Download Identification of Basolateral Amygdala Projection Cells and

J Neurophysiol 96: 3257–3265, 2006;
doi:10.1152/jn.00577.2006.
Identification of Basolateral Amygdala Projection Cells and Interneurons
Using Extracellular Recordings
Ekaterina Likhtik,1 Joe Guillaume Pelletier,2 Andrei T. Popescu,1 and Denis Paré1
1
Center for Molecular and Behavioral Neuroscience, Rutgers, The State University of New Jersey, Newark, New Jersey; and 2Département
de Physiologie, Université de Montréal, Montreal, Quebec, Canada
Submitted 2 June 2006; accepted in final form 10 July 2006
Likhtik, Ekaterina, Joe Guillaume Pelletier, Andrei T. Popescu, and
Denis Paré. Identification of basolateral amygdala projection cells and
interneurons using extracellular recordings. J Neurophysiol 96:
3257–3265, 2006; doi:10.1152/jn.00577.2006. This study tested whether
firing rate and spike shape could be used to distinguish projection cells
from interneurons in extracellular recordings of basolateral amygdala
(BLA) neurons. To this end, we recorded BLA neurons in isofluraneanesthetized animals with tungsten microelectrodes. Projection cells were
identified by antidromic activation from cortical projection sites of the
BLA. Although most projection cells fired spontaneously at low rates
(⬍1 Hz), an important subset fired at higher rates (up to 6.8 Hz). In fact,
the distribution of firing rates in projection cells and unidentified BLA
neurons overlapped extensively, even though the latter cell group presumably contains a higher proportion of interneurons. The only difference between the two distributions was a small subset (5.1%) of unidentified neurons with unusually high firing rates (9 –16 Hz). Similarly,
distributions of spike durations in both cell groups were indistinguishable,
although most of the fast-firing neurons had spike durations at the low
end of the distribution. However, we observed that spike durations
depended on the exact position of the electrode with respect to the
recorded cell, varying by as much as 0.7 ms. Thus neither firing rate nor
spike waveform allowed for unequivocal separation of projection cells
from interneurons. Nevertheless, we propose the use of two firing rate
cutoffs to obtain relatively pure samples of projection cells and interneurons: ⱕ1 Hz for projection cells and ⱖ7 Hz for fast-spiking interneurons.
Supplemented with spike-duration cutoffs of ⱖ0.7 ms for projection cells
and ⱕ0.5 ms for interneurons, this approach should keep instances of
misclassifications to a minimum.
The role of the basolateral amygdala (BLA) in the formation
of appetitive and aversive memories continues to attract much
interest (see Baxter and Murray 2002; Everitt et al. 2003;
Fanselow and Poulos 2005; LeDoux 2000; McGaugh 2004;
Phelps 2006; Walker and Davis 2002). One of the techniques
often used to investigate these questions involves extracellular
neuronal recordings (reviewed in Holland and Gallagher 2004;
Maren and Quirk 2004; Pelletier et al. 2005). However, the
BLA contains multiple cell types, including a prevalent group
of projection cells and multiple subtypes of local-circuit
GABAergic neurons (McDonald 1992), complicating the interpretation of extracellular data. This problem is compounded
by the fact that, in contrast with the hippocampus where
neurons are segregated in a laminar fashion, different cell types
are intermingled in the BLA. As a result, the position of BLA
cells cannot be used to infer their identity. Thus it would be of
great practical interest to define a set of criteria that could be
used to distinguish projection cells from interneurons on the
basis of their extracellularly recorded activity.
Intracellular and patch-clamp studies of morphologically
identified BLA neurons offer clues as to what properties might
be useful to identify BLA cells. Studies performed in vivo
(Chen and Lang 2003; Lang and Paré 1997a,b, 1998; Paré et al.
1995) and in vitro (Faber and Sah 2002, 2003; Faber et al.
2001; Faulkner and Brown 1999; Mahanty and Sah 1998;
Martina et al. 2001; Pape et al. 1998; Rainnie et al. 1993;
Szinyei et al. 2000; Washburn and Moises 1992) indicate that
most principal cells have a comparatively hyperpolarized
membrane potential, show little spontaneous firing at rest, and
generate trains of long-duration action potentials that exhibit
various degrees of frequency adaptation. In contrast, a subtype
of BLA interneurons (fast-spiking cells), thought to express
parvalbumin and accounting for roughly 50% of BLA interneurons (Mascagni and McDonald 2003), have a comparatively more depolarized membrane potential, are often spontaneously active at rest, and generate trains of short-duration
spikes that generally display little or no spike frequency adaptation. The latter property is thought to result from the expression of K⫹ channels with fast activation kinetics (McDonald
and Mascagni 2006).
In addition to their distinct intrinsic membrane properties,
synaptic factors also contribute to make interneurons more
active than projection cells. Indeed, intracellular studies in vivo
(Lang and Paré 1997a,b) revealed that the spontaneous activity
of projection cells is dominated by large-amplitude hyperpolarizing potentials generated by a combination of synaptic
␥-aminobutyric acid types A and B (GABAA and GABAB,
respectively) conductances (Rainnie et al. 1991; Washburn and
Moises 1992) and by a synaptically activated Ca2⫹-dependent
K⫹ current (Chen and Lang 2003; Danober and Pape 1998;
Faber et al. 2005; Lang and Paré 1997b). In contrast, fastspiking interneurons lack GABAB receptors (Huttman et al.
2006; Martina et al. 2001) and their GABAA reversal potential
is ⬎15 mV positive to that of projection cells, a difference
reflecting the presence of cotransporters that accumulate chloride in fast-spiking interneurons and extrude chloride in projection cells (Martina et al. 2001).
Together, these contrasting patterns of GABA responsiveness and intrinsic properties should conspire to make projection cells less active than fast-spiking interneurons. Thus differences in firing rates and action potential duration might
Address for reprint requests and other correspondence: D. Paré, CMBN,
Aidekman Research Center, Rutgers, The State University of New Jersey, 197
University Avenue, Newark, NJ 07102 (E-mail: [email protected]).
The costs of publication of this article were defrayed in part by the payment
of page charges. The article must therefore be hereby marked “advertisement”
in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
INTRODUCTION
www.jn.org
0022-3077/06 $8.00 Copyright © 2006 The American Physiological Society
3257
3258
LIKHTIK, PELLETIER, POPESCU, AND PARÉ
FIG. 1. Categorization criteria for our sample.
