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Volume 17, Number 14,
Issue of July 15, 1997
pp. 5509-5527
Copyright ©1997 Society for Neuroscience
GABAergic Neurons in Barrel Cortex Show Strong, Whisker-Dependent
Metabolic Activation during Normal Behavior
James S. McCasland1 and
Lyndon S. Hibbard2
1 Department of Anatomy and Cell Biology, State
University of New York Health Science Center at Syracuse, Syracuse, New
York 13210, and 2 Division of Experimental Neurology and
Neurological Surgery and McDonnell Center for Studies of Higher Brain
Function, Washington University School of Medicine, St. Louis, Missouri
63110
ABSTRACT
- INTRODUCTION
- MATERIALS AND METHODS
- RESULTS
- DISCUSSION
- FOOTNOTES
- REFERENCES
ABSTRACT 
Electrophysiological data from the rodent whisker/barrel cortex
indicate that GABAergic, presumed inhibitory, neurons respond more
vigorously to stimulation than glutamatergic, presumed excitatory, cells. However, these data represent very small neuronal samples in
restrained, anesthetized, or narcotized animals or in cortical slices.
Histochemical data from primate visual cortex, stained for the
mitochondrial enzyme cytochrome oxidase (CO) and for GABA, show that
GABAergic neurons are more highly reactive for CO than glutamatergic
cells, indicating that inhibitory neurons are chronically more active
than excitatory neurons but leaving doubt about the short-term stimulus
dependence of this activation. Taken together, these results suggest
that highly active inhibitory neurons powerfully influence relatively
inactive excitatory cells but do not demonstrate directly the relative
activities of excitatory and inhibitory neurons in the cortex
during normal behavior.
We used a novel double-labeling technique to approach the issue
of excitatory and inhibitory neuronal activation during behavior. Our
technique combines high-resolution 2-deoxyglucose (2DG),
immunohistochemical staining for neurotransmitter-specific antibodies,
and automated image analysis to collect the data. We find that putative
inhibitory neurons in barrel cortex of behaving animals are, on
average, much more heavily 2DG-labeled than presumed excitatory cells, a pattern not seen in animals anesthetized at the time of 2DG injection. This metabolic activation is dependent specifically on
sensory inputs from the whiskers, because acute trimming of most
whiskers greatly reduces 2DG labeling in both cell classes in columns
corresponding to trimmed whiskers. Our results provide confirmation of
the active GABAergic cell hypothesis suggested by CO and single-unit
data. We conclude that strong activation of inhibitory cortical neurons
must confer selective advantages that compensate for its inherent
energy inefficiency.
Key words:
barrels;
hamster;
neural inhibition;
somatosensory
cortex;
deoxyglucose;
cortical circuits
INTRODUCTION
GABAergic, presumed inhibitory, interneurons in
cerebral cortex are believed to play related roles in controlling
overall cortical excitability and enhancing temporal and spatial
resolution within neuronal receptive fields (Kyriazi et al., 1996
).
However, little is known about the extent to which inhibitory neurons
actually are engaged during normal sensory activity to achieve these
functions. Because signal transmission through the cortex is assumed to
involve excitatory neurons and their long-projecting axons, the most
energy efficient strategy for processing sensory inputs would entail minimal activation of inhibitory neurons, thus minimizing interference with feed-forward excitatory transmission.
However, physiological and histochemical evidence has accumulated in
recent years to argue against this minimal inhibition view of cortical
function. Single-unit recordings from the whisker/barrel cortex of
anesthetized/narcotized rodents (Simons, 1978, 1995; Armstrong-James,
1995) or from cortical slices (McCormick et al., 1985; Chagnac-Amitai
and Connors, 1989; Connors and Gutnick, 1990; Agmon and Connors, 1992)
show that, under controlled recording conditions and in response to
controlled stimulation, presumed inhibitory neurons are more
spontaneously active, more responsive to stimulation, and less
selective than excitatory neurons. Although much valuable information
has been obtained by these studies, poorly understood sampling problems
make it impossible to study large numbers of GABAergic cells. It is
also difficult to obtain single-unit recordings from animals during
normal exploratory behavior (but see Kodger et al., 1995). Thus, very
little direct information is available on the physiological activation
of GABAergic neurons during normal behavior.
The second line of evidence for active GABAergic cells in normally
functioning cortex comes from histochemistry for the mitochondrial enzyme cytochrome oxidase (CO). Wong-Riley and colleagues (1989, 1994;
Nie and Wong-Riley, 1995) have shown that GABAergic type C cells in
striate cortex of monkeys contain mitochondria that are darkly
CO-reactive. However, CO represents a relatively long-term measure
(hours or days) of oxidative metabolism (Wong-Riley and Welt, 1980;
Wong-Riley and Carroll, 1984; Deyoe et al., 1995). Thus the chronic
activation of inhibitory neurons, implied by these results, cannot be
attributed with any certainty to sensory stimulation as opposed to
ongoing or tonic activity.
The active GABAergic cell concept of cortical function suggested by the
above findings is inherently energy inefficient, somewhat analogous to
braking a car while pressing on the accelerator. The physiological and
histochemical data leading to this view, while strongly suggestive, are
indirect with respect to the activation of excitatory and inhibitory
neurons by normal sensory inputs during behavior. Both methods leave
open the possibility that some portions of the circuit show only
tonically high inhibitory activity, whereas others show
strong stimulus-driven activation of inhibitory neurons.
Other portions of the circuitry might exhibit neither or both
characteristics.
We have assessed these possibilities with a novel histological
double-labeling technique that combines high-resolution
2deoxyglucose (2DG) with immunohistochemistry for
transmitter-specific antibodies such as glutamate decarboxylase (GAD)
or glutamate (Glu). Our approach was to estimate the metabolic
activities of very large numbers of GABAergic (inhibitory) and
glutamatergic (excitatory) neurons from animals that were engaged or
not engaged in normal behavior, with or without selective deprivation
of sensory inputs. Using this 2DG/immunostaining approach, we
demonstrate that inhibitory neurons and their presumptive synapses are
activated heavily in barrel cortical circuits by normal sensory
activity during normal behavior. We also show that the relative levels
of activation in excitatory and inhibitory neurons vary systematically
by cortical lamina. Our data are consistent with hypotheses based on
limited available samples from single-unit recordings from barrels,
from the CO data of Wong-Riley et al. (1989, 1994; Nie and Wong-Riley, 1995) and from electron microscopic studies of synaptic distributions in barrel circuits (White, 1989).
MATERIALS AND METHODS
A complete 2DG/immunostaining histological protocol has been
published (McCasland, 1996) and is summarized briefly here. The data
collection procedures, presented here for the first time, are discussed
in detail (see also Hibbard et al., 1996).
Animal preparation
Adult golden hamsters of both sexes, weighing 80-150 gm, were
used for these experiments. Hamsters were used because of their avid
exploratory behavior and apparent behavioral sophistication, which we
hope to exploit in future behavioral discrimination paradigms. Although
relatively subtle differences exist in barrel field cytoarchitecture across rodent species (Welker and Woolsey, 1974; Land and Simons, 1985), several reports suggest that there are no marked differences in
the operation of barrel field circuitry in mice or rats (Simons and
Woolsey, 1984; Simons et al., 1984; McCasland et al., 1991). At the
gross cytoarchitectural level hamster barrel cortex resembles that of
the mouse rather than the rat, and we have obtained results from
behaving mice that are qualitatively similar to those presented here
(our unpublished observations). A total of 10 hemispheres from six
animals were analyzed in detail for this study (see Table 1). Subjects were developmentally normal
(n = 4 hemispheres) and had all whiskers intact at the
time of the experiment or were acutely deprived with all whiskers
except row C bilaterally trimmed the night before the experiment
(n = 6 hemispheres).
