud et al. (2000) found an
increasingly prominent peak at stimulus onset with increasing
modulation rate, a result that strongly parallels the findings of
the present study (see Comparison to previous fMRI and PET
studies). The present study and these previous reports suggest
that fMRI temporal patterns— or more specifically the temporal variations in neural activity underlying these patterns—may
be an important way in which sound is represented in the
auditory system.
Role of rate per se in determining fMRI responses
fMRI responses and underlying neural activity
To understand the significance of the different fMRI response waveshapes, it is necessary to first consider the extent
to which waveshape is governed by neural, metabolic, and
hemodynamic factors. While the relationship between neural
activity and fMRI responses is not fully understood, it is
generally accepted that neural activity and image signal are
ultimately linked through a chain of metabolic and hemodynamic events. For the form of fMRI in this study (BOLD
fMRI), this linkage is as follows. When there is an increase in
neural activity in the form of synaptic events or neural discharges,8 there is a corresponding increase in local brain me8
We consider both synaptic events and neural discharges as “neural activity,” because there is evidence in favor of each as a contributor. 2DG studies
suggest that synaptic events may dominate the metabolic response (and hence
the fMRI response; Auker et al. 1983; Jueptner and Weiller 1995; Nudo and
Masterton 1986). A recent study by Logothetis et al. (2001), which simultaneously recorded intracortical activity and fMRI responses, also suggests that
synaptic activity may dominate. However, there are also reports of a strong
coupling between discharges and fMRI responses (Heeger et al. 2000; Rees et
al. 2000), possibly because discharges and synaptic activity were themselves
J Neurophysiol • VOL
tabolism and oxygen consumption (Sokoloff 1989). The increase in oxygen consumption is accompanied by an increase
in blood flow and blood volume in the active brain region.
However, the increase in flow dominates, such that the local
concentration of deoxygenated hemoglobin actually decreases,
which is important because deoxygenated hemoglobin is paramagnetic (Pauling and Coryell 1936) and thus influences local
image signal levels. The net effect of a decrease in deoxygenated hemoglobin is an increase in image signal. When the
entire chain of events is considered together, increases and
decreases in neural activity result in concordant changes in
image signal strength (Kwong et al. 1992; Ogawa et al. 1993;
Springer et al. 1999). Since hemodynamic changes occur over
the course of seconds, fMRI effectively provides a temporally
low-pass filtered view of neural activity. More specifically,
since fMRI is sampling activity over small volumes of brain
(i.e., voxels), the responses can be thought of as showing the
time-envelope of population neural activity on a voxel-byvoxel basis.
Previous work has shown that the relative timing and magnitude of stimulus-evoked changes in blood flow, blood volume, and oxygen consumption can influence the waveshape of
the fMRI response (Buxton et al. 1998; Mandeville et al.
1998). While this raises the possibility that changes in waveshape from sustained to phasic reflect changes in hemodynamics rather than underlying neural activity, we believe this to be
unlikely for both of the main components of the phasic response, namely the off-peak and the on-peak. It is particularly
unlikely that a hemodynamic explanation accounts for the
off-peak. Previous hemodynamic modeling and experimentation has not predicted an off-peak following stimulus termination, and we know of no plausible model that could generate
such a component. Therefore the emergence of an off-peak
with increasing repetition rate in auditory cortex is almost
certainly attributable to a rate-dependent increase in neural
activity at stimulus offset.
The other major feature that distinguishes phasic from sustained responses, namely the sharp decline in signal that forms
the prominent onset peak, requires more detailed consideration
because it is known that declines in signal can theoretically
occur over the course of a prolonged stimulus for completely
hemodynamic reasons (Buxton et al. 1998). However, measurements of BOLD signal, blood flow, and blood volume
responses have failed to illustrate a case for which purely
hemodynamic features could generate a signal decline as dramatic as those seen here (e.g., Hoge et al. 1999a; Mandeville et
al. 1999). Additionally, separate evidence works against the
idea that the signal decline is driven primarily by hemodynamic rather than neural influences. The reasoning follows
from the fact that the same voxels were capable of showing
either a phasic response (and the associated dramatic signal
decline) or a sustained response depending on the stimulus.