A: example of BL neuron that was orthodromically
activated by insula (INS) stimuli but could not be
antidromically activated from any of the tested
cortical stimulation sites. This cell was therefore
classified as “unidentified.” A1: 23 superimposed
traces. A2: peristimulus histogram of responses
evoked from the insula, but based on a larger
number of trials. B: BL neuron that could be antidromically activated from the infralimbic (IL, B1)
and perirhinal (PRH, B2) cortex. This cell was
therefore classified as a projection cell. Note fixed
latency of antidromic spikes (B1 and B2, 10 superimposed sweeps in both cases) and collision with
spontaneously occurring spikes (B3). Arrowheads
in A and B mark the stimulus artifacts. C: frequency
distribution of firing rates in our entire sample (C1),
among physiologically identified projection cells
(C2) and in the unidentified neurons (C3). Firing
rates of projection cells and unidentified neurons
overlap extensively, with the exception of a few
unidentified cells that exhibited much higher firing
rates (C3).
constitute useful criteria to distinguish these two BLA cell
types. The present study tested this possibility using extracellular recordings of BLA cells, some of which could be unambiguously identified as projection cells using antidromic activation from known cortical projection sites of the BLA.
METHODS
All procedures were approved by the Institutional Animal Care and
Use committee of Rutgers State University, in compliance with the
Guide for the Care and Use of Laboratory Animals (Department of
Health and Human Services). Adult male cats (2.5–3.5 kg) were
preanesthetized with a mixture of ketamine and xylazine [15 and 2
mg/kg, administered intramuscularly (im)] and artificially ventilated
with a mixture of ambient air, oxygen, and isoflurane. Atropine (0.05
mg/kg, im) was administered to prevent secretions. The end-tidal
concentration in CO2 was kept at 3.7 ⫾ 0.2% and the body temperature was maintained at 37–38°C with a heating pad. The level of
anesthesia was assessed by continuously monitoring the electroencephalogram and electrocardiogram.
The bone overlying the regions of interest was removed and the
dura mater opened. High-impedance (10- to 12-M⍀) tungsten microelectrodes (FHC, Bowdoin, ME) were lowered to the basolateral
nucleus (BL) using a micromanipulator. To physiologically identify
BL projection cells, concentric stimulating electrodes were stereotaxically inserted in several cortical projection sites of the cat BL nucleus
including the perirhinal cortex, infralimbic cortex, and insula (Krettek
and Price 1977a,b; Paré et al. 1995; Room and Groenewegen 1986;
Smith and Paré 1994). To be classified as antidromic, evoked unit
responses had to meet at least two of the following three criteria
(Lipski 1981): 1) stable latency (⬍0.3-ms jitter), 2) collision with
orthodromically evoked or spontaneously occurring spikes, and 3)
ability to respond faithfully to high-frequency stimuli (300 Hz). The
spontaneous and evoked activity of responsive neurons was observed
on a digital oscilloscope, digitized at 20 kHz, and stored on disk for
off-line analysis. We considered only neurons generating spikes with
a high signal-to-noise ratio (ⱖ3).
To determine whether the duration of action potentials constituted
a fixed property of each neuron or whether it depended on the exact
position of the microelectrode tip with respect to the recorded cell, we
J Neurophysiol • VOL
performed a “distance test.” In this test, we first located a wellisolated, spontaneously active neuron and then moved the electrode
back in 5-␮m steps over a distance of ⱕ110 ␮m from the recording
site where the highest signal-to-noise ratio was observed.
At the end of experiments, the animals were administered an
overdose of sodium pentobarbital (50 mg/kg, administered intravenously) and extracellular recording sites were marked with electrolytic lesions (0.6 mA for 10 s). The brains were then taken out, fixed
in 2% paraformaldehyde and 1% glutaraldehyde, sectioned on a
vibrating microtome (at 100 ␮m), and stained with neutral red or
cresyl violet to reveal electrode placements. The microelectrode tracks
were reconstructed by combining the micrometer readings and histology. To be included in the analysis, cells had to be histologically
confirmed as being located in the BL nucleus.
Analyses were performed off-line with commercial software (IGOR,
WaveMetrics, Lake Oswego, OR; Matlab, The MathWorks, Natick, MA)
and custom-designed software running on personal computers. Spikes
were detected using a window discriminator after digital filtering of the
raw waves. All values are expressed as means ⫾ SE.
RESULTS
Spontaneous firing rates of BL neurons
To minimize the risk of missing BL neurons with low
spontaneous firing rates, microelectrodes were lowered gradually while applying electrical stimuli to the various stimulation
sites (0.3 Hz, 0.1– 0.5 mA, 100 –300 ␮s). When an ortho- (Fig.
1A) or antidromically (Fig. 1B) responsive and/or spontaneously active BL neuron with a high signal-to-noise ratio was
encountered, a 3-min period of spontaneous activity was recorded. Then, its responsiveness to perirhinal, insular, and
infralimbic stimuli was tested. In this case, our goal was to
determine whether antidromic discharges could be elicited
from one or more of these stimulation sites, to formally
establish whether this cell was a projection neuron. It should be
stressed that this technique is prone to false negatives because
failure to antidromically activate a cell might not mean that the
recorded cell is a local-circuit neuron but a projection cell
96 • DECEMBER 2006 •
www.jn.org
EXTRACELLULAR IDENTIFICATION OF BLA CELLS
3259
FIG. 2. Some physiologically identified
BL projection cells exhibit high spontaneous
firing rates. A: example of BL neuron that
was physiologically identified as a projection
cell on the basis of its antidromic responses
to stimulation of the insula. Note fixed latency of antidromic spikes (A1, 10 sweeps
overlaid) and collision with spontaneous
spikes (A2). B: spontaneous activity of the
same neuron. B1: sample of spontaneous
activity. This cell had a firing rate of 6.8 Hz.
B2 and B3: interspike interval histograms
with bins of 10 ms (B2) and 2 ms (B3). In
this cell, spike duration was 0.82 ms when
measured between the negative and positive
peaks with 0.1- to 10-kHz filtering. Abbreviations: X, average interspike-interval; N,
number of intervals in the depicted range.
whose axon does not end (or course) in close proximity to the
stimulating electrodes. Nonetheless, a positive finding, that is
the presence of electrically evoked antidromic discharges, can
be interpreted as unambiguous evidence that the recorded cell
is a projection neuron.
