Vision, Touch and Hearing Research Centre, Department of Physiology and Pharmacology, The University of Queensland, St. Lucia, Queensland 4072, Australia
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ABSTRACT |
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Spitzer, Matthew W., Michael B. Calford, Janine C. Clarey, John D. Pettigrew, and Anna W. Roe. Spontaneous and Stimulus-Evoked Intrinsic Optical Signals in Primary Auditory Cortex of the Cat. J. Neurophysiol. 85: 1283-1298, 2001. Spontaneous and tone-evoked changes in light reflectance were recorded from primary auditory cortex (A1) of anesthetized cats (barbiturate induction, ketamine maintenance). Spontaneous 0.1-Hz oscillations of reflectance of 540- and 690-nm light were recorded in quiet. Stimulation with tone pips evoked localized reflectance decreases at 540 nm in 3/10 cats. The distribution of patches "activated" by tones of different frequencies reflected the known tonotopic organization of auditory cortex. Stimulus-evoked reflectance changes at 690 nm were observed in 9/10 cats but lacked stimulus-dependent topography. In two experiments, stimulus-evoked optical signals at 540 nm were compared with multiunit responses to the same stimuli recorded at multiple sites. A significant correlation (P < 0.05) between magnitude of reflectance decrease and multiunit response strength was evident in only one of five stimulus conditions in each experiment. There was no significant correlation when data were pooled across all stimulus conditions in either experiment. In one experiment, the spatial distribution of activated patches, evident in records of spontaneous activity at 540 nm, was similar to that of patches activated by tonal stimuli. These results suggest that local cerebral blood volume changes reflect the gross tonotopic organization of A1 but are not restricted to the sites of spiking neurons.
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INTRODUCTION |
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Aside from the systematic
representation of frequency (Merzenich and Brugge 1973;
Reale and Imig 1980
), details of the functional organization of auditory cortex remain controversial. Several studies
have provided evidence of compartmental organization within the
tonotopic representation of primary auditory cortex (A1) in the cat
based on clustered or banded distributions of neurons with different
response properties (e.g., Imig and Adrian 1977
; Mendelson et al. 1993
; Middlebrooks and Pettigrew
1981
; Phillips et al. 1994
; Schreiner and
Mendelson 1990
). Due to limitations of conventional
microelectrode mapping techniques, however, a comprehensive view of the
functional organization of A1 has yet to emerge. In a single mapping
experiment, it is only possible to characterize effects of a few
stimulus parameters from a large number of recording sites.
Consequently, it is not yet clear how the various topographies of
response properties described by different studies relate to one
another. A second limiting factor is the lack of anatomical markers.
The discovery of histological markers for functionally distinct
neuronal subpopulations, most notably cytochrome oxidase, was a
considerable aid to elucidating the functional organization of visual
cortex (Horton and Hubel 1981
; Humphrey and
Hendrikson 1983
; Wong-Riley 1979
). In auditory
cortex, by contrast, the same markers provide little compelling
evidence for compartmentalization (Tootell et al. 1985
;
Wallace et al. 1991
). Resolution of this issue in
auditory cortex may have implications for our understanding of cortical
organization in general.
Optical imaging (OI) of intrinsic signals (Grinvald et al.
1986; Ts'o et al. 1990
) offers both vastly
increased spatial sampling density and the potential to sample cortical
responses to a larger stimulus repertoire in a single experiment than
is possible with microelectrode mapping, making it a promising
alternative approach for studying the physiological organization of A1.
This technique uses changes in light reflectance, associated with
physiological activity, to visualize the location of cortical tissue
activated by a given stimulus. The light reflectance signals derive
from multiple sources, including changes in local blood volume, which dominate the signal at wavelengths below 600 nm, and changes in the
oxygenation state of hemoglobin and light scattering, which provide a
major source of signals at longer wavelengths (Frostig et al.
1990
; Malonek and Grinvald 1996
; Malonek
et al. 1997
). Because neuronal responses are measured
indirectly, it is essential to verify results obtained by this method
with electrophysiological recording. Although most studies of visual
cortex have shown a high degree of correspondence between
stimulus-evoked optical and neurophysiological responses (Crair
et al. 1997
; Ghose and Ts'o 1997
;
Grinvald et al. 1986
; Roe and Ts'o 1995
;
Shmuel and Grinvald 1996
; Shoham et al.
1997
), under certain conditions, the areal extent of over which
evoked optical signals are recorded can be several times greater than
that of the neuronal spiking response (Das and Gilbert
1995
).
Recently, several groups have used OI of intrinsic signals to study the
topography of stimulus evoked activity in auditory cortex of rodents
(Bakin et al. 1996; Harrison et al. 1998
;
Hess and Scheich 1996
). Results of these studies differ
from those of single-unit mapping studies of cat A1 (Heil et al.
1994
; Phillips et al. 1994
) in several respects,
including threshold and time course of evoked activity, spatial
distribution of tone-evoked activity, and effects of changing sound
pressure level (SPL). It is not clear, however, to what extent these
apparent differences are due to species differences or recording
methodology because in only one case (Bakin et al. 1996
)
were optical images verified with electrophysiological recording.
For a number of reasons, including the large exposed surface area of
auditory cortex and the elaborate auditory cortical specializations, most mapping studies of A1 have been performed on the domestic cat
(Reale and Imig 1980). As a result, it is possible to
formulate relatively high-order questions about physiological
organization in this species. On the other hand, all published
intrinsic signal-based OI studies of A1 have been conducted on rodents,
about which much less is known. The present study was therefore
undertaken to characterize stimulus-evoked intrinsic optical signals at
different wavelengths in cat A1 and to compare optical responses to
neuronal spiking responses elicited by the same stimuli. Preliminary
accounts of the results were published in abstract form (Spitzer
and Clarey 1998
).
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METHODS |
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Anesthesia and surgical procedures
All surgical and experimental procedures were approved by the
Animal Experimentation Ethics Committee of the University of Queensland
and conformed to the guidelines for use of animals of the National
Health and Medical Research Council of Australia. Experiments were
performed on cats between 8 wk and 10 mo of age. Stimulus-evoked
optical signals at 540 nm, presented in the following text, were
obtained from three cats, aged 18, 20, and 27 wk. Young animals were
used because it has been reported that the signal-to-noise ratio of
optical signals is higher than in older animals (Hubener et al.
1997). Moreover, because response properties of auditory cortical neurons such as latency and threshold mature to adult-like levels within the first three to four postnatal weeks (Brugge et
al. 1988
), it is unlikely that the present results are
compromised by any physical immaturity of auditory cortex.
