A Critical Role for Neocortical Processing of Threat Memory

Article

A Critical Role for Neocortical Processing of Threat Memory Highlights d

Auditory cortex is selectively required for complex stimulus threat memory

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Adjacent temporal association cortex controls all forms of auditory threat memory

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Cortico-amygdala information transmission governs complex stimulus memory

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Amygdala-projecting neurons show population plasticity for stimulus discrimination

Dalmay et al., 2019, Neuron 104, 1–15 December 18, 2019 ª 2019 Elsevier Inc. https://doi.org/10.1016/j.neuron.2019.09.025

Authors Tamas Dalmay, Elisabeth Abs, Rogier B. Poorthuis, ..., Philip Tovote, Julijana Gjorgjieva, Johannes J. Letzkus

Correspondence [email protected]

In Brief Dalmay et al. demonstrate that the neocortex plays a key role in learning and memory, particularly when auditory stimuli are complex and naturalistic. Memory is mediated by plasticity in neurons projecting to the amygdala, the brain’s hub for emotions.

Please cite this article in press as: Dalmay et al., A Critical Role for Neocortical Processing of Threat Memory, Neuron (2019), https://doi.org/10.1016/ j.neuron.2019.09.025

Neuron

Article A Critical Role for Neocortical Processing of Threat Memory Tamas Dalmay,1,5 Elisabeth Abs,1,5 Rogier B. Poorthuis,1 Jan Hartung,1 De-Lin Pu,1 Sebastian Onasch,1 Yave R. Lozano,2 Je´re´my Signoret-Genest,2,3 Philip Tovote,2 Julijana Gjorgjieva,1,4 and Johannes J. Letzkus1,6,* 1Max

Planck Institute for Brain Research, 60438 Frankfurt, Germany €rzburg, 97078 Wu €rzburg, Germany of Clinical Neurobiology, University Hospital Wu 3Department of Psychiatry, Center of Mental Health, 97078 Wu €rzburg, Germany 4School of Life Sciences, Technical University of Munich, 85354 Freising, Germany 5These authors contributed equally 6Lead Contact *Correspondence: [email protected] https://doi.org/10.1016/j.neuron.2019.09.025 2Institute

SUMMARY

Memory of cues associated with threat is critical for survival and a leading model for elucidating how sensory information is linked to adaptive behavior by learning. Although the brain-wide circuits mediating auditory threat memory have been intensely investigated, it remains unclear whether the auditory cortex is critically involved. Here we use optogenetic activity manipulations in defined cortical areas and output pathways, viral tracing, pathway-specific in vivo 2-photon calcium imaging, and computational analyses of population plasticity to reveal that the auditory cortex is selectively required for conditioning to complex stimuli, whereas the adjacent temporal association cortex controls all forms of auditory threat memory. More temporal areas have a stronger effect on memory and more neurons projecting to the lateral amygdala, which control memory to complex stimuli through a balanced form of population plasticity that selectively supports discrimination of significant sensory stimuli. Thus, neocortical processing plays a critical role in cued threat memory.

INTRODUCTION An ultimate goal of much of modern neuroscience is to understand the function of the neocortex. Work on diverse cortical areas has by now produced a detailed understanding of the computations they perform. A case in point is the auditory cortex, which decomposes complex natural sounds into stimulus features such as frequency components and modulation rates, amplitude, and bandwidth (Mizrahi et al., 2014; Wang, 2018). In parallel, a large body of work has defined the architecture of the neocortical circuits that mediate these computations in great detail (Harris and Mrsic-Flogel, 2013). Conversely, we still understand much less about how these functions of neocortical areas enable defined behavioral capacities and the precise conditions

that govern this necessity (Hong et al., 2018; Otchy et al., 2015; €ttgen and Schwarz, 2018). This is non-trivial because inforStu mation that guides behavior is invariably processed in multiple parallel neuronal pathways in the brain. Moreover, for a brain area to instruct behavior efficiently, this information needs to be represented in a compatible format and sent to output pathways targeting the downstream structures relevant for the specific behavioral output. A large number of studies using lesions of the motor or sensory neocortex have reported no major deficits in several related behavioral functions (Campeau and Davis, 1995; Hong et al., 2018; Moczulska et al., 2013; Otchy et al., 2015; Peter et al., 2012; Romanski and LeDoux, 1992b; Sacco and Sacchetti, €ttgen and Schwarz, 2018), raising the fundamental 2010; Stu question to which behavioral capacities neocortical function makes a critical contribution. One controlled, highly tractable brain function is Pavlovian conditioning, in which acoustic cues are associated with aversive events and evoke learned defensive responses such as freezing (Herry and Johansen, 2014; LeDoux, 2000; Maren and Quirk, 2004; Pape and Pare, 2010). Although great progress has been made in delineating the brain-wide circuits that contribute to threat memory (Herry and Johansen, 2014; LeDoux and Daw, 2018; LeDoux, 2000; Tovote et al., 2015), whether and how the auditory cortex contributes remains contentious. Both pre- and post-training lesions of the auditory cortex often fail to cause memory deficits (Campeau and Davis, 1995; Moczulska et al., 2013; Peter et al., 2012; Romanski and LeDoux, 1992a, 1992b; Sacco and Sacchetti, 2010), whereas fewer studies have described impairments (Boatman and Kim, 2006; Campeau and Davis, 1995). A serious confound of lesions is, however, that they allow time for compensatory rearrangements of the substrates mediating a brain function (Hong et al., €ttgen and Schwarz, 2018; Talwar 2018; Otchy et al., 2015; Stu et al., 2001). More recent studies avoiding this confound by transient pharmacological or optogenetic inactivation of the auditory cortex during memory acquisition and expression have, however, also yielded diverse results, with evidence for (Banerjee et al., 2017; Yang et al., 2016) and against (Gillet et al., 2018; Zhang et al., 2018) critical involvement as well as for specific deficits in stimulus discrimination (Wigestrand et al., 2016). Similar discrepancies have been reported for other Neuron 104, 1–15, December 18, 2019 ª 2019 Elsevier Inc. 1

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forms of auditory discrimination behavior (e.g., Gimenez et al., 2015; Talwar et al., 2001), together indicating that the parameters that govern the critical role of the auditory cortex in instructing behavioral functions remain elusive. RESULTS Stimulus Complexity Determines the Requirement of the Auditory Cortex for Threat Memory Acquisition To inhibit the auditory cortex with tight temporal control and high specificity for the affected areas, we used adeno-associated viral vector (AAV)-mediated expression of the optogenetic inhibitor ArchT (Han et al., 2011) in pyramidal neurons under the Ca2+/calmodulin-dependent protein kinase II (CaMKII) promoter (Figures 1A and 1F). Both in vitro and in vivo recordings demonstrate the validity of this approach (Figures 1A–1D and S1A–S1F). ArchT expression was targeted bilaterally to the primary auditory cortex (Au1) and the ventral secondary auditory cortex (AuV). After fiber implantation, experimental mice expressing ArchT or GFP controls underwent discriminative threat conditioning with laser inactivation throughout the entire sound-footshock compound (Figure 1E). We first used a pair of frequency-modulated (FM) sweeps of opposing modulation direction as conditioned stimuli (CSs) (Gillet et al., 2018; Letzkus et al., 2011), a type of naturalistic stimulus (Mizrahi et al., 2014; Ohl et al., 2001; Rauschecker, 1998). One day after conditioning, freezing elicited by unreinforced re-exposure to CS+ and CS
in the absence of optogenetic perturbation was quantified. Animals expressing ArchT displayed a profound memory deficit relative to GFP controls selectively for CS+, rendering the memory non-discriminative (Figure 1G1). Reconditioning without auditory cortex inhibition resulted in significantly increased CS+ freezing levels (Figure S1G), indicating that the observed deficit is due to optogenetic inactivation rather than long-term effects such as damage. These data are consistent with previous experiments using optogenetics selectively during the footshock (Letzkus et al., 2011) and demonstrate that auditory cortex processing is critical for threat learning to complex CSs. However, by far the most widely used CSs in this research are pure tones, simple stimuli that are extremely rare in nature and, thus, less naturalistic than FM sweeps (Mizrahi et al., 2014; Ohl et al., 2001; Rauschecker, 1998). Analogous experiments using a pair of pure tones as CSs revealed that auditory cortex inhibition during the conditioning session caused only a small trend for memory impairment relative to GFP controls that furthermore did not affect discrimination (Figure 1G2 and S1H). These data demonstrate that the auditory cortex is selectively required for learning associations with complex naturalistic stimuli but not simple pure tones. More broadly, these results indicate that not all auditory behaviors depend on auditory cortex function. In line with this, we also found no effect of auditory cortex inactivation on startle responses to noise bursts (Figures S1I and S1J). Stimulus Complexity Determines the Requirement of the Auditory Cortex for Threat Memory Expression To further elucidate whether stimulus complexity may indeed be a key determinant of the critical involvement of the auditory cor-