Figure 1B shows an example of a BL neuron that could be
positively identified as such because electrical stimulation of
the infralimbic (Fig. 1B1) or perirhinal (Fig. 1B2) cortices
evoked antidromic spikes characterized by a fixed latency and
collision with spontaneously occurring action potentials (Fig.
1B3). Overall, 52.6% of recorded BL neurons (or 41 of 78)
were identified as projection cells using this technique. Most of
these cells could be antidromically activated from only one of
the tested sites (insula, 11.5%; infralimbic, 26.9%; perirhinal,
8.9%), but 5.1% projected to at least two of the tested sites, as
in the example of Fig. 1B.
Next, we compared the spontaneous firing rate of physiologically identified projection cells to that of unidentified
neurons. We reasoned that because this second group of BL
cells should contain a higher proportion of interneurons, differences in the distribution of firing rates between the two
groups might allow us to derive criteria that could be used to
distinguish projection cells from interneurons. Figure 1C
shows the distribution of firing rates in the entire sample (Fig.
1C1), in the subset of physiologically identified projection cells
(Fig. 1C2), and in the unidentified neurons (Fig. 1C3).
The spontaneous firing rates of BL cells ranged from 0 to 16
Hz (average 2.1 ⫾ 0.4 Hz; Fig. 1C1). Although nearly 51.2%
of projection cells fired ⱕ1 Hz (Fig. 1C2), we encountered
projection cells with spontaneous firing rates as high as 6.8 Hz
(Fig. 2). Overall, projection cells had a slightly lower average
firing rate (1.3 ⫾ 0.3 Hz, range, 0 – 6.8 Hz) than that of
unidentified neurons (3.1 ⫾ 0.7 Hz; Mann–Whitney U test,
J Neurophysiol • VOL
P ⬍ 0.05). Yet, distributions of firing rates in the two groups
overlapped extensively (Fig. 1, C2 and C3), with the exception
of a few unidentified neurons (5.1% of entire sample) with
unusually high rates of spontaneous firing (9.9 –16 Hz). Thus
these results suggest that a cutoff firing frequency of 7 Hz
might be used to distinguish projection cells from fast-spiking
interneurons. However, because counts of parvalbumin immunoreactive cells predict that random samples of BL neurons
will include 10% of fast-spiking cells (as estimated in rats;
Mascagni and McDonald 2003), and not 5.1% as in our
sample, a 7-Hz cutoff is likely to lead to the erroneous
classification of nearly half the fast-spiking cells as projection
neurons. Moreover, considering that extracellular recordings
are biased toward neurons with high firing rates, the latter
figure likely underestimates the incidence of misclassifications.
Spike durations in BL neurons
Because the above analyses suggest that firing rate is a
criterion of limited sensitivity to distinguish projection cells
from interneurons, we next turned our attention to spike duration. As reviewed in the INTRODUCTION, intracellular and patchclamp studies of morphologically identified BL neurons indicate that spike duration (typically measured at half-amplitude)
is significantly longer in projection cells (1.0 –1.5 ms) compared with fast-spiking interneurons (0.5– 0.7 ms). However,
these recordings are typically obtained from the soma of BL
cells. This differs from extracellular recordings where the
microelectrode tip (⬍1 ␮m) can be positioned at any point
around the axonal, somatic, or dendritic compartments of BL
cells.
In patch-clamp studies of cortical pyramidal cells, spike
durations were shown to vary depending on the cellular com-
96 • DECEMBER 2006 •
www.jn.org
3260
LIKHTIK, PELLETIER, POPESCU, AND PARÉ
A
10 µm
40 µm
80 µm
90 µm
100 µm
110 µm
0.2 mV
0 µm
B
1.6
1.5
0.9
1.4
0.8
1.3
0.7
1.2
0.6
0
0.5 ms
IS
0 µm
-110 µm
0.1 mV
1.7
1.0
0.2 mV
1.1
Spike amplitude (mV)
Spike duration (ms)
0.5 ms
40
80
120
Distance (µm)
partment from which the recording was obtained. In particular,
dendritic spikes generally last longer (sometimes twice as
much) than somatic spikes (e.g., see Gulledge and Stuart 2003;
Larkum et al. 2001). In addition, extracellularly recorded
axonal spikes were reported to be briefer (0.4 – 0.7 ms) than
somatodendritic spikes in multiple cell types (reviewed in
Lemon 1984).
Thus there is reason to suspect that the spikes generated by
single cells can have different durations depending on the
cellular compartment that is sampled by extracellular microelectrodes, a contention supported by a recent modeling study
(Gold et al. 2006). As a result, for interneurons and projection
cells to be reliably distinguished on this basis, within-cell
variations in spike durations arising from electrode location
must be smaller that the variations between cell types. To test
whether this is the case in the BL nucleus, we thus performed
experiments designed to estimate these two variables.
To assess the impact of microelectrode location on spike
duration, we performed a “distance test.” In this test, spikes
generated by spontaneously active BL cells (n ⫽ 11) were
recorded as the electrode was moved in 5-␮m steps over a
distance of ⱕ110 ␮m from the recording site where the highest
signal-to-noise ratio was observed. To minimize the risk of
confusing the action potentials generated by different cells, we
considered recordings where only one spontaneously active
neuron was present and where the signal-to-noise ratio was
⬎5. Last, we limited our attention to cells generating primarily
negative spikes followed by a slower positive potential, the
most frequently encountered spike shape in extracellular recordings. Spike duration was defined as the interval between
the negative and positive peaks because this method proved
useful to distinguish principal cells from interneurons in the
neocortex (Barthó et al. 2004) and hippocampus (Csicsvari et
J Neurophysiol • VOL
FIG. 3. Spike duration varies depending on the
microelectrode position with respect to the recorded
cell. A: examples of action potentials recorded at
various positions (numbers) around the same cell.
Initial recording is marked 0 ␮m. Electrode was
withdrawn gradually ⱕ110 ␮m from the original site.
