7, 326–336 (1998)
NI980329
NEUROIMAGE
ARTICLE NO.
Wavelength-Dependent Differences between Optically Determined
Functional Maps from Macaque Striate Cortex
Niall P. Mc Loughlin and Gary G. Blasdel
Department of Neurobiology, Harvard Medical School, Boston, Massachusetts 02115
Received September 23, 1997
This study investigates the role of wavelength in
determining the source and dynamic range of activitydriven reflectance changes in macaque striate cortex.
By using short (600 nm) and long (720 nm) wavelengths
to map ocular dominance, orientation, and position
from the same region of cortex on alternate trials, we
isolated wavelength-dependent differences in the contributions of different tissue compartments. In agreement with previous reports, 600-nm illumination was
found to produce optical signals that were more than
twice the size of those obtained with 720-nm illumination. In addition, 600- and 720-nm images were found to
correlate everywhere except in regions occluded by
blood vessels, where the images obtained at 600 nm
correlated with the overlying vasculature. Since the
720-nm images do not correlate with the vasculature,
this difference suggests that differential images obtained under 600-nm illumination are disproportionately sensitive to vascular events (e.g., changes in
blood flow, volume, etc.). This finding is supported by
the absorption spectra of hemoglobin and its derivatives, which absorb 600-nm light 4–1000 times more
strongly than 720-nm light. Hence, for the 40% of cortex
covered by blood vessels larger than 50 mm, images
obtained at 600 nm are dominated by the vascular
compartment to the exclusion of signals from the
neural compartment below. r 1998 Academic Press
INTRODUCTION
Light-based monitoring of neuronal activity has a
long and convoluted history. Early on, researchers
discovered that action potentials are accompanied by
transient changes in both light scattering and membrane birefringence (Cohen, 1973; Cohen et al., 1968;
Cohen and Keynes, 1971; Hill and Keynes, 1949; Tasaki
et al., 1968). The development of numerous membraneimpermeant voltage sensitive dyes, which transduce
membrane potential into larger optical signals, made it
practical to remotely monitor action potentials using
light (Cohen and Salzberg, 1978; Grinvald et al., 1977;
Gupta et al., 1981; Ross et al., 1977; Salzberg et al.,
1053-8119/98 $25.00
Copyright r 1998 by Academic Press
All rights of reproduction in any form reserved.
1977). However, it was well established prior to these
developments that even larger reflectance changes
could be observed at much slower time scales, presumably on account of activity-driven shifts in the distribution of water and electrolytes. As early as 1933, Penfield
noticed reflectance changes, perceptible to the unaided
eye, during localized seizure activity (Penfield, 1933).
Years later it was determined that much smaller
changes in the activity of normal cortex could be
detected optically on account of similar, activityassociated shifts in blood flow and volume (Lassen and
Ingvar, 1961; Jöbsis, 1977). A number of subsequent
studies observed that neuronal activity is often accompanied by oximetry signals that can be detected optically by monitoring the absorption and/or fluorescence
of intrinsic chromophores (Chance et al., 1962; Jöbsis et
al., 1977). Chance et al. (1962) noted that changes in
scattering, occasioned by the large flux of potassium
ions from active neurons into the interstitial fluids,
affected tissue transparency as well.
Though known, these changes were not used to
explore cortical organization until Blasdel and Salama
(1986) introduced the strategy of differentially imaging
them to map functional organizations associated with
ocular dominance and orientation in monkey striate
cortex. After showing that sustained activation leads to
sustained changes in local reflectance, they established
the practice of differentially imaging these changes to
visualize cortical function. Despite their use of a voltage sensitive dye (NK2367), Blasdel and Salama focused on the slow activity-associated intrinsic reflectance changes that were apparent before the cortex had
been stained. One of the key contributions of this work
was to shift attention from the fast optical changes
associated with action potentials to the approach of
differentially imaging the much slower ones associated
with local sustained activity.