tex, we performed FM sweep threat conditioning without laser stimulation and inhibited the auditory cortex during memory retrieval in an alternating fashion in 50% of CS presentations (Figures 2A and 2B). This revealed a strong deficit in CS+ FM sweep memory compared with presentation without laser, rendering the memory non-discriminative (Figure 2C1). Because of the rapid decline of freezing evoked by CS
(Figures 2D and 2E), accurately addressing the effect of optogenetic inhibition on CS
memory is not possible (Figures S2H and S3C). The impairment in CS+ freezing was fully reversible (Figures 2D and S2I) and not observed in GFP controls (Figures 2D, S2A, S2C1, and S2D). Conversely, analogous experiments using pure-tone CSs showed no deficit in memory expression (Figures 2C2, 2E, 2F, S2C2, and S2E–S2G). FM sweeps differ from pure tones in both frequency content and temporal modulation. To test whether frequency content is a key factor, we used a pair of band-pass-filtered noise within the same frequency range as FM sweeps and pure tones. Inhibition of the auditory cortex during memory expression caused only a small, non-significant reduction in freezing (Figures 2C3 and S2E–S2G). Importantly, post hoc quantification of ArchT expression indicates that differences in the size of the affected area between these experimental groups cannot account for the stronger effects observed for FM sweep memory (Figure S2B). Collectively, these results reveal that differences in stimulus complexity are a key determinant of whether the auditory cortex is critically required for threat memory acquisition and expression. Impairment of Non-discriminative Memory by Auditory Cortex Inhibition The observed deficits in memory acquisition and expression to complex FM sweeps could be due to impairments of memory or, alternatively, effects on stimulus discrimination (LeDoux, 2000; Wigestrand et al., 2016). To address this, we used auditory cortex inhibition during non-discriminative conditioning (Figure 3A), revealing that ArchT animals displayed a profound memory deficit relative to GFP controls in the subsequent retrieval session (Figures 3B–3D). Moreover, in animals that had undergone non-discriminative conditioning without laser stimulation, inhibition of the auditory cortex caused a robust decrease in freezing that was indistinguishable from similar data using discriminative conditioning (Figures 3E, 3F, S2J, and S3B). Together, these data demonstrate that auditory cortex function is indispensable for both discriminative and non-discriminative memory acquisition and expression to complex FM sweeps, indicating that CS identification is perturbed by this intervention. Because CS identification is a prerequisite for discrimination between different CSs, we conclude that the auditory cortex contributes to discriminative threat memory by mediating the identification of CSs. The Temporal Neocortex Is Critically Required for Threat Memory Expression and Acquisition Regardless of Stimulus Complexity The lack of pure-tone CS memory impairment by auditory cortex inhibition may suggest that this information can be conveyed to the amygdala by thalamic afferents (Bordi and

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Figure 1. The Auditory Cortex Is Selectively Required for Memory Acquisition with Complex Conditioned Stimuli (A) Experimental schematic. (B) Raster plot (top) and peristimulus time histogram (PSTH, bottom) of an example unit. Inset: spike waveform with (orange) and without laser stimulation (black). (C) Z-scored PSTH of 70 units recorded from 3 mice. (D) Laser stimulation caused significant inhibition in 74.3% of the recorded units (p < 0.05, Wilcoxon matched pairs signed-rank test). (E) Auditory cortex inhibition during threat conditioning. (F) ArchT expression in the Au1-AuV and optic fiber placement (orange). (G1 and G2) Freezing during memory retrieval without optogenetic perturbation (single animals, gray markers; population averages, bars; repeated measures (RMs) 2-way ANOVA with Tukey’s multiple comparisons test). (G1) ArchT-mediated inhibition of the Au1-AuV led to strongly reduced freezing to FM sweep CS+ (n = 7, orange) compared with GFP-expressing controls (n = 9, black). The residual memory was similar for CS
and CS+, whereas controls displayed discrimination. (G2) In contrast, no memory impairment was found for pure-tone CSs (ArchT, n = 11, orange; GFP, n = 6, black). See Table S1 for full results of statistical tests. Data are shown as mean ± SEM. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

LeDoux, 1994; Herry and Johansen, 2014; LeDoux, 2000; Romanski and LeDoux, 1992b; Weinberger, 2011). Alternatively, additional areas of the neocortex may exist that also contribute to threat memory irrespective of CS complexity. To address this, we performed slightly larger injections of AAVs into the temporal neocortex, leading to expression in the Au1 and AuV as well as the adjacent temporal association cortex (TeA) (Figures 4A and S3A). After conditioning without laser stimulation (Figure 4B), optogenetic inhibition of temporal neocortex led to a reduction of freezing in mice conditioned to FM sweeps (Figure 4C1) that was indistinguishable from the impairment observed for inhibition of the auditory cortex alone (Figure S3B). Strikingly however, animals conditioned to pure tones dis-

played a very similar impairment (Figure 4C2), in clear contrast to the results observed after inhibition of the auditory cortex alone (Figures 2C2 and S3B). Moreover, similar memory impairments were observed for noise CSs at both the standard 75-decibel (dB) sound pressure level (SPL) (Figures 4C3 and S3B) as well as at 90 dB SPL (Figures 4C4 and S3B). In addition, memory was also affected after non-discriminative conditioning using a continuous noise CS instead of the standard sound trains (Figures 4C5 and S3B). In line with the interpretation that the TeA area is crucial for this outcome, the extent of ArchT expression in the TeA correlates with the reduction in freezing observed across the different experimental conditions (Figure S3D). In addition to memory

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Figure 2. Auditory Cortex Is Selectively Required for Memory Expression with Complex Conditioned Stimuli (A) ArchT expression in the Au1-AuV. (B) Auditory cortex inhibition during memory retrieval. (C1–C3) Freezing without (gray) and with (orange) optogenetic perturbation (single animals, gray lines; population averages, bars; RMs 2-way ANOVA with Tukey’s multiple comparisons test). (C1) ArchT-mice conditioned to FM sweeps displayed robust discriminative memory in the control. Inhibition of auditory cortex caused a strong reduction of CS+ evoked freezing so that memory of CS+ and CS
in the presence of the laser was indistinguishable (n = 11). (C2) Inhibition did not affect pure-tone CS memory expression (n = 10). (C3) Inhibition did not affect band-pass-filtered noise CS memory expression (n = 13). (D) Freezing during each FM sweep presentation in ArchT mice (n = 11, gray) and GFP mice (n = 6, black) without (black/gray squares) and with laser stimulation (orange squares, RMs 2-way ANOVA with Tukey’s multiple comparisons test). (E) Same as (D) for mice conditioned with pure tones and noise CSs (n = 23) and additional GFP controls (n = 11, RMs 2-way ANOVA with Tukey’s multiple comparisons test). (F) Laser effect on CS+ freezing of ArchT mice across stimulus groups (1-way ANOVA with Tukey’s multiple comparisons test). See Table S1 for full results of statistical tests. Data are shown as mean ± SEM. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

expression, inhibition of the temporal neocortex was also sufficient to impair the acquisition of threat extinction (Figure S3E), further highlighting the central role of this area in processing aversive auditory cues. Importantly, despite the ubiquitous impairment of auditory memory, we found no effect of temporal neocortex inhibition on cued visual memory or on memory linked to context in these animals (Figures 4D and S3F– S3G2), indicating that our data cannot be explained by unspe-

cific, off-target effects because of diaschisis (Hong et al., 2018; Otchy et al., 2015). Finally, inhibition of the temporal neocortex during conditioning also caused a profound memory deficit for both FM sweep and simple CS+ (Figures 4E–4F2 and S3H– S3L). Together, these results firmly identify the temporal neocortex as a vital area for acquisition and retrieval of threat memory as well as for acquisition of extinction across a wide range of CS types and experimental conditions, consistent

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Figure 3. The Auditory Cortex Is Required for Non-discriminative Memory Acquisition and Expression with Complex Conditioned Stimuli (A) ArchT expression in the Au1-AuV and non-discriminative conditioning. (B) Auditory cortex inhibition during conditioning. (C) Freezing during memory retrieval without optogenetic perturbation (single animals, gray markers; population averages, bars; RMs 2-way ANOVA with Sidak’s multiple comparisons test). ArchT-mediated inhibition of the Au1-AuV led to strongly reduced freezing to the CS (n = 8, orange) compared with GFP-expressing controls (n = 6, black). (D) Reconditioning of the same ArchT mice without optogenetic perturbation (red bars; single animals, gray lines) yielded stronger memory (RMs 2-way ANOVA with Tukey’s multiple comparisons test). (E) Inhibition during memory retrieval. (F) Inhibition caused a strong reduction of CS-evoked freezing (n = 15, gray versus orange bars, RMs 1-way ANOVA with Tukey’s multiple comparisons test). See Table S1 for full results of statistical tests. Data are shown as mean ± SEM. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

with the requirement of this area for consolidation of recent memories (Cambiaghi et al., 2016). The Temporal Neocortex Areas AuV and TeA Contribute More Strongly to Threat Memory Expression Than Au1 Given the observed dichotomy between inactivation of the auditory and temporal cortex, we next aimed to define the relative contribution of the different cortical subdivisions by using restricted expression of ArchT in either the Au1 or AuV-TeA (Figures 5A–5B2). Inhibition of the Au1 alone during FM sweep memory retrieval only caused small, non-significant impairment (Figures 5B1 and 5C1). In stark contrast, memory expression was strongly reduced when only the AuV-TeA areas were inhibited in analogous experiments (Figures 5B2, 5C2, 5D, and S4A–S4C). Furthermore, inhibition of either the AuV or TeA alone resulted in FM sweep CS memory impairments (Figures S4I1–S4M), demonstrating the critical contribution of both areas to threat memory. In addition, inhibition of the AuV-TeA also caused robust memory impairment for pure-tone CSs that was indistinguishable from the results observed for FM sweeps (Figures 5C3 and 5D). Moreover, the distance of the ArchT expression area center to the rhinal fissure correlated with memory impairment across CS types (Figure S4C). Together, these data demonstrate that stimulus processing in the AuV-

TeA is critical for memory independent of CS complexity and that threat memory is differentially governed by different areas of the temporal neocortex organized along a medio-temporal gradient of impact. The Cortico-amygdala Pathway Is Organized along a Medio-temporal Gradient and Critically Contributes to Complex CS Threat Memory Expression Because the temporal neocortex provides strong input to the lateral amygdala (McDonald, 1998; Romanski and LeDoux, 1993), we hypothesized that the observed differential contribution of temporal neocortex areas could be related to information transfer through this pathway. In line, amygdala-projecting neurons display a medio-temporal density gradient with very few cells in the Au1 and increasingly greater numbers in the AuV and TeA both ipsi- and contralateral to the injection site (Figures 5E–5G, S4D-S4H, and S4N). These data highlight an intriguing parallel between the organization of the cortico-amygdala projection and the relative importance of temporal neocortex areas for threat memory. To address the contribution of information flow from the temporal neocortex to the amygdala to threat memory expression, optic fibers were targeted to ArchT-expressing axons of temporal neocortex neurons in the lateral amygdala (Figures 6A and