Since the time course of the phasic and sustained responses is
strongly correlated in those studies. It seems likely that the relative contribution of synaptic events versus discharges to the fMRI response will depend on
the specifics of the local neural circuitry (Bandettini and Ungerleider 2001),
and therefore may in fact vary across different regions of the brain (Auker et
al. 1983; Mathiesen et al. 1998), or even for different types of stimuli. For the
purposes of this discussion, we leave open the possibility that either or both
synaptic activity and discharges “contribute” to the fMRI responses we measured.
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In this study, noise burst duration was held constant while
rate was varied, so overall stimulus energy and sound-time
fraction (STF) covaried with rate (resulting in an approximately 12-dB differential in sound pressure level for 2/s vs.
35/s noise burst trains). While this raises the possibility that the
wide range of response waveshapes in auditory cortex was due
primarily to changes in parameters other than rate, we do not
believe this to be the case for two reasons. First, in a separate
study, we have found that varying the intensity of 2/s or 35/s
noise bursts over a 20- to 30-dB range has no effect on
response waveshape (Harms et al. 2001). Second, we have
found that changing rate from 2/s to 35/s while holding STF
constant (and therefore varying burst duration) still produces a
change in waveshape from sustained to phasic (although STF
does have some influence on response waveshape; Harms
2002; Harms and Melcher 1999). In the case of response
amplitude, the precise rate dependencies might be somewhat
different if stimulus energy and STF were held constant instead
of burst duration, because varying energy alone can produce
changes in response amplitude (Hall et al. 2001; Sigalovsky et
al. 2001), as may also be the case for changes in STF.
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M. HARMS AND J. MELCHER
very similar over the first 6 – 8 s, the “operational history” of
the hemodynamic system is presumably similar as well. In
light of this common initial response, a hemodynamic system
that could subsequently generate grossly different response
waveshapes seems unlikely, unless the differences reflect differences in underlying neural activity.
While response waveshape varied with rate within a structure, it also varied across structures for a given rate. It is known
that there can be spatial heterogeneity in tissue hemodynamics
(Chen et al. 1998; Davis et al. 1998), so the possibility that
regional variations in hemodynamics play some role in the
waveshape differences across structures cannot be discounted.
Still, the heterogeneities in hemodynamics that have been
documented are not sufficient to account for the dramatic
waveshape changes that occur across the pathway as a whole
(from the inferior colliculus to cortex).
Given that fMRI responses reflect the time-envelope of
population neural activity, the prominent declines in fMRI
signal that occur at high rates in MGB, HG, and STG provide
clear evidence for an overall decline in neural activity during
the first seconds of a train (⬍10 s). This decline likely includes
decreases in synaptic, as well as discharge activity, since both
forms of activity can be reflected in fMRI signals.
While an overall decrease in neural activity early in highrate trains is clear, the subsecond temporal details of activity
during this decrease remain unresolved because fMRI provides
a low-pass filtered view of neural activity. Previous electrophysiological data suggest various possible forms for the temporal details underlying the overall decline in neural activity.
For instance, it may be that the fMRI signal decline reflects a
burst-to-burst adaptation in neural activity in which each successive burst early in a train elicits progressively less activity
(Fruhstorfer et al. 1970; Ritter et al. 1968; Roth and Kopell
1969). Alternatively, a variant of this may occur in which
activity does not always decrease in a strictly progressive
fashion across consecutive bursts but sometimes shows an
increase from one burst to the next (e.g., facilitation or enhancement; Brosch et al. 1999; Budd and Michie 1994; Loveless et al. 1989). (As long as decreases from burst to burst
occur more often than increases, the time-envelope of neural
activity would still decrease.) Another possibility is that population neural activity is not synchronized to individual bursts
(Lu and Wang 2000; Lu et al. 2001) but instead occurs in
response to the train as a whole with an initial peak in activity
followed by a lower level of activity. All of these possibilities
would result in a decline in the time-envelope of population
neural activity and are therefore consistent with the prominent
declines seen in fMRI signal.
