Abstract
Network activity in the lateral central amygdala (CeL) plays a crucial role in fear learning and emotional processing. However, the local circuits of the CeL are not fully understood and have only recently begun to be explored in detail. Here, we characterized the intrinsic circuits in the CeL using paired whole-call patch-clamp recordings, immunohistochemistry, and optogenetics in C57BL/6J wild-type and somatostatin-cre (SOM-Cre) mice. Our results revealed that throughout the rostrocaudal extent of the CeL, neurons form inhibitory connections at a rate of ∼29% with an average amplitude of 20 ± 3 pA (at −40 mV). Inhibitory input from a single neuron is sufficient to halt firing in the postsynaptic neuron. Post hoc immunostaining for protein kinase Cδ (PKCδ) in wild-type mice and paired recordings in SOM-Cre mice demonstrated that the most common local connections were PKCδ(−) → PKCδ(−) and SOM(+) → SOM(+). Finally, by optogenetically activating either SOM(+) or SOM(−) neurons, we found that almost all neurons in the CeL were innervated by these neuronal populations and that connections between like neurons were stronger than those between different neuronal types. These findings reveal a complex network of connections within the CeL and provide the foundations for future behavior-specific circuit analysis of this complex network.
Keywords: anxiety, emotion, fear, learning
Significance Statement
Local inhibition in the lateral central amygdala (CeL) plays a crucial role in the processing of emotions, yet a complete understanding of these connections is still in its infancy. In this study, we show that CeL neurons are highly interconnected and that inhibition from a single neuron is sufficient to silence the postsynaptic neuron. Focusing on two well known CeL neuronal subtypes, protein kinase Cδ (PKCδ)-expressing and somatostatin (SOM)-expressing neurons, we show that the most common local connections are PKCδ(−) → PKCδ(−) and SOM(+) → SOM(+). Optogenetic activation of either the SOM(+) or SOM(−) neuronal populations revealed that inhibition was larger between like neurons. These findings show that within the CeL there is a complex network and provide the foundations for future behavior-specific circuit studies.
Introduction
The amygdala has long been known to play a crucial role in processing innate emotions, particularly fear (Klüver and Bucy, 1939; Weiskrantz, 1956; Sah et al., 2003). In Pavlovian fear conditioning, an associative learning paradigm widely used to study amygdala function, subjects learn to associate a neutral sensory stimulus [the conditioned stimulus (CS)], with an aversive one (the unconditioned stimulus; LeDoux, 2000). Following learning, the previously neutral CS now evokes a defensive response (i.e., freezing of movement or flight; Gross and Canteras, 2012). A converging body of evidence has established the amygdala as a central player in fear conditioning where the basolateral amygdala (BLA) and the central amygdala (CeA) are the key sites involved in the acquisition and expression of fear (LeDoux, 2000; Sah et al., 2003; Duvarci and Pare, 2014). The BLA has been extensively studied with respect to its cell types, intrinsic circuits, and extrinsic connections (LeDoux, 2000; Sah et al., 2003; Duvarci and Pare, 2014), while the CeA has received considerably less attention and the intrinsic circuits within this nucleus are less well understood.
The CeA is a GABAergic nucleus (McDonald and Augustine, 1993; Sun and Cassell, 1993) that is anatomically divided into the lateral sector of the CeA (CeL) and the medial sector of the CeA (CeM), with substantial unidirectional connections between the CeL and the CeM (McDonald, 1982; Grove, 1988; Jolkkonen and Pitkänen, 1998). Neurons in both regions also make extensive local connections (McDonald, 1982; Sun and Cassell, 1993; Jolkkonen and Pitkänen, 1998), with local glutamate excitation of CeL neurons evoking IPSCs in neighboring neurons (Lopez de Armentia and Sah, 2004). Recent studies have divided CeL neurons into distinct populations based on the expression of immunohistochemical markers, electrophysiological properties, and synaptic connections (Ciocchi et al., 2010; Haubensak et al., 2010; Li et al., 2013). Of these, one population expresses protein kinase Cδ [PKCδ(+)], and these neurons are predominantly described as late-firing (LF) neurons, exhibiting a substantial delay to action potential (AP) initiation in response to depolarizing somatic current injections. Following fear conditioning, these neurons respond to the CS with a reduction in activity and have therefore been called CeLOFF cells (Ciocchi et al., 2010; Haubensak et al., 2010). A second population of CeL neurons, which is largely separate from the PKCδ(+) population, expresses somatostatin [somatostatin-positive (SOM+); Li et al., 2013]. These neurons receive direct synaptic input from the lateral amygdala, which is potentiated following auditory fear conditioning (Li et al., 2013). Electrophysiologically, PKCδ(−) neurons which are predominantly SOM(+), have been described as either LF or regular spiking (RS). Following fear conditioning, PKCδ(−) neurons respond to the CS with an increase in activity, and have therefore been called CeLON neurons (Ciocchi et al., 2010; Haubensak et al., 2010), which likely also correspond to SOM(+) neurons (Yu et al., 2016). PKCδ(−) neurons inhibit PKCδ(+) neurons, which in turn project to the CeM (Haubensak et al., 2010).
This organization has led to one model in which fear expression is mediated by CS-related information driving PKCδ(−) neurons, presumably SOM(+) neurons, in the CeL via excitatory input from the BLA and thalamus. These neurons in turn inhibit PKCδ(+) neurons, resulting in disinhibition of the CeM and the expression of fear (Ciocchi et al., 2010; Haubensak et al., 2010). However, some SOM(+) neurons in the CeL also project to the periaqueductal gray (PAG; Penzo et al., 2014), and CS-driven activity of these neurons also contributes to fear expression (Tovote et al., 2016). Moreover, recent studies have reported that neurons in the CeL are also involved in feeding (Cai et al., 2014), and pain (Han et al., 2015). Neurons engaged during feeding and pain responses are also part of the PKCδ and SOM population, indicating that the intrinsic circuitry of the CeL is complex, and the strength, identity, and physiologic role of individual local connections are not fully understood. In this study, we provide a detailed investigation of local circuits in the CeL.
Materials and Methods
Animals
All studies were approved by the University of Queensland Animal Ethics Committee, and experiments were conducted in accordance with the Australian Code of Practice for the Care and Use of Animals for Scientific purposes. Adult (6–15 weeks old) male wild-type C57BL/6J mice were used for electrophysiology experiments. Where stated, we also used both male and female mice (8–12 weeks old) from a somatostatin-IRES-cre mouse line (SOM-Cre; C57BL/6J background; Ssttm2.1(cre)Zjh) that was acquired from The Jackson Laboratory. These mice express cre recombinase under the SOM promoter, thereby allowing selective targeting of SOM(+) neurons using cre-dependent viral constructs (described below). Mice were genotyped by the Australian Equine Genetics Research Center.