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(legend on next page)

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6B). In vitro experiments indicate the validity of this approach (Figures S5A–S5D). Axonal inhibition during memory retrieval revealed a robust memory deficit (Figures 6C1, S5E, S5G, and S5H), demonstrating the critical role of cortical information transfer to the amygdala for complex FM sweep CSs. In line, optogenetic activation of these axons caused a strong increase in freezing levels of naive animals (Figures 6D, 6E, and S5I–S5K). Importantly, retrieval of threat memory linked to context was completely unaffected by optogenetics in these animals (Figure S5F). In contrast, analogous experiments using simple pure-tone CSs indicated only smaller, non-significant memory impairment (Figures 6C2 and S5H), suggesting that parallel output pathway(s) from the temporal neocortex may exist that can transmit simple CS information indirectly to the amygdala. Direct cortico-amygdala information transfer is therefore more critical for FM sweeps than for pure tones. This finding resembles the dichotomy between pure tones and FM sweeps we found for auditory cortex function, together indicating that only complex naturalistic FM sweep CSs fully engage the neuronal circuits of threat memory. Amygdala-Projecting Neurons Display Balanced Population Plasticity that Mediates Stimulus Discrimination Given the critical role of temporal neocortex neurons with a direct projection to the lateral amygdala for threat memory expression to complex, naturalistic CSs, we next performed in vivo 2-photon calcium imaging to record the responses of the same amygdala-projecting neurons in the AuV area during habituation and memory retrieval (Figures 7A, S6A, and S6B; Abs et al., 2018). Animals either underwent FM sweep threat conditioning (Figure 7B) or pseudoconditioning as a control for stimulus exposure (Figure 7C). To verify memory expression during the imaging session in head fixation, pupil diameter changes in response to the CSs were also recorded (Figures S6C and S6D). During the habituation session, amygdala-projecting neurons displayed diverse responses to the CSs, including excitation and inhibition of ongoing firing. We first compared the top third of excitatory responses and bottom third of inhibitory responses between habituation and retrieval (Figures

7F, 7G, 7I, and 7J). Whereas individual neurons displayed pronounced reorganization of their responses, as evidenced by changes in rank order (Figures 7F, 7G, S6E, and S6F), on average, both excitatory and inhibitory transients were approximately maintained in the population during memory retrieval (Figures 7I and 7L). Although the responses to CS+ and CS
were similar during habituation, the memory-related changes led to CS discrimination in the excitatory responses during retrieval (Figure 7L). Similar results were obtained when analyzing neurons with significant excitatory or inhibitory CS responses (Figures S6G, S6H, S6J, S6K, and S6M) and for mice conditioned to pure-tone CSs (Figures S7A–S7F). In clear contrast, pseudoconditioning with FM sweeps caused a robust reduction in excitatory responses (Figures 7H, 7K, 7M, S6I, S6L, and S6N) along with a reduction in the number of significantly activated neurons (Figure S6I), consistent with similar observations in auditory cortex layer 2/3 pyramidal neurons and layer 1 interneurons (Abs et al., 2018; Gillet et al., 2018). To elucidate whether the rearrangements in the responsive neuronal population are related to conditioning or, alternatively, an inherent property of the circuit, we performed 2 days of habituation. The responsive neuronal population was rearranged considerably between these recording sessions (Figures S7B and S7C), indicating that these changes are likely a general feature (Chambers and Rumpel, 2017). Discrimination of stimuli that signal threat from behaviorally neutral information is a key attribute of aversive memory (LeDoux and Daw, 2018; LeDoux, 2000; Tovote et al., 2015). We therefore asked whether the observed balanced plastic changes may affect the discriminability of the CSs. Because there were no obvious changes in single-neuron CS selectivity (Figures S6O, S6P, S7G, and S7R), we surmised that CS discrimination might be encoded in the population response. To address this, we analyzed the spatiotemporal patterns evoked by CS presentation in the recorded population by computing a highdimensional vector of calcium imaging signals of all neurons over time. Each vector was stacked in a matrix consisting of the number of calcium signal time bins 3 the number of neurons (Stopfer et al., 2003). Principal-component analysis (PCA) was performed for dimensionality reduction, allowing us to plot the

Figure 4. The Temporal Neocortex Is Critically Required for Threat Memory Expression and Acquisition Regardless of Stimulus Complexity (A) ArchT expression in the temporal neocortex (Au1-AuV-TeA) and optic fiber placement (orange). (B) Inhibition during memory retrieval. (C1–C5) Freezing during memory retrieval without (gray) and with (orange) optogenetic perturbation (single animals, gray lines; population averages, bars; RMs 2way ANOVA with Tukey’s multiple comparisons test). (C1) ArchT mice conditioned to FM sweeps displayed robust discriminative memory in the absence of laser stimulation. Inhibition of the temporal neocortex caused a strong reduction in CS+ evoked freezing so that memory of CS+ and CS
in the presence of the laser was indistinguishable (n = 7). (C2) Same as (C1) for pure-tone CSs (n = 8). (C3) Same as (C1) for noise CSs (n = 12). (C4) Same as (C1) for louder noise CSs (90-dB SPL instead of the standard 75-dB SPL, n = 9). (C5) Same as (C1) for non-discriminative conditioning with continuous noise CSs (n = 5). (D) Retrieval of contextual memory in a subset of ArchT mice (n = 8) was unaffected by temporal neocortex inhibition (p = 0.88, paired t test). (E) Inhibition during conditioning. (F1 and F2) Freezing during memory retrieval without optogenetic perturbation (single animals, gray markers; population averages, bars; RMs 2-way ANOVA with Tukey’s multiple comparisons test). (F1) Compared with GFP controls (black, n = 6), temporal neocortex inhibition during conditioning in ArchT mice (orange, n = 5) significantly reduced CS+ freezing in animals conditioned to FM sweeps. (F2) Same as (F1) for ArchT (n = 9) and GFP mice (n = 7) conditioned to pure-tone and noise CSs. See Table S1 for full results of statistical tests. Data are shown as mean ± SEM. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

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Figure 5. Stronger Requirement of Cortical Areas AuV-TeA for Memory Expression and Medio-temporal Density Gradient of Amygdala-Projecting Cells (A) Inhibition during memory retrieval. (B1 and B2) ArchT expression and optic fiber placement (orange) in the Au1 (B1) or AuV-TeA (B2). (C1–C3) Freezing during memory retrieval without (gray) and with (orange) optogenetic perturbation (single animals, gray lines; population averages, bars; RMs 2way ANOVA with Tukey’s multiple comparisons test). (C1) Au1 inhibition caused a small trend for reduced FM sweep CS+ freezing (n = 10) that left discrimination intact. (C2) Inhibition of the AuV-TeA strongly reduced FM sweep CS+ freezing and abolished discrimination (n = 6). (C3) Animals conditioned to pure-tone CSs (n = 6) displayed strong memory impairment for the CS+ and no CS discrimination. (D) Laser effect on CS+ freezing of mice expressing ArchT in the Au1 or AuV-TeA (1-way ANOVA with Tukey’s multiple comparisons test). (E) Injection of a retrograde vector expressing Cre recombinase into the lateral amygdala of a Cre reporter strain. (F) Amygdala-projecting cells in the ipsilateral temporal cortex. Note the medio-temporal density gradient. (G) Proportion of amygdala-projecting cells in ipsilateral cortical areas (21 slices from 3 mice, RMs 1-way ANOVA with Tukey’s multiple comparisons test). See Table S1 for full results of statistical tests. Data are shown as mean ± SEM. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

temporal evolution of population responses to CS+ and CS
as trajectories in the space defined by the three main principalcomponents (PCs), explaining more than 80% of variance in the data (Figures 8A and S8A–S8D). Importantly, this analysis captures the directions of largest variability of the temporal profile of responses as well as their amplitudes. During habituation to FM sweep CSs, these trajectories were clearly distinct but relatively close to each other. Strikingly, the same population showed a doubling of the distance between CS+ and CS
trajectories during threat memory retrieval, indicating that the responses had become much more distinct with learning (Fig-

ures 8A–8C). In line with this, a similar enhancement of CS discrimination was also observed in a separate cohort of animals conditioned with pure-tone CSs (Figures 8D–8F). Consistent with this effect being related to conditioning, these mice showed discriminative freezing behavior (Figures 7B and S7A). In contrast, 2 consecutive days of habituation caused no change in stimulus discrimination (Figures S8K–S8M), whereas pseudoconditioning led to a strong decrease in trajectory distance (Figures 8G–8I). In line with the changes in CS discrimination after threat and pseudoconditioning, comparison of the trajectories in response to a given CS during habituation and