While we cannot conclusively determine the temporal details of activity during the declines in fMRI signal, it is worth
recognizing that aspects of our data are consistent with the idea
that there is a burst-to-burst adaptation in neural activity. For
instance, the fMRI responses to small numbers of noise bursts
are suggestive of an adaptation process because the fMRI
response amplitude did not increase in proportion to the number of bursts, but rather showed a slower than linear growth.
Whether neural activity and fMRI signal are coupled in an
approximately linear manner (and under what circumstances)
J Neurophysiol • VOL
9
One neural interpretation of the trends in onset amplitude follows from a
simple model in which there is a neural response to each successive burst in a
train and two competing effects of increasing rate: 1) an increased number of
bursts per unit time (working to increase total neural activity during the initial
seconds of the train, and hence onset amplitude) and 2) a decreased response
to individual bursts because of increased adaptation (working to decrease total
neural activity). If this latter effect due to adaptation were relatively unimportant at low rates, but dominant at high rates, the result would be an increase in
onset percent change with increasing rate at low rates and a plateau or decrease
at high rates—the trends we observed in MGB and HG.
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fMRI response onset and neural adaptation
is an open question under active investigation (Boynton et al.
1996; Dale and Buckner 1997; Gratton et al. 2001; Hoge et al.
1999b; Logothetis et al. 2001; Vazquez and Noll 1998). However, if they are, the slower than linear growth in the fMRI
responses to small numbers of noise bursts suggests that the
neural activity produced by each successive burst in a train is
progressively less (i.e., there is burst-to-burst adaptation in
neural activity). If the decline in neural activity from burst to
burst were to continue until there is little or no burst-evoked
activity, the time-envelope of neural activity would decline
substantially following the onset of the train, and so correspondingly would the fMRI signal (as observed for high-rate
trains). Another aspect of the data consistent with neural adaptation is the growth in onset amplitude with rate. If neural
activity and fMRI response amplitude vary in direct proportion, onset amplitude may be viewed roughly as an indicator of
the time-average neural activity during the first seconds of a
train. If there were no adaptation and each successive burst in
a train produced an identical increase in neural activity, the
time-average neural activity during the first seconds of a train
would increase in direct proportion to rate, and onset amplitude
would be expected to do the same. Instead, a proportional
increase in onset amplitude did not occur in any structure. This
is most obvious at high rates in MGB, HG, and STG, where
onset amplitude either declined or did not change with increasing rate.9 However, it can also be seen at lower rates and in the
IC. For instance, an increase in rate from 2/s to 10/s increased
onset amplitude by less than twofold in every structure, well
short of the fivefold increase expected if growth were proportional to rate. This result is consistent with neural adaptation
occurring in all of the studied structures, even the IC where
fMRI response waveshapes are sustained and do not immediately suggest an underlying adaptation.
Looking across structures, the data indicate that any neural
adaptation increased with increasing position in the pathway.
For instance, at any given rate, the percentage decline in signal
following the on-peak increased progressively from IC to
MGB to auditory cortex (HG and STG), suggesting an increasing degree of adaptation in the underlying population neural
activity. An increase in adaptation across structures is also
suggested by the fact that the growth in onset amplitude with
rate falls increasingly short of predictions assuming no adaptation. For instance, the increase in onset amplitude from 2/s to
10/s falls increasingly short of the fivefold increase predicted in
the absence of adaptation as one moves from IC (1.69) to MGB
(1.42) to auditory cortex (HG, 1.26; STG, 0.92). Similarly, the
increase in onset amplitude from 10/s to 35/s falls increasingly
short of the 3.5-fold prediction [IC, 1.42; MGB, 1.36; auditory
cortex, 0.80 (HG), 0.99 (STG)]. Thus if there is burst-to-burst
adaptation in population neural activity early in a train, it
increases from IC to MGB to auditory cortex.