Animals were fasted overnight with ad libitum water before
the (H3)2DG injection. 2-5 mCi of 1, 2,(H3)2DG (American Radiolabeled Chemicals, St.
Louis, MO), suspended in saline at a concentration of 2.5 mCi/ml, was
injected intraperitoneally. The subjects were released in a clean cage,
in lighted or dark conditions, and allowed to explore for 45 min,
during which time they were monitored at 5-10 min intervals to ensure
that they remained active. With the label theoretically cleared from
circulation, the subjects were anesthetized deeply with phenobarbital
(143 mg/kg) for 3-4 hr to shift label into macromolecular
compartments, including glycogen (McCasland and Woolsey, 1988;
McCasland, 1996). A final overdose of anesthetic was administered
before perfusion.
Tissue preparation
Immunohistochemical staining for GAD was done with the GAD-6
antibody (Chang and Gottlieb, 1988) (GAD-6, NICHD hybridoma bank contract N01-HD-6-2915). This monoclonal antibody was characterized thoroughly by its developers (Chang and Gottlieb, 1988) (J. Schwob, personal communication) and has been used in ELISA assays to extract GAD from brain homogenates (De Aizpurua et al., 1992; Davenport et al.,
1995). The antibody binds to a 59 kDa band purified by GAD-1
immunoaffinity columns; this band has GAD enzymatic activity. Staining
patterns with the GAD-6 antibody were compared with those of other GAD
antibodies (e.g., GAD-1) in ventral forebrain, olfactory bulb, and
cerebellum of rat and found to be substantially identical. Staining of
chick brain also showed similar patterns. We optimized the GAD-6
staining protocol for the so-called PPPFL (periodate paraformaldehyde
picric acid formaldehyde lysine) fixative (Miao and Lee, 1990), a
modified version of the periodate lysine paraformalde (PLP) fixative
for complex carbohydrates (McLean and Nakane, 1974). Perfusion was done
in two stages with pH 6.5 fixative for 1-2 min and then pH 7.4 fixative for 10-15 min. The glycolytic inhibitor iodoacetic acid
(0.5%) and sodium M-periodate (0.16%) were added to the modified PLP
fixative and chilled to 4°C just before use. The perfusion was done
at moderate flow rates (~14 ml/min for hamsters). After perfusion the
brains were removed, post-fixed overnight in 4°C pH 7.4 fixative
containing 30% sucrose, flattened between two glass slides separated
by Teflon spacers, and then sectioned tangential to the barrels at 40 µm on a freezing microtome.
Except as noted, immunocytochemistry was performed by standard
procedures, using ABC (Vectastain, Vector Labs, Burlingame, CA)
antibody kits. The most important modification was to add 0.5%
glycogen (Sigma type IX, St. Louis, MO) to all aqueous solutions to
which tissue sections were exposed, including buffers. This step was
crucial to achieve adequate retention of 2DG label via immunohistochemical processing but had no observable effects on stain
patterns (McCasland, 1996). Standard absorption experiments with small
amounts of GAD completely obliterated the appearance of staining with
the primary antibody. Omission of the primary antibody also eliminated
all staining in our procedure. These controls and extensive
characterization of the antibody by its developers (Chang and Gottlieb,
1988) ensured the specificity of staining patterns.
Glutamate immunostaining was done with specific antisera raised to
fixative-modified glutamate and shown to label pyramidal and other
non-GABAergic neurons (Beitz et al., 1986; Conti et al., 1987; Hepler
et al., 1988; Petrusz and Rustioni, 1989). Extensive characterization
of the antiserum by immunoabsorption, immunoblots, enzyme, and
radioimmunoassays demonstrated the specificity of the antibody, which
shows no or minimal cross-reactivity to structurally closely related
and biologically very important compounds such as GABA (Petrusz and
Rustioni, 1989). We adapted the protocol for a glutamate antibody
(Incstar 22523) to our use in the 2DG/immunostaining procedure by using
a three-stage perfusion procedure. The first two stages used the PPPFL
fixative as above. These stages were followed by 5 min of 0.5%
glutaraldehyde in 4% paraformaldehyde. This procedure yielded
excellent antibody staining of pyramidal and spiny stellate neurons as
well as good retention of 2DG label.
After the immunohistochemical procedures were completed, serial
sections were mounted on gelatin-coated slides and dried overnight. Test sections, taken from below the cortical series (containing hippocampus and basal ganglia), were mounted on separate slides. All
slides were rinsed thoroughly in distilled water, defatted in alcohols
and xylenes, and dipped in NTB2 photographic emulsion (Kodak,
Rochester, NY). Test sections were developed periodically to examine
the progress of the autoradiographic signal, which developed gradually
from few to many silver grains, indicating that positive chemography
was not responsible for our results. Additional exposed emulsion (brief
exposure of the liquid emulsion to light before dipping the slide)
controls for negative chemography showed uniform labeling over barrel
cortex, confirming that labeling in our 2DG/immunostained materials
represents 2DG labeling, not autoradiographic artifact. When good
contrast was seen between lightly and heavily 2DG-labeled structures in
the test slides, all slides were removed from the dark, developed in
D19, fixed, and coverslipped.
Photomicroscopy
Photomicrographs were taken with a Kodak Digital Camera System
(DCS420) directly from the histological section or with a Nikon Coolscan digital scanner from 35 mm slides. The 1500 × 1000 pixel array CCD images (or Coolscan images) were contrast-enhanced, using the
image level and unsharp mask commands in Adobe Photoshop 3.0 (Macintosh). These adjustments improved the quality of the images for
presentation while faithfully reflecting the spatial relationships
between 2DG silver grains and immunohistochemical stain in the original
sections.
Automated data collection
To make efficient use of the physical evidence inherent in these
materials, we developed automated techniques, using a template-matching algorithm to detect large samples of GAD+ and
GAD
neurons (Hibbard et al., 1992; McCasland et
al., 1992). The precision match between 2DG and GAD staining data was
preserved through every stage of the analysis. We wrote computer
programs to make regional metabolic maps of detected cells, 2DG grain
densities, and immunohistochemical stain intensities in barrel cortex.
This automated approach allowed the collection and objective
classification of very large samples of neurons. The computer-generated
montages, three-dimensional reconstructions of cortical slabs, and
applied barrel boundaries allowed comparisons of experimental groups
that were based on greatly compressed data but that nevertheless
represented very large samples of cortical neurons.
Stage scanning and image collection. Data were collected
with an automated system based on a work station (Digital Vaxstation 3200), video camera (Hamamatsu), image processor (Perceptics 9210), and
computer-controlled stage and filter wheel (Ludl) containing five
colored filters representing most of the visible spectrum. Resident
software in the image processor, controlled by Digital Command Language
(DCL) command procedures on the host work station, automated virtually
the entire process of raw data collection by quantifying grain
densities, stain distribution, and cell locations from sections on
slides.
Before data collection a barrel in the center of the field (usually C3)
was identified in the section or sections through layer IV. A prominent
radial blood vessel passing through or near this barrel was identified
as the center of the coordinate system used in data collection. The
same blood vessel or its equivalent position was identified on all
serial sections. A 2 × 2 mm area with the blood vessel at the
center was sampled in each section.