Brain slice preparation
Mice were anesthetized using isoflurane and decapitated, after which brains were quickly removed while submerged in an oxygenated ice-cold N-methyl-d-glucamine-based (NMDG) solution (NMDG 93 mm, KCl 2.5 mm, NaH2PO4 1.2 mm, NaHCO3 30 mm, HEPES 20 mm, glucose 25 mm, sodium ascorbate 5 mm, thiourea 2 mm, sodium pyruvate 3 mm, MgSO4 10 mm, and CaCl2 0.5 mm, pH 7.2, 290–300 mOsm). This NMDG-based solution is particularly suited for dissections of adult mice (Zhao et al., 2011). Coronal brain slices (300 μm thick) were then prepared using a vibratome (catalog #VT1000S, Leica) and placed to recover in oxygenated artificial CSF (aCSF; NaCl 118 mm, NaHCO3 25 mm, glucose 10 mm, KCl 2.5 mm, NaHPO4 1.2 mm, MgCl2 1.3 mm, and CaCl2 2.5 mm, pH 7.2, 290–300 mOsm) for 30 min at 34°C, and then at room temperature until required.
Electrophysiological recordings
Slices were visualized on an upright microscope (model BX51WI, Olympus), and whole-cell patch-clamp recordings were made using a Multiclamp 700B (Molecular Devices). The CeL was easily distinguishable in vitro based on the fire bundles that surround and clearly delineate this area (see Fig. 4A). These landmarks are readily visible under the microscope and ensured that cells chosen for recordings were situated within the CeL. In addition, for electrophysiological recordings, cells in the CeL are typically smaller than those in the BLA, and their cell density is higher than both the BLA and CeM. Data were filtered at 4 kHz and sampled at 20 kHz using an ITC-18 (Instrutech). Data were acquired and analyzed using AxoGraphX software (AxoGraph). Brain slices were continuously perfused with oxygenated aCSF (34°C; 3–4 mL/min), and recording electrodes (4–6 MΩ; glass capillaries, Harvard Apparatus; PC-10 Electrode Puller, Narishige) were filled with a KMeSO4-based internal solution (KMeSO4 135 mm, NaCl 8 mm, HEPES 10 mm, MgATP 2 mm, GTP 0.3 mm, phosphocreatine 7 mm, EGTA 0.2 mm, and biocytin 0.2%, pH 7.2 with KOH, osmolarity 295 mOsm/kg) unless otherwise stated, in which case a CsMeSO4-based internal solution was used (CsMeSO4 135 mm, NaCl 8 mm, HEPES 10 mm, MgATP 2 mm, GTP 0.3 mm, phosphocreatine 7 mm, and spermine 0.1 mm, pH 7.2 with CsOH, osmolarity 300 mOsm/kg). In some experiments GABA (10 mm) was added to the KMeSO4-based internal solution to avoid any run down of responses due to wash out during whole-cell recordings (Apostolides and Trussell, 2013), although no difference in response was observed when using GABA internal solutions. No corrections were made for junction potentials. The pairs of neurons chosen for recordings were located within 50–100 µm of each other in the coronal plane and 10–40 µm in the rostrocaudal plane. To probe for connections during paired recordings, one cell was held in current-clamp mode and injected with a 5 ms, 600–700 pA current pulse to evoke an AP. Meanwhile, the second (postsynaptic) neuron was held in voltage-clamp mode at −40 mV, well away from the chloride reversal potential (approximately −73 mV), given that neurons in the CeL are known to be GABAergic, forming inhibitory synapses (Sun and Cassell; Lopez de Armentia and Sah, 2004; Haubensak et al., 2010; Li et al., 2013). This protocol was repeated for at least 20 (but not >50) episodes, and sweeps were averaged for analysis. The same was then done in the opposite direction. Only connections with an amplitude of >5 pA were considered to be connected. Finally, in pharmacology experiments, bicuculline (10 μm; Sigma-Aldrich) or CNQX (10 μm; Tocris Bioscience) were bath applied to the slice.
Firing properties
APs were evoked using current injections applied in increments of 20 pA from −60 to 240 pA. AP threshold, amplitude, delay, half-width, rise time, and spike accommodation were analyzed off-line (described below). Spike accommodation was measured as the difference in AP frequency over at least eight APs at twice threshold. Although the two main firing types we observed ultimately had significantly different AP onsets, we used the absence or presence of spike accommodation to classify these firing types, as AP onset varied with small changes in holding membrane potential.
Data analysis
Electrophysiological properties
Resting membrane potential (Rm) was recorded on-line immediately after break-in, whereas input resistance (Ri) was measured off-line as Ri = dVm/l, where dVm is the change in membrane potential in response to a −20 pA (800 ms) current injection (l). For connections, decay was measured by fitting the average IPSC by a sum of two exponentials (simplex sum of squared errors) to calculate a weighted time constant: . Onset delay was calculated as the difference between the time of the presynaptic AP peak and the time of IPSC onset (time at 5% of peak). For firing properties, AP threshold was measured as the membrane potential at the start of the fast-rising phase. AP amplitude was measured from the threshold to peak, and delay was measured as the duration from the start of the current injection to the start of the fast-rising phase of the first AP.
Statistical tests
Datasets were tested for normality using the Shapiro–Wilks test. In the cases where a subset of the population was tested (e.g., drug application), we based our choice of statistical test on whether or not the overall dataset was normally distributed. We used parametric tests (t tests) when the data followed a normal distribution, whereas nonparametric tests (Wilcoxon and Mann–Whitney tests) were used for datasets that were too small to reliably test for or did not follow a normal distribution. Two-tailed tests were used unless otherwise stated, and differences were considered to be significant at p < 0.05.
Immunohistochemistry
Labeling for immunohistochemical characterization
For characterization of CeA neurons, mice were anesthetized by intraperitoneal injection of pentobarbitone sodium (3250 mg/kg; Virbac) and transcardially perfused with 40 ml of a 1% sodium nitrite solution (phosphate buffer, 0.1 m), followed by 40 ml of 4% paraformaldehyde (PFA; in 0.1 m phosphate buffer). Brains were then removed and left in 4% PFA at room temperature overnight and washed (3 × 15 min, PBS 0.1 m) before sectioning (50–60 μm sections). Brains were placed in 30% sucrose for 48 h and sectioned using a sliding microtome (model SM200R, Leica). Coronal subsections (50 µm) were then stained for PKCδ using a mouse anti-PKCδ antibody (1:500; BD Biosciences), for SOM using a rabbit anti-SOM antibody (1:1000; Millipore Bioscience Research Reagents/Millipore), and for NeuN using a chicken anti-NeuN antibody (1:1000; Millipore; 72 h at room temperature). In the case of virus-injected animals, fluorescence was amplified using either a rabbit anti-red fluorescent protein antibody (1:1000; Abcam) or chicken anti-green fluorescent protein (1:1000; Life Technologies). Sections were then washed and incubated with mouse-fluorophore 647 (for PKCδ; 1:2000; Invitrogen), rabbit-fluorophore 488 (for SOM; 1:2000; Invitrogen), rabbit-fluorophore 568, or chicken-fluorophore 488 (for fluorescence-enhanced sections; 1:2000; Invitrogen). Brain sections used for counts were immunolabeled for NeuN to allow reliable identification of mature neurons, and only NeuN(+) neurons were counted. Cell counts were made in both the right and left hemispheres, but, because these were not significantly different, the data were pooled for each bregma location.