RATE REPRESENTATION IN HUMAN AUDITORY PATHWAY
J Neurophysiol • VOL
and HG, changes in onset amplitude as a function of repetition
rate were consistent with what might be predicted based on
microelectrode recordings in animals of neural spiking in response to amplitude-modulated trains.
RELATIONSHIP TO ELECTRIC RECORDINGS IN HUMANS. A trend
of generally greater adaptation at cortical versus brainstem
levels of the auditory pathway fits with data concerning two of
the most studied components of human auditory evoked potentials: wave V of the brain stem– evoked potential and the
long latency potential N1. Wave V is likely generated by
neurons projecting to the IC (Melcher and Kiang 1996; Møller
1998), while the primary generators of N1 have been localized
to auditory cortex (e.g., Näätänen and Picton 1987; Reite et al.
1994). Wave V and N1 show different sensitivities to stimulus
repetition rate. For example, in responses averaged over many
click presentations, a high click rate (⬎50/s) is required to
generate a 30 –50% amplitude decrement in wave V of the
brain stem– evoked potential (Jiang et al. 1991; Suzuki et al.
1986; Thornton and Coleman 1975). In contrast, similar decrements in N1 occur at much lower rates (0.5–2/s), as seen in
averaged responses and by comparing the individual responses
to successive stimuli in a train (Davis et al. 1966, 1972;
Fruhstorfer 1971; Fruhstorfer et al. 1970; Picton et al. 1977;
Ritter et al. 1968; Roth and Kopell 1969). The substantially
different rate sensitivities for wave V and N1 indicate greater
stimulus-to-stimulus adaptation in the cortical neurons generating N1 than in the brain stem neurons generating wave V.
Phasic response “off-peak” and neural off responses
The off-peak in the phasic response may be related to
electrophysiological “off-responses” observed intracortically
following the termination of stimulus trains. For instance,
following 175-ms click trains, Steinschneider et al. (1998)
found transient increases in cortical multiunit activity in awake
monkeys. Using depth electrodes implanted in the vicinity of
human auditory cortex, Chatrian et al. (1960) showed gross
potential responses following 3-s click trains. In both of these
electrophysiological studies, the rate dependence of the offresponse resembled our fMRI cortical data in that an offresponse was present for high, but not low, repetition rates.
Given this similarity, it seems quite possible that the fMRI and
electrophysiological off-responses to stimulus trains arise from
similar underlying neural mechanisms.
The fMRI off-peak to high-rate stimulus trains may also be
physiologically related to off-responses that occur in evoked
potential and magnetic field recordings following the cessation
of prolonged individual stimuli. Support for this idea comes
from the fact that a cortical fMRI off-peak can be elicited by a
prolonged noise burst (30-s duration; Sigalovsky et al. 2001).
In humans, extracranial evoked potential (Hillyard and Picton
1978; Pfefferbaum et al. 1971; Picton et al. 1978; Spychala et
al. 1969) and magnetic field recordings (Hari et al. 1987;
Lammertmann and Lutkenhoner 2001; Pantev et al. 1996) have
also shown responses to the offset of prolonged noise or tone
bursts (with durations ranging from 0.5 to 10 s). The generation
site of electrophysiological off-responses has been localized to
auditory cortex (Hari et al. 1987; Noda et al. 1998; Pantev et al.