A DCL command procedure, running on the Vaxstation, moved the
computer-controlled microscope stage in a serpentine scanning pattern.
The CCD camera made a series of high-resolution (16 pixels/µm2) images of 125 × 125 µm fields,
with a total of 256 fields per section in a 16 × 16 array. At
each stage position a Ludl autofocus module was engaged to focus
sharply on the 2DG silver grains, after which a total of five images,
one for each color filter in the wheel, was collected. Each group of
high-resolution images represented precisely the same two-dimensional
tissue sample.
Programs written in FORTRAN were used to estimate stain intensity and
extract the silver grains from the high-resolution images. We took
advantage of the spectral differences between brown (DAB) immunostain
and black 2DG silver grains to disambiguate the two labels. The
variance of each pixel value across the five color-filtered images was
calculated. The resulting "variance images" were thresholded and
then combined (logically ORed) with low-thresholded (dark) pixels from
the original image to generate the binary silver grain image. Threshold
values were chosen empirically to yield accurate estimates of silver
grain distributions in the high-resolution images for representative
sections and then applied to all sections included in this study. The
stain values were set proportional to the variance for nongrain pixels
and scaled to represent the full dynamic range of tissue staining
within the 256 gray level limitation of the output montage.
The calculated stain and silver grain image tiles were assembled
serially via the stage scan sequence to create 512 × 512 pixel
montages representing 2 × 2 mm portions of the barrel field (see
Fig. 2). Montage image pixel values were taken as the average gray
level value of 15 × 15 pixel neighborhoods in the
high-magnification scanned images, excluding image edges. Each pixel of
the montage thus represented a 4 × 4 µm area in the original
section.
Fig. 2.
Whisker-dependent 2DG labeling of the barrel
field. Computer-generated montages depict the laminar distribution of
overall 2DG labeling in sections of barrel cortex stained for GAD.
Montages represent 2 × 2 mm regions of sections from
supragranular (top), granular (middle), and
infragranular layers (bottom) of behaving animals with all
whiskers present (left) or all but row C whiskers acutely
trimmed (right). White or lightly shaded
areas represent heavy label, whereas darker areas
represent relatively sparse label. Note the heavier labeling and the
clear barrel representation in layer IV of both cases. Barrel rows are
indicated with letter in these panels. Scale bar, 500 µm.
[View Larger Version of this Image (145K GIF file)]
Cell extraction and characterization. The stain and/or grain
images were segmented for object identification by another set of
FORTRAN programs (Hibbard et al., 1992; McCasland et al., 1992). Neuronal somata were detected by correlating templates with the digitized fields. Two templates were used, one each for
GAD+ and GAD cells. The
templates were constructed by selecting and averaging 64 × 64 pixel neighborhoods, centered on a manually selected cell center (using
a mouse with screen cursor), for 40-50 obvious examples of stained or
unstained cells in a tissue section representative of the data to be
analyzed. These templates by the nature of their construction were
somewhat blurred around the edges and were approximately spherically
symmetrical, unlike many of the cells to be detected. Potential cell
locations in the field images corresponded to maxima in the
two-dimensional correlation function R,
in which a and b are the image and
template arrays, respectively, F is the Fourier transform
operation, and F(b)* is the complex conjugate of
the transform of b (correlation theorem; Bracewell, 1986).
Peaks in R represented potential matches of image features
with the templates, and detection was confirmed for those features with
high correlations between the image portion containing the feature and
the template (vertical and horizontal lines of pixels intersecting at
detected cell center were correlated with vertical and horizontal lines
of pixels intersecting at template center).
Neural network scoring. The combined error rates of our cell
detection algorithm (sum of false-positive and false-negative GAD+ and GAD cells) were
estimated by two trained observers who independently scored a total of
250 automatically selected cells (125 each of GAD+
and GAD cells) in randomly selected image tiles.
The sum of false-positives and false-negatives was ~7% for
GAD+ and 5% for GAD cells.
This error rate was reduced further by subjecting the extracted stain
and grain measures to a neural network scoring procedure (Neural
Network Professional II; NeuralWare, Pittsburgh, PA). The network was
trained to match values assigned by trained observer on a 0-9 scale,
on which 0 represented artifacts such as red blood cells or radial
blood vessels and 9 represented an ideal example of true
GAD+ or GAD cells lying near
the plane of emulsion. A trained observer scored 200 randomly selected
GAD+ and GAD neurons (total of
400 cells) from the detected cell database and then trained a neural
network to reproduce these scores for the sample set. Then scores were
generated by the trained network for a novel test set of 200 cells, and
these were scored independently by the same observer, who had no
knowledge of the neural network score for each cell. Any cells showing
markedly different scores from observer and network were inserted, with
the observer's score, into the training database, and the network was
retrained to score correctly this cell and all previous cells. After
several iterations of this process, the network and observer scores
were in very close agreement. In every case for the updated test set,
the scores from the trained network and the observer were the same or
within one unit of each other. Red blood cells were correctly given
scores of 0-1, whereas obvious cells were given scores of 5 or higher. Correctly detected cells representing less ideal configurations of cell
soma and silver grains were given scores in the 3-5 range. All cells
with scores of 3 or higher were included in the analyses presented here
(see Fig. 10, Table 1). The total error rate after neural network
scoring was <2% for each cell class.
Fig. 10.
Measures of cell populations detected by
automated methods. A, Numbers of GAD+ and
GAD cells detected for each neural network score.
GAD+ cell numbers decline monotonically, whereas the
GAD counts appear as a positively skewed normal
distribution. Cells with scores of 3 and above were subject to
additional analysis. B, Total GAD+ and
GAD cells detected, a summary of the information
in A. A total of ~100,000 GAD+ cells
and 1,000,000 GAD cells received scores of 3 or
above. C, Cell densities in each lamina, by cell type. Each
bar represents the mean for 10 hemispheres (4 normal, 6 acute, row C spared), as shown in Table 1. Error bars represent SD for
the 10 hemispheres. These counts show slight but not statistically
significant trends toward greater densities in granular layers.
[View Larger Version of this Image (34K GIF file)]
Quantitative measures from individual cells. Once the center
of the cell was located and because the stain and grain images were
strictly in register, the distributions and densities of the 2DG silver
grains and the antibody label could be determined readily and were
quantified radially over annuli centered on the soma (peak somal value
in the correlation image) as a series of 32 means and SD. From these
"ring counts," which represented the primary data for each cell,
the following could be estimated (see Figs. 8, 9, 11, 12), using
procedures written for the commercial database product RS/1 (Bolt,
Beranek, and Newman, Cambridge, MA): (1) somal area, (2) label density
over soma, (3) label density over ring at perimeter of soma, and (4)
label density over near-surround of soma (32-µm-diameter
annulus).
Fig. 8.
Gallery of six randomly selected
GAD+ heavily 2DG-labeled neurons from the layer
IV-V boundary. The cells were detected by automated template-matching
algorithm and scored >3 by neural network. For each cell the five
color-filtered raw image portions are reproduced (upper left
and lower right), along with small squares
indicating the strengths of two types of template correlation (center) and disks representing
statistical/spatial models of the distributions of GAD stain
(lower left) and 2DG silver grains (upper right).