Animals were initially sedated with ketamine hydrochloride and
inspected to ensure there were no signs of external ear blockage or
middle ear infection. Surgical anesthesia was then induced with a
single dose of pentobarbital sodium (25 mg/kg ip). Atropine sulfate (10 mg/kg sc) was administered to minimize bronchial mucous secretion.
Anesthesia was maintained with supplemental doses of ketamine
hydrochloride (0.5-5 mg · kg1 · h
1 iv, typically starting 2 h after
barbiturate injection) throughout the subsequent surgery and recording
session. The subject's heart rate was monitored throughout the
recording session. During the optical and electrophysiological
recording sessions, anesthetic and supplementary fluids (lactated
Ringer solution) were administered by intravenous injection and/or
continuous intravenous infusion; anesthetic dose rate was adjusted to
maintain a constant heart rate and no signs of skeletal muscle activity
(indicated by high-frequency contamination of the electrocardiogram).
Anesthetic level was sufficient to attenuate the eye-blink reflex.
Throughout all procedures, the animal's temperature was maintained
with a homeothermic heating blanket (Harvard Instruments).
Following induction of surgical anesthesia, the trachea was cannulated and an intravenous line was established. A craniotomy was performed to expose the temporal cortical surface with the dura intact. A recording chamber was mounted over the skull opening with acrylic cement. The dura was then reflected to expose the cortical surface, and the recording chamber was filled with low-viscosity silicone oil (Corning) and sealed with a glass cover. Following the optical recording session, the top of the recording chamber was removed to permit introduction of microelectrodes. Dexamethosone was administered (1 mg/kg iv) prior to reflection of the dura and opening of the recording chamber to prevent cerebral edema. During the recording sessions, subjects were situated on a vibration isolation table (Newport Instruments) housed in an anechoic chamber. The cat's head was stabilized by attachment of the skull to a stereotaxic frame. Following experiments, animals were euthanized by overdose of pentobarbitone sodium (120 mg/kg iv).
Optical data acquisition and acoustic stimulation
Intrinsic optical signals were recorded using the Imager
2001 video acquisition system (Optical Imaging, Germantown, NY), the
details of which have been described previously (Bonhoeffer and
Grinvald 1996; Grinvald et al. 1986
, 1988
;
Ts'o et al. 1990
). The cortical surface was illuminated
with narrowband light, generated by band-pass filtering (band centers,
540, 630, or 690 ± 10 nm; interference filters from
Edmund Scientific, Barrington, NJ) the output of a tungsten light
source, and conveyed by two fiber-optic light guides. Video images of
the illuminated cortex were acquired at 30 frames per second with a CCD
camera (Bischke, Japan). The camera was positioned orthogonal to the
exposed surface of putative primary auditory cortex (gyral surface
between the tops of the anterior ectosylvian and posterior ectosylvian
sulci). Data were collected with the camera focused between 350 and 500 µm below the cortical surface to minimize artifacts due to the
presence of surface blood vessels. A typical imaging session began at
least 4 h after induction of anesthesia and continued for 12 h. While 12-bit cameras permit the encoding of absolute reflectance
value, the Imager 2001 system employs differential imaging methodology to allow the use of standard 8-bit video technology. In this method, a
reference image (average of 64 video frames) is obtained prior to each
block of recording trials. This reference image is subtracted from each
subsequent recorded image within a block of trials. The
reference-subtracted images constitute the stored data files on which
further analyses are performed. Such initial reference subtraction
allows the encoding of the differential signal within the 8-bit limit
and has comparable signal-to-noise resolution as that obtained by
storing the absolute reflectance values. Absolute reflectance values
can be reconstructed from the reference images. In this study,
intrinsic optical signals were acquired by summing video frames
captured over 0.5 s for stimulus-evoked signals, or 1 s for
spontaneous signals, followed by reference subtraction. The resulting
differential image was then amplified, digitized (8 bits), and stored
on computer disk. The stored image produced by this process will be
referred to hereafter as a data frame.
Acoustic stimuli were generated digitally (MALab, Kaiser Instruments, Irvine, CA) and presented via a calibrated piezo-electric transducer (Motorola KSN 1141) positioned 25 cm from the ear contralateral to the imaged cortex and approximately 50° in azimuth from the cat's midline. Individual acoustic stimuli consisted of trains of five to seven identical tone pips (10-ms cosine rise and fall, 70-ms duration, 730-ms inter-pip interval), as illustrated in Fig. 5. Detailed stimulus parameters are included in figure legends. Stimuli were presented in sets consisting of five or six combinations of frequency and sound pressure level (SPL) and a no-stimulus condition, presented in random order.
Acoustically evoked optical signals were recorded by acquiring a sequence of data frames during presentation of each acoustic stimulus. Acquisition of optical data was synchronized to acoustic stimulus presentation, starting one frame (500 ms) prior to stimulus onset and continuing for up to 6.5 s after stimulus offset (see Figs. 3 and 5). Following this data acquisition period, there was a silent interval of 12 s. To remove periodic spontaneous variation of the optical signal (see RESULTS), it was necessary to sum optical data recorded during multiple repeated trials of each stimulus set. Typically, data were collected and stored on-line in blocks of four or five repeated stimulus sets.
Acoustically evoked and spontaneous optical signals were visualized by
off-line analysis (SumAnal program) (Ghose and Ts'o 1997). For acoustically evoked images, corresponding data
frames (i.e., same stimulus and time after stimulation) from multiple blocks of stored data were first summed. Each successive summed frame
collected after stimulus onset was then divided by the frame collected
prior to stimulus onset within the trial. (n.b. This differential
analysis procedure should not be confused with the initial reference
image subtraction performed prior to saving data, on-line.) The
resulting values indicate the change in light reflectance
(
R) at each measured pixel following stimulus onset. Further analyses are described in RESULTS. To display
R data as images, images were clipped (top and bottom
19% of the pixel value distribution removed) to eliminate large
signals due to blood vessel artifact and fit to a common linear 33 color scale (shown in each figure). Spontaneous optical signals were
visualized using a similar procedure of frame-by-frame, temporal
analysis of signal development. However, data from individual trials
(20-s data acquisition period, no stimulus) were analyzed separately without summation across blocks.
The present data were obtained in 10 experiments performed on cats maintained under ketamine anesthesia. An earlier series of experiments was performed on 15 cats maintained under barbiturate anesthesia (pentothal sodium). In general, results obtained using barbiturate anesthesia were inconclusive due to excessive variability. However, a few references are made to results from these experiments, where appropriate.