It should be noted that, even though the electrode was
moved 110 ␮m, the total travel distance between the
electrode tip and recorded cell was probably lower
because the movement of the electrode likely caused
the tissue to move with it. B: graph plotting spike
duration (left axis, filled circles) and amplitude (right
axis, empty squares) as a function of electrode distance from original site (x-axis). Changes are illustrated with an expanded superimposition of the spikes
recorded at these various positions. Values marked
by the red and black circles in the graph on the left,
respectively, correspond to the spikes drawn with red
and thick black traces. Bottom right: scheme showing
hypothesized trajectory of the electrode in relation to
the recorded cell. Because spikes recorded toward the
end of the trajectory (red circle) were briefer and
exhibited a more obvious break between the initial
segment (IS) and somatodendritic (SD) components
than those recorded at the original site (black circle),
we presume that the thinner spikes were recorded in
proximity to the initial axonal segment, whereas the
longest one was recorded in the dendrites. That initial
segment spikes are generally of lower amplitude than
somatodendritic spikes was established in experiments where simultaneous extra- and intracellular
recordings of action potentials were performed (Terzuolo and Araki 1961).
al. 1999) where, in simultaneous extra- and intracellular recordings, a close correspondence was found between this
parameter and the duration of intracellularly recorded action
potentials.
Figure 3 shows examples of spikes recorded at seven different locations around a representative cell with a slow (Fig.
3A) and a fast time base (Fig. 3B). Throughout the distance
test, the spike amplitude remained well above noise and firing
rates remained stable. The left inset of Fig. 3B plots spike
duration (left axis) and amplitude (right axis) as a function of
distance from the original recording site. In this cell, spike
durations changed by as much as 45% from the first (black
line) to the last recording site (red line). Overall, depending on
the electrode position relative to recorded cells, spike durations
varied by as much as 45.5 ⫾ 4.5% (range 17– 66%) of the
longest measured value (1.5 ⫾ 0.67 ms). Thus these observations imply that depending on the position of the recording
electrode, one can expect maximal within-cell variations of
spike durations of about 0.70 ms.
Next, we compared within-cell variations in spike durations arising from electrode position to the variability seen
between different neurons. With respect to firing rates, we
compared the subset of neurons that were physiologically
identified as projection cells to the group of unidentified
neurons that presumably included a higher proportion of
local-circuit cells. However, measuring spike durations in
extracellular recordings is not trivial because spike shapes
vary. Although most cells generated primarily negativegoing action potentials (72.2%, as in Fig. 3), some generated
primarily positive-going spikes (18.1%), and others positive-negative ones (9.7%). Importantly, the particular spike
shape was not a specific property of each neuron because it
96 • DECEMBER 2006 •
www.jn.org
EXTRACELLULAR IDENTIFICATION OF BLA CELLS
A
B1
3261
B2
0.5 mV
1 ms
1 ms
2 ms
C1
E114
All Cells
Cell counts
8
6
4
6
4
2
0
0..0
0..5
1..0
1..5
2..0
2..5
Spike duration (ms)
10
E2
1
2
3
4
5
6
4
10
8
1..0
1..5
2..0
0
Spike duration (ms)
10
Unidentified Cells
X=0.85
8
6
4
2
2
4
6
8 10 12 14 16
F2
4
2..5
4..0
0
E314
1
2
3
4
5
Spike duration (ms)
12
Cell counts
D312
0..5
1..0
Firing rate (Hz)
6
0
0..0
Presumed ITN
1..5
Projection Cells
X=1.9
2
0
Unidentified Cell
0..5
12
2
Projection cell
Spike duration (ms)
14
Projection Cells
X=0.91
8
0
Unidentified Cells
X=1.85
10
8
6
Spike duration (ms)
Cell counts
8
0
D212
Cell counts
10
2
Cell counts
Cell counts
10
F1 2..0
All Cells
12
Spike duration (ms)
D112
C2
3..5
3..0
FIG. 4. Spike shape and duration are unreliable indicators of BL cell identity with highimpedance extracellular electrodes. A: spike
polarity, amplitude, and duration vary depending on the position of the microelectrode with
respect to the recorded cell. Overlaid examples
of spikes generated by the same cell, but recorded at different positions. B and C: 2 methods used to measure spike durations. B: in the
first method, spike duration was defined as the
interval between trough and peak for prevalently negative spikes (B1) or peak to trough
for prevalently positive spikes (B2). C: in the
second method, spike duration was defined as
the interval between the initial change from
baseline to return to baseline for both spike
shapes. D and E: frequency distribution of
spike durations as estimated with the first (D)
and second (E) method in our entire sample
(1), in the subset of physiologically identified
projection cells (2), and among the unidentified neurons (3). F: plots of spike duration
(y-axis) vs. firing rate (x-axis) using the first
(1) and second (2) method for measuring spike
duration. Different symbols are used depending on the identity of the cells: red circles for
physiologically identified projection cells,
black circles for unidentified cells, empty circles for cells presumed to be interneurons
because of their high spontaneous firing rates
(ⱖ7 Hz). Abbreviation: X, average spike
duration.
2..5
2..0
1..5
4
2
1..0
0
0
0..0
0..5
1..0
1..5
2..0
Spike duration (ms)
2..5
0
1
2
3
4
0
5
Spike duration (ms)
4
6
8 10 12 14 16
Firing rate (Hz
could change from one type to another when the electrode
was moved around the recorded cell (Fig. 4A).
This variability in spike shapes led us to measure spike
durations in two ways. In the first approach, spike durations
were measured as in the distance test (interval between the
negative and positive peaks for neurons generating primarily
negative action potentials, Fig. 4B1; interval between positive
and negative peaks for cells generating primarily positive
spikes, Fig. 4B2). In the second approach, spike duration was
defined as the interval between the initial change from baseline
to return to baseline (Fig. 4C), including all components of the
spike shape, as in previous studies on BL neurons (Rosenkranz
and Grace 1999, 2001). It should be noted that because this
approach includes the slower potential associated with spike
afterhyperpolarizations, it sometimes yields long spike durations (⬎4 ms).
Figure 4, D and E compares the distribution of spike durations as measured with the two methods in the entire sample of
BL neurons (Fig. 4, D1 and E1), in the subset of identified
projection cells (Fig. 4, D2 and E2), and in the unidentified
J Neurophysiol • VOL
2
neurons (Fig. 4, D3 and E3). The two methods yielded different ranges of values with a correlation coefficient of r ⫽ 0.65.
However, in both cases the distributions ranged widely, with
considerable overlap between projection cells and unidentified
neurons. The difference in average spike durations (listed in
the top right corner of each graph) between projection cells
and unidentified neurons did not reach significance with either
method (t-test, P ⬎ 0.05) and qualitatively identical results
were obtained with different filtering settings (1 Hz to 3 kHz
vs. 100 Hz to 10 kHz). Importantly, no correlation was observed between spike duration and firing rate with either
method (Fig. 4F). However, most of the cells with extremely
high firing rates did have spike durations at the lower end of the
distributions (empty circles in Fig. 4F).