To generate the disks, we used the RS/1 function "PROBNORM-INV,"
which computes the value of a normal distribution corresponding to a
given probability. For each pixel in the disk, the distance from disk
center was computed and normalized to a range of 1-32 to determine
which concentric ring density values to apply. The angle of the pixel relative to disk center in radians was divided by
to normalize to a range of 0-1. This number served as the
probability input to the PROBNORM-INV function, which returned values
from approximately 
3 to +3, representing the number of SD from the
mean (for the computed data ring) that the pixel should represent. Then
the pixel value was computed as the algebraic sum of ring mean and the
product of ring SD and the value returned by the PROBNORM-INV function.
In effect, the disks created by this algorithm represent the range of
values expected from the computed mean and SD for all 32 data rings.
Our convention in generating the 2DG disks was that pixel values
representing dense 2DG grains (white) or GAD stain
(dark) were plotted to the right, both above and
below the axis of bilateral symmetry used for making the plot.
[View Larger Version of this Image (80K GIF file)]
Fig. 9.
Gallery of six randomly selected
GAD lightly 2DG-labeled neurons. These images,
computed by the same algorithm as those in Figure 8, illustrate the
generally very low 2DG labeling over GAD neuronal
somata.
[View Larger Version of this Image (67K GIF file)]
Fig. 11.
Cross-sectional profiles of 2DG labeling in
GAD+ and GAD neurons. This
analysis was restricted to cells from barrel rows B, C, and
D in four hemispheres from normal behaving hamsters and depicts cells from supragranular (top), granular
(middle), and infragranular layers (bottom). The
curves were generated from the concentric ring density
measurements and drawn as a mirror image from the detected cell center.
They represent averaged 2DG densities (in arbitrary units representing
the gray scale dynamic range of the raw images) for the
numbers of cells indicated in the key. These
curves and those in Figure 12 reveal several features of detected
cells, as discussed in the text. Vertical dashed lines indicate the apparent mean cell perimeter for GAD+
and GAD cells. The contrast between curves for
GAD+ and GAD neurons is most
pronounced in layer IV (middle), less so in infragranular layers (bottom), and relatively subtle in supragranular
layers (top).
[View Larger Version of this Image (20K GIF file)]
Fig. 12.
Cross-sectional profiles of GAD stain intensity
in GAD+ and GAD neurons in
supragranular (top), granular (middle), and
infragranular layers (bottom) of barrel cortex from a normal
behaving hamster. These curves represent the same cell
groups as those portrayed in Figure 11 and were generated in the same
manner. See legend to Figure 11 and text for discussion.
[View Larger Version of this Image (19K GIF file)]
The template-matching procedure, in combination with quantitation of
stain and grain densities, allowed us to obtain detailed information
for very large numbers (Fig. 10, Table 1) of individual neurons labeled
by both antibodies and silver grains. The detected objects were mapped
in another montage that was in strict register with the stain and
silver grain montages described above.
It is important to emphasize that our strategy was based on a
systematic and reproducible procedure that was fully automated, requiring no operator intervention once the scanning sequence was
initiated. Although many image-processing strategies might be used to
collect the data, our strategy works well for our purposes, is
reproducible, and gives consistent results. The strength of our
analysis is that our data acquisition procedure is standardized across
experimental conditions, laminae, barrel rows, et cetera. In other
words, the algorithm is always looking for precisely the same thing,
and it reproducibly generates a numerical estimate of the similarity
between any given object in the tissue and the test template. Thus,
differences in metabolic activities of GABAergic cells within a
cortical hemisphere or across experimental conditions cannot be
attributed to the data collection procedure. It is true, however, that
systematic errors may be introduced by use of a standardized template
across all sections, when known size differences across laminae might
be used to guide design of different templates for different sections.
This trade off between standardized and customized data collection
deserves and will be subject to further investigation.
Alignment of serial sections. Montages representing serial
sections were rotated and aligned by an automated procedure (Hibbard and Hawkins, 1988; Hibbard et al., 1992; McCasland et al., 1992), assuring accurate alignment of sections in a hemisphere. At this point
the database represented the entire cortical slab in the surveyed
portion of barrel cortex. It therefore was possible to determine the
label density throughout a column defined by a barrel. All of the cell
extraction and three-dimensional reconstruction (see below) programs
were built with the DIANA image analysis software system (Hibbard et
al., 1987).
Controls for image processing
To control for artifacts of image processing in data
collection, we used immunohistochemically stained sections on slides dipped in emulsion that had been exposed previously to light (these sections also served as controls for chemography, as described above).
The previous light exposure of the emulsion created a relatively
uniform grain distribution in the developed autoradiogram. These
controls showed no significant differences between grain counts over
GAD+ and GAD neurons (data not
shown).
RESULTS
Somatotopically appropriate 2DG labeling in barrel fields of
normal animals
As shown in Figures 1 and 2, our 2DG/immunostaining method
demonstrates metabolic activation in somatotopically appropriate zones
of somatosensory cortex from behaving hamsters. Figure 1 (top) is a low-magnification photomicrograph of a 2DG
labeled/GAD-stained section from hamster barrel cortex, cut tangential
to the barrel representation in layer IV. The image was
contrast-enhanced to show the pattern of 2DG label, which reveals a
full body map of 2DG label (a "hamsterunculus"), including the
whisker-related barrel field, in somatosensory cortex. In this normal
animal all barrels were heavily 2DG-labeled. The clear demarcation of
individual barrels near the center of the section, reminiscent of the
barrel pattern observed with Nissl stains in this species (Fig. 1,
bottom), suggests that each barrel represents a relatively
independent functional unit in the behaving animal. Interestingly, 2DG
labeling within the barrel is much heavier than in the barrel walls or the septum between barrels (Fig. 1, bottom), despite the
greater density of Nissl staining in the barrel walls. Sections stained only for GAD do not show such a clear representation of the body map or
of individual barrels.
Fig. 1.
Somatotopically appropriate 2DG labeling in normal
hamster barrel field. Top, Low-magnification photomicrograph
of a 2DG-labeled/GAD-stained section from hamster, including barrel
cortex, cut tangential to the barrel representation in layer IV. The
image was contrast-enhanced to show the pattern of 2DG label, which
reveals a full body map of 2DG label (a "hamsterunculus"),
including the whisker-related barrel field, in somatosensory cortex.
Note the clear demarcation of individual barrels near the center of the
section, marked A-E to denote barrel rows corresponding to
rows of whiskers on the contralateral face. V, Visual
cortex; T, trunk representation; F, forelimb;
H, hindlimb; LL, lower lip. Scale bar, 500 µm.
Bottom, A similar low-magnification photomicrograph of a
Nissl-stained section from another hamster, also cut tangential to the
barrels. Individual barrels are defined clearly with this stain, which is denser in barrel walls than in the septum between barrels. This
pattern is similar to that shown with 2DG.
[View Larger Version of this Image (108K GIF file)]
Whisker-dependent 2DG labeling after selective
acute deprivation
Intact whiskers are necessary for strong 2DG labeling in our
paradigm. Figure 2 illustrates this point with silver
grain montages generated by our automated data collection procedure. As
in Figure 1, 2DG labeling patterns in barrel fields from normal animals (left panels) show strong metabolic activation of all
barrels. The left center panel represents the same section as that
shown in Figure 1 and serves as a point of reference for the labeling of all whisker-related columns in both supragranular (top)
and infragranular (bottom) sections. By contrast, 2DG
labeling in animals with all but row C whiskers acutely trimmed before
2DG injection (right panels) is confined mainly to the
spared row C columns. These data clearly demonstrate that strong
regional metabolic activation with this technique is dependent on
normal whisker inputs. Note the heavier labeling and the clear barrel representation in layer IV of both normal and acutely deprived barrel
fields. The asymmetrically distributed label outside spared row C
columns in the right panels confirms an earlier prediction related to
functional gradients in the barrel field (McCasland et al., 1991) and
will be discussed more fully elsewhere.