By demonstrating a close relation with electrophysiologically determined patterns, moreover, this work
established a neuronal basis for the patterns they
observed that led to the first detailed maps of orientation selectivity, the first images of any response prop-
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erty in vivo, and the first images of different response
properties from the same cortical tissue whose subsequent analysis indicated local interactions that were
previously unknown (Blasdel, 1992a,b; Bartfeld and
Grinvald, 1992; Obermayer and Blasdel, 1993; Blasdel
and Campbell, 1997).
Following on from this work, Grinvald et al. (1986)
employed an array of photodiodes to explore the spectral and temporal dynamics of these intrinsic reflectance changes in unstained cortex. However, due to the
limited resolution of the 12 3 12 photodiode array
employed, it was not until Ts’o et al. (1990) implemented a variant of differential imaging that the first
high-resolution maps, comparable to those of Blasdel
and Salama (1986), were generated exclusively from
intrinsic reflectance changes. Since then a great many
intrinsic imaging studies have been published (for
example, Bonhoeffer and Grinvald, 1991, 1993; Bosking et al., 1997; Das and Gilbert, 1995, 1997; Kim and
Bonhoeffer, 1994; Wang et al., 1996).
Although the maps generated by the various differential imaging approaches appear simple and intuitive,
the mechanisms responsible for producing them depend on a number of poorly understood factors that link
neuronal activity to reflectance. Apart from physiological parameters, one of the most important variables is
the wavelength of incident illumination at which the
reflectance changes are monitored, as different tissue
compartments are characterized by different absorption spectra. While Blasdel and Salama (1986) did not
explore imaging at wavelengths shorter than 700 nm,
they did verify their optical patterns with reference to
histochemical stains as well as to physiologically determined preferences at strategic locations. The preferences inferred from both optical and electrode maps
agreed to a remarkable degree.
The first systematic exploration of wavelength was
conducted by Frostig et al. (1990), who reported that
differential images obtained under a range of narrowband wavelengths, 480 to 940 nm, appeared similar.
However, as the focus of this study was on analyzing
the temporal dynamics of their responses, no detailed
spatial comparisons between patterns obtained under
different wavelengths were made. Malonek and Grinvald (1996) reexamined the sources of intrinsic optical
signals in the cat using an imaging spectroscope, which
allowed them to simultaneously collect spatiotemporal
data for a range of wavelengths (520 to 650 nm). They
estimated contributions from oxyhemoglobin (HbO2 ),
deoxyhemoglobin (Hb), and light scattering by collapsing their data over space and fitting the spatial average
of their measured reflectance changes at different
wavelengths to the textbook absorption spectra. Unfortunately, to estimate the spatial precision of each signal
component the authors collapsed their data across
wavelength (from 520 to 650 nm), so no wavelength-
327
dependent differences were noted. Similarly, by averaging their spectral data across the cortical surface, they
failed to report on differential contributions from the
different tissue compartments.
In order to address the possibility of there being
differences between differential images collected under
different illumination wavelengths, we compared two
sets of images acquired under virtually identical conditions, using two different wavelengths: the 720 nm used
initially by Blasdel and Salama (1986) and a shorter
600-nm wavelength favored by Grinvald et al. (1986)
and others since (Ts’o et al., 1990; Bonhoeffer and
Grinvald, 1991, 1993). In agreement with previous
observations, the 600-nm signals were more than twice
the size of the 720-nm signals at most locations (Frostig
et al., 1990), and patterns obtained at 600 nm correlated with those obtained at 720 nm in regions that
were not covered by blood vessels. However, the patterns obtained at 600 nm did not correlate with those
obtained at 720 nm in regions that were covered by
blood vessels. In these regions the patterns collected at
600 nm correlated with the superficial vasculature.
While the active interference of blood vessels can most
likely be reduced or eliminated through appropriate
additional precautions, the passive loss of signals from
cortex covered by blood vessels appears to result from
the far greater absorption of 600-nm light by hemoglobin and the resulting inability of light at this wavelength to penetrate blood vessels to monitor the activities of cortical neurons below.