Post hoc labeling of recorded neurons
Alexa Fluor 568 (1 ng/ml internal solution) was added to the internal recording solution, and images of dendritic morphology were taken during recordings to correctly identify the presynaptic and postsynaptic cells after recovery of recorded neurons. Following electrophysiological recordings, slices were fixed in 4% PFA (in 0.1 m phosphate buffer) for either 1 h at room temperature or overnight at 4°C, and then washed for 3 × 15 min in 0.1 m PBS. Slices were then placed in blocking solution (1% BSA, 0.05% saponin, and 0.05% sodium azide) for 1–2 h at room temperature before incubation with an Alexa Fluor 555-bound streptavidin (overnight at room temperature; 1:2000 in blocking solution; Life Technologies). Slices were then washed (3 × 15 min, 0.1 m PBS), mounted (DABCO), and imaged using either an upright fluorescent microscope (5× and 20×; Zen Software, Zeiss) or spinning disk confocal microscope (20× and 40× water-immersion objective, model #CSU-W1, Yokogawa; Slidebook software). All images were analyzed using FIJI (ImageJ). For protein PKCδ staining, slices were subsequently embedded in 4% agarose and subsectioned (50 μm sections; VT1000S vibratome, Leica) before being incubated with the PKCδ mouse-antibody (72 h at room temperature; 1:500; BD Biosciences). Sections were then washed and incubated with mouse-fluorophore 647 (1:2000; Invitrogen), and the nuclei of the cells stained with DAPI, before being mounted and imaged as described above. Although PKCδ clearly labeled somas, the somatostatin antibody did not deliver reliable post hoc staining, as a result of which we focused on PKCδ for postrecording labeling experiments.
Morphology
Biocytin-recovered neurons that were used for morphologic reconstruction were imaged using a spinning disk confocal microscope (40 × 1.2 numerical aperture water-immersion objective, 0.156 × 0.156 × 0.33 μm3/pixel resolution; model #CSU-W1, Yokogawa; Slidebook software). Neurons were manually traced using Neurolucida (MBF Bioscience) and analyzed using Neurolucida Explorer. For spine counts, dendrites were reimaged using a 63 × 1.4 numerical aperture oil-objective (0.099 × 0.099 × 0.15 μm3/pixel resolution) and underwent deconvolution. Spines were counted automatically and manually verified (Neurolucida 360, MBF Bioscience; including the z-plane) over 60 μm of secondary dendrites. Three segments (each from a different secondary dendrite) were counted and averaged for each cell.
Viral injections and optical stimulation
Mice (21–28 d old) were anesthetized (100 mg/kg ketamine, 10 mg/kg xylazil in saline) and placed in a stereotaxic frame. Bilateral injections were made into CeL using the following coordinates (Paxinos and Watson, 2001): −1.6 mm (anteroposterior); ± 2.8 mm (mediolateral); and −4.8 mm (dorsoventral from skull).
A small hole was drilled in the skull, and virus was injected using a glass needle (pressure injection Picospritzer; 10–20 ms, 10–30 psi). Animals were injected stereotaxically with an AAV (adeno-associated virus; 0.1–0.3 μl, 0.1 μl/min Vector Core) containing one of the following constructs: AAV2/5- EF1α-DIO-tdTomato (titer: 1.0 × 1011); AAV2/5- EF1α.dflox.hChR2(H134R)-mCherry (titer: 1.31 × 1013); or AAV2/5-EF1α-DIO-Fwd.hChR2(H134R)-EYFP (enhanced yellow fluorescent protein; titer: 1.0 × 1011).
Animals were quarantined for 48 h then allowed to recover for at least 4 weeks postinjection. Brain slices were prepared as described above for electrophysiological experiments, and cells were only recorded well within the spread of the virus to ensure that nonfluorescent neurons were indeed SOM(−) rather than simply not infected. To verify the expression of channelrhodopsin (ChR2) and to activate ChR2 in infected cells, an LED system (470 nm, 1.4 mW; pE-2 LED System, CoolLED) attached to the microscope (via the rear C-mount port) was used. A prolonged light pulse (100 ms) was used to verify that cells expressed functional ChR2. In the case of AAV2/5- EF1α.dflox.hChR2(H134R)-mCherry experiments, for example, neurons were considered SOM(+) if they were both fluorescent and displayed a prolonged depolarization in response to prolonged light stimulation (470 nm, 100 ms), whereas a SOM(−) neuron was not fluorescent and showed no excitation to the light pulse. A light pulse of 2 ms (n = 57 neurons) or 1 ms (n = 10 neurons) was used to evoke responses in the CeL.
Results
Characterization of neurons in the central lateral amygdala
Immunohistochemical characterization
Neurons in the CeL have been separated based on the expression of a range of neuropeptides and markers that include PKCδ, SOM, corticotropin-releasing factor, oxytocin receptors, enkephalin, and others (Cassell and Gray, 1989; Haubensak et al., 2010). Of these, the two most highly expressed and clearly distinct neuropeptides are PKCδ and SOM (Haubensak et al., 2010; Li et al., 2013). Immunostaining of brain sections from four locations posterior to bregma (−1.20, −1.40, −1.60, and −1.80 mm; ±0.05 mm; Fig. 1A, top diagrams) shows that PKCδ labeling within the amygdala was specific to the CeL, whereas SOM expression was also present outside the central amygdala. In the CeL, 48 ± 5% of neurons expressed PKCδ, and 38 ± 3% SOM (Fig. 1A), with the two populations largely nonoverlapping, and dual-labeled [PKCδ(+)/SOM(+)] neurons accounting for only 1.5 ± 0.5% of neurons. The remaining neurons (13 ± 2%) were negative for both markers. It was notable that whereas the proportions of PKCδ(+)/SOM(−) and PKCδ(−)/SOM(+) neurons were similar between bregma −1.40 and −1.60 mm, the difference between the total numbers of the two cell types changed at bregma −1.20 and −1.80 mm, the rostral and caudal limits of the CeL (Fig. 1B).
Electrophysiological properties
Based on their response to somatic current injections, three general types of CeL neurons have previously been described, with the two major types being LF neurons, which show a significant delay before onset of the first AP (∼100–200 ms), and early-spiking (ES) neurons (also described as regular-spiking; AP onset, ∼50 ms). A third, smaller population of low-threshold bursting neurons has also been described (Dumont et al., 2002; Lopez de Armentia and Sah, 2004; Haubensak et al., 2010; Li et al., 2013; Hou et al., 2016). We characterized the firing properties of 151 CeL neurons. However, while classifying neurons we found that AP onset varied with changes in holding potential, whereas the presence of spike frequency accommodation was more reliable. Using this measure, neurons were classified either as nonaccommodating (NA), where AP frequency remained relatively consistent (∼17 Hz), or accommodating (Ac), where there was clear spike frequency adaptation (AP1-2, ∼32 Hz; AP7-8, 13 Hz; p < 0.001 Wilcoxon matched-pairs test; Fig. 2C). The large majority of our neurons were nonaccommodating (n = 80 neurons; Fig. 2A) or accommodating (n = 59 neurons; Fig. 2B). Nonaccommodating neurons also had a significantly longer mean onset compared with that of accommodating neurons (Table 1), and these neurons generally corresponded to the LF and ES types (Haubensak et al., 2010; Amano et al., 2012). Thus, for consistency we have termed these LF-NA and ES-Ac neurons. Apart from resting membrane potential, which was significantly more depolarized in ES-Ac neurons, other membrane properties such as input resistance, threshold potential, AP amplitude, rise time, and half-width did not differ significantly between LF-NA and ES-Ac neurons (Table 1).