1996). Therefore both the electrophysiological and fMRI offresponses imply increased activity in auditory cortical neurons
following sound offset, a form of response that has ample
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RELATIONSHIP TO ELECTROPHYSIOLOGICAL DATA IN ANIMALS. A
trend of increasing adaptation with increasing position in the
auditory pathway has emerged from several animal neurophysiological studies explicitly designed to compare responses
across structures. For instance, microelectrode recordings of
the response to paired stimuli with different interstimulus intervals have shown an increase in recovery time with increasing level in the auditory pathway. In unanesthetized animals
(cats and rabbits), the average interval required for 50% recovery of the response to the second of two clicks is 2 ms in the
auditory nerve, cochlear nucleus, and superior olivary complex, but 7 ms in the inferior colliculus and 20 ms in auditory
cortex (Fitzpatrick and Kuwada 1999). In unanesthetized
guinea pig, Creutzfeldt et al. (1980) recorded responses to
amplitude modulated tones simultaneously from thalamic and
cortical neurons (specifically 9 thalamo-cortical unit pairs for
which the correlation of spontaneous activity suggested a direct
synaptic connection). Activity in the cortical neurons declined
more rapidly over successive cycles of the AM tone than did
the activity in the thalamic neurons, indicating greater adaptation in the cortical neurons. Finally, recording near-field potentials from the IC and auditory cortex in response to brief
noise bursts in unanesthetized chinchilla, Burkard et al. (1999)
found that the mean response amplitude (averaged across noise
burst presentations) decreased more in cortex than IC as repetition rate was increased. Their results are again consistent
with greater adaptation in cortex than lower structures in the
auditory pathway.
The extensive animal literature regarding modulation transfer functions (MTFs) also suggests a change in temporal response properties from the IC to auditory cortex. Here we
focus on studies that quantify their results in terms of rate
MTFs (rMTF; average firing rate vs. modulation frequency),
rather than temporal MTFs, since changes in the “synchronization” or phase locking of neural activity (in the absence of
average rate changes) are unlikely to be reflected in fMRI
activity. Furthermore, since most animal studies use shortduration stimulus trains (approximately 1 s), the most appropriate measure of the present study for comparison to the
animal results is onset amplitude. In the IC, the best modulation frequency (BMF; the frequency at which the rMTF has its
largest value) for individual neurons is generally greater than
approximately 30 Hz (Krishna and Semple 2000; Langner and
Schreiner 1988; Muller-Preuss et al. 1994). In contrast, BMFs
in auditory cortex tend to be less than approximately 20 Hz
(Bieser and Muller-Preuss 1996; Eggermont 1991; Schreiner
and Raggio 1996; Schreiner and Urbas 1988). These values are
consistent with this study in that onset amplitude steadily
increased in the IC for noise burst rates ⱕ35/s (the highest rate
employed), but peaked in HG at a lower rate (10/s). While the
variation in onset amplitude with rate in HG was rather small
(and in STG, onset amplitude did not vary at all), a similarly
weak “tuning” also holds in population neural activity, in that
the rMTF averaged across cortical neurons is primarily lowpass, or only weakly band-pass (Eggermont 1994, 1998; Eggermont and Smith 1995; Schreiner and Urbas 1986). The
relatively flat nature of the average rMTF in cortex probably
reflects a weak tuning in the rMTFs of many individual neurons (Eggermont 1998; Schreiner and Raggio 1996) but could
also be due in part to the summation of activity across sharply
tuned units having a wide range of BMFs. Overall, in both IC
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M. HARMS AND J. MELCHER
Phasic response recovery
In addition to the prominent on- and off-peaks of fMRI
phasic responses, there is a third component, namely the steady
signal increase that begins approximately 10 s after train onset
and continues to the end of the train. A similar “signal recovery” has been observed in auditory cortex in response to tone
bursts (Robson et al. 1998) and in the supplementary motor
area in response to a finger tapping task (Nakai et al. 2000).