To test whether taking into account the entire spike shape
rather than a particular attribute (such as duration) would better
differentiate between cell types, we performed principal-component analysis (PCA) on our sample. Previously, PCA was
used to search for better classification criteria than the immediately observable data points, solving the problem of reducing
96 • DECEMBER 2006 •
www.jn.org
3262
LIKHTIK, PELLETIER, POPESCU, AND PARÉ
data dimensionality (Harris et al. 2000). All spikes were
centered on their peak and the amplitudes normalized. Feature
vectors were extracted based on PCA and used to project the
original data (Fig. 5). The first 10 components (explaining 91%
of the variability) were used to cluster the spikes in two groups,
using the K-means method. In general, the first component
contained most of the information used to generate the two
groups. A ␹2 analysis revealed that the incidence of projection
cells and presumed interneurons did not differ between the two
groups (␹2 ⫽ 0.68, P ⫽ 0.41). Similarly, the ratio of projection
cells to presumed interneurons in the two groups did not differ
from that expected by chance on the basis of the incidence of
the two cell types reported in previous studies of GABA
immunoreactivity (group 1, ␹2 ⫽ 0.51, P ⫽ 0.48; group 2,
␹2 ⫽ 0.01, P ⫽ 0.77). Similar results were obtained when
using the first two to ten components. Overall, these observations are in keeping with the fact that spike shapes can change
depending on electrode position around recorded cells and thus
cannot serve as a reliable criterion to differentiate projection
cells from interneurons (Figs. 3 and 4A).
DISCUSSION
The present study was undertaken to test whether projection
cells and interneurons of the BL nucleus can be distinguished
reliably in extracellular recordings with high-impedance electrodes using criteria such as firing rate and spike waveform.
Given that a complete understanding of emotional learning will
require an analysis of the interaction between projection cells
and interneurons, developing reliable criteria for the extracellular identification of different BL cells types is of great
importance. In the following account, we will first consider
earlier attempts at classifying cell types on the basis of extracellular data and then consider the applicability and reliability
of such criteria in the BL nucleus.
1.5
100
70
40
10
Principal component #2
1
P
I
U
0.5
0
Group 2
Group 1
Projection Cells
Interneurons
Unidentified Cell Type
-0.5
-1
-1.5
-1
-0.5
0
0.5
1
1.5
Principal component #1
2
2.5
FIG. 5. Clustering of BL cells based on principal-component analysis of
spike shapes. Cells were separated in two groups (red symbols, group 1; black
symbols, group 2) based on the first 10 principal components (representing
most of the variability in spike shape), using the K-means technique (see text).
Graph plots first (x-axis) vs. second (y-axis) component for all recorded
neurons using different symbols depending on their identity (see legend on
bottom right). Inset: percentage of projection cells (P), presumed interneurons
(I, based on the 7-Hz cutoff in firing rate), and unidentified (U) cells in each
of the 2 groups (red bars, group 1; black bars, group 2).
J Neurophysiol • VOL
In some structures, extracellular data can be used to
distinguish different cell types
In a number of cortical areas and subcortical structures, it
was reported that different cell types have easily distinguishable firing properties. In the thalamus, for instance, cortically
projecting and reticular neurons can be distinguished by their
bursting behavior. Thalamocortical cells generate bursts with
interspike intervals of progressively increasing durations,
whereas reticular thalamic neurons produce bursts where successive interspike intervals initially show a progressive decrease and then a gradual increase in duration (Domich et al.
1986). Unfortunately, this criterion cannot be used in the
amygdala because bursting projection cells account for a minority of projection neurons (Paré and Gaudreau 1996) and
local-circuit cells with an indistinguishable burst-firing pattern
exist in the BL nucleus (Rainnie et al. 1993).
Other classification schemes have been based on the differential responsiveness of neurons to particular drugs. In the
substantia nigra, for instance, physiologically identified nigrostriatal neurons were reported to be inhibited by D2 receptor
agonists, whereas other types of nigral neurons were unresponsive (Hainsworth et al. 1991). This differential responsiveness
coincided with distinct electrophysiological signatures such as
long-duration action potentials (⬎2.5 ms) and low firing rates
in nigrostriatal cells compared with spikes of short-duration
and elevated basal discharge rates in presumed interneurons or
thalamically projecting cells (Deniau et al. 1978; Grace and
Bunney 1983; Guyenet and Aghajanian 1978; Hainsworth et
al. 1991; Paladini and Tepper 1999; Tepper et al. 1987).
However, to the best of our knowledge, there is no wellestablished pattern of pharmacological responsiveness that
could be used to distinguish BL projection cells and interneurons. Besides, routinely conducting such tests would be impractical.
In the hippocampus, where the position of recorded neurons
with respect to the pyramidal layer is already a strong indication of their identity, it was reported that a combination of
extracellular properties including spike duration, firing rate,
and pattern could be used to reliably distinguish pyramidal
cells from interneurons (e.g., see Csicsvari et al. 1999; Fox and
Ranck 1975). However, this task is more difficult in the
amygdala because interneurons and projection cells are intermingled and their dendrites are randomly oriented.
A possible solution to this problem was proposed by Buzsáki
and colleagues (Barthó et al. 2004). By performing highdensity neuronal recordings with silicon probes in layer 5 of
the somatosensory cortex, this group examined the functional
interactions between simultaneously recorded neurons and
identified a subset of cells whose activity was negatively
correlated to that of simultaneously recorded neurons. Such
cells were regarded as putative GABAergic interneurons,
whereas cells with positively correlated activity were classified
as pyramidal neurons. The spike shapes of these presumed
interneurons and projection cells were then used as templates
to classify all recorded cells. Spike duration (defined as the
interval between the negative and positive peaks, as in the
present study) measured from unfiltered data (1 Hz to 5 kHz)
was found to be the most reliable criterion to distinguish the
two cell types.