Table 1:
Firing type | Nonaccommodating (n = 80) |
Accommodating (n = 59) |
Stuttering (n = 12) |
---|---|---|---|
Incidence | 53% | 39% | 8% |
Input resistance (mΩ) | 416 ± 17 | 419 ± 28 | 387 ± 64 |
Resting potential (mV) | -64 ± 1 | -59 ± 1a | -62 ± 2 |
Threshold (mV) | -33 ± 0.5 | -34 ± 0.5 | -34 ± 1.8 |
Onset (ms) at T | 330 ± 25 | 209 ± 23b | 122 ± 54 |
Onset (ms) at 2T | 77 ± 5 | 59 ± 7c | 28 ± 19 |
Amplitude (mV) | 66 ± 1 | 69 ± 1 | 53 ± 4d,e |
Rise time (ms) | 0.4 ± 0.02 | 0.4 ± 0.02 | 0.2 ± 0.02f,g |
Half-width (ms) | 1.2 ± 0.03 | 1.1 ± 0.03 | 0.6 ± 0.04f,h |
Values are the mean ± SEM. Low-threshold bursting neuron properties are not represented in this table since n = 1 for this firing type. T, Threshold; 2T, twice threshold.
p < 0.001 vs NA (two-tailed t test).
p < 0.001 vs NA (Mann–Whitney test).
p < 0.01 vs NA (Mann–Whitney test)
p < 0.001 vs NA (two-tailed t test).
p < 0.0001 vs Ac (two-tailed t test).
p < 0.0001 vs NA (Mann–Whitney test).
p < 0.001 vs Ac (Mann–Whitney test).
p < 0.0001 vs Ac (Mann–Whitney test).
In the remaining 12 neurons (8%; Fig. 3), we found a distinct stuttering firing type that resembled that of some interneurons in the BLA (Woodruff and Sah, 2007; Sosulina et al., 2010; Spampanato et al., 2011). These neurons were easily distinguishable due to their distinctive firing pattern, with bursts of high-frequency APs (∼60 Hz; Fig. 3A). Moreover, these neurons had significantly briefer APs with a half-width of 0.6 ± 0.04 ms compared with 1.1 ± 0.03 ms in ES-Ac neurons and 1.2 ± 0.03 ms in LF-NA (Table 1; Fig. 3B). Stuttering neurons also displayed a higher frequency of spontaneous synaptic events compared with LF-NA and ES-Ac neurons (Fig. 3C). For stuttering neurons, we were unable to recover the entire cell; however, dendrites were filled, and visible, and showed that, unlike LF-NA and Es-Ac neuron, stuttering neurons were aspiny.
Twenty-five recorded neurons were successfully recovered with biocytin and labeled for PKCδ. Of these, PKCδ(+) neurons (n = 8) were either LF-NA or ES-Ac at equal incidence (50%), whereas PKCδ(−) neurons (n = 17) were more likely to be LF-NA (∼59%) than ES-Ac (∼23%). As previously described using Golgi methods (McDonald, 1982; Cassell and Gray, 1989), the majority of CeL neurons resembled medium-spiny neurons (see Fig. 5). Stuttering neuron somas that were successfully recovered and stained (n = 3) were all PKCδ(−) (Fig. 3D,E). These results show that PKCδ-expressing (48%), and SOM-expressing (38%) neurons are the major cell types in the CeL, with very few neurons expressing both markers (1.5%). These neurons have one of two firing properties, LF-NA or ES-Ac. We also identified a previously unrecognized population of stuttering neurons (8%) that express neither PKCδ or SOM (see below).
Local inhibitory connections
To determine the nature of local connections between neurons in the CeL, paired whole-cell recordings were made in acute coronal slices of wild-type mice (Fig. 4A). A total of 152 pairs were tested, of which 45 (29%) were connected. This was a monosynaptic connection with an onset latency of 0.85 ± 0.06 ms after the AP peak and a high release probability (failure rate, 23 ± 3%), which is consistent with a monosynaptic connection (Fig. 4B,C). At a holding potential of −40 mV, the IPSC had a mean amplitude of 20 ± 3 pA, a 10–90% rise time of 1.7 ± 0.1 ms, and a decay time constant of 19.2 ± 1.5 ms. Connections were predominantly unidirectional (n = 42 of 45 connected pairs; Fig. 4B), with only 3 connected pairs displaying bidirectional connectivity (Fig. 4C,D). Apart from the stuttering cells, these neurons resembled medium-spiny neurons, (Fig. 5A–C), and spine density did not differ significantly between presynaptic and postsynaptic neurons (Fig. 5B); nor were differences observed in soma diameter, soma volume, number of primary dendrites, number of nodes, or total dendrite length (Table 2). Recordings were made throughout the rostrocaudal extent of the CeL, and the resulting map of connected and unconnected pairs revealed no obvious location preference (Fig. 5D).
Table 2:
Soma length (μm) | Soma volume (μm3) | Number of primary dendrites | Number of nodes | Total dendrite length (μm) | |
---|---|---|---|---|---|
Total (n = 8) | 15.6 ± 0.8 | 1117 ± 232 | 5.5 ± 0.4 | 13.2 ± 1.0 | 1389 ± 88 |
Presynaptic (n = 4) | 14.4 ± 0.9 | 929 ± 354 | 5.2 ± 0.6 | 14.7 ± 0.6 | 1309 ± 152 |
Postsynaptic (n = 4) | 16.9 ± 1.2 | 1304 ± 322 | 5.7 ± 0.5 | 11.7 ± 1.7 | 1469 ± 93 |
Values are the mean ± SEM. Four connected pairs (total of eight neurons) were recovered, and their morphologies were analyzed. When these properties were compared between presynaptic and postsynaptic neurons, no significant differences were observed (Mann–Whitney test).
Neurons in the CeL are predominantly GABAergic, and in our connected pairs the IPSC reversal potential was −72 mV, which corresponds to the calculated chloride reversal potential (approximately −73 mV; Fig. 6A). Application of the GABAA receptor (GABAA-R) antagonist, bicuculline (10 µM) blocked these IPSCs (Fig. 6B; n = 5 paired recordings), confirming that they were GABAA-R-mediated chloride currents. In current clamp, these connections were hyperpolarizing, with a mean amplitude of −1.1 ± 0.3 mV (n = 17), which is sufficient to halt firing in the postsynaptic cell (Fig. 6C; n = 5 paired recordings), and in some cases this inhibition was followed by a rebound increase in spike probability (Fig. 6D). These results demonstrate that neurons throughout the CeL form local inhibitory connections at a relatively high rate, which are capable of shaping the activity of the postsynaptic cell.
Distinct connection patterns exist between local CeL neurons
To determine the identity of recorded pairs, recovered neurons were processed using immunohistochemistry. As expected (Ciocchi et al., 2010; Haubensak et al., 2010), we found local connections between presynaptic PKCδ(−) and postsynaptic PKCδ(+) neurons [PKCδ(−) → PKCδ(+)] in 27% of successfully recovered pairs (Fig. 7B,D,E). However, the most common connection type was between two PKCδ(−) neurons [PKCδ(−) → PKCδ(−)] (∼55%; Fig 7A,D,E). In two cases, both the presynaptic and postsynaptic neurons were PKCδ(+) (18%; Fig 7C–E). No PKCδ(+) → PKCδ(−) connections were found. Connected cells displayed a variety of discharge properties (Fig. 7F), with the most common connections being either LF-NA → LF-NA (∼26%; n = 5 of 19 paired recordings) or ES-Ac → LF-NA connections (∼21%; n = 4 of 19 paired recordings). Although less common, we also found ES-Ac → ES-Ac connections (∼10%; n = 2 of 19 paired recordings). Stuttering neurons were always presynaptic (n = 3), with two connections to LF-NA neurons and one to an ES-Ac neuron.