This signal recovery, which in this study can be seen in the
responses to intermediate (10/s) and high-rate (35/s) trains of
various durations (e.g., 30 – 60 s; Figs. 4 and 9), has no obvious
analog in electric- or magnetic-evoked responses to prolonged
auditory stimuli, since these responses do not generally increase over the course of the stimulus but remain fairly constant, or even decrease (Lammertmann and Lutkenhoner 2001;
Picton et al. 1978). However, most of the stimulus durations
used in this literature are far less than those used in this study,
so it is possible that an electric or magnetic analog would be
identified if longer stimulus durations were tried. It is also
possible that the phasic response signal recovery is related to
subjects’ anticipation of the termination of high-rate trains (and
thus may be loosely analogous to electrophysiological correlates of expectation such as the contingent negative variation;
Donchin et al. 1978). To explore this idea, we performed two
pilot experiments in which, for some runs, the subject’s attention was diverted to the visual domain with a highly demanding, ongoing visual task, while presenting a 35/s noise burst
train in our standard “30 s on, 30 s off” protocol. Comparison
of task versus no task showed the expected decrease in auditory
response amplitude in the visual task condition (Woodruff et
al. 1996), but no obvious change in the form of the signal
recovery. If the signal recovery does indeed reflect anticipation
of train termination, it would appear to be a “low-level” expectation that is not readily eliminated by overt diversion of
attention.
J Neurophysiol • VOL
Comparison to previous fMRI and PET studies—auditory
and nonauditory
Several imaging studies have examined rate effects in auditory cortex using stimuli limited to low rates. fMRI studies
presenting blocks of syllables or words at rates ⱕ2.5/s show
primarily sustained responses in auditory cortex and an increase in response amplitude with increasing rate (Binder et al.
1994; Dhankhar et al. 1997; Rees et al. 1997). Both the time
courses and amplitude variations are consistent with the results
of this study at low rates. PET studies have also investigated
the effect of changes in the repetition rate of words or long
duration (500 ms) tone bursts presented at rates ⱕ1.5/s (Frith
and Friston 1996; Price et al. 1992; Rees et al. 1997). Because
PET requires the collection of photon counts over an extended
duration (e.g., tens of seconds), response time courses cannot
be obtained, and results are reported only in terms of the
overall percent signal change between stimulus and control
periods. Computed in this “time-averaged” manner, these PET
rate studies report a monotonic relationship between rate and
percent change. The findings of this study are in agreement
with this trend at low repetition rates.
The fMRI studies of Tanaka et al. (2000) and Giraud et al.
(2000) examined a wider range of rates and presented data in
agreement with some aspects of the present study. Using trains
of tone bursts presented at rates ranging from 0.5 to 20/s,
Tanaka et al. found that “time-averaged” percent change in the
transverse temporal gyrus was maximal at a rate of 5/s, consistent with our observation of a maximum in time-average
percent change between 2–10/s in auditory cortex. To reduce
the influence of acoustic imaging noise on auditory activation,
Tanaka et al. took a very different approach from the present
study and used a paradigm that leaves a long interval (12 s)
between image acquisitions. The fact that Tanaka et al. still
found a “time-average” rate dependence in agreement with the
present study indicates that this dependence is not sensitive to
scanner-generated acoustic noise.10
Giraud et al. (2000) examined the time course, as well as the
amplitude of activation, and for high rates identified a cortical
response component that is likely analogous to the “on-peak”
in the phasic responses of the present study. The stimulation
paradigm presented AM noise and unmodulated noise in alternating 30 s blocks. Although the use of unmodulated noise as
the comparison condition differs from the present study, a
relationship between AM rate and changes in cortical response
waveshape was found that is consistent with the present results.
For auditory cortex and high AM rates (greater than approximately 32 Hz), image signal changes during AM presentations
were modeled best by a transient response at AM noise onset.
In contrast, for low rates, the signal changes were better modeled by a sustained response. Thus the Giraud et al. study, like
the present one, indicates the emergence of a prominent onpeak with increasing rate in auditory cortex. For rates greater
than those used in the present study, Giraud et al. also reported
rate-dependent changes in response waveshape for subcortical
10
The insensitivity of our reported rate dependencies to scanner noise is
further supported by separate replication of the time course findings for 2/s and
35/s noise burst trains using clustered image acquisition with a long interimage interval (8 –10 s; Edmister et al. 1999), combined with a sampling
approach that allows time courses to be reconstructed with high temporal
resolution (e.g., 2 s; Harms 2002; I. Sigalovsky, personal communication).