Strong metabolic activation of GAD+ neurons
Some neurons in barrel cortex of behaving animals show strikingly
heavy 2DG labeling, and most of these are GAD+.
Figure 3 illustrates this finding with examples from
supragranular (top), granular (middle), and
infragranular (bottom) layers of barrel cortex from a normal
behaving animal. The left panels are taken from overexposed
autoradiograms that clearly depict, especially in layer IV, the stark
contrast between heavily 2DG-labeled, mostly GAD+
(black dots) neurons and lightly labeled
GAD "headlights" (smaller blank
patches in the silver grain emulsion; absence of brown
GAD stain). Because the silver grains in these overexposed
autoradiograms can obscure the underlying GAD stain, we include smaller
panels at the right from the same laminae of different specimens,
showing representative labeled cells from less heavily exposed
sections. In all cases, dense clusters of silver grains precisely
overlie most neurons stained for the GAD antibody. In cases in which
the microtome knife sliced through the cell and its nucleus, the
pattern of heavy grains is interrupted by the nuclear boundary. There
are also small numbers of lightly 2DG-labeled GAD+
cells; we have evidence that many of these cells are colocalized with
the calcium-binding protein calbindin, whereas many of the heavily
2DG-labeled GAD+ cells are colocalized with
parvalbumin (Maier and McCasland, 1997).
Fig. 3.
Strong metabolic activation of GABAergic neurons
during normal behavior. High-magnification photomicrographs of
double-labeled GAD+ and GAD
somata in supragranular (top), granular (middle),
and infragranular layers (bottom) of barrel cortex from a
normal behaving hamster show a stark contrast between heavily
2DG-labeled mostly GAD+ (black dots)
neurons and lightly labeled GAD "headlights"
(smaller blank patches in the silver grain emulsion; absence
of brown GAD stain). These phenomena are recognizable but
somewhat more subtle in supragranular and infragranular layers. Several
examples of heavy 2DG labeling in ridges surrounding
GAD somata can be seen in the bottom
panel, representing infragranular layers (see also Fig. 4).
Smaller photos at right, taken from the same laminae but
different specimens with less heavily exposed autoradiograms, better
indicate the GAD stain underlying 2DG silver grains. Scale bar, 25 µm.
[View Larger Version of this Image (153K GIF file)]
At moderate magnification it also was clear that concentrations of 2DG
silver grains are found over patches of relatively heavy GAD staining,
whether for neuropil or neuronal somata. This finding is consistent
with the hypothesis that GAD+ processes are, like
their parent somata, metabolically active in the behaving animal.
2DG-labeled GAD+ "ridges" surrounding
unlabeled GAD neurons
Particularly in deep laminae, many of the lightly
2DG-labeled GAD neurons in barrel cortex are
ringed by heavy 2DG label associated with GAD+
puncta. Examples of this effect are shown in Figures 3 and
4. At high magnification it appears that these
curvilinear arrays of 2DG silver grains at the perimeters of cell
somata are positioned precisely over the pericellular nests or
"baskets" of GAD+ terminals surrounding many
GAD neurons and are visible just deep to the
emulsion [see White (1989) for a discussion of this terminology]. The
tight spatial apposition of 2DG grains and perisomatic
GAD+ puncta suggests that inhibitory inputs to these
GAD cells were active. We refer to the ring of
grains around the perimeter of the cell soma at the cut surface of the
section as an "inhibitory ridge" (the name refers to the visual
appearance of the curvilinear arrays of grains, not to a true ridge or
elevation). In some instances there was a pronounced inhibitory ridge,
but the cell soma was also significantly 2DG-labeled (Fig.
4B, open arrows), suggestive of cells for which the
excitatory inputs have elicited significant numbers of action
potentials despite strong inhibitory inputs.
Fig. 4.
Top. Additional examples of ridges of 2DG
grains closely apposed to perisomatic GAD+ puncta.
These photomicrographs, taken from infragranular layers of barrel
cortex stained for GAD, show many examples of GAD
neurons with curvilinear arrays of 2DG grains and
GAD+ puncta at their somal perimeters (some of these
are indicated by filled arrows). We refer to these grain
arrays as "inhibitory ridges" (see Results and Discussion).
Inset to A shows one example of such a ridge at two
different focal depths, one at the plane of the silver grains
(left) and one just below the grains, showing the
GAD+ puncta (right). B, Two
examples of GAD cells (open arrows),
which exhibit inhibitory ridges but are nevertheless moderately
2DG-labeled (the labeling is comparable to that of the surrounding
neuropil). In our interpretation these cases show that
GAD cells can be activated metabolically during
normal behavior although subject to strong proximal inhibition. Scale
bars: 10 µm for inset to A; 25 µm for all
other panels.
[View Larger Version of this Image (144K GIF file)]
Most neurons stained with a glutamate antibody are
lightly 2DG-labeled
From our observations in GAD-stained cortices, we predicted that
sections stained for Glu would show the opposite relationship between
immunostain and 2DG label. This prediction was confirmed, as shown in
Figure 5. Most Glu+ neurons were
lightly 2DG-labeled or unlabeled, and many of these lightly 2DG-labeled
Glu+ neurons were bordered by curvilinear arrays of
grains (arrows). However, these materials also showed that a
small number of Glu+ neurons were heavily
2DG-labeled (our unpublished data; we have not quantified this
phenomenon, but the number of metabolically active
Glu+ neurons seems to be <5%), suggesting that a
functional subgroup of Glu+ neurons is highly active
in normal behavior.
Bottom. Relatively inactive neurons
stained with an antibody to glutamate. Heavily 2DG-labeled neurons in
this high-magnification photomicrograph of a layer IV section from
hamster barrel cortex are not stained for the antibody. Stained neurons
generally show very little 2DG label, although some
Glu+ cells (not shown) are heavily 2DG-labeled.
Arrows mark examples of Glu+ cells with
sparse somatic 2DG label and a ridge of heavy label at the cell
perimeter. Scale bar, 25 µm.
Fig. 5.
No observable bias toward GAD+ 2DG labeling in
anesthetized animals
As an additional control for stimulus-dependent 2DG labeling in
barrel cortex, we performed two experiments in which animals were
anesthetized with phenobarbital at the time of 2DG injection (Fig. 6). Careful visual inspection of barrel field
neurons in these animals showed no detectable differences in labeling
of GAD+ and GAD neurons (see
also McCasland, 1996). Exposure times for autoradiograms from cortices
of these animals were dramatically (~3×) longer than for those from
normal behaving animals, and grain densities were extremely low even
after the long exposures (terminated because of rising background
levels with time in our autoradiograms). Unfortunately, the grain
densities in these materials were so low as to preclude automated
analysis, because the autofocus function of the microscope controller
was unable to lock on the sparse grains and did not exhibit stable
behavior. However, our visual microscopic inspection of barrel cortex
sections from anesthetized animals indicated, if anything, a slight
bias in the opposite direction from that observed in experimental
GAD/2DG materials, i.e., more grains over GAD than
GAD+ structures.
Fig. 6.
Absence of 2DG labeling bias in
GAD+ and GAD cells from animals
anesthetized simultaneously with 2DG injection. Each of the three
panels shows a different subfield of barrel cortex (layer IV), in which
very sparse silver grains (some indicated by arrows) show no
obvious spatial relationship with GAD+ or
GAD cells. A similar figure has been published
previously (McCasland, 1996). Scale bars, 25 µm.