Electrophysiological recording
Electrophysiological mapping was performed in four of the
experiments. Following the imaging session, a dose of dexamethosone (1 mg/kg iv) was administered, and the lid of the recording chamber was
removed. A reference image of the cortical surface collected under
illumination with green light (590 nm) was used to guide electrode
placement and for later alignment of optical and electrophysiological data. Glass-coated, tungsten microelectrodes (0.5-2.5 M impedance at 1 kHz) were introduced perpendicular to the cortical surface using a
stepping motor microdrive (Herb Adams, Caltech) controlled from outside
the anechoic chamber. The electrode signal was amplified (×1,000 gain)
and filtered 0.3-5 kHz. Recordings of multi- and single-unit activity
were obtained by level triggering (MALab). Multiunit recordings
were only obtained at sites where action potentials of the constituent
units could be clearly distinguished visually using an oscilloscope.
Single-unit recordings were also distinguished by visual criteria. In
each electrode penetration, an attempt was made to record from the
closest site to the cortical surface exhibiting acoustically driven
unit activity. In practice, the majority of recordings were obtained at
depths between 400 and 800 µm.
Neuronal responses to the stimulus set used for optical imaging were collected at each recording site. In addition, an attempt was always made to determine the frequency/level combination that gave an optimal response. The response to the "optimal" stimulus was also recorded if it was considerably greater than responses to any of the stimuli used for optical imaging. Slight modifications to the stimulation protocol were made for electrophysiological recording: stimuli were presented as 70-ms-duration tone pips (10-ms cosine rise/fall), 730-ms interstimulus interval, 20 repetitions. The time of occurrence of triggered action potentials and stimulus events were logged (more than 1-µs resolution) and stored in a FIFO buffer, from which they were retrieved by the host computer and stored on disk.
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RESULTS |
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Spontaneous optical signals
Spontaneous changes of light reflectance in auditory cortex were
observed in the absence of acoustic stimulation in 9/10 cats. In most
cases, the spontaneous signals were manifest as irregular variations of
reflectance in the no-stimulus condition but were obscured by averaging
signals from multiple trials. To visualize the spontaneous signal
directly, optical signals were recorded without averaging from one cat.
An example of the spontaneous optical signal recorded under
illumination with 540-nm light is shown in Figs.
1 and 2.
The change in cortical light reflectance (R) during a
20-s sampling period was measured by recording 21 consecutive 1-s data
frames and dividing frame 2 through 21 by frame 1. During the sampling
period, the reflectance of 540-nm light increased and decreased
relative to the initial frame in an oscillatory manner at a frequency
of 0.1 Hz (Fig. 1). The optical signal thus recorded has two
components. The first component is a coherent oscillation of
reflectance at all points on the cortical surface (Fig. 1C).
A second component consists of systematic variation of the amplitude of
signals recorded at different sites. The local variations of signal
amplitude can be dissociated from the global variations by dividing the
reflectance change at each site by the mean reflectance change measured
over the entire image. Such analysis (Fig. 1D) reveals that
the amplitude of reflectance change at each site oscillates relative to
the image mean at a frequency of 0.1 Hz and that the phase of
oscillation shifts with changes of position across the cortical
surface. The local variation of optical signals can also be visualized
by displaying the reflectance change at each measured pixel as an image
(individual frames in Fig. 2; see METHODS for details). In
this case, the global reflectance changes are removed by fitting the
histogram of pixel values of each frame to a common color map, a
similar transformation to division by the image mean. When viewed as an
image, it becomes apparent that in each frame areas of similar relative
reflectance change form distinct patches, several millimeters across. A
similar 0.1-Hz oscillation of cortical light reflectance was also
recorded using 690-nm light (not shown).
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Stimulus-evoked optical signals
Stimulus-evoked intrinsic optical signals were clearly evident in
only 3/10 cats imaged with 540-nm light. A similar success rate has
been reported for imaging at 540 nm in chinchilla auditory cortex
(Harrison et al. 1998). In two of the unsuccessful
cases, normal multiunit responses were recorded after the imaging
sessions, indicating that our failure to record optical signals was not a result of a physiologically unresponsive auditory cortex. In successful cases, it was necessary to average responses to large numbers of stimulus presentations to obtain acceptable image quality and to negate the spontaneous signals. An example of the optical signal
recorded at 540 nm by averaging responses to 50 presentations of a tone
pip train is illustrated in Figs. 3 and
4. Stimulus-evoked optical signals were
measured by recording optical data for 10.5 s, starting just prior
to stimulation, and dividing data frames collected after stimulus onset
by the frame collected before stimulus onset (Fig. 3C).
Within 3 s of stimulus onset, reflectance begins to decrease at
nearly all points on the cortical surface (Fig. 3D).
Reflectance continues to decrease throughout the duration of the 4.8-s
stimulus sequence and then returns to prestimulation levels following
stimulus offset. The magnitude of the stimulus evoked reflectance
changes at most sites is much greater than that of any fluctuations
evident in the no stimulus condition (- - -). The absence of large
reflectance changes in the no-stimulus condition indicates that signal
averaging was sufficient to negate the contribution of spontaneous
signals. Local variations of the amplitude of stimulus-evoked
reflectance changes are clearly evident in Fig. 3D. Division
by the image mean reveals a restricted region exhibiting relative
decrease of reflectance near the center of presumptive A1 (Fig.
3E). The image representation (Fig. 4A) shows development of an "activated" area (red and orange), corresponding to the location of maximum reflectance decrease, and, presumably, maximum blood volume increase, within 1 s of stimulus onset. The activated area remains relatively stable from 2-s postonset until stimulus offset and then quickly dissipates. Note, also, the late "activation" of posteromedial and anterolateral regions that showed relative increases of reflectance during stimulation. In comparison to
the stimulated condition, the unstimulated condition shows only minor
and unsystematic fluctuations of reflectance (Fig. 4B).
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The position of the activated region revealed by optical signals at 540 nm is dependent on stimulus frequency. Optical signals recorded from
the same cortex in response to six different frequency-level combinations are shown in Fig. 5.
Responses to stimuli presented at 65 dB SPL show a clear progressive
shift of the activated region from caudal to rostral as stimulus
frequency is incremented from 5 to 20 kHz, in agreement with the known
tonotopic organization of cat A1 (Reale and Imig 1980).