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Figure 6. The Cortico-amygdala Pathway Is Required for FM Sweep CS Memory Expression and Induces Freezing Behavior (A) Inhibition during memory retrieval. (B) ArchT expression in the temporal neocortex (Au1-AuV-TeA) with optic fiber placement (dashed line) in the lateral amygdala (solid lines) for axonal inhibition. (C1 and C2) Freezing during memory retrieval without (gray) and with (orange) optogenetic perturbation (single animals, gray lines; population averages, bars; RMs 2-way ANOVA with Tukey’s multiple comparisons test). (C1) Inhibition of cortical axons caused a strong reduction in FM sweep CS+ freezing (n = 13). (C2) In contrast, mice conditioned to pure-tone CSs (n = 12) displayed only a trend for reduced freezing during axonal inhibition. (D) Same as (B) for ChR2 expression. (E) Freezing of ChR2-expressing mice before (baseline) and in the 30 s after optogenetic stimulation onset (laser, n = 5). Stimulation of cortical axons caused successively stronger freezing (RMs 1-way ANOVA with Tukey’s multiple comparisons test). See Table S1 for full results of statistical tests. Data are shown as mean ± SEM. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

retrieval indicated changes in CS representation for both behavioral conditions (Figures S8E–S8J). These data demonstrate that amygdala-projecting neurons display a form of balanced plasticity that improves stimulus discrimination at the population level in the absence of strong changes in overall activity. Finally, to elucidate whether these effects are specific to amygdala-projecting cells, we performed analogous experiments on AuV neurons projecting to the striatum (Figures S7H– S7Q), a pathway implicated in operant auditory discrimination behavior (Xiong et al., 2015a). Similar to the above experiments, threat conditioning was associated with reorganization of neuronal CS responses between habituation and retrieval (Figures S7J–S7M). Importantly, however, this population displayed a robust decrease in CS trajectory distance during threat memory retrieval (Figures 8J–8L). Taken together, these experiments reveal that enhanced CS discrimination at the population level is selectively observed in amygdala-projecting neurons after threat conditioning, in line with our results demonstrating that this pathway is required for threat memory expression to complex FM sweep CSs. These findings thus highlight the central role of the cortico-amygdala pathway in encoding naturalistic sensory information with learned aversive relevance (Figure 8M). DISCUSSION The present results demonstrate that the temporal neocortex contributes substantially and directly to expression of threat

memory and define how CS complexity, different cortical areas, and brain-wide output pathways interact to shape this behavior. One striking result is that the auditory cortex is selectively required for memory acquisition and retrieval to FM sweep CSs. Given that FM sweeps are ubiquitous components of naturally occurring vocalizations (Mizrahi et al., 2014; Rauschecker, 1998), this raises the testable hypothesis that the auditory cortex may be an obligatory part of the network that mediates threat memory under real-world conditions that are also relevant to human experiences leading to adaptive and maladaptive outcomes. Previous studies using lesions of the auditory cortex have reported memory deficits for some naturalistic CSs but not others (Moczulska et al., 2013; Peter et al., 2012). On the one hand, our data validate FM sweeps as a stimulus type that captures the vital role of the auditory cortex while offering much greater control over parameters such as frequency content and rate of modulation than natural sounds (Letzkus et al., 2011; Ohl et al., 2001). This is further underpinned by the recent finding that the auditory cortex is also selectively required for discrimination between FM sweeps and pure tones in an appetitive go/no go task (Ceballo et al., 2019, in this issue of Neuron). On the other hand, this raises the question of which stimulus parameters contribute to making a sound complex as defined by auditory cortex-dependent processing. Our data suggest that one critical factor may be temporal modulation, but broad frequency content may also contribute. Future work may use the methodology established here to test the effect of

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(legend on next page)

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elementary sound features such as bandwidth, amplitude modulation (Yin et al., 2016), frequency modulation (Tian and Rauschecker, 2004), brief gaps in ongoing sounds (Weible et al., 2014), and tone contours on auditory cortex-dependent processing in a principled manner. Cortical processing of complex sounds occurs primarily along the ventral auditory stream, where neurons display large receptive fields and both preference and selectivity for broadband sounds, in particular FM sweeps (Rauschecker, 1998; Tian and Rauschecker, 2004). In line with this, we found only modest impairment of complex FM sweep memory during inhibition of the Au1 alone but a strong reduction in freezing when the AuV or TeA were inhibited. These results are therefore consistent with the interpretation that complex CS-related information flows in cortical ‘‘cascades’’ from the primary sensory cortex via secondary cortical areas toward the higher-order association cortex (McDonald, 1998; Rauschecker, 1998; Romanski and LeDoux, 1993). Our data refine this model by demonstrating the increasing connection strength to the lateral amygdala along this pathway, the central role of cortico-amygdala communication in FM sweep memory, and the population plasticity of amygdala-projecting neurons. Collectively, these results indicate that complex CS information transferred from the temporal neocortex to the lateral amygdala is indispensable for threat memory. This also reveals an intriguing parallel with the role of the auditory cortex in category learning (Ohl et al., 2001), raising the question of whether amygdala-projecting neurons may also contribute to this function. In contrast, flight behavior has been linked to auditory cortex projections to the inferior colliculus (Xiong et al., 2015b), highlighting the different mechanisms of innate versus learned defensive behavior (Tovote et al., 2015). Going forward, future work is needed to address the similarities and differences of our results with appetitive operant memory (Ceballo et al., 2019), including the role of the TeA (Eichenbaum et al., 2007) and of cortico-amygdala communication (Malvaez et al., 2019). Our work highlights a number of differences in the cortical mechanisms that mediate FM sweep and pure-tone CS memory. First, pure-tone learning and memory are unaffected by auditory cortex inhibition. Although previous work has often hypothesized that higher-order thalamic afferents to the lateral amygdala alone may be able to carry out this function (Herry

and Johansen, 2014; LeDoux, 2000; Romanski and LeDoux, 1992b), our results refine this view by demonstrating that the AuV-TeA areas are also obligatory substrates for learning and memory independent of CS identity, CS intensity, and paradigm design. While future work on non-discriminative learning is required, we propose an updated model for discriminative threat memory in which both the AuV-TeA and higher-order thalamus are critical, and several lines of evidence suggest that interactions between these areas may be required. Although puretone memory is independent of the auditory cortex, it critically requires the AuV-TeA. This suggests that the necessary afferent information does not derive from lower cortical areas. Instead, afferents from the high-order thalamus are likely candidates because this area projects strongly and preferentially to the AuV-TeA (Kimura et al., 2003), displays learning-related plasticity (Weinberger, 2011), and provides relatively coarse auditory information that may be able to rescue perception of simple stimuli (Bordi and LeDoux, 1994). Second, we find that direct cortico-amygdala transmission is less critical for simple compared with complex CS memory, suggesting the existence of additional output pathways from the AuV-TeA that can convey simple CS information to the amygdala indirectly. Given the strong corticofugal projections to the higher-order thalamus (Winer, 2006), it appears likely that cortico-thalamic transmission may underlie this indirect route to the amygdala. Future work is therefore needed to elucidate the interactions between the AuV-TeA and the higher-order thalamus for simple and complex CS memory. By what mechanism does the cortico-amygdala pathway contribute to FM sweep memory expression? In vivo calcium imaging of amygdala-projecting neurons uncovered that information encoded in these neurons is highly sensitive to the learned relevance of sensory stimuli. Thus, repeated exposure to the CSs during pseudoconditioning caused a pronounced decrease in excitatory responses together with a reduction of CS discrimination at the population level. In contrast, associative learning elicited rearrangements of the population response that occurred with only modest changes in absolute response size for both CSs. This balanced form of plasticity was mediated by both increases and decreases in CS responses of individual neurons, in line with recent experiments in the sensory neocortex and the amygdala that have reported both potentiation and

Figure 7. Plasticity of Amygdala-Projecting Neuron Responses in Area AuV after Threat Conditioning (A) Discriminative threat and pseudoconditioning with awake in vivo 2-photon calcium imaging. (B) Freezing of mice conditioned to FM sweep CSs (n = 21, RMs 1-way ANOVA with Tukey’s multiple comparisons test). (C) Freezing of pseudoconditioned mice (n = 5, F (1.159, 4.635) = 0.3649, p = 0.61, RMs 1-way ANOVA with Tukey’s multiple comparisons test). (D) Amygdala-projecting neurons in the AuV expressing an ultra-sensitive protein calcium sensor (GCaMP6s) and tdTomato for motion correction. (E) Responses of an example neuron during habituation (single trials, gray; average, green) and memory retrieval (average, red). (F) Responses of all recorded neurons (154 cells in 8 mice) to the CS+ before (top) and after threat conditioning (bottom), ranked separately according to response integral for habituation and memory retrieval (solid line, CS+; dashed line, population thirds (I–III); gray scale, rank order during habituation). Note the widespread changes in rank order of individual neurons. (G) Same as (F) for CS
. (H) Same as (F) for pseudoconditioning (99 cells in 5 mice). Because both CSs are behaviorally equivalent (C), CS1 and CS2 were pooled for this analysis. (I–K) Average responses of the top third (I) and bottom third (III) of neurons from (F)–(H) (blue bars, CS) during habituation (green) and memory retrieval (red). (L) Response integral of the top third (I) and bottom third (III) of neurons in threat-conditioned mice. Excitatory responses to CS+ and CS
were similar during habituation (green) but became discriminative during memory retrieval (red, 1-way ANOVA with Tukey’s multiple comparisons test). (M) Same as (L) for pseudoconditioning, showing reduced excitatory responses (Mann Whitney test). See Table S1 for full results of statistical tests. Data are shown as mean ± SEM. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

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Figure 8. Selective Enhancement of Population Stimulus Discrimination by Threat Conditioning in Amygdala-Projecting Neurons (A) Experimental paradigm (left) and responses to FM sweep CSs in the population of amygdala-projecting neurons (154 cells in 8 mice), represented as trajectories over time after CS onset (gray line in the color scale bar) in the space defined by the first 3 PCs. During habituation, CSs cause divergent trajectories, (legend continued on next page)

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depression of CS responses (Gdalyahu et al., 2012; Gillet et al., 2018; Grewe et al., 2017). The population plasticity was able to enhance discrimination of both simple and complex CSs markedly and selectively in amygdala-projecting neurons but not in striatum-projecting cells, revealing a pathway-specific, population-level plasticity mechanism (cf. Chen et al., 2013). In turn, this may allow robust, energy-efficient information storage while preserving the capacity of the circuit to encode future memories. Furthermore, this suggests that memory expression can be elicited in the absence of pronounced response potentiation, likely by either rearrangement of the auditory cortex neurons that encode the CSs or by further plasticity in the amygdala (Herry and Johansen, 2014; LeDoux, 2000; Maren and Quirk, 2004; Pape and Pare, 2010; Tovote et al., 2015). Future studies are therefore needed to elucidate whether the connectivity of these neurons with defined circuit elements in the lateral amygdala affects their memory-related plasticity and how the population plasticity we find in this key afferent pathway instructs complex CS encoding in the amygdala. STAR+METHODS

d d d

d d

KEY RESOURCES TABLE LEAD CONTACT AND MATERIALS AVAILABILITY EXPERIMENTAL MODEL AND SUBJECT DETAILS B Animals METHOD DETAILS B Surgery B Threat conditioning and optogenetics B Perfusions and histological analysis B In vivo calcium imaging B Analyses of in vivo calcium imaging data B Measurement of pupil diameter B In vivo extracellular recordings B Slice preparation and whole-cell recordings B Acoustic startle B Visual threat conditioning QUANTIFICATION AND STATISTICAL ANALYSIS DATA AND CODE AVAILABILITY B FreezingScoring B AudioGame