[View Larger Version of this Image (97K GIF file)]
Automated measures of detected GAD+ and
GAD cells confirm our qualitative observations
Automated methods, using a scanning microscope/image processing
system, were used to detect and quantify 2DG label in individual GAD+ and GAD neurons in barrel
cortex. All measurements reported here were calculated from databases
consisting of cells detected by these automated procedures and scored
by neural network (see Materials and Methods). The heavy 2DG labeling
of GAD+ neurons, evident by visual inspection of our
materials, was quantified in the data collected by these automated
procedures.
Figure 7 shows a typical example of the raw output from
our automated cell detection protocol. This map or montage,
representing a 2 × 2 mm portion of a section from the layer IV-V
boundary in normal barrel cortex, shows the raw candidate cell output
from the template-matching algorithm. This output then is refined with neural network scoring, based on a manually scored training set (see
Materials and Methods). Approximately 12,000 candidate cells are
depicted in this image.
Fig. 7.
Detected cell map (montage) from a layer IV
section of barrel cortex in a normal behaving hamster. Blue
shading represents 2DG density from the silver grain montage (as
in Fig. 2; see Materials and Methods); green stars represent
candidate GAD+ cells; red dots represent
candidate GAD cells. For each cell our automated
routines quantify grain and stain densities in an annular array about
the detected cell center (Figs. 8, 9, 11).
[View Larger Version of this Image (222K GIF file)]
To provide a sense of the quantitative information available for each
neuron in the database, we wrote an RS/1 procedure to generate
reconstructed cell images. Figure 8 shows a
computer-generated gray scale representation of six randomly selected
GAD+ heavily 2DG-labeled neurons detected by
automated template-matching algorithm and achieving neural network
scores >3 (see legend for a description of how these images were
synthesized). Note the heterogeneity of cell morphologies among
GAD+ heavily 2DG-labeled cells. Figure
9 shows similarly computed images representing randomly
selected GAD cells in the database. These images
provide a visual impression of the cell representation in our database,
which does not include an image of the cell but does preserve
information that can be used to reconstruct, in many cases, a
reasonable facsimile of the original cell. A comparison of Figures 8
and 9 illustrates the generally heavy labeling in
GAD+ neurons and light labeling in
GAD cells and gives a sense of the data base from
which our quantitative analysis was conducted.
A summary of the cell count statistics from our database is presented
in Figure 10 and Table 1. A plot of cell count versus score (Fig. 10A) shows an exponential decline for
GAD+ cells and a roughly gaussian distribution for
GAD cells. In neither case were true cells sharply
separated from false cells along the axis of cell score. The binary
decision to include a given cell for analysis was, therefore, a
relatively arbitrary choice of threshold along the scale of neural
network score, constrained by low sample sizes on the upper end and
more heterogeneous cell populations on the lower end. For this report we set the threshold at 3 for both cell classes (Fig.
10B). We stress that the conclusions drawn here are
not dependent on setting this threshold at any particular level.
This database represents one possible strategy (see Materials and
Methods) for building a representation of the information available
from our 2DG/immunostained cortical tissue. We were constrained in
choosing this strategy by the requirement that the analysis be limited
to cells lying near the autoradiographic emulsion. With this constraint
in mind, we emphasize that our data are not corrected stereologically
to represent true cell counts. However, it is reassuring to note that
our cell count data, in which GAD+ cells account for
~10% of the total detected (compare GAD+ and
GAD bars in Fig.
10B,C), fall at the low end but within the range of
other studies based on Golgi (Woolsey et al., 1975; Simons and Woolsey,
1984) or immunohistochemical (Lin et al., 1985; Spreafico et al., 1988)
procedures (using different GAD antibodies and different rodent
species). When the analysis is confined to the central portion of the
barrel field (rows B, C, and D; see Figs. 11, 12) to
avoid potential problems of including outlying cells, the percentage of
GAD+ cells is closer to 20%, suggesting that
GAD+ cells are less numerous at the edges of the
field. Further investigation will be required to adequately interpret
these percentages; our cell counts may over- or underestimate the true
numbers, but we are confident that they represent an accurate and
reproducible estimate of GAD+ and
GAD cells that contribute to the autoradiographic
2DG signal.
Laminar patterns
As indicated in Figure 3, GAD+ neurons were
more heavily 2DG-labeled than GAD neurons in every
cortical lamina of the barrel field. Figure 11 shows that this
difference was dramatic in layers IV, less so in layers V-VI, and
relatively subtle in layers II-III. For this figure we summarized the
information represented in the reconstructed images of Figures 8 and 9
as mean cross-sectional profiles. The profiles of Figure 11 depict mean
2DG densities in detected (network-verified) GAD+
and GAD neurons from the central portion of the
barrel fields (rows B, C, and D) of all four normal hemispheres. Figure
12 shows similarly computed curves representing GAD
stain profiles for the same groups of neurons as in Figure 11. Vertical
dashed lines, positioned to intersect the curves as they cross the
background 2DG densities, indicate the apparent mean cell perimeter for
GAD+ and GAD cells. These
profiles, which represent the central tendencies for thousands of cells
(see Fig. 11, key), reveal or provide quantitative detail
for several features of detected cell populations. First, the stain and
2DG density curves are very similar for both object classes in all
laminae, suggesting that grains and stain are colocalized for
GAD+ cells and that both are absent in
GAD cells (compare curves in Figs. 11, 12).
Second, detected GAD+ neurons are larger (mean
diameter ~18 µm) than detected GAD neurons
(mean diameter ~12 µm). These figures are consistent with published
measures from Golgi materials (smooth cells; Woolsey et al., 1975;
Simons and Woolsey, 1984; Simons, 1995) or GAD stains (Lin et al.,
1985). Third, for GAD (presumed spiny) cells both
stain and 2DG curves showed a significant ridge at the perimeter of the
cell. This ridge corresponds in the stain curve to the
GAD+ "basket" terminals and in the 2DG curve to
the silver grains overlying the GAD terminals. In contrast, the
2DG labeling profile for GAD+ neurons slopes
smoothly down to background levels, suggesting minimal inhibition on
the somata of these neurons in behaving animals. This observation is
consistent with electron microscopic observations of synaptic
distributions in barrel cortex (White, 1989) (see Discussion).
Figure 13 summarizes the laminar 2DG labeling
pattern of GAD+ and GAD neurons
and overall 2DG labeling (mean values from the silver grain montage)
from a single normal hemisphere. This plot illustrates that both cell
classes are activated more strongly in laminae that show higher overall
2DG densities. We saw no evidence that increases in activation of
inhibitory cells were accompanied by decreased activation in excitatory
cells within a lamina or hemisphere. Indeed, we have preliminary
evidence (our unpublished data) that 2DG labeling in single
GAD+ neurons is correlated strongly and positively
with mean 2DG labeling in near-neighbor GAD
cells.
Fig. 13.
Laminar distribution of 2DG labeling in somata of
GAD+ and GAD neurons.