In contrast to the pattern of neuronal activation, described previously
(Heil et al. 1994
; Phillips et al. 1994
;
Schreiner et al. 1992
), the position of optical
activation evoked by 15-kHz tones remains constant across a 30-dB range
of stimulus level. The decaying phase of the activation sequence follows a curious course in responses to the 15-kHz, 80-dB and 20-kHz,
65-dB stimuli. In both cases, following stimulus offset the region of
decreased reflectance shifts in a caudal direction. Similar shifts of
the activated region after stimulus offset or during the later stages
of the stimulation period were observed in all three successful 540-nm
imaging experiments. In this particular case, as a result of the shift,
the images obtained for over 2 s following offset of the 20-kHz
stimulus are very similar to the stimulus-evoked images recorded in
response to the 5-kHz stimulus. Thus in this and the other two cases, a
topographic relationship of reflectance changes was apparent only for
the period during stimulus presentation.
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Weak stimulus-evoked intrinsic optical signals were also evident in
9/10 cats imaged using 690-nm light and ketamine anesthesia, including
those exhibiting stimulus-evoked signals at 540 nm. The magnitude of
evoked reflectance changes at 690 nm was approximately one-tenth that
observed at 540 nm. In contrast to the optical signals recorded at 540 nm, at 690 nm, reflectance changes were uniformly distributed across
the cortical surface, and their topography was uninfluenced by stimulus
frequency. Multiunit mapping was performed in four cats exhibiting
stimulus evoked signals at 690 nm, and in all cases, revealed normal
multiunit responses with stimulus-dependent topography. Cross-condition
analyses of the evoked optical signals (e.g., dividing responses to
different frequencies and levels, subtracting responses to a single
condition from the sum of all conditions, dividing across binaural
conditions in barbiturate anesthetized animals), such as those
performed in studies of visual cortex (Bonhoeffer and Grinvald
1993; Frostig et al. 1990
; Ts'o et al.
1990
), did not reveal any stimulus-dependent topography at 690 nm. Finally, similarly uniform and stimulus-feature-independent topography of intrinsic optical signals was observed in barbiturate anesthetized cats using light band-pass filtered at 690 or at 636 nm or
long-pass filtered with 650-nm cutoff.
Comparison of optical and multiunit responses
Multiunit mapping was performed in 2/3 animals that exhibited stimulus-evoked optical signals at 540 nm. Results of one experiment in which optical signals and multiunit electrophysiological data were recorded from the same cortex are shown in Fig. 6. The optical images form a tonotopic sequence with the region of activation shifting progressively from caudal to rostral as stimulus frequency is incremented from 5 to 20 kHz. Multiunit responses were classified as either clear responses, no response, or near-threshold responses (details in legend). In each image, there are a number of multiunit recording sites at which the optical and electrophysiological responses are in apparent agreement (e.g., 5 kHz: 1, 6, 9; 10 kHz: 3, 5, 8, 9; 15 kHz: 4, 5, 6; 20 kHz: 1, 3, 4, 5, 6, 8) and a number at which they clearly disagree (e.g., all stimuli at sites 10 and 11; 10 kHz: 1, 2, 4; 15 kHz: 3; 20 kHz: 7). Multiunit records and optical signals recorded at each site in response to the 20 kHz stimulus are shown in Fig. 7 to permit detailed comparisons. This case was chosen for illustration because it represents the best agreement between optical and multiunit responses. While there appears to be good agreement between optical and multiunit responses at the majority of sites (e.g., 1-6, and 8), there are also sites where the two measures clearly disagree (10 and 11), and others where the relationship is questionable (7 and 9).
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Examination of cases where the optical and multiunit responses disagreed revealed several examples of both false positive (decrease of reflectance relative to image mean, but no multiunit response; e.g., Fig. 6, site 3 at 15 kHz, sites 1 and 4 at 10 kHz) and false negative (clear multiunit response but no reflectance decrease relative to image mean; e.g., Figs. 6 and 7, sites 10 and 11 for all stimuli) optical signals. In experiment 98s11, 55 comparisons of optical and multiunit responses (responses to 5 stimulus conditions compared at 11 sites) revealed 22 clear mismatches of which 9 were false positives and 13 false negatives. Cases where the multiunit response was classified in the threshold category were not counted in this analysis. In experiment 98s5, 95 comparisons (5 stimulus conditions, 19 sites) revealed 30 mismatches of which 8 were false positives and 22 were false negatives. Thus in both experiments in which multiunit data are available, the majority of errors were false negatives.
It is not obvious from the preceding qualitative comparisons whether the level of agreement of optical signals and multiunit responses is higher than that expected by chance. To address this issue, quantitatively, the magnitude of optical and multiunit responses was compared at each electrophysiological recording site in two cats. A strong association would be expected to result in a significant negative correlation between these two response measures. Scatter plots relating the magnitudes of optical signals and multiunit responses, recorded at the same sites, in response to all stimuli that elicited clear, stimulus-evoked optical signals, are shown in Fig. 8 (see legend for details). To enable comparison of multiunit responses across recording sites, response magnitude at each site is expressed as a percentage of the response to the optimal stimulus at that site. Sites at which an optimal stimulus could not be determined were excluded from analysis. The level of correlation between magnitudes of optical signals and multiunit responses was assessed separately for each stimulus condition and also for all responses pooled across stimuli within each experiment, by calculating Spearman's rank order correlation coefficient (Rs; a nonparametric measure was used because the multiunit response data were not normally distributed due to a strong ceiling effect). Correlation values for individual stimulus conditions are shown in Table 1. In only 3 of the 10 cases was a significant (P < 0.05) correlation detected between optical and multiunit response magnitudes. Furthermore, in one of the three cases showing significant correlation, the direction of relationship was reversed. Due to the small sample sizes, it remains possible that a weak association existed in the remaining cases but was not detected. However, analysis of data pooled across conditions in each experiment (Fig. 8) provided no evidence of a significant correlation. Thus the present data provide little evidence of an association between the magnitude of stimulus-evoked optical and multiunit responses.
