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«Distribution Agreement In presenting this thesis or dissertation as a partial fulfillment of the requirements for an advanced degree from Emory ...»

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It is notable that the currents mentioned above (IT, IA, and IH) are all sensitive to membrane hyperpolarization, particularly in the voltage range between rest and action potential threshold (Rudy, 1988; Perez-Reyes, 2003; Robinson and Siegelbaum, 2003). Specifically, IT channels are de-inactivated by hyperpolarization in this range and IH channels are activated, while IA channels are activated by depolarization in this range. We have shown that compound IPSPs facilitate the MPO in the absence of spiking, and this is likely due to hyperpolarization-mediated de-inactivation of IT and activation of IH. While we did not find an effect of IH blockade on the MPO, it is possible this is an artifact of the degree to which we depolarize the membrane to enhance the MPO. The MPO is likely also active in a more subtle form at membrane potentials only slightly depolarized from rest, where IT and IA are active and IH would enhance the rebound from an IPSP and may contribute directly to the MPO.

In addition, the conductances of these currents, and therefore the magnitude of the MPO itself, are not fixed but sensitive to modulation. Importantly, the channels mediating IT and IH increase their activity in response to PKA phosphorylation (Ingram and Williams, 1996;

Gerhardstein et al., 1999; Kamp and Hell, 2000; Vargas and Lucero, 2002; Kim et al., 2006a;

Ramadan et al., 2009). Conversely, activity of K+ channels mediating IA is decreased by PKA phosphorylation (Hoffman and Johnston, 1998). Together, these would enable neurotransmitter systems which modulate PKA activity to have synergistic effects to bi-directionally modulate the MPO.

The frequency of rhythmic, compound IPSPs is also sensitive to modulation. We have previously shown that dopamine acts to increase IPSP frequency into a range of 2-6 Hz (Rainnie, 1999b; Loretan et al., 2004; Muly et al., 2009). This would bring the IPSP frequency closer to the peak resonance frequency of BLA principal neurons, ensuring principal neurons respond to incoming rhythmic IPSPs with maximal voltage deflections and also serve to enhance the interaction with the MPO. In our hands, the oscillation does not occur spontaneously but is initiated by the IPSP and is naturally damped, quickly decaying from its initial amplitude. More frequent synchronized IPSPs would better reset drift in the phase relationship between cells and better maintain the amplitude of the oscillation.

It is interesting to note that the intracellular cascades activated by many of the neuromodulators that promote the MPO (e.g., Gs-coupled activation of cAMP and PKA) are also critical for synaptic plasticity. Through this mechanism, cells primed by neuromodulators to exhibit an MPO may be more likely to contribute to a fear memory engram.

6.5.3 Implications for learning and memory Recent studies by several groups have emphasized the importance of amygdala network oscillations and synchronized oscillations across multiple brain regions in regulating long-term fear memory (Quirk et al., 1995; Collins et al., 2001; Pare et al., 2002; Seidenbecher et al., 2003;

Pelletier and Pare, 2004; Pape et al., 2005; Bauer et al., 2007; Paz et al., 2008). Importantly, phase-locked stimulation of the amygdala and hippocampus at theta frequency during extinction training prolongs fear expression, suggesting synchronized network oscillations between these regions are an essential neurological component of fear memory (Lesting et al., 2011). The high delta / low theta oscillations in the LFPs of the BLA, hippocampus, and prefrontal cortex during fear acquisition and expression(Madsen and Rainnie, 2009; Sangha et al., 2009) match the frequency of the MPO and the peak resonance in BLA neurons, suggesting the intrinsic properties of BLA neurons contribute to the network oscillation. As we have argued, a candidate mechanism to promote these network oscillations is the interaction of synchronized IPSPs with MPOs in BLA principal neurons. MPOs could contribute to fear learning by promoting network oscillations, and by improving spike-timing precision they could support fear memory formation through enhanced spike-timing dependent plasticity (Dan and Poo, 2004; Jutras and Buffalo, 2010).

The sub-cellular mechanism of the intrinsic MPO is well-suited to facilitate plasticity in the BLA and thereby promote fear learning. The MPO requires activation of voltage-gated calcium currents (Pape and Driesang, 1998), causing calcium influx and subsequent activation of cAMP and PKA (Zaccolo and Pozzan, 2003). This can, in turn, reinforce the oscillation through phosphorylation of ion channels. In fact, the oscillation is weak or nonexistent under our baseline experimental conditions, but must be uncovered by application of the PKA activator, forksolin.