96 • DECEMBER 2006 •
www.jn.org
EXTRACELLULAR IDENTIFICATION OF BLA CELLS
Previous attempts to distinguish BL projection cells versus
interneurons with extracellular data
Although many investigators have conducted extracellular
recordings in the BL nucleus, to the best of our knowledge,
only two groups have proposed criteria to distinguish projection cells from interneurons (Paré and Gaudreau 1996; Rosenkranz and Grace 1999, 2001). Both classifications were based
on firing rate. In the classification of Rosenkranz and Grace
(1999, 2001), neurons with firing rates ⬍0.5 Hz were classified
as projection cells, whereas neurons with higher discharge
rates where classified as interneurons. This bipartite classification of firing rate was reported to coincide with a bimodal
distribution of spike durations (Rosenkranz and Grace 1999,
2001) where projection cells generated spikes of longer duration than interneurons. In contrast, Paré and Gaudreau (1996)
proposed a cutoff firing rate of 10 Hz but did not examine the
relation between firing rate and spike durations.
Applied to the present sample of BL neurons, the criteria
proposed by Rosenkranz and Grace would have led to the
erroneous classification of many projection cells as interneurons. Indeed, although the present study and a previous one in
unanesthetized animals (Paré and Gaudreau 1996) reported that
many projection cells fire at extremely low rates, more than
half of them had firing rates ⬎0.5 Hz, with some firing at rates
as high as 6.8 Hz. Also, spike durations were not distributed
bimodally in our sample and several physiologically identified
projection cells had relatively short spike durations. Consistent
with this, the sample of BL cells described by Rosenkranz and
Grace (1999) contained a high proportion of presumed interneurons, roughly threefold higher than that expected on the
basis of counts of GABA immunoreactive neurons in the rat
BL nucleus (Mascagni and McDonald 2003).
However, methodological differences might account for
some of the discrepancies between these studies. First, Rosenkranz and Grace (1999, 2001) used a different anesthetic
(chloral hydrate) that might have reduced the excitability of
projection cells. Second, our study was conducted in cats,
whereas Rosenkranz and Grace (1999, 2001) used rats. Given
that the rat BL nucleus was reported to contain a higher
proportion of principal cells with rapid spike frequency adaptation (Sah et al. 2003), this fact might also explain the higher
proportion of principal cells with low firing rates in the studies
reported by Rosenkranz and Grace (1999, 2001). In contrast,
there is no evidence in the literature to explain the extremely
high proportion of fast-spiking cells they observed. Although
the percentage of GABAergic interneurons in the cat amygdala
is unknown, observations in primates (McDonald and Augustine 1993) suggest it increases with phylogeny, as was observed in the thalamus (Arcelli et al. 1997; Winer and Larue
1996) and cortex (Fairen et al. 1984; Jones 1993; Rakic 1975).
3263
Similarly, there was much overlap between the distribution
of spike durations seen in physiologically identified projection
cells and unidentified neurons. As mentioned earlier, neurons
with high firing rates did tend to generate spikes of short
duration, but so did many projection cells. Although this seems
to contradict earlier intracellular and patch-clamp studies (see
references in INTRODUCTION), we believe the discrepancy reflects the fact that high-impedance extracellular electrodes can
be located at any point along the soma, dendrites, or axons of
recorded cells, whereas intracellular and patch-clamp measurements are typically obtained from the soma. Consistent with
this, we observed that spike shapes and durations were not a
fixed property of each cell but that they varied depending on
the electrode position with respect to the recorded cell.
Thus our results suggest that the only conclusive way to
identify projection cells remains antidromic activation from BL
projection sites. By reason of the fact that projection cells
account for most BL neurons, a majority of unidentified neurons, especially those with low firing rates, are likely projection
cells. In contrast, instances of misclassifications will likely be
higher for interneurons. Using a cutoff of 7 Hz on our data
implies that only about 5% of recorded cells were interneurons.
This figure is roughly half the incidence expected on the basis
of previous immunohistochemical studies, even ignoring the
fact that extracellular recordings are biased toward cells with
high firing rates. These considerations lead us to conclude that
with a cutoff firing rate of 7 Hz, the rate of false positive
identification of fast-spiking neurons is probably low, but that
the rate of false negatives is probably very high, around 50%.
This high rate of false negatives might be explained by the fact
that there are multiple physiological subtypes of parvalbuminpositive interneurons in the BL nucleus. In support of this
contention, Woodruff and Sah (2005) reported that ⬎20% of
parvalbumin-positive BL interneurons have a regular firing
pattern that is indistinguishable from that of projection cells,
Misclassification using firing rates and spike durations
In the present study, comparing the distributions of firing
rates in physiologically identified versus unidentified BL neurons revealed only one nonoverlapping region: neurons with
firing rates ⬎7 Hz (seen only in the unidentified neurons). It is
therefore probable that cells with such high firing rates are
interneurons. This contention is supported by the fact that most
of these cells had spike durations at the low end of the
distributions.
J Neurophysiol • VOL
FIG. 6. Estimated probability that a BL cell is a projection cell or interneuron depending on its firing rate. Exponential curves were fitted to the
frequency distribution of firing rates among projection cells and divided by the
number of recorded cells firing at each frequency. Thus the black line
represents the probability that a cell at a particular firing rate is a projection
cell. Dashed line is the inverse of the solid line and thus an estimate that cells
with a particular firing rate are interneurons.
96 • DECEMBER 2006 •
www.jn.org
3264
LIKHTIK, PELLETIER, POPESCU, AND PARÉ
except for the short duration of the action potentials they
generate.
In an attempt to describe the probability that a cell with a
particular firing rate is a projection cell or an interneuron, we
fitted exponential curves to the distribution of firing rates for
both identified projection cells and the whole neuronal population in our data. Figure 6 (continuous line) shows the ratio of
known projection cells to the entire population at all encountered frequencies. The one’s complement of this probability
curve yields an estimate (dashed line) of the likelihood that
neurons with particular firing rates are interneurons. On this
basis, we propose that the optimal approach to obtain pure
samples of projection cells and interneurons is to use two firing
cutoffs: ⱕ1 Hz for projection cells and ⱖ7– 8 Hz for fastspiking interneurons. Indeed, the probability functions of Fig.
6 suggest that, at a cutoff frequency of 7.4 Hz, the probability
of incorrectly identifying an interneuron as a projection cell is
⬍5%. The incidence of misclassifications should be very low
with this approach, especially if used in combination with two
spike duration cutoffs (ⱖ0.7 ms for projection cells and ⱕ0.5
ms for fast-spiking interneurons). The drawback of this approach is that it forces investigators to ignore many of the
recorded cells. On the other hand, one can be confident about
the identity of recorded neurons. A challenge for future studies
will be to test the validity of this approach by directly identifying the morphology of presumed interneurons using juxtacellular labeling (Pinault 1996) of BLA neurons with neurobiotin.