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Relation of stimulus-evoked and spontaneous optical signal
Finally, an intriguing similarity between spatial patterns of spontaneous and stimulus-evoked optical signals was observed in the single case for which both forms of data are available. A comparison of spontaneous and stimulus-evoked images obtained at 540 nm from the same cortex, is shown in Fig. 9. The top row of images are single frames (frame 5, 2,000- to 2,500-ms poststimulus onset) taken from sequences of stimulus-evoked activity, which form a tonotopic sequence as shown in Fig. 6. The images in the middle row are single frames, arbitrarily selected, from sequences of spontaneous optical activity recorded from the same cortex. The apparent similarity of patterns of stimulus-evoked and spontaneous optical signals was confirmed by subtracting each image in the middle row from the image above it (results shown on bottom row). The resulting difference images show little overall variation from the mean value, with most of the remaining peaks and troughs corresponding to major blood vessels. The similarity of topography of stimulus-evoked and spontaneous images might appear to suggest that all of the images resulted from spontaneous activity. This suggestion is, however, untenable because it cannot account for synchronization of reflectance changes to onset of acoustic stimulation, persistence of stimulus-evoked signals despite averaging images from multiple presentations, dependence of the topography of evoked images on stimulus frequency, nor for absence of major reflectance changes in the no-stimulus condition after extensive averaging. An alternative, albeit speculative explanation is that the neuronal or vascular functional units that are activated by acoustic stimulation are also spontaneously active in the absence of stimulation.
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DISCUSSION |
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The major findings of the present study are that both spontaneously occurring and stimulus-evoked optical signals were observed in auditory cortex of anesthetized cats; the spatial distribution of stimulus-evoked optical signals recorded at 540 nm was consistent with the known tonotopic organization of A1; and despite the stimulus-dependent topography of optical signals, the distribution of maximum reflectance changes was apparently not related to the distribution of extracellularly recorded neuronal activity.
The mismatch between the spatial distribution of optical signals and multiunit responses suggests that the optical signal at 540 nm corresponds to local blood volume changes within vascular functional units, recruited in response to physiological activation, whose fine structure is imprecisely matched to that of the multiunit response. The following sections discuss the rationale for these conclusions as well as alternative explanations of our results.
Differences in recording depth of optical and multiunit responses
Due to constraints imposed by light penetration and scattering,
intrinsic optical signals are recorded from the superficial cortical
layers (Bonhoeffer and Grinvald 1996). In these
experiments, optical signals were recorded at a depth of 500 µm below
the cortical surface. Because the superficial cortical layers contained
few electrically responsive units, multiunit responses were recorded at
depths ranging from 320 to 1,200 µm, with mean recording depths of
821 and 580 µm for experiments 98s5 and
98s11, respectively. The difference in depths at which
optical and multiunit responses were recorded raises two issues
pertinent to interpretation of the present results and may help to
explain the low success rate of the imaging experiments. First, do
neuronal response properties vary with depth in A1? In agreement with
the general concept of columnar organization, several studies have
shown that response properties, including type of response to
contralateral ear stimulation and frequency tuning, remain consistent
throughout the depth of A1 (Abeles and Goldstein
1970
; Imig and Adrian 1977
), including the superficial layers (Reser et al. 2000
). A major
exception is that, under binaural conditions, the influence of
ipsilateral ear stimulation may vary with cortical depth
(Phillips and Irvine 1979
; Reser et al.
2000
). Despite the variation of ipsilateral ear influences,
however, Clarey et al. (1994)
found that preferred azimuth remained homogenous throughout cortical depth in 72% of electrode penetrations. Thus although it is possible that the response
to the contralaterally located speaker, used in the present study,
would change with depth at some recording sites, such effects would be
limited to a minority of sites.
Furthermore it is difficult to evaluate the extent to which such depth
effects might confound the comparison of multiunit and optical
responses because it is unknown whether the optical signals are
generated in response to subthreshold neuronal activity in the
superficial cortical layers or suprathreshold activity in the middle
layers. The depth disparity is only relevant in the latter case. A
second issue is whether the 540-nm optical signals could be displaced
laterally from their site of origin due to blood flow with an
orientation parallel to the cortical surface. This would appear
unlikely because the arterioles and venules that supply the cortical
parenchyma are oriented orthogonal to the cortical surface
(Edvinsson et al. 1993). Reflecting this anatomical
arrangement, numerous studies have demonstrated that local blood flow
within the cerebral cortex follows a course parallel to that of
neuronal columns (e.g., Bryan and Duckrow 1995
). Thus optical signals with a hemodynamic basis should remain aligned with
their column of origin regardless of recording depth. Finally, the
paucity of neuronal responses in the superficial layers of A1, where
optical signals are recorded, is an obvious potential cause of the low
success rate of the imaging experiments. Future attempts at intrinsic
signal based imaging of A1 might benefit from use of anesthetics, such
as isofluorane, which are less disruptive to cortical activity
patterns, or an awake preparation.
Spontaneous optical signals
In the present study we observed spontaneous oscillatory changes
of reflectance of both red and green light at frequencies close to 0.1 Hz. Spontaneous optical signals with similar properties have been
reported previously (Mayhew et al. 1996). Using similar optical recording methods, Mayhew and coworkers observed 0.1-Hz oscillations of reflected dichromatic light (570 and 660 nm) from the
surfaces of diverse brain structures in anesthetized rats and
unanesthetized cats. Furthermore they demonstrated that the change of
intensity of reflected light was highly correlated with changes of
local blood flow measured in the same tissue by laser-Doppler flowmetry. Thus it is highly likely that the spontaneous optical signal
recorded by Mayhew et al., and in the present study results from
vasomotion, a 0.1-Hz oscillation of local cerebral and somatic blood
flow that has been widely reported (Dirnagl et al. 1989
; Fagrell et al. 1980
; Golanov et al. 1994
;
Meyer et al. 1988
; Morita-Tsuzuki et al.
1992
).
Spontaneous optical signals are a major source of noise that must
be overcome to record stimulus-evoked signals. The spontaneous signals
measured by Mayhew et al. could be as large as 1-2% of the total
reflected light signal; whereas, the "mapping signals" used to
define functional architecture by intrinsic signal based optical
imaging are typically an order of magnitude smaller (Frostig et
al. 1990; Grinvald et al. 1988
). At least one
other optical imaging study of auditory cortex has reported
"spontaneous waves of cortical activity" with a cycle of 5-10 s
that acted as a major source of interference (Harrison et al.
1998
). In the present study, averaging data from multiple
stimulus presentations (more than 16) proved sufficient to remove major
contributions of the spontaneous signals.
Comparison with previous studies of auditory cortex
Results of recent optical imaging studies of
auditory cortex in ketamine-anesthetized chinchillas (Harrison
et al. 1998) and barbiturate-anesthetized cats (Dinse et
al. 1996
) are similar to those obtained with 540-nm light in
the present study. Harrison et al. (1998)
recorded
tone-evoked optical signals at 540 nm with similar time course
and topography to those reported here. Threshold levels for reliably
eliciting optical signals were between 40 and 50 dB SPL. Both previous
imaging studies reported monotonic growth of activated regions as
stimulus SPL was increased and tonotopic organization of activated
regions at suprathreshold SPL.