These results show that local CeL connections occur between a variety of immunohistochemically and electrophysiologically distinct neuronal types with the most common connection between PKCδ(−) neurons. Given that ∼75% of PKCδ(−) neurons are SOM(+) (Fig. 1), we turned to a SOM-Cre mouse line to reliably identify and selectively activate SOM(+) neurons in vitro. It was important to confirm that neurons considered to be PKCδ(−) were not false negatives due to protein washout during whole-cell recordings. To label SOM(+) neurons, we injected an adeno-associated virus containing a DIO-td-tomato vector (AAV-DIO-tdTom) into the CeL of SOM-Cre mice (Fig. 8). SOM-tdTom and PKCδ labeling in the CeL revealed proportions of these markers that were similar to those in wild-type mice (Fig. 8A,B; n = 3 mice; at bregma, −1.40 to −1.60 mm). We also determined the firing properties of SOM(+) and SOM(−) neurons (Fig. 8C). In agreement with recordings in wild- type mice, SOM(+) neurons were mostly LF-NA (∼81%; n = 13 of 16 neurons; ES-Ac: ∼19%; n = 3 of 16 neurons), whereas the SOM(−) neurons were mostly ES-Ac (∼65%; n = 11 of 17 neurons; LF-NA: ∼29%; n = 5 of 17 neurons). Notably, the one stuttering neuron found in these recordings was SOM(−). Given that the stuttering neurons observed in wild-type mice were PKCδ(−), it is possible that these neurons are a major contributor to the population of PKCδ(−)/SOM(−) neurons.
Next, paired whole-cell recordings were obtained using identified SOM(+) neurons (Fig. 8D–F). Thirty-one pairs of neurons were recorded, as follows: 8 pairs between SOM(+) neurons; 16 pairs between a SOM(+) neuron and a SOM(−) neuron; and 7 pairs between SOM(−) neurons (Fig. 8D–G). Nine of the 31 pairs were connected (∼29%), which included eight unidirectional connections and one bidirectional connection (Fig. 8D). In these connections, the mean IPSC amplitude (at −40 mV) was 21 ± 5 pA (n = 9) and had an onset latency of 0.76 ± 0.11 ms, which is not significantly different from the results obtained in wild-type mice (wild-type mean IPSC: 20 ± 3 pA; p = 0.7, Mann–Whitney test). The IPSC 10–90% rise time was 1.3 ± 0.1 ms and had a decay time constant of 13.2 ± 1.9 ms. The most common connection (∼56%) was between SOM(+) neurons (Fig. 8E), with the remaining connections being SOM(−) → SOM(−) (∼22%) and SOM(−) → SOM(+) (∼22%; Fig. 8E–G). When we compared the number of connected pairs to the total number of recordings for each combination, the least likely connection was between SOM(+) and SOM(−) neurons, with only ∼12% (n = 2 of 16 recordings) of these pairs being connected. In contrast, ∼62% (n = 5 of 8 pairs) of SOM(+)/SOM(+) recordings and ∼28% (n = 2 of 7 pairs) of SOM(−)/SOM(−) recordings were connected (Fig. 8H). No SOM(+) → SOM(−) connections were found.
Population-driven inhibition is greater between like neurons
Somatostatin-positive neurons
As described above, paired recordings in coronal brain slices from both wild- type, and SOM-Cre mice show that connections were most frequent between somatostatin expressing PKCδ(−) neurons. However, previous studies indicate that the inhibition of SOM(−) neurons by SOM(+) cells not only exists, but plays a key role in fear expression (Li et al., 2013; Hou et al., 2016). Such a motif is also suggested by inhibition of PKCδ(+) neurons by PKCδ(−) neurons (ON neuron → OFF neuron; Ciocchi et al., 2010; Haubensak et al., 2010). One possibility for our low incidence of SOM(+) → SOM(−) connections is that we are sampling local connections (∼50–100 µm apart) in the coronal plane, and SOM(+) → SOM(−) connections may be more common among “distal” (i.e. >100 µm) connections. To address this, we injected an AAV-containing DIO-channelrhodopsin-mCherry into the CeL of SOM-Cre mice (Fig. 9A,B) to directly activate SOM(+) terminals.
Whole-cell recordings were made from SOM (+) and SOM(−) neurons, and synapses made by SOM(+) neurons were activated optically. All SOM(−) cells received input from SOM(+) neurons with a mean IPSC of 162 ± 24 pA (n = 15; holding voltage, −40 mV; Fig. 9C). Next, paired recordings were made using a Cs-based internal solution, allowing voltage clamping of cells at the ChR2 reversal potential (∼0 mV) to test for SOM(+) → SOM(+) connections. In this configuration, all SOM(−) and SOM(+) neurons received large IPSCs when SOM(+) terminals were activated [SOM(−) = 22 neurons; SOM(+) = 10 neurons; Fig. 9D–F]. IPSCs in response to SOM(+) terminal activation were fully blocked by bicuculline (10 µm, n = 5, Fig. 9G), reversed at approximately −67 mV (n = 4), and were able to halt firing in the postsynaptic cell. From this cohort, 10 SOM(−) neurons were recovered, of which 5 were PKCδ(+), showing direct SOM(+) → PKCδ(+) and SOM(+) → PKCδ(−) connections (Fig. 9H). While all neurons received input from SOM neurons in the CeL, overall input to SOM(+) neurons was significantly larger than to SOM(−) neurons (Fig. 9I). This difference is consistent with our paired recordings where five of eight SOM(+) → SOM(+) pairs were connected, but none of the SOM(+)/SOM(−) pairs were (n = 16 pairs). In the course of these recordings, it was clear that, using SOM as a neuronal marker, a wide variety of connections are present in the CeL. Thus, for example, in one SOM(−) → SOM(−) single connected pair (illustrated in Fig. 9J), both cells also received input from local SOM(+) neurons.
Somatostatin-negative neurons
Our paired recordings also showed that SOM(−) → SOM(−) and SOM(−) → SOM(+) local connections, while not frequent, were present (Fig. 8E,F). However, with the technique we used (Fig. 8) there was a risk that noninfected (and therefore nonfluorescent) SOM(+) neurons could be misidentified as SOM(−). Although the number of SOM(+) neurons in SOM-Cre mice (Fig. 8A,B) was consistent with that of wild-type mice (Fig. 1), and despite the fact that we made sure to restrict recordings to well within the spread of infection, we used an alternative approach to confirm the existence of these connections. We again used an optogenetic approach to target SOM(−) neurons of the CeL in SOM-Cre mice with an AAV containing a DIO-Fwd-hChR2(H134R)-EYFP construct (Fig. 10A). With this construct, the ChR2-EYFP sequence is “cut out” in the presence of Cre recombinase, thereby ensuring that only Cre− [in this case SOM(−)] neurons express ChR2-EYFP. Combining these injections with a DIO-tdTom-containing AAV (1:1 ratio) allowed simultaneous identification of SOM(+) neurons (tdTom fluorescent) and SOM(−) neurons (eYFP fluorescent and ChR2-expressing). We could therefore selectively activate SOM(−) neurons, all while avoiding misidentification of neurons due to lack of fluorescence. These injections typically covered the majority of the width of the CeL (Fig. 10B). However, although a small volume of virus (∼100-200 nl) was injected to minimize spread outside the CeL, we did observe eYFP(+) somas in the basal amygdala and the amygdalostriatal area, which is located dorsally to the CeL. Within the CeL, ∼62% of all fluorescently labeled neurons were eYFP(+)/tdTom(−), whereas tdTom(+)/eYFP(−) neurons accounted for ∼36%. Processing slices for PKCδ revealed that the majority of eYFP(+) neurons were PKCδ(+) (∼77%; Fig. 10C,D).