Curves depict relative 2DG grain densities (in arbitrary units as in Figs. 11, 12) for detected GAD+ cells
(filled squares), GAD cells
(filled triangles), and overall mean 2DG labeling
(filled circles) in barrel cortex from a single
normal hemisphere. Numbers along the abscissa
correspond to tangential section numbers from the specimen, starting
just deep to the pia. GAD+ neurons are more heavily
2DG-labeled than GAD neurons in every layer. For
each tissue section this difference was highly significant
(t test, p < 0.001); error bars are omitted for clarity of presentation. The difference in 2DG labeling of GAD+ and GAD cells is most
pronounced in layer IV (sections 9-13), less so in infragranular
layers, and relatively subtle in supragranular layers.
[View Larger Version of this Image (25K GIF file)]
DISCUSSION
Numerous studies have suggested an important role
for GABAergic inhibition in shaping receptive fields; examples are
visual cortex (Sillito, 1975, 1992; Hata et al., 1988; Kisvarday et
al., 1993, 1994; Eysel and Shevelev, 1994), somatosensory cortex
(Gardner and Costanzo, 1980; Dykes et al., 1984), and subcortical
auditory nuclei (Fujita and Konishi, 1991; Park and Pollak, 1993a, b;
Ebert and Ostwald, 1995). Recent modeling work suggests that GABAergic inhibition plays critical, and probably related, roles in controlling overall cortical excitability (Somers et al., 1995; Kyriazi et al.,
1996) and synchronizing the activities of excitatory cell populations
(Bush and Sejnowski, 1994). Largely for technical reasons,
comparatively little attention has been devoted to the activity
patterns displayed by GABAergic cells themselves (Swadlow, 1995), and
almost no information is available from such cells in normal behaving
animals.
For this report we used a novel high-resolution
2DG/immunostaining protocol to visualize metabolic and
immunohistochemical markers in the same tissue section. Our technique
assesses metabolic activation in putative excitatory or inhibitory
neurons, thus filling a gap in existing 2DG methodology by supplying
the "sign"
positive or negative, excitatory or inhibitoryfor any
given activated neuron in barrel cortex. Using this approach, we show
that inhibitory neurons are much more metabolically active than
excitatory neurons in stimulated columns of a well defined cortical
system during normal behavior. To our knowledge this is the
first large cell sample demonstration of active GABAergic cells in
normally functioning cortical circuitry.
The present results provide a global picture of function in cortical
circuits that can be related to results from microelectrode recordings
and other methods. Our data confirm and extend those from previous
single-unit electrophysiological studies in rodent barrel cortex and
histochemical studies with CO in primate visual cortex. Thus,
convergent conclusionsthat GABAergic cells are highly active in
cortical circuitsare reached by three different methods.
Heavy 2DG labeling in GAD+ neurons
The strong metabolic activation of most GAD+
neurons confirms, in behaving animals, hypotheses based on
physiological data from limited samples of neurons recorded in
vitro (McCormick et al., 1985; Chagnac-Amitai and Connors, 1989;
Connors and Gutnick, 1990; Agmon and Connors, 1992) or in
unanesthetized, urethane-anesthetized, or narcotized animals (Simons,
1978, 1995; Armstrong-James, 1995)that GABAergic inhibitory neurons
are far more active than glutamatergic excitatory neurons in barrel
cortex. More generally, concentrations of 2DG silver grains are found
over patches of relatively heavy GAD staining, whether for neuropil or
neuronal somata. As expected from this pattern, sections stained for
Glu and labeled for 2DG show that most glutamatergic neurons are
lightly 2DG-labeled or unlabeled (see Fig. 5).
The heavy 2DG label in GABAergic neurons and neuropil cannot be
attributed to spontaneous activity, because GABAergic neurons in barrel
columns corresponding to stimulated whiskers are more heavily
2DG-labeled than those in columns corresponding to acutely trimmed
whiskers (see Fig. 2). The latter do receive reduced stimulation via
multiwhisker inputs from thalamocortical projection neurons (Simons and
Carvell, 1989); hence, it is not surprising that GABAergic cells are
heavily labeled relative to glutamatergic cells within these acutely
deprived columns. Furthermore, sections from animals anesthetized at
the time of 2DG injection (a condition known to reduce regional
metabolism to very low levels) showed no such bias toward heavy 2DG
labeling in GABAergic neurons (see Fig. 6; McCasland, 1996). The bias
toward heavy 2DG labeling of GABAergic neurons was also noticeably less
prominent in animals that were relatively somnolent after 2DG injection
(our unpublished observations).
These features of our histological specimens were reflected in
the distinctive mean 2DG profiles for GAD+ and
GAD neurons, as measured by our automated
algorithm (see Figs. 11, 12). They are also clear in the reconstructed
cell images of Figures 8 and 9.
2DG-labeled GAD+ ridges surrounding unlabeled
GAD neurons
We found a similarly striking correspondence between silver grains
and GAD stain in the "ridges" of 2DG label closely apposed to
perisomatic GAD+ puncta on many otherwise unlabeled
GAD somata, strongly suggestive of active
inhibitory inputs to these cells. We infer from these data that the
electronically favorable position of GABAergic synapses on spiny cell
somata and proximal dendrites, quantified in barrel cortex by the
electron microscopic studies of White (1989), is actively used by
inhibitory interneurons during normal sensory responses. This localized
metabolic activation may well reflect both presynaptic and postsynaptic
components of proximal inhibition on excitatory neurons.
Our interpretation of these phenomena as an inhibitory ridge can be
challenged on two principal grounds. First, our data do not indicate
directly the degree to which postsynaptic cells are inhibited, because
they say nothing about the receptor number, position, and subunit
make-up that determine synaptic strength, nor do they address
transmitter release, reception, or postsynaptic transduction
mechanisms. However, ultrastructural studies (White, 1989; Wong-Riley
et al., 1989, 1994) show that excitatory neurons receive only
symmetrical (presumed inhibitory) synapses on their somata. Thus, the
simplest explanation of our data is that they indicate the extent to
which proximal inhibitory synapses are activated metabolically.
Although synaptic strength is an independent variable, the degree of
metabolic activation of known inhibitory synapses can serve as a useful
index of the strength of inhibition in a given experimental setting.
Second, the resolution of light microscopy is too limited to
distinguish, in any single instance, between true
GAD+ puncta and cut dendrites. Thus a randomly
placed silver grain could be closely apposed to a single stained
process, and this process might be a cut dendrite (or axon), but our
materials show systematic ridges or curvilinear arrays of silver grains
specifically associated with neuronal somata sectioned by the microtome
knife, closely apposed to very similar rings of puncta-like
GAD+ processes. To our knowledge there is no
evidence that cut GAD+ dendrites are distributed in
a nonrandom manner with respect to cell somata of only one
neurotransmitter class (Glu+ cells); even if such
cut dendrite arrays did exist, they should be placed similarly around
the edges of GAD+ neurons. Even this remote
possibility of systematically misidentifying putative
GAD+ terminals cannot account for the statistical
improbability of the curvilinear arrays of silver grains positioned
precisely over GAD+ processes and aligned with the
edges of GAD, not GAD+, somata.
These observations are consistent with available ultrastructural and
electrophysiological evidence and (in our interpretation) represent the
most direct demonstration to date of active GABAergic terminals during
normal behavior.
Laminar patterns
Little is known about the relative levels of activation of
excitatory and inhibitory neurons in different cortical laminae of
rodent barrel cortex during behavior. In our materials
GAD+ neurons in every cortical lamina are much more
heavily 2DG-labeled during normal exploratory behavior than
GAD neurons (see Figs. 3, 8, 9, 11, 13). In
absolute terms this difference was dramatic in layers IV-V, less so in
layer VI, and relatively subtle in layers II-III. Thus, the heavy
labeling of GABAergic neurons corresponds approximately to the zones in
which densities of thalamocortical synapses are high, and activation of
inhibitory neurons is relatively less prominent in supragranular
laminae, which represent predominantly intracortical excitatory and
inhibitory synapses (White, 1989).