ACKNOWLEDGMENTS
We thank J. Tepper and members of the Paré lab for comments on an earlier
version of this manuscript.
GRANTS
This work was supported by National Science Foundation Grant 0208712
and by National Institute of Mental Health (NIMH) Grants R01MH-073610-01
to D. Paré and NIMH National Research Service Award Grant 1 F31 MH076415-01 to E. Likhtik.
REFERENCES
Arcelli P, Frassoni C, Regondi MC, De Biasi S, and Spreafico R. GABAergic neurons in mammalian thalamus: a marker of thalamic complexity?
Brain Res Bull 42: 27–37, 1997.
Barthó P, Hirase H, Monconduit L, Zugaro M, Harris KD, and Buzsáki G.
Characterization of neocortical principal cells and interneurons by network
interactions and extracellular features. J Neurophysiol 92: 600 – 608, 2004.
Baxter MG and Murray EA. The amygdala and reward. Nat Rev Neurosci 3:
563–573, 2002.
Chen JC and Lang EJ. Inhibitory control of rat lateral amygdaloid projection
cells. Neuroscience 121: 155–166, 2003.
Csicsvari J, Hirase H, Czurko A, Mamiya A, and Buzsáki G. Oscillatory
coupling of hippocampal pyramidal cells and interneurons in the behaving
rat. J Neurosci 19: 274 –287, 1999.
Danober L and Pape HC. Mechanisms and functional significance of a slow
inhibitory potential in neurons of the lateral amygdala. Eur J Neurosci 10:
853– 867, 1998.
Deniau JM, Hammond C, Riszk A, and Feger J. Electrophysiological
properties of identified output neurons of the rat substantia nigra (pars
compacta and pars reticulata): evidences for the existence of branched
neurons. Exp Brain Res 32: 409 – 422, 1978.
Domich L, Oakson G, and Steriade M. Thalamic burst patterns in the
naturally sleeping cat: a comparison between cortically projecting and
reticularis neurones. J Physiol 379: 429 – 449, 1986.
Everitt BJ, Cardinal RN, Parkinson JA, and Robbins TW. Appetitive
behavior: impact of amygdala-dependent mechanisms of emotional learning. Ann NY Acad Sci 985: 233–250, 2003.
J Neurophysiol • VOL
Faber ES, Callister RJ, and Sah P. Morphological and electrophysiological
properties of principal neurons in the rat lateral amygdala in vitro. J Neurophysiol 85: 714 –723, 2001.
Faber ES, Delaney AJ, and Sah P. SK channels regulate excitatory synaptic
transmission and plasticity in the lateral amygdala. Nat Neurosci 8: 635–
641, 2005.
Faber ES and Sah P. Physiological role of calcium-activated potassium
currents in the rat lateral amygdala. J Neurosci 22: 1618 –1628, 2002.
Faber ES and Sah P. Ca2⫹-activated K⫹ (BK) channel inactivation contributes to spike broadening during repetitive firing in the rat lateral amygdala.
J Physiol 552: 483– 497, 2003.
Fairen A, DeFelipe J, and Regidor J. Nonpyramidal neurons; general
account. In: Cellular Components of the Cerebral Cortex, edited by Peters
A and Jones EG. New York: Plenum, 1984, p. 201–253.
Fanselow MS and Poulos AM. The neuroscience of mammalian associative
learning. Annu Rev Psychol 56: 207–234, 2005.
Faulkner B and Brown TH. Morphology and physiology of neurons in the rat
perirhinal-lateral amygdala area. J Comp Neurol 411: 613– 642, 1999.
Fox SE and Ranck JB Jr. Localization and anatomical identification of theta
and complex spike cells in dorsal hippocampal formation of rats. Exp Neurol
49: 299 –313, 1975.
Gold C, Henze DA, Koch C, and Buzsáki G. On the origin of the extracellular action potential waveform: a modeling study. J Neurophysiol 95:
3113–3128, 2006.
Grace AA and Bunney BS. Intracellular and extracellular electrophysiology
of nigral dopaminergic neurons—1. Identification and characterization.
Neuroscience 10: 301–315, 1983.
Gulledge AT and Stuart GJ. Excitatory actions of GABA in the cortex.
Neuron 37: 299 –309, 2003.
Guyenet PG and Aghajanian GK. Antidromic identification of dopaminergic
and other output neurons of the rat substantia nigra. Brain Res 150: 69 – 84,
1978.
Hainsworth AH, Roper J, Kapoor R, and Ashcroft FM. Identification and
electrophysiology of isolated pars compacta neurons from guinea-pig substantia nigra. Neuroscience 43: 81–93, 1991.
Harris KD, Henze DA, Csicsvari J, Hirase H, and Buzsáki G. Accuracy of
tetrode spike separation as determined by simultaneous intracellular and
extracellular measurements. J Neurophysiol 84: 401– 414, 2000.
Holland PC and Gallagher M. Amygdala-frontal interactions and reward
expectancy. Curr Opin Neurobiol 14: 148 –155, 2004.
Huttmann K, Yilmazer-Hanke D, Seifert G, Schramm J, Pape HC, and
Steinhauser C. Molecular and functional properties of neurons in the
human lateral amygdala. Mol Cell Neurosci 31: 210 –217, 2006.
Jones EG. GABAergic neurons and their role in cortical plasticity in primates.
Cereb Cortex 3: 361–372, 1993.
Krettek JE and Price JL. Projections from the amygdaloid complex to the
cerebral cortex and thalamus in the rat and cat. J Comp Neurol 172:
687–722, 1977a.
Krettek JE and Price JL. Projections from the amygdaloid complex and
adjacent olfactory structures to the entorhinal cortex and to the subiculum in
the rat and cat. J Comp Neurol 172: 723–752, 1977b.
Lang EJ and Paré D. Similar inhibitory processes dominate the responses of
cat lateral amygdaloid projection neurons to their various afferents. J Neurophysiol 77: 341–352, 1997a.
Lang EJ and Paré D. Synaptic and synaptically activated intrinsic conductances underlie inhibitory potentials in cat lateral amygdaloid projection
neurons in vivo. J Neurophysiol 77: 353–363, 1997b.
Larkum ME, Zhu JJ, and Sakmann B. Dendritic mechanisms underlying the
coupling of the dendritic with the axonal action potential initiation zone of
adult rat layer 5 pyramidal neurons. J Physiol 533: 447– 466, 2001.