The close relationship of the MPO with the adenylyl cyclase-cAMP signaling cascade is particularly important because its downstream targets have been implicated in fear learning and memory (Schafe et al., 1999; Josselyn et al., 2001; Kida et al., 2002; Josselyn et al., 2004), and in regulating theta oscillations in the amygdala in vivo (Josselyn et al., 2001; Kida et al., 2002;

Josselyn et al., 2004; Josselyn and Nguyen, 2005; Pape et al., 2005). It is also noteworthy that downstream targets of this signaling cascade, particularly the cAMP response element binding protein (CREB), have been used to identify those neurons activated specifically during fear memory formation (Han et al., 2007; Han et al., 2009).





One neurotransmitter receptor known to modulate the cAMP-PKA pathway, the dopamine D1 receptor, is also implicated in fear learning. Release of dopamine and subsequent activation of D1 receptors in the BLA are critically involved in the acquisition and consolidation of fear memory (Lamont and Kokkinidis, 1998; Greba et al., 2001). Additionally, we have recently shown that D1-receptor activation is necessary for long-term potentiation of sensory afferents to the BLA (Li et al., 2011). Aside from direct effects on synaptic plasticity, D1 receptor activation may promote fear learning by facilitating an MPO. Considering that the MPO must be uncovered by activation of PKA in vitro, D1-receptor activation could provide the requisite PKA activation to initiate a self-reinforcing high delta / low theta oscillation in vivo.

Importantly, in the prefrontal cortex, application of dopamine mimics the effect of a working memory task to entrain firing of principal neurons to an ongoing theta oscillation (Benchenane et al., 2010). One possible explanation, which parallels our observations in the BLA in vitro, is that interneurons maintain a network oscillation by providing a background of rhythmic activity to which principal neurons are, at baseline, minimally sensitive. In this model, the principal neurons become more sensitive to rhythmic inhibitory input from the interneurons by activation of PKA, either directly (as in our hands, with forskolin) or with the introduction of dopamine (either artificially or endogenously through a behavioral task) (Benchenane et al., 2010; Young, 2011).

Interestingly, activation of D1 receptors has also been shown to enhance spike-timing dependent plasticity, potentially compounding with the effects of D1 activation on spike-timing precision via the MPO (Zhang et al., 2009).

We have shown that inhibition of adenylyl cyclase and cAMP production by activation of group II metabotropic glutamate receptors completely abolished the high amplitude MPO induced by forskolin and 4-AP. Activation of these receptors has been associated with reductions in fear learning, as well as de-potentation of synapses and long-term depression (Pin and Duvoisin, 1995; Lin et al., 2000; Lin et al., 2005), providing further support for a role for MPOs in BLAdependent fear learning. Interestingly, the Gi-coupled type 1 cannabinoid receptor has been shown to reduce neural synchrony and dampen theta and gamma oscillations in the hippocampus (Robbe et al., 2006), further suggesting changes in cAMP levels can bi-directionally modulate the propensity of a network to oscillate.

While network oscillations contribute to normal brain functions, including fear learning, aberrant oscillations have been implicated in the pathophysiology of psychiatric disorders. For example, it is well established that diminished synchrony between pyramidal neurons, and consequently aberrant network oscillations in the gamma band, are involved in the pathophysiology of schizophrenia (Lewis et al., 2005). Interestingly, the changes in oscillations observed in schizophrenia have been specifically linked to diminished function in the PV + subpopulation of interneurons in the cortex (Lewis et al., 2005). It is worth noting that theta oscillations are thought to modulate the gain of gamma oscillations, and both are produced through the action of PV+ interneurons (Canolty et al., 2006; Bartos et al., 2007; Jensen and Colgin, 2007). Gamma-frequency oscillations are observed in the BLA both in vivo and in vitro (Sinfield and Collins, 2006; Randall et al., 2011), and may be generated by similar mechanisms in the BLA as in the cortex due to their similar composition and architecture (Carlsen and Heimer, 1988). Considering the importance of neural oscillations and that compound IPSPs may influence gamma oscillations through their effects on high delta / low theta oscillations, future studies should address changes in oscillations and PV+ interneurons in the amygdala in various psychiatric disorders, particularly post-traumatic stress disorder and others linked to fear learning (Rainnie and Ressler, 2009).

Figure 6.1: Spontaneous, compound IPSPs in the BLA were synchronized across principal neurons and with bursts in inhibitory interneurons Figure 6.

