| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
1Center 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
 |
ABSTRACT |
|---|
|
1 Hz for projection cells and 
7 Hz for fast-spiking interneurons. Supplemented with spike-duration cutoffs of 0.7 ms for projection cells and 0.5 ms for interneurons, this approach should keep instances of misclassifications to a minimum. |
|
INTRODUCTION |
|---|
|
; 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, fast-spiking 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 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 |
|---|
|
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 (3).
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 performed a "distance test." In this test, we first located a well-isolated, spontaneously active neuron and then moved the electrode back in 5-µm steps over a distance of 110 µm 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 µm), 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 |
|---|
|
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 µs). 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 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.
|
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 1 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, 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.
|
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 patch-clamp 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 µm) 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 compartment 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-µm steps over a distance of 110 µm 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 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.
|
|
, 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 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 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 |
|---|
|
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 well-established 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 high-density 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.
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).
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.
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 parvalbumin-positive 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, 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: 1 Hz for projection cells and 7–8 Hz for fast-spiking 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 (0.7 ms for projection cells and 0.5 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.
|
|
|
GRANTS |
|---|
|
|
|
ACKNOWLEDGMENTS |
|---|
|
|
|
FOOTNOTES |
|---|
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: pare{at}axon.rutgers.edu)
|
|
REFERENCES |
|---|
|
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.[CrossRef][Web of Science][Medline]
Chen JC and Lang EJ. Inhibitory control of rat lateral amygdaloid projection cells. Neuroscience 121: 155–166, 2003.[CrossRef][Web of Science][Medline]
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.[CrossRef][Web of Science][Medline]
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.[Web of Science][Medline]
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.[CrossRef][Web of Science][Medline]
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.[CrossRef][Web of Science][Medline]
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.[CrossRef][Web of Science][Medline]
Faulkner B and Brown TH. Morphology and physiology of neurons in the rat perirhinal-lateral amygdala area. J Comp Neurol 411: 613–642, 1999.[CrossRef][Web of Science][Medline]
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.[CrossRef][Web of Science][Medline]
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.[CrossRef][Web of Science][Medline]
Gulledge AT and Stuart GJ. Excitatory actions of GABA in the cortex. Neuron 37: 299–309, 2003.[CrossRef][Web of Science][Medline]
Guyenet PG and Aghajanian GK. Antidromic identification of dopaminergic and other output neurons of the rat substantia nigra. Brain Res 150: 69–84, 1978.[CrossRef][Web of Science][Medline]
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.[CrossRef][Web of Science][Medline]
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.[CrossRef][Web of Science][Medline]
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.[CrossRef][Web of Science][Medline]
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.[CrossRef][Web of Science][Medline]
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.[CrossRef][Web of Science][Medline]
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.[CrossRef][Web of Science][Medline]
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.[CrossRef][Web of Science][Medline]
Mahanty NK and Sah P. Calcium-permeable AMPA receptors mediate long-term potentiation in interneurons in the amygdala. Nature 394: 683–687, 1998.[CrossRef][Medline]
Maren S and Quirk GJ. Neuronal signaling of fear memory. Nat Rev Neurosci 5: 844–852, 2004.[CrossRef][Web of Science][Medline]
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.
Mascagni F and McDonald AJ. Immunohistochemical characterization of cholecystokinin containing neurons in the rat basolateral amygdala. Brain Res 976: 171–184, 2003.[CrossRef][Web of Science][Medline]
McDonald AJ. Projection neurons of the basolateral amygdala: a correlative Golgi and retrograde tract tracing study. Brain Res Bull 28: 179–185, 1992.[CrossRef][Web of Science][Medline]
McDonald AJ and Augustine JR. Localization of GABA-like immunoreactivity in the monkey amygdala. Neuroscience 52: 281–294, 1993.[CrossRef][Web of Science][Medline]
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.[CrossRef][Web of Science][Medline]
McGaugh JL. The amygdala modulates the consolidation of memories of emotionally arousing experiences. Annu Rev Neurosci 27: 1–28, 2004.[CrossRef][Web of Science][Medline]
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.[CrossRef][Web of Science][Medline]
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.[CrossRef][Web of Science][Medline]
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.[CrossRef][Web of Science][Medline]
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.
Rakic P. Local circuit neurons. Neurosci Res Prog Bull 13: 295–416, 1975.[Medline]
Room P and Groenewegen HJ. Connections of the parahippocampal cortex in the cat. II. Subcortical afferents. J Comp Neurol 251: 451–473, 1986.[CrossRef][Web of Science][Medline]
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.[CrossRef][Web of Science][Medline]
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.[Abstract]
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.[Web of Science][Medline]
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.[CrossRef][Web of Science][Medline]
Washburn MS and Moises HC. Electrophysiological and morphological properties of rat basolateral amygdaloid neurons in vitro. J Neurosci 12: 4066–4079, 1992.[Abstract]
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.
This article has been cited by other articles:
|
|
 |
|
  S. J. Shabel and P. H. Janak Substantial similarity in amygdala neuronal activity during conditioned appetitive and aversive emotional arousal PNAS, September 1, 2009; 106(35): 15031 - 15036. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 G. Li, S. S. Nair, and G. J. Quirk A Biologically Realistic Network Model of Acquisition and Extinction of Conditioned Fear Associations in Lateral Amygdala Neurons J Neurophysiol, March 1, 2009; 101(3): 1629 - 1646. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 S. Maren and J. A. Hobin Hippocampal regulation of context-dependent neuronal activity in the lateral amygdala Learn. Mem., April 12, 2007; 14(4): 318 - 324. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| Visit Other APS Journals Online |
TOP
ABSTRACT
INTRODUCTION