MATERIALS AND METHODS
These results are based on general observations from
six animals (one cat, five macaque monkeys) and
detailed comparisons of patterns obtained from two
additional macaque monkeys (Macaca mulatta) expressly for this study. In order to curtail extraneous
contributions, all comparisons were restricted to images collected using identical parameters (of visual
stimulation and image acquisition) that were obtained
alternately at least two or three times at 600 and 720
nm. Except for the fact that voltage-sensitive dyes were
not used, and longer durations of visual stimulation
and frame averaging were therefore required (to compensate for smaller signals), all equipment, procedures,
and parameters of visual stimulation remained the
same as in previous publications (see Blasdel, 1992a,b;
Blasdel et al., 1995).
On the day of recording each animal was induced
either with a single injection of ketamine/xylazine or
with Isoflurane delivered through a mask in a 2:1
mixture of N2O and O2. After the animal had been
intubated, and a catheter for infusing electrolytes and
drugs had been placed in the cephalic vein, it was
ventilated with a 2:1 mixture of N2O:O2 and anesthe-
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tized with thiopental (0.2–2.5 mg/kg/h for monkeys and
0.1–1.0 mg/kg/h for the cat), as the initial anesthetic
agent (ketamine or Isoflurane) wore off.
Following the completion of surgical procedures,
each animal was paralyzed partially with vecuronium
bromide (or Tubocurarine), to stabilize the eyes, which
were then protected with hard, gas-permeable contact
lenses. Curvatures for the latter were chosen to bring
both eyes into focus on the screen of a 20-in. monitor
placed 2 m away. Throughout the duration of each
experiment, anesthesia was maintained with thiopental (0.2–2.5 mg/kg/h for monkeys and 0.1–1.0 mg/kg/h
for the cat) and verified with respect to the electrocardiogram, blood pressure, and end-tidal carbon dioxide
(CO2 ), all of which were monitored continuously. In
addition to these measures, the adequacy of anesthesia
was also verified at regular intervals from the absence
of reflexes (e.g., lateral canthal), which were assessed
after verifying that the level of neuromuscular suppression, if present, remained below 50%. Although this
level of suppression eliminates all but the smallest eye
movements (Blasdel, 1992a), it allows spinal reflexes to
be elicited when and if they appear (which, under the
worst scenario, would occur long before the animal
awakens).
Optical Imaging
During the acquisition of differential images, light
from a 100-W quartz halogen lamp was focused by a
parabolic cold mirror through heat and interference
filters (720 6 20 or 600 6 20 nm) onto the end of a
0.25-in. diameter fiberoptic bundle. Monochromatic
light from this bundle was collimated and deflected
through the edge of a 100-mm objective and then
through a protective coverslip onto the cortical surface.
Light that was not absorbed or scattered was then
reflected through the same objective to a projection lens
that focused it onto a Newvicon camera (COHU Model
5300), as described previously (Blasdel, 1989, 1992a,b;
Blasdel and Salama, 1986). Visual stimuli in all cases
consisted of four, superimposed nonharmonic squarewave gratings, which moved back and forth at 1.5–3°/s,
and that appeared with an average luminance and
contrast of 3 cd/m2 and 80%. Between periods of visual
stimulation, these patterns were replaced with a blank
gray screen with the same average mean luminance (3
cd/m2 ). All frames recorded by the camera were grabbed
at 60 Hz, with a pixel resolution of 512 3 480, which
later was compressed, by binning, to 128 3 120, with
each pixel representing approximately 17.5 3 17.5 µm
of cortex.
Ocular Dominance
Differential images of ocular dominance were obtained by stimulating each eye alternately with grat-
ings at a single orientation, while images of the cortex
were averaged and compared. During each of five
successive trials, 180–300 60-Hz frames were summated during each of five presentations to the left eye
(at intervals of 10–15 s) and subtracted from the sum of
a similar number of frames that were added while
stimuli were presented to the right eye. The final tally
of 900–1500 positive and 900–1500 negative frames
was then divided by 2n, with 1 # n # 3, to produce the
final image that was reduced from 512 3 480 to 128 3
120 by binning (Blasdel and Salama, 1986).