LeDoux JE. Emotion circuits in the brain. Annu Rev Neurosci 23: 155–184,
2000.
Lemon R. Methods for Neuronal Recordings in Conscious Animals. Chichester, UK: Wiley, 1984.
Lipski J. Antidromic activation of neurones as an analytic tool in the study of
the central nervous system. J Neurosci Methods 4: 1–32, 1981.
Mahanty NK and Sah P. Calcium-permeable AMPA receptors mediate
long-term potentiation in interneurons in the amygdala. Nature 394: 683–
687, 1998.
Maren S and Quirk GJ. Neuronal signaling of fear memory. Nat Rev
Neurosci 5: 844 – 852, 2004.
Martina M, Royer S, and Paré D. Cell-type-specific GABA responses and
chloride homeostasis in the cortex and amygdala. J Neurophysiol 86:
2887–2895, 2001.
96 • DECEMBER 2006 •
www.jn.org
EXTRACELLULAR IDENTIFICATION OF BLA CELLS
Mascagni F and McDonald AJ. Immunohistochemical characterization of
cholecystokinin containing neurons in the rat basolateral amygdala. Brain
Res 976: 171–184, 2003.
McDonald AJ. Projection neurons of the basolateral amygdala: a correlative
Golgi and retrograde tract tracing study. Brain Res Bull 28: 179 –185, 1992.
McDonald AJ and Augustine JR. Localization of GABA-like immunoreactivity in the monkey amygdala. Neuroscience 52: 281–294, 1993.
McDonald AJ and Mascagni F. Differential expression of Kv3.1b and Kv3.2
potassium channel subunits in interneurons of the basolateral amygdala.
Neuroscience 138: 537–547, 2006.
McGaugh JL. The amygdala modulates the consolidation of memories of
emotionally arousing experiences. Annu Rev Neurosci 27: 1–28, 2004.
Paladini CA and Tepper JM. GABA(A) and GABA(B) antagonists differentially affect the firing pattern of substantia nigra dopaminergic neurons in
vivo. Synapse 32: 165–176, 1999.
Pape HC, Paré D, and Driesang RB. Two types of intrinsic oscillations in
neurons of the lateral and basolateral nuclei of the amygdala. J Neurophysiol
79: 205–216, 1998.
Paré D and Gaudreau H. Projection cells and interneurons of the lateral and
basolateral amygdala: distinct firing patterns and differential relation to theta
and delta rhythms in conscious cats. J Neurosci 16: 3334 –3350, 1996.
Paré D, Pape HC, and Dong J. Bursting and oscillating neurons of the cat
basolateral amygdaloid complex in vivo: electrophysiological properties and
morphological features. J Neurophysiol 74: 1179 –1191, 1995.
Pelletier JG, Likhtik E, Filali M, and Paré D. Lasting increases in basolateral amygdala activity after emotional arousal: implications for facilitated
consolidation of emotional memories. Learn Mem 12: 96 –102, 2005.
Phelps EA. Emotion and cognition: insights from studies of the human
amygdala. Annu Rev Psychol 57: 27–53, 2006.
Pinault D. A novel single-cell staining procedure performed in vivo under
electrophysiological control: morpho-functional features of juxtacellularly
labeled thalamic cells and other central neurons with biocytin or neurobiotin.
J Neurosci Methods 65: 113–136, 1996.
Rainnie DG, Asprodini EK, and Shinnick-Gallagher P. Excitatory transmission in the basolateral amygdala. J Neurophysiol 66: 986 –998, 1991.
Rainnie DG, Asprodini EK, and Shinnick-Gallagher P. Intracellular recordings from morphologically identified neurons of the basolateral amygdala.
J Neurophysiol 69: 1350 –1362, 1993.
J Neurophysiol • VOL
3265
Rakic P. Local circuit neurons. Neurosci Res Prog Bull 13: 295– 416, 1975.
Room P and Groenewegen HJ. Connections of the parahippocampal cortex
in the cat. II. Subcortical afferents. J Comp Neurol 251: 451– 473, 1986.
Rosenkranz JA and Grace AA. Modulation of basolateral amygdala neuronal
firing and afferent drive by dopamine receptor activation in vivo. J Neurosci
19: 11027–11039, 1999.
Rosenkranz JA and Grace AA. Dopamine attenuates prefrontal cortical
suppression of sensory inputs to the basolateral amygdala of rats. J Neurosci
21: 4090 – 4103, 2001.
Sah P, Faber ES, Lopez De Armentia M, and Power J. The amygdaloid
complex: anatomy and physiology. Physiol Rev 83: 803– 834, 2003.
Smith Y and Paré D. Intra-amygdaloid projections of the lateral nucleus in the
cat: PHA-L anterograde labeling combined with postembedding GABA and
glutamate immunocytochemistry. J Comp Neurol 342: 232–248, 1994.
Szinyei C, Heinbockel T, Montagne J, and Pape HC. Putative cortical
and thalamic inputs elicit convergent excitation in a population of GABAergic interneurons of the lateral amygdala. J Neurosci 20: 8909 – 8915,
2000.
Tepper JM, Sawyer SF, and Groves PM. Electrophysiologically identified
nigral dopaminergic neurons intracellularly labeled with HRP: light-microscopic analysis. J Neurosci 7: 2794 –2806, 1987.
Terzuolo CA and Araki T. An analysis of intra- versus extra-cellular
potential changes associated with activity of single spinal motoneurons. Ann
NY Acad Sci 94: 547–558, 1961.
Walker DL and Davis M. The role of amygdala glutamate receptors in fear
learning, fear-potentiated startle, and extinction. Pharmacol Biochem Behav
71: 379 –392, 2002.
Washburn MS and Moises HC. Electrophysiological and morphological
properties of rat basolateral amygdaloid neurons in vitro. J Neurosci 12:
4066 – 4079, 1992.
Winer JA and Larue DT. Evolution of GABAergic circuitry in the
mammalian medial geniculate body. Proc Natl Acad Sci USA 93:
3083–3087, 1996.
Woodruff AR and Sah P. Four distinct interneuronal networks in the
basolateral amygdala. Program No. 271.11. 2005 Abstract Viewer/Itinerary
Planner. Washington, DC: Society for Neuroscience, 2005, Online.
96 • DECEMBER 2006 •
www.jn.org