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precedent at the single neuron level in auditory cortex (Abeles
and Goldstein 1972; Goldstein et al. 1968; He et al. 1997;
Howard et al. 1996; Recanzone 2000).
The fMRI and electrophysiological off-responses resemble
one another functionally in that both show a decrease in magnitude with decreasing sound duration. For instance, evoked
potential off-responses following tone bursts have been shown
to decrease in magnitude as burst duration is reduced from 9 to
1 s (Hillyard and Picton 1978; Picton et al. 1978), or from 2.5
to 0.5 s (Pfefferbaum et al. 1971). The fMRI off-response to
high-rate trains is lower in magnitude for a 15-s train compared
with longer duration trains (ⱖ30 s; Fig. 9). The duration
dependence of fMRI and electrophysiological off-responses
may indicate that a diminishing percentage of cortical neurons
respond to sound offset as sound duration decreases, or that the
neurons responding to the offset of sound do so less robustly as
duration decreases. The fact that the fMRI and electrophysiological data were obtained for very different ranges of duration
(15– 60 s vs. 0.5–9 s) suggests that changes in sound duration
may be reflected in cortical population neural activity in the
same basic way over a broad range of sound durations.
RATE REPRESENTATION IN HUMAN AUDITORY PATHWAY
Relationship between fMRI response waveshape
and sound perception
The fact that the various noise burst trains used in this study
differ considerably from a perceptual standpoint raises the
possibility that some of the observed trends in the fMRI responses are correlates of perception, rather than simply rate.
The most striking of the observed trends, the change in cortical
response waveshape, showed a qualitative correlation with the
perceptual attributes of the stimuli. For the low rate that elicited sustained responses (i.e., 2/s), the individual bursts in the
train were distinguishable. For the high rate that elicited the
most phasic responses (i.e., 35/s), the noise bursts fused so that
the overall train sounded continuous (although modulated).
Thus distinctly different cortical response waveshapes occurred for two distinctly different perceptual regimens.
J Neurophysiol • VOL
The possible correlation between fMRI response waveshape
and perception fits with the idea that population neural activity
in auditory cortex encodes the beginning and end of perceptually distinct acoustic “events.” At a 2/s rate, individual noise
bursts of a train are the distinguishable events, so there would
be successive neural responses to each burst, resulting in a
sustained fMRI response. In contrast, 35/s noise burst trains are
perceptually continuous and would be treated as a single event,
thus producing neural responses primarily at the onset and
offset of the train, and therefore the phasic fMRI response.
Coordinated fMRI and psychophysical experiments will be
necessary to more fully explore the potential coupling between
perception and neural activity as seen through fMRI response
waveshape.
The trend in fMRI response waveshape across structures
suggests that the coding of perceptually distinct acoustic events
in population neural activity occurs to different degrees depending on structure. While auditory cortex showed a clear,
qualitative correlation between perception and response waveshape, the MGB showed less correlation in that there was less
difference in waveshape for 2/s versus 35/s bursts. In the IC,
there was no apparent correlation since waveshape was the
same regardless of whether the individual bursts of a train were
distinct (as at 2/s) or fused (35/s). Thus any correlation between perception and response waveshape diminished from
high to lower stages of the pathway.
Overall, our results suggest a population neural representation of the beginning and the end of distinct perceptual events
that, while weak or absent in the midbrain, begins to emerge in
the thalamus and is robust in auditory cortex.
The authors thank J. Guinan, M. Tramo, I. Sigalovsky, C. Lane, P. Cariani,
and M. Hawley for numerous helpful comments and suggestions, and B. Norris
for considerable assistance in figure preparation.
This study was supported by National Institute on Deafness and Other
Communication Disorders Grants PO1DC-00119, RO3DC-03122, and
T32DC-00038 and a Martinos Scholarship.
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