Orientation Preference
Differential images of orientation were obtained by
comparing responses to orthogonally oriented contours.
Specifically, the frames accumulated during five interleaved presentations of gratings at one orientation
were subtracted from the sum of frames acquired
during a comparable period of stimulation by an orthogonal grating. All other aspects of frame collection and
processing were otherwise identical to those described
for ocular dominance.
Retinotopic Position
Differential maps of retinotopically defined strips of
space were obtained in a similar manner. However,
instead of comparing frames accumulated during the
stimulation of each eye, or with orthogonal orientations, we compared frames averaged during the presentation of monocular stimuli in one of two complementary sets of strips of visual space (see Campbell and
Blasdel, 1995; Blasdel and Campbell, 1997, for details).
Correlational Analysis
In order to quantify and visualize the similarity
between images, crosscorrelation and autocorrelation
analyses were performed. Crosscorrelograms were obtained by incrementally shifting one image relative to
another, in two dimensions, and calculating a correlation coefficient at each offset location. The resulting
two-dimensional distribution of coefficients yields the
crosscorrelogram, which depicts the degree of similarity and distribution of regular features. When this
operation is performed on two identical images, the
result is an autocorrelogram, which consists of a peak
at location (0,0), where the image matches itself completely, surrounded by a gradual decay whose shape
imparts information about the two-dimensional structure of the image. The shape of a crosscorrelogram can
be used to identify similarities between two images
that vary between no correlation (a flat gray distribution) and exactly the same structure (an autocorrelogram).
Since vascular artifacts may induce either positive
(dark) or negative (light) values, in contrast to actual
CORTICAL ACTIVITY AND REFLECTANCE
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FIG. 1. Optical images of striate cortex obtained with 600- and 720-nm wavelength light. (A) An image of the surface vasculature seen
under 600-nm light. The V1/V2 border runs approximately parallel to the bottom of this image, with the foveal region being up and to the left.
The surface vasculature is clearly visible, because oxyhemoglobin and deoxyhemoglobin absorb 600-nm light efficiently. (B) The exact same
region of cortex imaged under 720-nm light. The largest blood vessels are barely detectable. (C) A differential image of ocular dominance for the
same region obtained under 600-nm light. Video images of the striate cortex obtained during visual stimulation of the left eye (four to six
presentations for 1.5–3.0 s) were subtracted from images obtained during stimulation of the right eye to generate this image. Vascular noise
occludes much of this optical map. (D) The exact same map of ocular dominance obtained under 720-nm light. Dark and light stripes,
corresponding to left and right eye dominance bands, run approximately perpendicular to the V1/V2 border throughout the imaged region. (E)
An example of a differential image of orientation preference obtained under 600-nm light. This differential image was produced by collecting
video images of the striate cortex when both eyes were stimulated by moving vertical square-wave gratings and subtracting images obtained
when the eyes were stimulated by moving horizontal gratings. Darker spots correspond to regions which preferred vertical to horizontal, while
lighter spots represent the complement. As in C, vascular components occlude the underlying orientation map. (F) The same differential map
of orientation preference obtained under 720-nm light. No obvious vascular artifacts are present.
blood vessels, which always appear positive (dark), it
was necessary to half-wave rectify differential images
before obtaining crosscorrelograms with the vasculature. This was achieved by inverting each pixel value
that was greater than the image mean around the
image mean. Thus, for each optical map, any pixel
value that exceeded the mean by x was set to the mean
value minus x. This results in a half-wave rectified
image in which all vascular artifacts appear dark with
respect to the surrounding cortex. These rectified images were used to calculate the crosscorrelograms
between the response maps and the vasculature. The
original unprocessed maps were used to calculate the
autocorrelograms and the cross-wavelength correlograms.