Using a Cs-based internal solution, whole-cell recordings were obtained from either SOM(+) (Fig. 10E) or SOM(−) neurons (Fig. 10F). As eYFP(+) neurons were present in the basal amygdala (Fig. 10B), we bath applied CNQX (10 μm) during these recordings to ensure that the recorded IPSCs were monosynaptic. Under these conditions, in ∼91% of SOM(+) neurons (10 of 11 neurons) and all SOM(−) neurons (n = 9 neurons), stimulation of SOM(−) terminals evoked an IPSC (Fig. 10G), and these responses were GABAA-R mediated (Fig. 10H). Moreover, SOM(−) → SOM(−) IPSCs were significantly larger than SOM(−) → SOM(+) IPSCs (Fig. 10I).
Together with our connected paired recordings, these results are consistent with the presence of SOM(−) → SOM(+) and SOM(−) → SOM(−) connections within the CeL. Furthermore, they suggest that, as with SOM(+) neurons, a high proportion of CeL neurons receive inhibitory local connections from SOM(−) neurons, and with inhibition within the population being stronger than that between populations.
Discussion
The CeA is generally considered to be the main output nucleus of the amygdalar complex and is divided into the lateral and medial sectors. It contains GABAergic neurons that have been divided into several distinct populations using immunohistochemical and electrophysiological markers. These cells form local, as well as long-range connections, and different cell types have been associated with distinct functional roles (McDonald, 1982; Sun and Cassell, 1993; Jolkkonen and Pitkänen, 1998; Ciocchi et al., 2010; Haubensak et al., 2010; Li et al., 2013). Here, using whole-cell paired recordings and optogenetics, we characterized neurons of the CeL and their intrinsic connections. We find that neurons in the CeL are extensively interconnected, with local connections apparent between all types of neurons, but strongest between like neurons. Moreover, we describe a new type of neuron in the CeL with distinct firing properties. These results highlight the complex intrinsic circuits within the CeL and suggest that particular cell groups identified using current methods, rather than mediating specific behaviors, participate in a range of different circuits.
Local networks in the CeL
Consistent with previous studies, we found that PKCδ and SOM labeled two separate populations of neurons in the CeL (∼48% and ∼38%, respectively), with very little overlap (∼1–2%), that account for 88% of the total cell population. In response to current injection, these neurons show two types of discharge patterns, late firing (LF-NA) and early spiking (ES-Ac), and their overall incidences (∼52% and ∼39% respectively) were comparable to those previously described in the mouse (Haubensak et al., 2010; Hou et al., 2016). While SOM(+) neurons were mostly LF-NA (∼81%) and SOM(−) neurons (largely PKCδ expressing) were more likely to be ES-Ac (∼65%), these electrophysiological properties could not be used to separate the two populations. A smaller number of neurons (∼12%) were PKCδ(−) and SOM(−). These neurons may express CRF or one of the other peptides that are known to be present in CeL neurons (Cassell and Gray, 1989; Haubensak et al., 2010).
A small number of neurons (∼8%), had faster action potentials and a stuttering phenotype, with bursts of high-frequency AP discharge. This type of neuron has not been previously reported in the mouse CeL, although a similar “fast-spiking” neuron has been described in rare cases in the CeL and CeM of the guinea pig and cat (Martina et al., 1999; Dumont et al., 2002). These neurons were PKCδ(−) in wild-type mice, and the one stuttering neuron in SOM-Cre mice was SOM(−), suggesting that they may reflect a distinct PKCδ(−)/SOM(−) population. Although the role of this particular type of neuron is not clear, paired recordings showed that stuttering neurons were always presynaptic, and in cases where we had successful recovery of dendrites they had an aspiny morphology, different from that of the typically recovered CeL neurons. This, together with its fast-spiking properties, suggests the presence in the CeL of a local interneuron-like cell as opposed to the principal-type neurons typically found in the CeL.
Paired recordings demonstrated that neurons in the CeL were connected with an incidence of ∼29%. In these recordings, we find that at the local level (∼50–100 μm in coronal slices), the most common connection was unidirectional and between two PKCδ(−) or two SOM(+) cells. In agreement with a recent report (Hou et al., 2016), connections between other pairs, as well as bidirectional connections were present but were much less prevalent. We did not, however, find cells that showed clear evidence of autapses, which were reported in ∼15% of neurons in the Hou et al. (2016) study. In contrast, when SOM(+) or SOM(−) neurons were transduced with ChR2, we found that nearly all cells received a large input from both cell types. This difference in connectivity indicates that neurons make long-range connections within the CeL, perhaps in the rostrocaudal plane.
For the SOM neurons, using paired recordings, the monosynaptic connection had a mean amplitude of ∼20 pA (at −40 mV), whereas when SOM neurons were transduced with ChR2, the optically driven IPSC had a mean amplitude of ∼160 pA, showing that on average approximately eight SOM(+) neurons innervate each SOM(−) neuron. In paired recordings, the IPSC had rapid rise times, suggesting that these contacts were likely to be somatic, or close to the soma (Delaney and Sah, 2001), which is consistent with the ability of these connections to halt spiking.
The CeL and behavior
The role of the CeL in cued fear expression is clear: a large body of data supports a model whereby conditioned stimulus-mediated disinhibition of CeM output drives conditioned fear (Ciocchi et al., 2010; Haubensak et al., 2010; Li et al., 2013). However, it remains unclear how the high level of CeL connectivity (both intra-CeL and extra-CeL afferents) can be reconciled with the increasing number of important behaviors in which CeL activity has been implicated. For example, fear expression has also been suggested to require activation of the parabrachial nucleus (PB) input to the CeL (Han et al., 2015; Sato et al., 2015), and yet this PB → CeL circuit has also been implicated in appetite suppression (Carter et al., 2013; Cai et al., 2014). Meanwhile, other CeL circuits have been shown to underlie the switch between innate and conditioned fear (Isosaka et al., 2015), and anxiety generalization (Botta et al., 2015). Last, as well as forming local inhibitory connections (Li et al., 2013), SOM(+) neurons are also projection neurons that target the PAG (Penzo et al., 2014), and this CeA → PAG projection is engaged in mediating defensive behaviors (Tovote et al., 2016). We have shown that these neurons are also highly interconnected both within and between distinct neuronal populations. Our results suggest that within the CeL, neither cytosolic markers (PKCδ and SOM) nor their electrophysiological properties alone can be used to identify cells engaged in particular behavioral roles.