EyeTracker and Camera Acquisition Processing of Calcium Imaging Data B CellCounter B

SUPPLEMENTAL INFORMATION Supplemental Information can be found online at https://doi.org/10.1016/j. neuron.2019.09.025. ACKNOWLEDGMENTS €thi; M. Kaschube; E. Schuman; We thank all members of the Letzkus lab; A. Lu M.S. Fustinana Gueler and members of the FENS Kavli Network of Excellence for comments and discussions; A. Wrana for outstanding technical assistance; S. Junek, C. Polisseni, and F. Vollrath for help with image processing; F. Kretschmer, G. Tushev, and Juan Luis Riquelme for assistance with programming and data analysis; L.L. Looger, J. Akerboom, D.S. Kim, and the GENIE Project at Janelia Farm for making GCaMP available; and K. Deisseroth, E.S. Boyden, and H. Zeng for generously sharing reagents. This work was supported by the Max Planck Society, the European Research Council (StG 335587 to J.J.L.), and the German Research Foundation (CRC1193 [Project B02] and SPP1665 to J.J.L. and TO1124 to P.T.). AUTHOR CONTRIBUTIONS

Detailed methods are provided in the online version of this paper and include the following: d

B

J.J.L., T.D., and E.A. initiated the project. Experiments and data analyses were performed as follows: Figures 1 and S1A–S1H by T.D., R.B.P., and J.J.L.; Figures S1I, S1J, and S3F–S3G2 by Y.R.L., J.S.-G., and P.T.; Figures 2, 3, 4, 5, 6, S2, S3A–S2E, S2H–S2K, S4A–S4I, S4N, and S5E–S5K by T.D. and J.J.L.; S4J– S4M by T.D., D.-L.P., and J.J.L.; Figures S5A–S5D by J.H. and J.J.L.; and Figures 7, 8, and S6–S8 by E.A., S.O., J.G., and J.J.L. J.J.L. conceived the project and wrote the manuscript with input from all authors. All authors contributed to the experimental design and interpretation and commented on the manuscript. DECLARATION OF INTERESTS The authors declare no competing interests. Received: February 26, 2019 Revised: August 10, 2019 Accepted: September 17, 2019 Published: November 11, 2019 REFERENCES Abs, E., Poorthuis, R.B., Apelblat, D., Muhammad, K., Pardi, M.B., Enke, L., Kushinsky, D., Pu, D.L., Eizinger, M.F., Conzelmann, K.K., et al. (2018). Learning-Related Plasticity in Dendrite-Targeting Layer 1 Interneurons. Neuron 100, 684–699.e6.

indicating stimulus discrimination that can be quantified as their normalized distance (here, 0.80; Video S1). Threat conditioning caused an increase in trajectory distance (to 1.57), indicating enhanced CS discrimination. (B) CS trajectory distance over time during habituation (green) and memory retrieval (red; blue bars, CS). (C) Bootstrapped distributions of these data indicate enhanced stimulus discrimination (5,000 iterations, Mann-Whitney test). (D–F) same as (A)–(C) for amygdala-projecting neurons with pure-tone CSs (82 cells in 5 mice), indicating an increase in trajectory distance (from 0.21 to 0.34; Video S2). (G–I) same as (A)–(C) for amygdala-projecting neurons after FM sweep pseudoconditioning (99 cells in 5 mice), revealing a decrease in trajectory distance (from 0.18 to 0.11; Video S3). (J–L) same as (A)–(C) for striatum-projecting neurons after FM sweep conditioning (200 cells in 5 mice), indicating a decrease in trajectory distance (from 1.57 to 1.05; Video S4). (M) Main results of the study. Left: the auditory cortex is selectively required for threat memory to complex FM sweep stimuli. Center: more temporal areas have a stronger effect on memory independent of CS complexity. Naturalistic FM sweep CSs critically depend on cortico-amygdala information transfer, whereas pure tones likely employ additional output pathways. Right: neurons projecting to the lateral amygdala display a balanced form of population plasticity that selectively supports the discrimination of sensory stimuli with learned relevance. See Table S1 for full results of statistical tests. Data are shown as mean ± SEM. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

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STAR+METHODS KEY RESOURCES TABLE

REAGENT or RESOURCE

SOURCE

IDENTIFIER

AAV2/1-CAG.Flex.GCaMP6s.WPRE.SV40

PennVector Core

AV-1-PV2818

AAV2/1.CAG.Flex.tdTomato.WPRE.bGH

PennVector Core

AV-1-ALL864

AAV2/1-phSyn1-FLEX-tdTomato-T2A-SypEGFPWPRE

Salk Vector Core

51509

CAV2-CMV-Cre-SV40

Plateforme de Vectorologie de Montpellier

CAV Cre

AAV2/5.CAMKII.ArchT.GFP.WPRE.SV40

PennVector Core

AV-5-PV2527

AAV2/5.CamKII0.4.eGFP.WPRE.rBG

PennVector Core

AV-5-PV1917

AAV2/5.CamKIIa.hChR2(H134R)-EYFP.WPRE.hGH

PennVector Core

AV-5-26969P

DAPI

Thermo Fisher

D1306

FluoSpheres

Thermo Fisher

F8813

Carboxylate-modified polystyrene latex beads

Sigma-Aldrich

L3280

Bacterial and Virus Strains

Chemicals, Peptides, and Recombinant Proteins

Experimental Models: Organisms/Strains Mouse: C57BL/6J

N/A

Mouse: B6.Cg-Gt(ROSA)26Sortm9(CAG-tdTomato)Hze/J

The Jackson Laboratory

007909

Software and Algorithms MATLAB

MathWorks

N/A

Prism

GraphPad

N/A N/A

Fiji

https://fiji.sc/

pClamp

Molecular Devices

N/A

Zen

Carl Zeiss

N/A

PlexControl

Plexon

N/A

OfflineSorter

Plexon

N/A

RPvdsEx

Tucker-Davis Technolgies

N/A

RStudio

https://rstudio.com/

N/A

NeuroExplorer

Nex Technologies

N/A

LEAD CONTACT AND MATERIALS AVAILABILITY Further information and requests for resources and reagent should be directed to and will be fulfilled by the Lead Contact, Johannes J. Letzkus ([email protected]). EXPERIMENTAL MODEL AND SUBJECT DETAILS Animals Male C57BL6/J and Ai9 Cre reporter mice (2-6 months old) were housed under a 12 h light/dark cycle and provided with food and water ad libitum. After surgical procedures, mice were individually housed. Prior to threat conditioning, mice were habituated to handling R 5 times. All animal procedures were executed in accordance with institutional guidelines, and approved by the prescribed €sidium Darmstadt, Bezirksregierung Unterfranken and Veterinary Department of the Canton of authorities (Regierungspra Basel-Stadt).