RESULTS AND DISCUSSION
General Observations
Optical maps of the surface vasculature, ocular dominance, and orientation preference were obtained, as
elaborated under Materials and Methods, with 600and 720-nm illumination. Except for the wavelength
and intensity of illumination (which were adjusted to
compensate for differences in camera sensitivity), all
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FIG. 2. Vascular and avascular compartments. (A) The surface vasculature imaged at high resolution under 570-nm illumination. This
image was processed to produce a sequence of gray-level compartments through which pixel variances were measured. (B) All regions of the
vascular map whose gray scale value is less than the mean pixel value. This image corresponds roughly to the vascular regions and contains all
the larger blood vessels. (C) All regions whose pixel values are greater than or equal to the mean pixel value. This image corresponds roughly to
the avascular regions. Only small, lighter colored, vessels remain within this image. It is possible to make a sequence of such compartments by
extracting image regions whose gray scale values lie within a limited range of pixel values. This can then be used to determine the effects of the
surface vascularity on the strength of the measured optical signals.
parameters of visual stimulation, frame acquisition,
and averaging were the same for images on the right
and left sides of Fig. 1. Figures 1A and 1B in the top row
depict the surface vasculature under 600- and 720-nm
illumination. Figures 1C and 1D display the differential images of ocular dominance, and Figures 1E and 1F
display the differential images of orientation that were
obtained at 600- and 720-nm wavelengths.
Most of our observations follow from the absorption
spectra of hemoglobin and its derivatives, for example,
the millimolar extinction coefficients of Hb and HbO2
are 3.2 and 0.8 for 600 nm and 0.38 and 0.09 for 720 nm
(van Assendelft, 1970) and can be understood qualitatively by comparing the images in Figs. 1A and 1B.
From Lambert–Beer’s law we note that the transmittance, defined as T 5 102e · c · l (where e is the millimolar
extinction coefficient, c is the concentration in millimolars per liter, and l is the total light pathlength), of
600-nm light through vessels ranging in thickness from
50 to 250 µm is 4 to 1000 times less than that of 720-nm
light. Hence superficial blood vessels appear much
darker in the image on the left, where they contrast
strongly with the cortex.
This situation is further complicated by changes in
the perfusion of blood vessels that can also produce
signals. Blood vessels can appear as selective for a
particular response property as the most highly tuned
cortical regions (Blasdel and Salama, 1986; Grinvald et
al., 1986)—presumably because they supply or drain
such regions. Unfortunately, these changes usually
reflect the preferences of displaced neurons and not
those of neurons located underneath. As there is no way
of determining which regions they represent, these
signals must be considered a form of noise in intrinsic
optical imaging. For these reasons, the differential
images of ocular dominance and orientation in Figs. 1C
and 1E exhibit larger contributions from the vasculature than those in Figs. 1D and 1F, even though all
images were collected from the same cortical region, in
response to the same stimuli, and at virtually the same
time.
Distribution of Signal Strength
In order to quantify the differences between images
obtained at different wavelengths, three independent
CORTICAL ACTIVITY AND REFLECTANCE
measures were developed, the simplest of which is to
divide the imaged region into vascular and avascular
compartments. This can be done most easily by applying a 50% gray scale threshold to a high-resolution
image of the vasculature. When this is done (see Fig. 2),
approximately 40 and 60% of the imaged area fall into
vascular and avascular compartments. By measuring
the pixel variance of ocular dominance patterns within
each compartment, it is possible to dissect the signal
components. When this is done for the ocular dominance pattern imaged at 600 nm, for example, the
variance of pixels in the vascular compartment exceeds
that in the avascular compartment by 65%. Taking this
analysis one step further, one can easily subdivide the
cortex in Fig. 2 into a series of compartments. The
lowest thresholds extract regions corresponding to the
centers of the largest and thickest blood vessels because these reflect the least light. The next compartment, corresponding to pixels with values lying between 18.0 and 21.5% of the maximum pixel value,
extracts regions corresponding to the edges of large
blood vessels along with the centers of smaller ones.