Results obtained with optical imaging at 540 nm differ in several
respects from the previously described maps of single-unit (Phillips et al. 1994), and multiunit (Heil et
al. 1994
; Schreiner et al. 1992
), responses to
tones in A1. First, single-unit thresholds were often lower than 10 dB
SPL, and patches of neighboring sites containing units responding to a
given frequency were evident at 20 dB SPL, at least 20 dB below
threshold for optical signals. Second, as SPL was increased, the
location of patches of active neurons changed such that the location of
neurons responding maximally at the highest SPL was usually very
different from the location of neurons active near threshold SPL. All
maps contained at least some patches of recording sites where neuronal
activity decreased as a function of increasing SPL. By contrast, the
area of activated cortex indicated by optical recording grows
monotonically with increasing SPL, remaining centered on the site
showing activation at threshold (Dinse et al. 1996
;
Harrison et al. 1998
). Finally, at high SPL (e.g., at
least 60 dB), the distribution of single-unit activity consisted of
multiple, discrete, patches of active neurons separated by intervening
areas of unresponsive neurons. The largest continuous patches of active
neurons were not more than 3 mm across. Optical imaging, on the other
hand, typically reveals a single, large region of activation
(Dinse et al. 1996
; Harrison et al. 1998
;
present results). In our hands the activated region could span more
than 5 mm (Figs. 4 and 5). Finally, the lack of correspondence between
multiunit responses and optical signals at 540 nm reported here is
unsurprising, given the major discrepancies between the previously
reported optical and single-unit maps.
It might be argued that the maps of unit activity obtained using
contralateral ear stimulation, are not directly comparable to the
images obtained in the present study, using free-field stimulation.
Certainly, the single-unit maps obtained with different procedures are
likely to differ, primarily as a result of differences in the
contribution of ipsilateral ear stimulation. However, the effects of
SPL on the distribution of active neurons primarily reflect the patchy
organization of neurons with different forms of sensitivity to SPL
(Clarey et al. 1994; Heil et al. 1994
;
Phillips et al. 1994
; Sutter and Schreiner
1995
), as opposed to binaural interaction, and are therefore
unlikely to be dramatically altered by the difference in stimulation procedures.
Two studies, thus far, have reported tone-evoked optical signals at
wavelengths greater than 600 nm in auditory cortex. Hess and
Sheich (1996) recorded at 610 nm in auditory cortex of
unanesthetized gerbils, and Bakin et al. (1994)
recorded
at 630 nm in barbiturate-anesthetized rats and guinea pigs. In
agreement with the present findings, in both studies optical imaging
indicated activation of very large, continuous regions of A1. The
contiguous and broad distribution of activated regions revealed by
optical imaging at 630 nm in rats (Bakin et al. 1996
)
differs from the patchy distribution of single-unit activity reported
by Phillips et al. (1994)
in cats. Subsequent multiunit
mapping, however, revealed much better agreement between optical and
neuronal responses in rats than was evident in the present study. The
mapping data presented by Bakin et al. (1994)
permit 214 comparisons of optical and multiunit responses (2 stimulus conditions
at 107 sites) of which only 33 showed clear disagreement between the
two measures. Thus the difference between imaging results in rats and
single-unit mapping results in cats may indicate a fundamental
difference in the topographic representation of suprathreshold tones in
auditory cortex of the two species. In the gerbil, the location of the
activated region revealed by optical imaging shifted at later stages of
the stimulation period and continued to shift after cessation of
stimulation (Hess and Scheich 1996
). Similar shifts were
observed in all three cats successfully imaged at 540 nm in the present
study. Hess and Sheich suggested that shift of the activated area is
indicative of a corresponding shift of subthreshold
electrophysiological activity. However, in the absence of corroborating
electrophysiological data, and in light of the lack of correspondence
between optical signals and neuronal responses demonstrated here, it is
equally likely that the phenomenon reflects the spatiotemporal
properties of the hemodynamic response rather than those of the
neuronal response.
Comparison with previous studies of visual and somatosensory cortex
The findings of the present study differ from those of intrinsic
signal-based optical imaging studies of visual and somatosensory cortices in two regards. First, in the present study optical signals with stimulus-dependent topography were recorded at 540 nm but not at
longer wavelengths. By contrast, in visual cortex, similar functional
maps of ocular dominance and orientation preference can be obtained at
wavelengths ranging from 480 to 940 nm (Frostig et al.
1990). In rodent somatosensory cortex, stimulation of a single
facial vibrissa also evokes optical images with similar spatial
location and extent at 550, 610, and 850 nm (Narayan et al.
1994
). The wavelength dependence of optical signals in cat auditory cortex suggests that stimulus-evoked changes in local blood
volume, oxygen saturation of hemoglobin, and light scattering, which
provide the major sources of optical signals recorded at 540, 630, and
690 nm, respectively (Frostig et al. 1990
;
Malonek and Grinvald 1996
; Malonek et al.
1997
), are not as well coupled in this cortex as they are other
sensory cortices and, presumably, in auditory cortex in other species
(Bakin et al. 1996
; Harrison et al. 1998
;
Hess and Scheich 1996
). The reason for this apparent difference is not readily apparent. A second difference is that, in the
present study, the topographies of optical signals and neuronal spiking
responses evoked by the same stimulus were unrelated. By contrast,
several studies have demonstrated a tight correspondence between the
spatial distribution of optical and spiking responses in visual cortex
(Crair et al. 1997
; Ghose and Ts'o 1997
;
Grinvald et al. 1986
; Roe and Ts'o 1995
;
Shmuel and Grinvald 1996
; Shoham et al.
1997
) and rodent somatosensory cortex (Masino et al.
1993
; Peterson et al. 1998
). In the following
text, we consider three possible explanations for the latter difference.