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METHOD DETAILS Surgery Mice were anesthetized with isoflurane (induction: 4%, maintenance: 2%) in oxygen-enriched air (Oxymat 3ª, Weinmann, Hamburg, Germany) and fixed in a stereotaxic frame (Kopf Instruments, Tujunga, USA). Core body temperature was maintained at 37.5 C by a feed-back controlled heating pad (FHC, Bowdoinham, ME, USA). Analgesia was provided by local injection of ropivacain under the scalp (Naropinª, AstraZeneca, Switzerland) and systemic injection of metamizole (100 mg/kg, i.p., Novalgin, Sanofi) or buprenorphine (0,1 mg/kg), and meloxicam (2 mg/kg, i.p., Metacamª, Boehringer-Ingelheim, Ingelheim, Germany). Adeno-associated viral vectors (AAV, serotype 2/1 or 2/5, 50-500 nl, depending on the experiment) were injected from glass pipettes (tip diameter 10-20 mm) connected to a pressure ejection system (PDES-02DE-LA-2, NPI, Germany) into auditory cortex at the following coordinates: 2.54 mm posterior of bregma, 4.6 mm lateral of midline, depth below cortical surface varied for experiments. Experiments were performed after 1-3 months of expression time. For optogenetic inhibition, mice were injected with 100-200 nL AAV2/5-CaMKIIa-eArchT3.0-eYFP (Penn vector core) from 0 to 400 mm below the cortical surface for expression in Au1, 0 to 600 mm for expression in Au1-AuV, and 0 to 900 mm for expression in Au1-AuV-TeA. For expression in AuV-TeA, auditory cortex was approached more laterally (4.9 mm lateral of midline), the AAV was diluted 1:1 in sterile ringer solution and injected at a single depth (600 mm below the cortical surface). For light delivery, mice were implanted with custom-built connectors holding optic fibers (0.39 NA, 200 mm diameter, Thorlabs, Newton, NJ, US). For inhibition of Au1, Au1-AuV, AuV-TeA, and Au1-TeA, optic fiber ends were inserted 200-300 mm into neocortex at the injection site. For inhibition of AuV or TeA alone, fiber ends were inserted at depths of 800 mm or 1200 mm below the cortical surface, respectively. To target light delivery to the lateral amygdala, optic fiber implants were inserted at the coordinates: 1.6-7 mm posterior bregma, 3.5 mm lateral of midline and 3.1 mm below cortical surface. Implants were fixed to the skull with cyanoacrylate glue (Ultra Gelª, Henkel, €sseldorf, Germany) and dental cement (Paladurª, Heraeus, Hanau, Germany). Du For retrograde labeling and expression of Cre in amygdala-projecting cells, 50-100 nL of CAV2-Cre mixed with 0.5 mm fluorescent microspheres (green: FluoSpheres, Thermo Fisher, Waltham, MA, US, red: carboxylate-modified polystyrene latex beads, Sigma-Aldrich, St. Louis, MO, US) was injected into the lateral amygdala at the following coordinates: 1.6-1.7 mm posterior bregma, 3.5 mm lateral of midline and 3.5-4.0 mm below cortical surface. For targeting striatum-projecting neurons the before mentioned mixture was injected at the following coordinates: 1.7 mm posterior bregma, 3.5 mm lateral of midline and 3 mm below cortical surface. For calcium imaging, a craniotomy was performed over the right auditory cortex using a sterile biopsy punch (3 mm, Integra Miltex, Rietheim-Weilheim, Germany). AAV2/1-CAG-flex-tdTomato-WPRE-bGH and AAV2/1-CAG-flex-GCaMP6s-WPRE-SV40 (mixed 1:1) were co-injected at several sites in the craniotomy (300-500 nL total volume). A round cover glass (diameter 3 mm) glued to a section of hypodermic tubing (outer diameter 3 mm, 0.5 mm deep) was used to cover the craniotomy, and fixed using Cyanoacrylate glue €sseldorf, Germany) and dental cement (Paladur, Heraeus, Hanau, Germany). The window was protected (Ultra Gel, Henkel, Du from dirt and light with silicone adhesive (Kwik-Cast, World Precision Instruments). Threat conditioning and optogenetics Habituation and threat conditioning were performed in the same context, and retrieval took place in a distinct context (contexts A and B, respectively). Contexts A and B were cleaned before and after each session with 70% ethanol or 0.2% acetic acid, respectively. Conditioned stimuli were trains of 500 ms auditory stimuli (10 s trains during conditioning, 30 s trains during habituation and retrieval) with 50 ms rise and fall, delivered at 1 Hz at a sound pressure level of 75 dB SPL (or 90 dB SPL where stated) at the speaker (MF1 speakers and RZ6 processor, Tucker-Davis Technologies, Alachua, FL, US). Auditory stimuli were generated in Real-Time Processor Visual Design Studio (RPvdsEx, Tucker-Davis Technologies). For complex conditioned stimuli, the trains consisted of logarithmically modulated sweeps of opposing modulation direction (between 5 and 20 kHz). Simple conditioned stimuli were trains of pure tones (5 kHz versus 12 kHz or 7.5 kHz versus 12.5 kHz) or band-pass filtered white noise (filtered at 5-10 kHz and 12-17 kHz). Non-discriminative threat conditioning was performed with continuous, band-pass filtered noise (filtered at 5-10 kHz or 12-17 kHz; Figure 4C5), or an FM sweep of one modulation direction (between 5 and 20 kHz, 75 dB SPL, counterbalanced between animals, Figure 3). The choice of the frequency range for FM sweeps was guided by previous work (Letzkus et al., 2011), and in addition by pilot experiments addressing which FM sweeps produce robust discriminative threat memory (e.g., Figure 1G1 GFP group, Figure 2C1 laser off, Figure 4C1 laser off). The pure tone frequencies were similarly chosen based on published data (Ciocchi et al., 2010; Courtin et al., 2014; Wolff et al., 2014) with two additional criteria: First, we wished to remain in the audible range of the experimenter for optimal experimental control. Second, we aimed to achieve similar levels of CS discrimination at the behavioral level as for FM sweeps for optimal comparability (e.g., Figure 1G2 GFP group, Figure 2C2 laser off, Figure 4C2 laser off). We therefore chose pure tone frequencies that fall within the range covered by FM sweeps, but not the extreme frequencies of those stimuli. The identities of the CS+ and CS- were counterbalanced between animals in all experiments. The CS+ was paired with a foot-shock US (1 s, 0.6 mA AC, Coulbourn Precision Animal Shocker, Coulbourn Instruments, Holliston, MA, US) in 15 CS+/foot-shock pairings. In the non-discriminative paradigm, mice received 5 (Figure 4C5) or 15 (Figure 3) CS/foot-shock pairings. The onset of the foot-shock coincided with the onset of the last sound or tone in the CS+ train. CS- presentations were interleaved with CS+/foot-shock presentations, but were never reinforced (15 CSpresentations). The inter-trial interval was 20–180 s. For pseudoconditioning in the calcium imaging experiments, the same sound stimuli (termed CS1 and CS2) and foot-shocks were used and presented separately in a random fashion (15 presentations each,

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inter-trial interval: 20–180 s). Importantly, we observed no correlation between animal age in days and freezing (%) evoked by the CS+ without intervention (r = 0.04, p = 0.71, n = 117), indicating that age-related hearing loss is not a confound. For optogenetic inhibition, both implanted connectors were connected to a DPSS laser (594 nm, Cobolt Mambo, Cobolt, Solna, Sweden) via optic fibers connected to a fiber splitter or rotating commutator (Doric Lenses, Quebec, Canada) immediately before the experiment, allowing mice to move freely in the context. In experiments with optogenetic inhibition during the memory retrieval session, a habituation session took place one day before threat conditioning. Habituation consisted of 4 presentations of each CS in an alternating fashion. The laser was switched on during every second trial (onset 100 ms before the first FM sweep/ tone of the trial, offset 1 s after the final FM sweep/ tone of the trial), in order to habituate animals to the CS/laser compound. In experiments with optogenetic inhibition during conditioning, the laser was switched on during every CS+ trial (onset 100 ms before the first sound or tone of the trial, offset 1-4.5 s after the foot-shock). Conditioned mice were submitted to retrieval of threat memory approximately 24 h later in context B, during which they received 4 presentations of CS- and CS+. Mice that received optogenetic inhibition during conditioning were not connected to the laser during memory retrieval. In experiments with optogenetic inhibition during retrieval (mice were not connected to the laser during conditioning), the laser (594 nm, 10-15 mW) was switched on during every second trial (onset 100 ms before the first sound or tone of the trial, offset 1 s after the final FM sweep/ tone of the trial). Given the low number of CS+ and CS- presentations that are typical for these experiments (4 each, Courtin et al., 2014; Letzkus et al., 2011; Wolff et al., 2014), a random design would have carried the risk of generating asymmetries in the datasets. To score freezing behavior, we used a webcam (HD C270, Logitech, Lausanne, Switzerland) and custom written MATLAB (Mathworks, Natick, MA, US) software (FreezingScoring, see link below). Mice were considered to be freezing if no movement was detected for 2 s and the measure was expressed as a percentage of time spent freezing. In experiments with optogenetic inhibition during retrieval, mice that displayed average CS+ freezing without laser stimulation of less than 40% were excluded as non-learners. To assess the effect of temporal neocortex or cortical axon inhibition on contextual threat memory, a subset of animals that previously completed auditory cued threat memory retrieval with optogenetic inhibition were placed into context A for 5 min and presented with the laser stimulation 4 times (594 nm, 30 s) with 30 s inter-trial intervals. Freezing was measured during the laser on and laser off epochs. In the axon stimulation experiment, naive mice expressing hChr2(H143R) in auditory cortex and optic fibers implanted to the lateral amygdala were connected to a DPSS laser (473 nm, Omicron, Rodgau, Germany), placed into context A and presented with either 9 s train of laser stimulation (10 mW, 3 ms pulses presented at 100 Hz with a 60 s ITI (Mahn et al., 2016; Figure 6E), or with CS (FM sweep), laser stimulation, and CS/ laser compound (Figures S5J and S5K). Freezing was assessed during a 30 s window starting with the onset of the laser, the CS, or the CS/ laser presentation. Perfusions and histological analysis After completion of the experiment, mice were anesthetised with a mixture of ketamine (300 mg/kg) and xylazine (20 mg/kg) and transcardially perfused with 4% PFA. Brains were post-fixed in PFA overnight at 4 C and subsequently stored in PBS. For analysis of ArchT or ChR2 expression and optic fiber placement, 100-150 mm thick coronal sections were made with a Campden Instruments (Loughborough, UK) or Leica (Leica Biosystems, Nussloch, Germany) vibratome. For analysis of retrogradely labeled amygdala-projecting cells using CAV2-Cre injections, 50 mm sections were made with a Leica vibratome. Every second slice was mounted in Mowiol and imaged with a Zeiss (Carl Zeiss, Oberkochen, Germany) microscope (AxioZoom for ArchT and ChR2 expression, LSM 880 for retrograde tracing). To quantify the expression of ArchT, images of the same anterior-posterior coordinate corresponding to the typical center of injection (bregma
2.5) were manually registered to the mouse brain atlas (Paxinos and Franklin, 2001). The total area of ArchT expression, and the area in each region (Au1, AuV, TeA), was calculated in Zeiss Zen software. Counting of amygdala-projecting cells was done using a custom written MATLAB program (CellCounter, see link below). To define the cell numbers in different brain regions €rth et al., 2018). The following images are compounds obtained by ‘stitching’ images were registered to the Allen Brain Atlas (Fu of different fields-of-view: Figures 5B, 5E, 5F, 6B, 6D, S4E, S4I, S4N, and S7I. In vivo calcium imaging The methodology for in vivo calcium imaging in combination with threat conditioning has been previously reported (Abs et al., 2018). After 4 to 5 weeks for AAV expression and localization of auditory cortex by intrinsic imaging under anesthesia, animals were water restricted and habituated 3 times to handling and subsequently 3-4 times to head-fixation under the microscope, where they received water ad libitum before the experiment. During the habituation imaging session, each CS was presented 8 to 12 times. The CSs consisted of trains of 5 FM sweeps (500 ms duration, logarithmically modulated between 5 and 20 kHz, 50 ms rise and fall) or pure tones (500 ms duration, 5 and 12.5 kHz, 50 ms rise and fall) delivered at 1 Hz at a sound pressure level of 75 dB SPL at the speaker (MF1 speakers and RZ6 processor, Tucker-Davis Technology). We used a pure tone at 5 kHz instead of 7.5 kHz as for the optogenetic experiments to clearly distinguish them from the continuous sound of the microscope scanner (7.8 kHz, below ambient SPL). CS+ and CS- were presented in an alternating fashion. During the retrieval imaging session 24 h after threat conditioning (and 48 h after the habituation imaging session), the same neurons were imaged again and 16 CSs were presented (CS- and CS+ alternating, 8 each). In the pure tone experiment an additional habituation session was performed (Figures S7B–S7F). In a subset of the data an additional retrieval imaging session was performed 2.5-3.5 h after the conditioning (data not shown). Calcium imaging was performed with a resonant scanner microscope (Bruker Investigator or custom built) and a femtosecond laser (Spectra Physics