This progresses to the point at which the highest
threshold compartment, corresponding to the brightest
pixels, represents unoccluded regions of the cortical
surface. Since 600 nm lies near an isobestic point in the
absorption of hemoglobin, moreover, arterioles and
venules are treated similarly by this procedure. While
similar to the previous procedure, this technique dissects the cortex into 16 different compartments corresponding (approximately) to 16 different thicknesses of
blood through which the optical signals must pass.
Figure 3 shows the distribution of pixel variance from
the ocular dominance and orientation images in Fig. 1,
with respect to each cortical compartment.
As one can see in Fig. 3A, for images collected at 600
nm the pixel variance is greatest in regions corresponding to the centers of the largest blood vessels. On the
other hand, the pixel variance for images collected at
720 nm remains relatively flat across compartments, as
one might expect from previous analysis (Blasdel,
1992a). Figure 3B displays similar distributions for the
differential images of orientation collected at each
wavelength in Figs. 1E and 1F.
Ocular dominance images obtained in an identical
manner from a second animal appear in Fig. 4. As in
Fig. 3, the surface vasculature interferes with the
image of the underlying ocular dominance columns
when 600-nm illumination is employed (Fig. 4B).
Figure 5 depicts the representation of vertical stripes
imaged at 600 and 720 nm that take the form of light
and dark bands running parallel to the V2 border,
which is aligned with the bottom of each frame. Since
the topography of V1 is well established, there can be
no doubt that these images represent the vertically
directed strips that were stimulated (Blasdel and Camp-
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FIG. 3. The spatial distribution of intrinsic optical signals. (A)
The computed variance of the pixel values (signal) for the ocular
dominance maps, Figs. 1C and 1D, as processed through a sequence
of vascular compartments as described in Fig. 2. The open squares
correspond to the variances computed for Fig. 1C, while the open
circles correspond to the same measures for Fig. 1D. The horizontal
axis codes the range of gray scale values in each compartment, which
in this case ranges from 18 to 100%. The low % corresponds to the
darker pixels in Fig. 2A, while the higher ranges correspond to the
brightest pixels. There is a clear correlation between the range of
gray scale values used (vascularity) and the signal strength for the
images collected under 600-nm illumination. The recorded signal
strength is greatest from the regions corresponding to the biggest
darkest blood vessels and falls off in the more avascular regions. In
contrast, there is no significant difference between the recorded
signal strength coming from the vascular and avascular regions of
the 720-nm optical maps. (B) The same computed metrics for the
orientation preference maps depicted in Figs. 1E and 1F.
bell, 1997; Campbell and Blasdel, 1995; McLoughlin
and Blasdel, 1996).
As in the previous examples, the images obtained at
600 nm (see Fig. 5A) appear noisier with respect to the
vasculature. The variance measured in each of the 16
gray-level vascular compartments produces a similar
result. The shallower slope of the line corresponding to
600 nm suggests that vascular artifacts are smaller in
this image than those in the ocular dominance and
orientation images displayed previously. This could
possibly be because these patterns occur at a larger
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MC LOUGHLIN AND BLASDEL
FIG. 4. Examples of ocular dominance maps from a second animal. (A) The surface vasculature of a region of striate cortex taken from a
second animal, this time closer to the horizontal meridian. (B) A differential image of ocular dominance for the same region obtained under
600-nm light. Video images of the striate cortex obtained during visual stimulation of the left eye (four to six presentations for 1.5–3.0 s) were
subtracted from images obtained during stimulation of the right eye to generate this image. Similar to our previous examples, vascular
contributions are obvious in much of this optical map. (C) The exact same map of ocular dominance obtained under 720-nm light. Dark and
light stripes, corresponding to left and right eye dominance bands, run approximately parallel to the V1/V2 border throughout the imaged
region, although they are less regular than those seen in Fig. 1.
scale that more closely approximates the scale at which
the activity of blood vessels actually reflects the activity
of the local neuropil.