Visualization of subthreshold and interneuron activity
Two studies of visual cortex, using spatially restricted stimuli,
provide evidence that intrinsic optical signals reflect both sub- and
suprathreshold neuronal activity. In contrast to previous imaging
studies that used full-field visual stimulation, these studies
demonstrated that partial field stimulation evokes optical signals
distributed over a much larger cortical area than the corresponding
neuronal spiking responses. Using stimuli designed to simulate the
receptive field size of a single cortical neuron, Das and
Gilbert (1995) reported that the areal extent of evoked optical
signals was approximately 20 times greater than that of evoked spiking
activity. In a second study, using partial field stimulation,
single-unit data provided evidence of subthreshold physiological
activity within regions containing evoked optical signals but no
spiking neurons (Toth et al. 1996
). Thus in visual cortex, stimuli that elicit spatially restricted patterns of spiking activity, as do tones in auditory cortex, reveal large discrepancies between the spatial distribution of optical and unit responses, most
likely due to visualization of subthreshold physiological activity. It
is therefore possible that visualization of subthreshold activity could
be a primary cause of the mismatch between patterns of optical
activation and spiking activity observed in the present study.
Another potential source of the mismatch between optical and unit
activity is the difficulty of recording spiking activity from
inhibitory interneurons with extracellular electrodes.
Mountcastle et al. (1969) reported that the majority of
extracellular recordings obtained with metal electrodes in
somatosensory cortex exhibited a type of waveform referred to as
"regular spikes." Only occasionally were recordings obtained from
neurons with "thin spikes," characterized by a comparatively
shorter initial negative phase, smaller amplitude, and spatially
restricted electrical field. A slightly higher proportion of
"thin," or "fast spiking," neurons have been recorded in
studies using glass micropipette electrodes (e.g., Simons
1978
). More recently, intracellular experiments have confirmed
Mountcastle's proposal that regular spiking neurons have pyramidal
morphology and thin spiking neurons have aspiny stellate morphology,
typical of GABAergic inhibitory interneurons (McCormick et al.
1985
). These findings provide strong circumstantial evidence
that metal extracellular electrodes record preferentially from
pyramidal neurons. The temporal properties of stimulus-evoked spike
bursts recorded extracellularly in auditory cortex are consistent with this view (Bowman et al. 1995
; Phillips et al.
1996
). Consequently a mismatch between multiunit and optical
responses could potentially arise in areas where spiking activity of
interneurons generates a strong optical signal, while simultaneously
inhibiting nearby pyramidal neurons.
There is certainly considerable evidence that stimulation with tones at
high SPL elicits subthreshold electrophysiological activity throughout
a substantial proportion of auditory cortex, including a major
component due to inhibitory processes and, presumably, spiking activity
in inhibitory interneurons. First, because many A1 neurons have
frequency-domain inhibitory side bands (Calford and Semple
1995; Calford et al. 1993
; Phillips
1988
; Phillips and Cynader 1985
;
Schreiner and Mendelson 1990
; Shamma and Symmes 1985
; Shamma et al. 1993
), a tonal stimulus of a
given frequency will induce widespread inhibition of neurons with
higher and lower best frequencies. Second, many A1 neurons exhibit
level-dependent suppression (Phillips and Irvine 1981
),
referred to as "nonmonotonicity," a substantial proportion of which
is likely generated by intracortical inhibitory mechanisms
(Barone et al. 1996
; Prieto et al. 1994
). Because nonmonotonic neurons are distributed in patches (Clarey et al. 1994
; Phillips et al. 1994
), the spatial
pattern of cortical activity induced by tones at high SPL should be
expected to include patches of spiking inhibitory interneurons,
together with inhibited pyramdial neurons. Consistent with this
contention, a recent voltage-sensitive dye-based imaging study of
guinea pig auditory cortex demonstrated that tonal stimulation produces
regions of activation, flanked by regions of inhibition, and that the
area of excitatory activation could be dramatically expanded by local
application of GABA antagonists (Horikawa et al. 1996
).
Finally, although binaural inhibitory mechanisms would not be expected
to have a major influence on the distribution of unit responses to
contralaterally presented tonal stimuli, such stimuli might induce
subthreshold activation of "predominantly binaural" cortical
neurons (Kitzes et al. 1980
).
In addition to the difference between optical and multiunit responses
within A1, failure to distinguish between supra- and subthreshold activity might also account for the difference in the
degree of correspondence of optical and unit responses
between A1 and visual cortex. In visual cortex, columns of
neurons with similar orientation preference are interconnected by a
lattice-like array of axonal projections (Gilbert and Wiesel
1983, 1989
; Rockland and Lund 1983
). As a
result, stimulation of a single orientation column gives rise to
subthreshold excitatory and inhibitory synaptic inputs preferentially
directed to surrounding columns with similar orientation preference
(Grinvald et al. 1994
; Weliky et al.
1995
). Consequently, the spatial pattern of subthreshold
electrophysiological activation induced by partial visual field
stimulation corresponds very closely to the pattern of suprathreshold
activation induced by full-field stimulation. Thus for full-field
stimulation, the failure to distinguish between sub- and suprathreshold
activation has little effect on the correspondence of optical and unit
responses. In auditory cortex, by contrast, it is likely that the
patches of activated and actively inhibited pyramidal neurons are
spatially separated. Thus visualization of subthreshold physiological
activity could have a far greater effect on results in auditory cortex than in visual cortex.
The hypothesis that the discrepancy between optical and unit responses in cat auditory cortex results entirely from visualization of subthreshold activity, and/or inability to record spiking activity of interneurons, is inconsistent, however, with the fact that the majority of mismatches that we observed were false-negative errors. In either case, one expects the majority of errors to be false positives. Although our data do not allow us to rule out a contribution of visualized subthreshold activity or interneuronal spiking activity, it is unlikely that either is the major cause of discrepancies between optical and multiunit responses observed here.
Timing of auditory cortical neuronal responses
The characteristic temporal response properties of neurons in
auditory cortex of anesthetized cats might account for the difference in results of optical imaging in auditory and other cortical areas. Most neurons in A1 respond to best-frequency tonal stimulation with one
or two spikes synchronized to stimulus onset, followed by suppression
of spiking activity for the duration of the stimulus (Fig. 7). In
addition, because repeated stimulation at short interstimulus intervals
results in decreased responsiveness, it is necessary to use a
stimulation duty cycle with a low ratio (~1:10) of stimulus-on time
to stimulus-off time to obtain maximum responsiveness to individual
stimuli (Hocherman and Gilat 1981; Phillips et
al. 1989
). As a consequence, using optimal stimulus parameters,
suprathreshold neuronal responses are only elicited during a very small
fraction of the duration of a repetitive tone-pip sequence. By
contrast, sustained neuronal responses lasting several seconds are
elicited by optimal stimuli in visual cortex of barbiturate
anesthetized animals (Hubel and Wiesel 1959
). Because of
this difference in response properties, discharge rates of optimally
stimulated neurons in visual cortex, recorded throughout a 5-s stimulus
presentation, may exceed those of auditory cortical neurons by more
than a factor of 15 (Roe and Ts'o 1995
; Sengpiel
and Blakemore 1994
). In barrel cortex, periodic vibrissal
stimulation elicits sustained spike discharges, with two spike bursts
per stimulus period (Peterson et al. 1998
). The 4- to
5-Hz stimulation frequencies, typically used in optical imaging
studies, would thus elicit discharge rates several times those
typically observed in auditory cortex.