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MaiTai or InSight) at 920 nm. The average excitation power under the objective (Nikon 16x, 0.8 N.A., 3 mm WD) was below 50 mW. Images (416x416 or 512x512 pixels) were acquired at 19 to 30 Hz. The imaging field of view was chosen at depths 450-600 mm below dura (see Figure S4H). Image acquisition, CS delivery and camera for pupil tracking (see below) were controlled using custom written software (AudioGame, see link below). Post hoc processing of the acquired time series consisted of motion correction using custom MATLAB code and 2-4x temporal binning resulting in a frame rate of approximately 5 Hz. The mean projection of the red channel was used to outline the regions of interest (ROIs) using ImageJ (Fiji) or a MATLAB based custom-written software. Fluorescent values were extracted from the image stacks for each ROI, and df/f was calculated as (F-F0)/ F0 x 100, where F0 is the mean fluorescence during 10 s of the trial before stimulus onset. The response integral was calculated using trapezoidal numerical integration of the df/f trace from stimulus onset to the end of the trial (15 s). Analyses of in vivo calcium imaging data All fluorescent traces were re-binned with a linear interpolation algorithm with a bin-width of 0.04 s. For these analyses, the first 4 stimulus presentations of CS- and CS+ during the habituation session were excluded to balance the number of trials analyzed during habituation and retrieval (8 each), and to minimize effects of stimulus novelty. The responses of individual neurons were averaged across trials and sorted according to the CS response integral of 15 s starting with CS onset. Grubb’s outlier test was performed on the average response integral over all trials and sessions for all neurons, leading to the exclusion of one neuron with aberrant activity (G = 5.82 > Gcrit N = 83 = 3.152). We note that experiments using pseudoconditioning produced lower responses during habituation compared to the experiments on threat conditioned animals, which is likely due to inter-experimental variability. Importantly, since the same neurons were imaged during both habituation and retrieval, these data nonetheless provide controlled experimental conditions to assess changes in neuronal encoding of the CSs. For analyses based on significant responses (Figures S6G–S6N, S7F, and S7Q), neurons were counted as responsive based on two criteria: 1. the average z-score was significant (> 1.96 or <
1.65) during the stimulus; 2. In at least two trials the z-score was > 1.96 or <
1.65 for at least 0.5 s during the response window (onset + 15 s). Z-scores were computed for each individual trial, using the standard deviation during the 5 s before stimulus onset. When neurons were both positive and negative responsive they were treated as non-responsive. To perform the principal component analysis (PCA), the principal components were determined using python (toolkit sklearn.decomposition.PCA) on the df/f traces averaged over all trials and habituation as well as retrieval. For each day of imaging separately, the fluorescent traces were averaged within the condition and projected into the space of the first three PCs determined beforehand. The distance between two trajectories in the projected space is given by the average Euclidian distance, calculated pairwise between the data points of the two trajectories for every time-step in a time window starting 3 s before the stimulus and lasting for 18 s. To determine the statistical significance of the distance between the trajectories we performed a bootstrapping approach: In every step we sub-selected a set of M neurons with replacement from the original set with N neurons with M = N. Every bootstrapped set (n = 5000) was treated like the original set to determine the distance of the two trajectories, and the resulting populations were compared. For the analyses in Figures S6O, S6P, S7G, and S7R, we calculated the selectivity index (SI) for each neuron based on its average response integral (AUC) to the CS+ and CS- in each session: SI = ðAUCCS +
AUCCS
Þ=ðjAUCCS + j + jAUCCS
j Þ Neurons were ranked according to their SI for each recording session separately. A threshold of SI > 0.75 and SI <
0.75 was used to determine discriminating/selective neurons. Measurement of pupil diameter Recording of pupil diameter was performed using a camera (Basler acA1920-25um) and custom-written software (EyeTracker) at a frame rate of approximately 20 Hz under infrared illumination (LED, l = 620 nm). Data were binned in the time domain to reach a sampling rate of approximately 5 Hz. The change in pupil diameter (Dd/d) was calculated as (d-d0)/ d0, where d0 is the mean diameter during the first < 15 s of each trial before stimulus onset. The response integral was calculated using trapezoidal numerical integration of the Dd/d trace during the 10 s following stimulus onset. In vivo extracellular recordings Mice injected with AAV2/5.CAMKII.ArchT.GFP.WPRE.SV40 in auditory cortex were anesthetized and fixed in a setereotaxic frame (see Surgery). After the scalp was retracted and the skull was cleaned, a silver chloride ground wire was inserted into the recording €sseldorf, Germany) and dental cement (Paladurª, Heraeus, Hanau, Gerwell formed with cyanoacrylate glue (Ultra Gelª, Henkel, Du many). A craniotomy (approximately 2 mm diameter) was performed centered on the virus injection site. The exposed cortical surface was superfused with sterile rat ringer, and the multichannel silicon probe (A2x32-5mm-25-200-177-H64LP, NeuroNexus Technologies, Ann Arbor, MI, US) with an optic fiber attached above the recording sites was slowly lowered into the brain using a micromanipulator (SM-6, Luigs & Neumann, Ratingen, Germany). Once the probe reached the desired initial depth (> 500 mm below cortical surface), the craniotomy was covered with 1.5% agarose (in sterile rat ringer). Recording commenced approximately 5 min later. Mice were anesthetised (1% isoflurane) throughout the recording. Two to four recordings were made from each mouse at increasing depths (max. 1000 mm) at the same insertion site. The laser (10 mW, 594 nm, Cobolt Mambo) was presented for 10 s with an ITI of 90 s, with 9-37 laser trials in each recording. The silicon probe was connected to two 32-channel unity-gain headstages (Plexon, Dallas,

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TX, US) via an adaptor (A64-OM32x2-sm, NeuroNexus). The headstages were connected to a computer-controlled preamplifier (Plexon digiAmp), which acquired wideband data at 40 kHz with a 50x gain and bandpass filtered from 0.05 Hz to 8 kHz. Spiking data were obtained by performing common average referencing on all channels and high-pass filtering the wideband data at 300 Hz. Single-unit spike sorting was performed manually using Offline Sorter (Plexon). A group of waveforms was considered to originate from a single neuron if it defined a discrete cluster in principal component space and displayed a clear refractory period (no more than 0.1% of spikes occurring within an inter-spike interval of 1.2 ms). Cross-correlograms were computed to avoid analysis of the same neuron recorded on different channels. If a target neuron displayed a peak of activity at the same time as the reference neuron fired, only one of the two neurons was considered for further analysis. Analysis of the effect of the laser was confined to neurons displaying a mean spike rate of > 0.3 Hz in the 10 s prior to laser onset throughout the recording session. To calculate the percentage of units significantly inhibited by the laser, a Wilcoxon matched-pairs signed rank test was performed for each unit comparing the firing rate in the 10 s preceding the laser and the 10 s during the laser (p < 0.05). The peristimulus time histograms were z-scored using NeuroExplorer (Nex Technologies, Colorado Springs, CO, US). Slice preparation and whole-cell recordings The methodology for slicing and recording in adult auditory cortex has been previously reported (Abs et al., 2018; Poorthuis et al., 2018). C57BL/6J mice were injected with AAV2/5.CAMKII.ArchT.GFP.WPRE.SV40 in auditory cortex of the right hemisphere. After 23-46 days of expression, slices were obtained from auditory cortex of adult mice. Animals were anesthetized with isoflurane (4%) in oxygen-enriched air (Oxymat 3, Weinmann, Hamburg, Germany), and decapitated into ice-cold slicing solution containing (in mM) 125 NaCl, 26 NaHCO3, 10 D-glucose, 7 MgSO4, 3 KCl, 1.25 NaH2PO4, and 0.5 CaCl2. After placing the brain on a holder in coronal orientation, 350 mm thick slices were prepared on a vibratome (Leica VT 1200S), and transferred to an immersion style holding chamber filled with standard aCSF containing (in mM) 125 NaCl, 26 NaHCO3, 10 D-glucose, 1 MgSO4, 3 KCl, 1.25 NaH2PO4, and 2 CaCl2, in which they recovered for at least one hour. All aCSF solutions were continuously bubbled with carbogen gas (95% O2, 5%CO2), and had an osmolality of 300 mOsm. For recording, slices were transferred to the recording chamber and perfused with aCSF (2-3 mL/min). All experiments were performed at 31-34 C. Cells were visualized using differential interference contrast microscopy. Patch pipettes (3-6 MU) were pulled from standard-wall borosilicate capillaries and were filled with intracellular solution (in mM): 140 K-gluconate, 10 KCl, 10 HEPES, 4 Na2-phosphocreatine, 4 ATP-Mg, 0.4 GTP. pH was adjusted to 7.3 with KOH, and osmolality was 290-300 mOsm. After establishment of the whole-cell configuration (series resistance below 20 MU, left uncompensated) voltage-clamp and current- clamp recordings were made using Multiclamp 700B amplifiers (Axon Instruments, CA), low-pass filtered to 10 kHz and digitized at 10 kHz (Digidata 1550, Molecular Devices) using pClamp software (Molecular Devices). To test for efficiency of inhibiting action potential firing of auditory cortex pyramidal neurons by ArchT, cells were stimulated with current injections (250 pA, 30 s) to evoke firing. After 10 s, ArchT was stimulated with yellow laser light (594 nm, 10 s, 10 mW, Cobolt Mambo) delivered through an optic fiber (0.39 NA, 200 mm diameter, Thorlabs) directed to the slice. The effect of ArchT stimulation on action potential firing was quantified by comparing the action potential frequency in a 5 s window before and after onset of the laser. ArchT-induced currents were measured at a holding potential of
70 mV. Similar recordings were made from the uninjected contralateral hemisphere as a control. All data were quantified using Clampfit (Axon instruments, CA). To test for efficiency of inhibiting amygdala-projecting axons from temporal neocortex pyramidal neurons, a bipolar electrode (Tungsten stereotrodes, ISO-STIM ISO-01D-100, NPI electronics) was placed in the external capsule. Lateral amygdala principal cells were recorded in whole-cell patch-clamp configuration at a holding potential of
70mV and the bipolar current was adjusted to produce EPSCs in a range of 40-160 pA. EPSCs elicited by electrical stimulation (100ms) were measured in interleaved trials with or without ArchT activation. ArchT was activated for 500ms (starting 300ms before electrical stimulation) using yellow laser light (594 nm, 21-29 mW at fiber tip, Cobolt Mambo) delivered through a optic fiber, or green epifluorescence LED light (565 nm, 10.6 mW, Cool LED). Acoustic startle Virus injection and optical fiber implantation were performed in separate surgeries. Acoustic startle experiments were performed 1-2 weeks after implantation of the optic fibers. The protocol for measurement of startle responses has been previously reported (Daldrup et al., 2015). Briefly, a motion-sensitive platform with 3 piezoelectric sensors that transduced the animal’s motion into a voltage signal was enclosed in a transparent plastic cage within a sound-attenuating chamber, dimly lit from above by a circular LED lamp (LED-240; Proxistar). The piezoelectric voltage signal was continuously recorded at a 5 kHz sampling rate. The triggering of the laser was generated using Radiant software (Plexon). The startle stimuli consisted of a wide band white noise burst (100 dB SPL, 50 ms duration) generated by an RZ6 multi-processor (Tucker-Davis Technologies) and delivered via a multi-field magnetic speaker (MF1; Tucker-Davis Technologies) located 20 cm above the motion sensitive platform. The triggering of the startle stimuli was controlled by RPvdsEx (Tucker-Davis Technologies). A camera located on the side of the cage controlled by video tracking software (CinePlex Studio, Plexon) was used to record the animal’s behavior during the startle experiments. A day before testing, the animals were gently restrained and a custom end-piece was connected to each of the implanted fiber connectors in order to habituate the animals to the experimental procedures. All animals received the same trial and laser sequence for a given experiment. After connecting the optical fiber cable to the implanted fiber connectors, the animals were placed in their home cage for 30 s prior to e5 Neuron 104, 1–15.e1–e7, December 18, 2019 NEURON 14982