Both GAD+ and GAD neurons
showed higher 2DG labeling in laminae with higher overall label (see
Fig. 13), with GAD+ neurons more heavily labeled in
every instance. Profiles representing 2DG labeling in different laminae
indicate that relative levels of excitation are increased, and relative
levels of inhibition somewhat decreased, in supragranular relative to
granular layers. These relationships suggest that inhibition is less
prominent in more advanced stages of signal processing within barrel
cortex.
Correspondence to anatomically and physiologically defined classes
of neurons
Barrel neurons comprise two morphological groupssmooth
and spinybased on quantitative measures from Golgi materials (Woolsey et al., 1975; Simons and Woolsey, 1984). Smooth cells are predominantly GABAergic, presumed inhibitory, interneurons, whereas spiny cells are
glutamatergic and are presumably excitatory. Single-unit physiology indicates that the spiny cells correspond to the most frequently encountered cell type, the "regular spiking unit" or RSU, which produces spike waveforms of longer duration than a second, rarer group
corresponding to the smooth cells, the "fast-spiking unit" or FSU
(Simons, 1978).
The most striking feature of FSU neuron physiology is the ability to
fire at very high rates with little or no adaptation, a trait that
distinguishes these cells from all other known categories of cortical
neurons. FSUs respond more reliably and over a broader range of
frequencies than RSUs. Intracellular recordings from tissue slices
support this functional dichotomy (Chagnac-Amitai and Connors, 1989;
Connors and Gutnick, 1990; Agmon and Connors, 1992) and show that FSUs
rapidly repolarize after firing (McCormick et al., 1985; Connors and
Gutnick, 1990). Thalamic inputs to FSUs are concentrated on somata and
proximal dendrites, whereas RSUs receive most excitatory inputs on
distal dendrites (White, 1989), suggesting that spiny barrel cells
require greater thalamic convergence for activation (Rall, 1967;
Tsukahara et al., 1975; Simons, 1995). FSUs and RSUs perform different
transforms on the incoming thalamic signal (Simons, 1995), such that
FSU output represents the entire (or any) combination of its
inputs, whereas RSU output represents only the most common
input. RSUs have small receptive fields, are strongly inhibited by
adjacent whisker deflections, and respond disproportionately to strong
versus weak inputs (Simons, 1995), whereas FSUs resemble the thalamic
input with high spontaneous activities, strong adjacent whisker
excitatory responses, and relatively weak inhibitory surrounds.
Recent studies by Swadlow (1989, 1991, 1994, 1995) of "suspected
interneurons" (SINs) in rabbit primary somatosensory cortex show many
characteristics consistent with our observations and with physiological
studies of FSUs. SINs are not activated antidromically by thalamic
stimulation and are, therefore, presumed interneurons. They can follow
very high rates of stimulation with maximum firing frequencies >600
Hz. Most receive heavy convergent and divergent inputs from large
numbers of thalamocortical projection neurons. Such a richly divergent
and convergent network approaches the description of a "complete
transmission line" characterized by a very "high reliability" but
at a cost of sacrificing "complexity of task" (Swadlow, 1995).
In our materials, most of the large heavily 2DG-labeled neurons in
barrel cortex are stained positively for GAD. We propose that these
heavily 2DG-labeled GAD+ cells are FSUs and that the
far more numerous lightly 2DG-labeled neurons, which are smaller and
stained for Glu, but not GAD, are RSUs. One possible interpretation of
our data is that the lightly 2DG-labeled, GAD
neurons are silent during the animal's behavior, perhaps because of
strong tonic inhibition. However, the available physiological data
strongly suggest that such cells are electrophysiologically active in
important ways (to the animal's perception), but the nature of the
activity is such that it leads to minimal 2DG uptake. For example,
pyramidal neurons may fire phasicallyin short bursts with each
"whisk"followed by longer periods of intervening silence, produced perhaps by active inhibition. A preliminary report on small
numbers of single units recorded in awake, behaving animals (Kodger et
al., 1995) tends to support this view.
Correspondence to CO data
Our findings are also consistent with the elegant CO studies of
Wong-Riley in cat (Kageyama and Wong-Riley, 1986) and primate (Wong-Riley et al., 1989, 1994) visual cortex. With respect to our
findings, the most definitive of these is an ultrastructural double-labeling study for CO and GABA (Nie and Wong-Riley, 1995) in
macaque striate cortex. Their data show that GABAergic type C neurons
receive both excitatory and inhibitory axosomatic synapses and contain
darkly CO-reactive mitochondria, whereas non-GABA neurons receive only
GABAergic axosomatic synapses and have lightly CO-reactive
mitochondria. They propose that the higher level of oxidative
metabolism in GABAergic neurons can be explained by their proximal
excitatory inputs.
CO histochemistry represents a longer term measure of oxidative
metabolism, because CO reactivity changes over a period of days in
response to sensory deprivation, whereas 2DG represents a shorter term
measure of metabolism (Wong-Riley and Welt, 1980; Hevner et al., 1995).
Thus, longer term tonic differences in GABAergic and glutamatergic cell
activity could account for differences in CO reactivity between the two
cell populations. Wong-Riley et al. (1995) recently have addressed this
temporal resolution issue by correlating changes in CO staining with
electrophysiological responses in a given recording site in primate
visual cortex before and after monocular TTX injections (Deyoe et al.,
1995). They showed that TTX-induced reductions in CO staining were
correlated with, but preceded by, reductions in spike rates, suggesting
that the adjustment of CO levels partially reflects changes in
physiological responses. However, this correlation of increased
multiple unit activity with changes in CO reactivity was not attributed
to either smooth or spiny cells but rather to the neuronal population
recorded by the electrode. They also acknowledged that the binocular
circuitry of primate visual cortex complicates the interpretation of
physiological changes because of monocular TTX injections. Our
2DG/immunostaining data from barrel cortex more clearly distinguish
between GABAergic cell activity driven by sensory inputs and that
attributable to tonic inhibition.
What selective advantages do active GABAergic cells provide?
Why does the normally functioning cortex put so much of its energy
into inhibition? If this kind of engineering were implemented in an
automobile, the car would be braking constantly as the driver continued
to press the accelerator. Logically, there is an inherent inefficiency
in heavy activation of inhibitory neurons in an organ (the brain) that
uses a large percentage of total body glucose. This inefficiency
implies a selective disadvantage that must be countered by other
aspects of circuit function.
Recent reports suggest two related answers to this puzzle. First,
relatively nonspecific inhibition may contribute critically to the
generation of specific (narrowly tuned) excitatory responses (Somers et
al., 1995; Kyriazi et al., 1996). Second, active inhibition may be
necessary to prevent epileptic imbalances in activity because of
abundant positive feedback in cortical circuitry, which itself contributes critically to the sharpening of cortical responses (Somers
et al., 1995).
In barrel cortex recent evidence suggests that inhibition acts
nonspecifically to amplify contrast between weak and strong inputs
(Simons, 1995; Kyriazi et al., 1996). Local iontophoretic application
of GABA disproportionately suppresses, and similarly applied
bicuculline disproportionately enhances, responses to inputs that are
normally only weakly effective. These data suggest that bicuculline
produces a general increase in excitability that acts in
conjunction with neuronal nonlinearities to produce the ob
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