The physiologic role, if any, of SOM and PKCδ are not known; however, they clearly label separate populations of neurons in the CeL. Developmentally, the CeL has a striatal origin (Medina et al., 2011), and SOM and PKCδ, rather than specifying different populations that mediated different functional roles, should be thought of as lineage markers. We suggest that PKCδ-expressing and SOM-expressing neurons form heterogeneous populations of neurons, with different populations contributing to different behavioral outcomes. Understanding the flow of information through the CeA and its outputs, in a behaviorally specific and relevant manner, will be a challenge for future experiments. Similarly, it will be important to take these additional local circuits into account in further investigations of the CeL circuitry, particularly when judging the effects of pharmacological treatments during in vivo studies.
Synthesis
Reviewing Editor: Pablo Castillo, Albert Einstein College of Medicine
Decisions are customarily a result of the Reviewing Editor and the peer reviewers coming together and discussing their recommendations until a consensus is reached. When revisions are invited, a fact-based synthesis statement explaining their decision and outlining what is needed to prepare a revision will be listed below. The following reviewer(s) agreed to reveal their identity: Roger Clem, Mario Penzo.
Your manuscript has been evaluated by two reviewers. The following is a synthesis of the online discussion on your study. The reviewers agree on the high quality of the results and overall merit of your findings. They have also raised some criticisms regarding the interpretation of the results that you should address in your revised manuscript. For example, you should acknowledge that the viral transfection efficiency is not necessarily 100%. They understand you should be able to estimate the typical expression efficiency using co-immunolabeling. They also agree you should articulate more clearly the meaning of your findings beyond indicating that the intrinsic circuitry is more complex than previously thought. Below you will find the reviewers' specific comments. It is expected that the revision of your manuscript requires less than 2 months' work.
----------------------------------------------------------------------------
Reviewer #1
The lateral CeA is involved in many types of behavioral regulation. Recent studies suggest that it contains multiple distinct neuronal populations and that these cell types exhibit differential long-range synaptic connectivity. However, the organization of local connections between various cell types remains less clear. Here the authors provide a detailed examination of inhibitory connections among and between somatostatin and PKC-delta expressing populations. The results indicate that more frequent connections exist within than between cell classes. These findings will help establish what types of local computation are supported by CeA neurons by providing a basic wiring diagram of the two major cell classes and demonstrating the function of associated GABAergic connections. The experiments were performed to a high standard, with complementary analyses leaving little room for alternative interpretation. For a somewhat more specialized audience the results will be of high interest. I have just a few main concerns:
1. The most significant, and surprising, finding is that connection probability is highest within PKCdelta or SST+ neurons. This would not necessarily be predicted given the existing model for threat memory expression involving SST--
2. After struggling to understand authors' perspective, I feel like it would be beneficial for them to articulate more clearly why they think that the intrinsic circuitry exhibits more complexity than previously appreciated. Do they mean that there is more heterogeneity of firing phenotypes? If so, why did they not further parse their analysis of connectivity according to these subclasses? Or by complexity are they referring to the fact that there is more local connectivity within different classes than they anticipated?
3. An interesting question to which the authors seem to have alluded is what types of local interactions exist between projection-specific ensembles in the lateral CeA. Do they have any evidence on this topic or can they explain what implications their data might have?
Reviewer #2
The central nucleus of the amygdala, particularly the lateral portion (CeL), has become a center of attention in the study of fear memory following groundbreaking findings in the early 2000s by the groups of Stephen Maren and Joseph LeDoux. However, while several claims have been made regarding the local circuit organization of the CeL, a thorough interrogation of its intrinsic circuits have been missing. The present manuscript by Hunt et al. does much to fill that gap. Following a set of elegant experiments, they reach the important conclusion that connections between like-neurons are typically stronger than those between neurons of different types in CeL. And although, in light of previous publications, it is hard to interpret the overall meaning of these findings, at the very least they raise an important issue about the complexity of central amygdala circuits. This is of vital importance, because it confirms that most models of central amygdala fear circuits are extremely oversimplified and should be revised. Therefore, the contribution of the present manuscript is significant.
In general, the authors have done a good job at addressing the concerns raised during a previous review of their manuscript. And while I am satisfied with most changes, I want to highlight a few points that deserve attention:
1) Towards the end of the 3rd paragraph of the introduction the authors claim that SOM(+) neurons of CeL are likely to be the so-called CeLon neuons of the classic Ciocchi et al., 2010 paper, but that this has not been demonstrated. I disagree. I paper published this summer by Bo Li and colleagues (Yu et al., 2006 - Journal of Neuroscience) shows that SOM(+) neurons of CeL are activated following presentation of the CS+ in conditioned mice. The manuscript should include this important citation.
2) The results presented in Figure 8, 9 and 10 has an important caveat the authors should highlight. Because in these experiments SOM(+) neurons were labeled using a virus approach, it is possible that neurons that are non-fluorescent in these experiments (referred to as SOM(-)) are in fact SOM(+) neurons that were not infected with the virus. Therefore, connectivity rates in these experiments may be slightly compromised because of this. Particularly for SOM(-)-SOM(-) and SOM(+)-SOM(-) connections. It is not surprising to me that they found the single stuttering neuron in Figure 8c to be SOM(-). This could have been a SOM(+) neuron that didn't get infected by the virus. This problem could have been avoided using a Cre-dependent R26 reporter line. And, while I don't think the authors need to repeat these experiments, they should warn the reader that their approach limits the interpretation of the results.