1: Spontaneous, compound IPSPs in the BLA were synchronized across principal neurons and with bursts in inhibitory interneurons. (A) A representative pair of primate BLA principal neurons, held at -60 mV, showing compound IPSPs that are rhythmic and highly synchronized, observed during gap-free recordings. (B) A histogram plotting instantaneous frequency of compound IPSPs during 30-second recordings from 12 primate BLA principal neurons. (C) Paired recordings in the primate BLA of a principal neuron receiving compound IPSPs and a burst-firing parvalbumin interneuron, both held at -60 mV. (D) An example of a burst-IPSP pair shown at higher temporal resolution. (E) A compound IPSP can be induced in a BLA principal neuron by using current injection to drive bursting activity in the interneuron.

Figure 6.2: Spontaneous, compound IPSPs coordinated spike timing and promoted rhythmic firing in the primate BLA Figure 6.

2: Spontaneous, compound IPSPs coordinated spike timing and promoted rhythmic firing in the primate BLA. (A) Spontaneous, compound IPSPs exhibited by a representative pair of primate BLA projection neurons, depolarized to action potential threshold (-45 to -40 mV) using a DC current injection. A raster plot highlights the relative synchrony of spikes following the IPSPs, highlighted in gray boxes. Action potentials are cropped at -30 mV (n = 6). (B) A spike correlation metric (see Methods) is plotted for 6 pairs of primate BLA principal neurons exhibiting compound IPSPs and depolarized to threshold, as in A. Correlation is plotted for each pair as an individual, smoothed trace (thin black lines) representing the mean correlation surrounding every spontaneous, compound IPSP, with the peak of each IPSP aligned to time 0. The mean of all 6 pairs is superimposed as a dotted black line. (C-D) A representative single (C, n = 4) and pair of (D, n = 2) primate BLA principal neurons exhibiting rhythmic firing upon rebound from spontaneous, compound IPSPs. Neurons were depolarized to threshold, as in A. IPSPs and rebound firing are highlighted with gray boxes in C. Action potentials were cropped at -30mV. (E) A primate BLA principal neuron, depolarized as in A, exhibiting a damped membrane potential oscillation in response to a spontaneous, compound IPSP.

Figure 6.3: Spike-timing precision diminishes in spike trains and is reset by compound IPSPs Figure 6.

3: Spike-timing precision diminishes in spike trains and is reset by compound IPSPs. (A) A single sweep recorded from a spiking BLA principal neuron, held at -45 mV by steady-state current injection, displaying a typical regular firing pattern. (B) Multiple sweeps like that in A overlaid and aligned by their first spikes. A raster plot illustrates decay of spike-timing reliability. (C) Injection of artificial IPSPs recovers spike-timing precision.

Figure 6.4: Artificial and evoked compound IPSPs improved spike-timing precision in individual BLA principal neurons Figure 6.

4: Artificial and evoked compound IPSPs improved spike-timing precision in individual BLA principal neurons. (A) Five superimposed traces from a representative principal neuron, held at -60 mV, showing a train of action potentials in response to a depolarizing current step in the presence of DNQX (20 µM), RS-CPP (10 µM) and CGP (2 µM);

note the loss of spike-timing precision as the spike train progresses. (B, C) Similar traces to A with the injection of evoked (B) or artificial (C) compound IPSPs to demonstrate improvement of spike-timing precision following a compound IPSP. (D, E, F) Comparisons of spike-timing precision for neurons with no IPSPs (Control, n = 11), evoked IPSPs (n = 11), and artificial IPSPs (n = 11), assessed with a spike correlation metric (see Methods) and plotted as mean ± SEM.

Comparisons were made using a two-way ANOVA (see Results), and windows of significant differences (p 0.05) in spike correlation are denoted with grey boxes.

Figure 6.5: Artificial, compound IPSPs coordinated spike timing across pairs of BLA principal neurons Figure 6.

5: Artificial, compound IPSPs coordinated spike timing across pairs of BLA principal neurons. (A) Five overlaid, consecutive traces of action potentials during paired recordings of BLA principal neurons, held at -60 mV, in response to a depolarizing current injection without IPSPs and (B) with two IPSPs. (C) Spike correlation metric calculated across pairs of neurons when artificial IPSPs are injected compared to the control condition (n = 6 pairs), plotted against time. Comparisons were made using a two-way ANOVA (see Results), and grey boxes denote windows of significant differences (p 0.05) in spike correlation.

Figure 6.6: BLA principal neurons exhibited a modifiable intrinsic resonance and a membrane potential oscillation that was facilitated by compound IPSPs Figure 6.



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