The gross differences in amount of suprathreshold neuronal responses
throughout the period of stimulation in different cortical areas may be
reflected in the corresponding optical signals. Specifically, the
paucity of neuronal spiking responses may be insufficient to generate a
reliable and robust optical signal at 540 nm in auditory cortex. This
explanation is consistent with the unreliability of the optical signal,
which was only detectable in 3/10 cats tested, and then only after
extensive averaging. This hypothesis may, at first, appear inconsistent
with the fact that optical imaging has been shown to reveal both
suprathreshold and subthreshold activity (Das and Gilbert
1995; Toth et al. 1996
), as the paucity of spike
discharges in A1 is at least partly due to an abundance of
stimulus-evoked inhibitory processes some of which may last beyond the
stimulus duration (Volkov and Galazjuk 1991
, 1992
). It
should be noted, however, that optical signals accompanying suprathreshold activity are larger than those accompanying subthreshold activity and can be distinguished on this basis using appropriate analysis (Toth et al. 1996
). Furthermore it is not clear
how inhibitory activity is reflected by intrinsic optical signals. Thus
it remains entirely plausible that the failure of optical signals
recorded at 540 nm to reveal physiologically relevant functional
topographies in cat auditory cortex is due in large part to the
sparseness of neuronal spiking responses. If this is the case, one
potential approach to improving the quality of optical signals recorded from auditory cortex would be the use of an awake, behaving
preparation. Previous studies have indicated that tones evoke a wider
range of response types in A1 of unanesthetized animals, including a greater prevalence of sustained responses (Abeles and Goldstein 1972
; Brugge and Merzenich 1973
; Pfingst
et al. 1977
). One previous optical imaging study of auditory
cortex used an awake preparation (Hess and Scheich
1996
), but in the absence of corroborating physiological data,
there is no reason to suppose that the results were any different to
those of the present study.
Vascular organization
Finally, we consider the possibility that the success of optical
imaging in visual and barrel cortex depends on specializations of the
vascular supply that may be absent in cat auditory cortex. The primary
and secondary visual cortices of primates are distinguished by a finely
structured modular organization that is demarcated by the pattern of
cytochrome oxidase staining (Horton and Hubel 1981;
Livingstone and Hubel 1984a
;
Murphy et al. 1995
; Wong-Riley 1979
).
Neurons within the different cytochrome oxidase defined modules have
different response properties (Hubel and Livingstone 1987
; Livingstone and Hubel 1984a
;
Silverman et al. 1989
; Ts'o and Gilbert
1988
). Furthermore the modularity of functional organization is
reflected in the distribution of metabolic activity (Humphrey and Hendrikson 1983
; Tootell et al. 1983
) as
well as the patterns of intrinsic and interareal cortico-cortical
connections (Hubel and Livingstone 1987
; Levitt
et al. 1994
; Livingstone and Hubel 1984b
, 1987
;
Rockland and Lund 1983
; Yoshioka et al.
1996
), and in the distribution of a number of neurochemical
markers including CAT-301 (Hendry et al. 1984
) and
parvalbumin (Blumcke et al. 1990
). At least in macaque
monkeys, the modular neuronal organization within visual cortex is also
reflected by the distribution of small diameter blood vessels
(Zheng et al. 1991
). The density of capillaries and
radial penetrating vessels is greater in cytochrome oxidase-rich blobs
and stripes than in the intervening cortical tissue. Similarly, in
barrel cortex each histologically defined barrel is delimited by a
dense capillary plexus within the barrel core (Patel
1983
). There is not, however, a one-to-one relationship between
individual barrels and vascular supply, as individual penetrating
arterioles give rise to capillaries in several adjacent barrels
(Woolsey et al. 1996
). Nevertheless, in both visual and barrel cortex, there is evidence that the microvasculature is organized
into functional units that bear a close spatial relationship to the
anatomically and physiologically defined neuronal functional modules.
Because optical imaging relies primarily on blood-borne signals, it is
possible that the organization of vascular elements sets constraints on
the spatial pattern of activation that can be visualized with this
technique. Consistent with this view, the areal extent of activation in
barrel cortex produced by near-threshold deflection of a single
vibrissa (Peterson et al. 1998) corresponds very well
with the areal extent of the minimal vascular functional unit,
consisting of the capillaries supplied by a single penetrating arteriole (Woolsey et al. 1996
). If an equivalent
complementarity of neuronal and vascular functional units does not
exist in auditory cortex, the local blood volume changes, presumably
the major source of optical signals recorded at 540 nm, might be
spatially decoupled from the pattern of electrophysiological activity.
It is difficult to further evaluate this conjecture at present,
however, because it is not known whether A1 exhibits modular neuronal
organization, comparable to that in visual cortex, and because the
microscopic organization of blood supply to A1 has not been described.
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ACKNOWLEDGMENTS |
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We thank S. Kaiser, M. Ward, and the staff of Optical Imaging, without whose technical expertise these experiments would not have been possible. We also thank D. Vaney for contributing the vibration isolation table, D. Ts'O for use of the SumAnal program, and K. Keller for help with image analysis.
This work was supported by National Health and Medical Research Council of Australia Project Grants 971147 and 96/NHMRC9002G to M. W. Spitzer and J. D. Pettigrew, respectively, and by NHMRC Project Fellowship Grant 961226 to M. B. Calford.
Present addresses: M. B. Calford, Faculty of Medicine and Health Sciences, The University of Newcastle, Callaghan, NSW 2308, Australia; J. C. Clarey, Dept. of Otolaryngology, The University of Melbourne, Melbourne, VIC 3010, Australia; A. W. Roe, Dept. of Neurobiology, Yale University School of Medicine, New Haven, CT 06520.
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FOOTNOTES |
---|
Present address and address for reprint requests: M. W. Spitzer, Institute of Neuroscience, University of Oregon, Eugene, OR 97403 (E-mail: spitzer{at}uoneuro.uoregon.edu).
Received 14 June 2000; accepted in final form 3 November 2000.
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REFERENCES |
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