References
- Amano T, Amir A, Goswami S, Pare D (2012) Morphology, PKCδ expression, and synaptic responsiveness of different types of rat central lateral amygdala neurons. J Neurophysiol 108:3196–3205. 10.1152/jn.00514.2012 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Apostolides PF, Trussell LO (2013) Rapid, activity-independent turnover of vesicular transmitter content at a mixed glycine/GABA synapse. J Neurosci 33:4768–4781. 10.1523/JNEUROSCI.5555-12.2013 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Botta P, Demmou L, Kasugai Y, Markovic M, Xu C, Fadok JP, Lu T, Poe MM, Xu L, Cook JM, Rudolph U, Sah P, Ferraguti F, Lüthi A (2015) Regulating anxiety with extrasynaptic inhibition. Nat Neurosci 18:1493–1500. 10.1038/nn.4102 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Cai H, Haubensak W, Anthony TE, Anderson DJ (2014) Central amygdala PKC-δ(+) neurons mediate the influence of multiple anorexigenic signals. Nat Neurosci 17:1240–1248. 10.1038/nn.3767 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Carter ME, Soden ME, Zweifel LS, Palmiter RD (2013) Genetic identification of a neural circuit that suppresses appetite. Nature 503:111–114. 10.1038/nature12596 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Cassell MD, Gray TS (1989) Morphology of peptide-immunoreactive neurons in the rat central nucleus of the amygdala. J Comp Neurol 281:320–333. 10.1002/cne.902810212 [DOI] [PubMed] [Google Scholar]
- Ciocchi S, Herry C, Grenier F, Wolff SB, Letzkus JJ, Vlachos I, Ehrlich I, Sprengel R, Deisseroth K, Stadler MB, Müller C, Lüthi A (2010) Encoding of conditioned fear in central amygdala inhibitory circuits. Nature 468:277–282. 10.1038/nature09559 [DOI] [PubMed] [Google Scholar]
- Delaney AJ, Sah P (2001) Pathway-specific targeting of GABA(A) receptor subtypes to somatic and dendritic synapses in the central amygdala. J Neurophysiol 86:717–723. [DOI] [PubMed] [Google Scholar]
- Dumont EC, Martina M, Samson RD, Drolet G, Paré D (2002) Physiological properties of central amygdala neurons: species differences. Eur J Neurosci 15:545–552. [DOI] [PubMed] [Google Scholar]
- Duvarci S, Pare D (2014) Amygdala microcircuits controlling learned fear. Neuron 82:966–980. 10.1016/j.neuron.2014.04.042 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Gross CT, Canteras NS (2012) The many paths to fear. Nat Rev Neurosci 13:651–658. 10.1038/nrn3301 [DOI] [PubMed] [Google Scholar]
- Grove EA (1988) Neural associations of the substantia innominata in the rat: afferent connections. J Comp Neurol 277:315–346. 10.1002/cne.902770302 [DOI] [PubMed] [Google Scholar]
- Han S, Soleiman MT, Soden ME, Zweifel LS, Palmiter RD (2015) Elucidating an affective pain circuit that creates a threat memory. Cell 162:363–374. 10.1016/j.cell.2015.05.057 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Haubensak W, Kunwar PS, Cai H, Ciocchi S, Wall NR, Ponnusamy R, Biag J, Dong HW, Deisseroth K, Callaway EM, Fanselow MS, Lüthi A, Anderson DJ (2010) Genetic dissection of an amygdala microcircuit that gates conditioned fear. Nature 468:270–276. 10.1038/nature09553 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hou WH, Kuo N, Fang GW, Huang HS, Wu KP, Zimmer A, Cheng JK, Lien CC (2016) Wiring specificity and synaptic diversity in the mouse lateral central amygdala. J Neurosci 36:4549–4563. 10.1523/JNEUROSCI.3309-15.2016 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Isosaka T, Matsuo T, Yamaguchi T, Funabiki K, Nakanishi S, Kobayakawa R, Kobayakawa K (2015) Htr2a-expressing cells in the central amygdala control the hierarchy between innate and learned fear. Cell 163:1153–1164. 10.1016/j.cell.2015.10.047 [DOI] [PubMed] [Google Scholar]
- Jolkkonen E, Pitkänen A (1998) Intrinsic connections of the rat amygdaloid complex: projections originating in the central nucleus. J Comp Neurol 395:53–72. [DOI] [PubMed] [Google Scholar]
- Klüver H, Bucy PC (1939) Preliminary analysis of functions of the temporal lobes in monkeys. 1939. J Neuropsychiatry Clin Neurosci 9:606–620. 10.1176/jnp.9.4.606 [DOI] [PubMed] [Google Scholar]
- LeDoux JE (2000) Emotion circuits in the brain. Annu Rev Neurosci 23:155–184. 10.1146/annurev.neuro.23.1.155 [DOI] [PubMed] [Google Scholar]
- Li H, Penzo MA, Taniguchi H, Kopec CD, Huang ZJ, Li B (2013) Experience-dependent modification of a central amygdala fear circuit. Nat Neurosci 16:332–339. 10.1038/nn.3322 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Lopez de Armentia M, Sah P (2004) Firing properties and connectivity of neurons in the rat lateral central nucleus of the amygdala. J Neurophysiol 92:1285–1294. 10.1152/jn.00211.2004 [DOI] [PubMed] [Google Scholar]
- Martina M, Royer S, Paré D (1999) Physiological properties of central medial and central lateral amygdala neurons. J Neurophysiol 82:1843–1854. [DOI] [PubMed] [Google Scholar]
- McDonald AJ (1982) Cytoarchitecture of the central amygdaloid nucleus of the rat. J Comp Neurol 208:401–418. 10.1002/cne.902080409 [DOI] [PubMed] [Google Scholar]
- McDonald AJ, Augustine JR (1993) Localization of GABA-like immunoreactivity in the monkey amygdala. Neurosci 52:281–294. [DOI] [PubMed] [Google Scholar]
- Medina L, Bupesh M, Abellán A (2011) Contribution of genoarchitecture to understanding forebrain evolution and development, with particular emphasis on the amygdala. Brain Behav Evol 78:216–236. 10.1159/000330056 [DOI] [PubMed] [Google Scholar]
- Penzo MA, Robert V, Li B (2014) Fear conditioning potentiates synaptic transmission onto long-range projection neurons in the lateral subdivision of central amygdala. J Neurosci 34:2432–2437. 10.1523/JNEUROSCI.4166-13.2014 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Sah P, Faber ES, Lopez De Armentia M, Power J (2003) The amygdaloid complex: anatomy and physiology. Physiol Rev 83:803–834. 10.1152/physrev.00002.2003 [DOI] [PubMed] [Google Scholar]
- Sato M, Ito M, Nagase M, Sugimura YK, Takahashi Y, Watabe AM, Kato F (2015) The lateral parabrachial nucleus is actively involved in the acquisition of fear memory in mice. Mol Brain 8:22 10.1186/s13041-015-0108-z [DOI] [PMC free article] [PubMed] [Google Scholar]
- Sosulina L, Graebenitz S, Pape HC (2010) GABAergic interneurons in the mouse lateral amygdala: a classification study. J Neurophysiol 104:617–626. 10.1152/jn.00207.2010 [DOI] [PubMed] [Google Scholar]
- Spampanato J, Polepalli J, Sah P (2011) Interneurons in the basolateral amygdala. Neuropharmacol 60:765–773. 10.1016/j.neuropharm.2010.11.006 [DOI] [PubMed] [Google Scholar]
- Sun N, Cassell MD (1993) Intrinsic GABAergic neurons in the rat central extended amygdala. J Comp Neurol 330:381–404. 10.1002/cne.903300308 [DOI] [PubMed] [Google Scholar]
- Tovote P, Esposito MS, Botta P, Chaudun F, Fadok JP, Markovic M, Wolff SB, Ramakrishnan C, Fenno L, Deisseroth K, Herry C, Arber S, Lüthi A (2016) Midbrain circuits for defensive behaviour. Nature 534:206–212. 10.1038/nature17996 [DOI] [PubMed] [Google Scholar]
- Weiskrantz L (1956) Behavioral changes associated with ablation of the amygdaloid complex in monkeys. J Comp Physiol Psychol 49:381–391. [DOI] [PubMed] [Google Scholar]
- Woodruff AR, Sah P (2007) Networks of parvalbumin-positive interneurons in the basolateral amygdala. J Neurosci 27:553–563. 10.1523/JNEUROSCI.3686-06.2007 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Yu K, Garcia da Silva P, Albeanu DF, Li B (2016) Central amygdala somatostatin neurons gate passive and active defensive behaviors. J Neurosci 36:6488–6496. 10.1523/JNEUROSCI.4419-15.2016 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zhao S, Ting JT, Atallah HE, Qiu L, Tan J, Gloss B, Augustine GJ, Deisseroth K, Luo M, Graybiel AM, Feng G (2011) Cell type-specific channelrhodopsin-2 transgenic mice for optogenetic dissection of neural circuitry function. Nat Methods 8:745–752. [DOI] [PMC free article] [PubMed] [Google Scholar]