Spatial Correlations
In order to identify the common and vascular components of the optical maps, crosscorrelograms were
calculated for the images collected at different wavelengths and the vasculature. The results for ocular
dominance and orientation appear in Figs. 6A and 6B.
The autocorrelogram for the vascular image in Fig. 6A
exhibits a typical pattern of decay for an image which
contains little or no repeated spatial structure. The
central peak falls off rapidly in all directions. The
autocorrelograms for the 600- and 720-nm maps of
ocular dominance display a very different pattern of
activity. Activity falls off much more rapidly in the
horizontal direction than in the vertical direction. This
indicates that the images contain a spatial structure
that is elongated in the vertical direction. The resurgence of activity after the image is shifted even further
in the horizontal direction indicates that the vertical
pattern is repeated after a characteristic shift. Hence,
the autocorrelograms for the optical maps of ocular
dominance succinctly capture the fact that ocular dominance columns are elongated along the vertical axis
and are periodic. Since the ocular dominance bands are
irregular, the only exact match (correlation 5 1.0) occurs at the center, such that the intensity of the
flanking bands fades over a distance of several
millimeters.
From the autocorrelogram of the 600-nm ocular
dominance image, one can see that regular banding,
though present, is greatly suppressed relative to that in
the 720-nm image, suggesting the presence of extraneous noise. However, the presence of a central peak in
the crosscorrelograms of the 600/720-nm ocular dominance images indicates that the optical maps correlate
strongly with each other, almost as strongly as the
720-nm image correlates with itself. From their crosscorrelations with the vasculature one can also see that
the differences between the ocular dominance patterns
obtained at these wavelengths derive from the fact that
the 600-nm pattern, in contrast to the 720-nm pattern,
also correlates with the image of the vasculature.
As all the correlograms are bounded by 1 and 0, their
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FIG. 5. Intrinsic maps of retinotopy. (A) A differential image of a series of elongated strips in space. Video images of the animal’s cortex
were collected while it was visually stimulated in strips of space. Images collected while the animal was visually stimulated in complementary
strips were then subtracted to produce this retinotopic map. Elongated dark and bright regions, corresponding to two sets of complementary
vertical strips, run approximately parallel to the V1/V2 border, which is aligned with the bottom of each frame. Once again this differential
image, obtained under 600-nm light, has obvious vascular components. (B) The optical map for the same stimulus collected under 720-nm
light. The position of the light and dark bands are shifted relative to those in A, indicating that the animal made an eye-movement between
these presentations. (C) The variance of the pixel values measured through the same gray scale compartments as in Figs. 3A and 3B, this time
computed for the images shown in A and B.
similarity can be estimated by subtracting the average
difference between them from 1. If the remainder is 1.0,
the correlograms are identical, since their average
difference is 0.0. A remainder of 0.0 indicates that they
are exact opposites or anticorrelated with an average
difference of 1.0, while a value of 0.5 indicates the
absence of any correlation at all. When this metric is
applied between the vascular/600-nm ocular dominance crosscorrelogram and the vascular/vascular autocorrelogram, the remainder is 0.586. This indicates
that 17.2% of the 600-nm ocular dominance pattern
derives from vascular elements. A similar calculation
applied to the 720-nm ocular dominance pattern produces a value of 0.507 and suggests that only 1.4% of
the map’s spatial structure is derived from vascular
elements.
The autocorrelograms for the 600- and 720-nm orientation maps depicted in Fig. 6B exhibit a very different
pattern of activity. The central peak decays equally in
all directions and is surrounded some distance out by a
ring of activity. The slight elliptical shape of this ring
reflects the local orthogonality of ocular dominance
boundaries and iso-orientation contours, resulting in a
slightly exaggerated distance of repetition perpendicu-
FIG. 6. Spatial correlations. (A) A table of all possible spatial correlograms between the two optically derived maps of ocular dominance
and the image of the overlying vasculature. The autocorrelograms run diagonally top–left to bottom–right while the crosscorrelograms appear
off-diagonal. Complete images were shifted up to 25 pixels in each direction for both the autocorrelograms and the crosscorrelograms. Edge
effects were avoided by scaling the difference metric by the number of contributing points. An arbitrary scale was used to plot each correlogram
to aid visualization of salient structures within the data. As the vasculature noise can appear in the optical maps as either lighter or darker
than the background, the ocular dominance maps were rectified around their mean for comparison to the vascular map (Materials and
Methods). (B) A table of all possible spatial correlograms between the two optically derived maps of orientation preference and the image of the
overlying vasculature. As in A, the autocorrelograms run down diagonally from the top–left while the crosscorrelograms appear off-diagonal.