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being placed into the startle box. A recording session consisted of two phases: habituation and acoustic startle testing. Mice were habituated to the context for 300 s during which no sound or laser stimulation was presented. This was immediately followed by 16 startle-eliciting white noise bursts (100 dB SPL, 50 ms duration) presented with a randomized inter-stimulus interval of 30-50 s. The white noise bursts were pseudorandomized into two conditions: 1) white noise only, 2) white noise and laser stimulation. The laser was activated 100 ms before the white noise onset and deactivated 100 ms after white noise offset. The acoustic startle reflex was analyzed using a detection method previously reported (Grimsley et al., 2015). The startle amplitude was defined as the voltage signal occurring within a 100 ms time window after the onset of each white noise burst whose threshold was above the mean ± 2 standard deviations of a 500 ms pre-stimulus voltage signal. Visual threat conditioning Light fear conditioning was conducted 4-5 weeks after virus injection and optical fiber implantation. Two different sound-attenuated chambers (length: 100 cm, width: 80 cm, height: 116 cm) were used for the behavioral experiments. One chamber was covered with white insulation foam and contained a Petri dish filled with 70% ethanol (Conditioning Context A). The second chamber had dark insulation foam and a Petri dish filled with 1% acetic acid (Retrieval Context B). Two externally powered fans provided ventilation and temperature control to the chambers. In Context A, a square red Plexiglas box (30 cm x 30 cm x 50 cm) was placed above a stainless steel grid floor that was used to deliver the controlled electric foot shocks generated with an isolated pulse stimulator (A-M Systems, 2100). The grid floor rested over a smooth plastic white base. In Context B, a white circular Plexiglas cylinder (27.5 cm diameter, 33 cm height) was placed above a smooth plastic white floor. Ambient light to Context A and B was provided by a circular LED lamp (LED-240; Proxistar). A flexible array of LEDs (4 cm x 4 cm), folded approximately 90 was attached to one corner of the square arena (Context A) and to the inner side of the cylindrical arena of Context B (30 cm above the grid or plastic floor). The light intensity of the overhead light and LED array was regulated by a laboratory power supply (BaseTech, BT155) placed outside the chamber. Light intensity inside both chambers was measured with a luxmeter (Voltcraft, MS-1300) placed at the center of the square or cylindrical arena. Ambient light inside the conditioning Context A was adjusted to 15 lux and within the retrieval Context B to 40 lux. The LED array used as the visual cue was adjusted to 500 lux within both Context A and Context B when the ambient light was turned off. Behavioral protocols were generated by an RZ6 multi-processor (Tucker-Davis Technologies). The triggering of the shock, overhead lamp and LED array were controlled with a Real-time Processor Visual Design Studio software (RPvdsEx; Tucker-Davis Technologies). The light fear conditioning protocol was adapted from a previous study (Kelley et al., 2011). On the first day, mice were acclimated to Context A for 180 s in order to adapt the animal to the environment. This was followed by four presentations of a visual stimulus consisting of a sequence of 2 s light on-light off stimuli with a total duration of 16 s that was used as the conditioned stimulus (CS). The stimulus was produced by the LED array. The final 2 s when the LED array was turned on co-terminated with an electric foot-shock (0.75 mA, DC) that was used as the unconditioned stimulus (US). This period was followed by 4 s of darkness and subsequently ambient light on until the next CS-US presentation. The conditioning session lasted 20 min during which the mice were exposed to five CS-US pairings with a pseudorandom inter-stimulus interval of 170-230 s. On the second day, the same conditioning protocol as the first day was conducted. Prior to the start of a recording session, the conditioning box, grid floor, and supporting base were wiped with 70% ethanol. On day 3, mice were tested in a different context (Context B) to the one that they were conditioned. After connecting the optical fiber cable to the implanted fiber stubs, the animals were placed in their home cage for 60 s prior to the start of the retrieval session. In the retrieval session, a 180 s baseline period was followed by 4 CS-only presentations (inter-stimulus interval of 110-140 s) with alternating laser off-on trials. Continuous laser light was delivered from 100 ms before the first dark period of the trial to 1 s after the final light-on period of the trial. Light output of the Ce:YAG optical head was controlled by a Ce:YAG driver (Doric) that was triggered by a computer (Radiant; Plexon). For optical stimulation of ArchT or GFP, no bandpass filter was used for the Ce:YAG optical head. The light intensity (l = 565) at the tip of the optical fiber patch cable measured with an optical power meter (Thorlabs) was 10-17 mW. During the recall sessions, the cylindrical context and supporting base was wiped down with 1% acetic acid prior to the start of a recording session. For all the recording sessions, mice were brought to the experimental room with the ceiling lights on that were turned off before the start of the recording. Animals were taken back to their home cages and transferred to the Scantainer cabinets immediately after the recording session. The experimenter was blinded to the treatment condition and the order of behavioral testing was pseudorandomly selected. Due to the periods of darkness during the CS presentation, a thermal video camera (FLIR, A655sc) located above the arena recorded the animal’s position (25 Hz sampling rate). A second camera above the arena controlled by video tracking software (CinePlex Studio; Plexon) was used to monitor the light stimulation events during the optogenetic experiments. The animal’s motion was computed from the thermal video recordings by determining the pixel change across frames with custom written MATLAB (MathWorks) code. Freezing episodes were defined as periods when the motion was below a threshold value (the same was used for all the animals). Only events longer than 1 s were defined as freezing episodes, and events closer than 200 ms were merged. Freezing episodes were visually inspected and grooming episodes were excluded. The effect of laser stimulation (laser OFF versus ON) within group (ArchT or GFP) on light fear conditioning recall was assessed by comparing the percentage of time spent freezing during a 30 s time window prior to (baseline) and after CS presentation. Animals with unilateral or unidentifiable viral expression were excluded.

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Please cite this article in press as: Dalmay et al., A Critical Role for Neocortical Processing of Threat Memory, Neuron (2019), https://doi.org/10.1016/ j.neuron.2019.09.025

QUANTIFICATION AND STATISTICAL ANALYSIS The number of experimental recordings and animals used in each experiment is indicated in the figure legends. Statistical tests were performed using GraphPad Prism and MATLAB, and the statistical tests used are indicated in the figure legends. The resulting p values are indicated in each figure, and a full report of statistical results is provided in Table S1. Data were first subjected to a Shapiro-Wilk test of normality, and based on the result to the indicated parametric and non-parametric tests. Comparison of more than two groups were performed using the indicated one or two-way ANOVA tests. Post hoc multiple comparisons were performed using Tukey’s (in comparisons of 3 or more conditions) or Sidak’s test (in comparisons of 2 conditions). DATA AND CODE AVAILABILITY The custom-written software used for data acquisition and analysis is available under the following links: FreezingScoring https://github.molgen.mpg.de/MPIBR/FreezingAnalysis https://www.defense-circuits-lab.com/resources AudioGame Synchronization of sound presentation, pupil tracking and 2-photon imaging https://github.molgen.mpg.de/MPIBR/AudioGameGUI EyeTracker and Camera Acquisition Recording of pupil videos and tracking of pupil dilation https://github.molgen.mpg.de/PylonRecorder/PylonRecorder https://github.molgen.mpg.de/PylonRecorder/TrackerPlugin_EyeTracker Processing of Calcium Imaging Data Motion correction of Calcium imaging data https://github.molgen.mpg.de/MPIBR/CellSortPCAICA CellCounter Simple MATLAB cell counter https://github.molgen.mpg.de/MPIBR/CellCounter

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