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lar to the trajectories of the ocular dominance columns,
shown previously by Obermayer and Blasdel (1993).
Once again the autocorrelogram of the pattern imaged at 720 nm exhibits much greater regularity than
that imaged at 600 nm, even though the corresponding
crosscorrelograms show that the images correlate
strongly with one another. Crosscorrelations with the
vasculature additionally show that the 600-nm orientation pattern, in contrast to 720-nm pattern, correlates
with the vasculature. Applying the similarity estimate
to the correlograms, one finds that vascular elements
contribute 25.1% of the spatial structure to the 600-nm
image, while they contribute only 5.2% to the 720-nm
pattern.
In contrast to previous studies that concentrated on
the spectrotemporal aspects of intrinsic reflectance
changes in cortex (Frostig et al., 1990; Malonek and
Grinvald, 1996), this study reports the first highresolution spatial comparison between optical maps
obtained at different wavelengths and the overlying
vasculature through which these maps are recorded. In
agreement with the present findings, Frostig et al.
(1990) reported that blood vessel artifacts were significantly larger for shorter wavelengths of light. Similarly, Malonek and Grinvald (1996) noted significant
involvement of the superficial vasculature in all images
collected at wavelengths shorter then 620 nm, prompting them to exclude these regions from further analysis
(Fig. 2, p. 552).
Mayhew et al. (1996) reported a pronounced 0.1-Hz
oscillation in the reflected light from cortex. This
oscillation correlated strongly with laser Doppler flow
measurements of cerebral blood flow (CBF). Our imaging procedure minimizes any such contribution, because we synchronize our data collection to the animal’s
respiration and heart beat, subtract data collected at
alternating time slices, and limit our collection times to
short periods during visual stimulation. In addition,
thiobarbiturate anesthesia has recently been shown to
dramatically reduce CBF (Lindauer et al., 1993). This
suggests that the effects we report are, if anything,
reduced by our choice of anesthesia.
The current findings suggest that the changes in
reflectance measured with 720-nm wavelength light
are not due to the selective responses of surface blood
vessels. While the mechanisms responsible for producing such changes are not well understood at present,
they are most likely derived from a number of different
events, including glial cell swelling due to potassium
release (MacVicar and Hochman, 1991), dilation of the
capillary beds (Pawlik et al., 1981), and cell swelling
due to increased chlorine transport (Holthoff and Witte,
1996). Malonek and Grinvald (1996) also noted, by
their signal decomposition procedure, that light scattering signals tend to dominate (.70%) optical responses
measured with wavelengths greater than 660 nm. In
short, our analyses indicate (1) that 600- and 720-nm
optical maps are strongly correlated in tissue compartments not underlying the surface vasculature, (2) that
while 720-nm optical maps exhibit a flat distribution of
signal strength across the various tissue compartments, the regions associated with the surface vasculature exhibit much stronger signals in all the 600-nm
images examined, and (3) that the signals recorded
from these vascular compartments are spatially correlated with the surface vessels when 600-nm illumination is employed.
ACKNOWLEDGMENTS
This work was supported in part by a McDonnell-Pew training
fellowship and funded by the NIH (EY06586 and EY10862) and the
Human Frontiers Foundation. We thank Lawrence Sincich, Jonathan
Levitt, and Wade Regehr, for useful comments on the manuscript,
and Darlene Campbell for her excellent technical assistance.
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