Non-canonical cortico-thalamic dynamics gate avoidance decisions

doi:10.21203/rs.3.rs-2787982/v1

Download PDF

Article

Non-canonical cortico-thalamic dynamics gate avoidance decisions

https://doi.org/10.21203/rs.3.rs-2787982/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 04 Aug, 2024

Read the published version in Nature Communications →

Version 1

posted

You are reading this latest preprint version

The mammalian cortex exerts top-down control of the thalamus by either enhancing or suppressing incoming sensory signals. This is thought to be accomplished via direct and indirect corticothalamic pathways that respectively increase and decrease the activity of thalamic relays^1–4. Here, we discovered that in a reciprocally connected thalamocortical loop involving the dorsal midline thalamus, and unlike for sensory systems, top-down control by the cortex is predominantly indirect and through the thalamic reticular nucleus (TRN). Specifically, we found that the prelimbic area of the medial prefrontal cortex—a region mediating the selection and execution of emotional and motivated behavior^5–8—shapes the decision to engage in and initiate instrumental avoidance responses via TRN projections to the paraventricular thalamus. Our findings reveal novel intra-thalamic circuit dynamics that gate cortical cognitive signals to shape goal-oriented actions.

Biological sciences/Neuroscience/Cognitive neuroscience/Decision

Biological sciences/Neuroscience/Motivation

Biological sciences/Neuroscience/Emotion/Prefrontal cortex

Prelimbic cortex

thalamic reticular nucleus

paraventricular nucleus of the thalamus

fear

active avoidance

instrumental behavior

Current models of corticothalamic circuit organization have largely been drawn from studies of sensory systems, including sensory perception and attention^9,10. As a result, our understanding of the mechanisms by which the cortex exerts higher cognitive functions, such as learning and memory and decision making remains limited. In particular, the functional organization of corticothalamic circuits involving the medial prefrontal cortex (mPFC), a region of the brain which is critical for higher-order cognitive processes such as decision making^11,12, is poorly understood.

Recent studies have demonstrated that excitatory prefronto-thalamic projections shape cognitive processes such as working memory by promoting cortico-cortical communication via the mediodorsal thalamus, and that disrupting these projections leads to deficits in cognitive performance^13,14. The mPFC targets additional limbic thalamic circuitry that guide emotional processing and motivated behaviors. For example, thalamic projections from the prelimbic region of the mPFC (PL) to the paraventricular thalamus (PVT) are thought to drive the selection and execution of adaptive responses to appetitive and aversive stimuli^15–18. But whether top-down control in this pathway ascribes to archetypal corticothalamic organization is unknown.

We began to address this gap by first investigating the anatomical organization of PL projections to the PVT. For this, we used transgenic lines in which expression of Cre recombinase is restricted to either layer 5 (L5) or layer 6 (L6) pyramidal neurons of the neocortex¹⁹ (since both layers are thought to innervate the thalamus) and used Cre-dependent adeno-associated viral vectors (AAVs) to specifically label the axonal projections of either lamina of PL (Extended Data Fig. 1a-c). In addition, we performed retrograde labeling experiments to identify the cell bodies of PL neurons projecting to PVT (Extended Data Fig. 1d-h). Using these methods, we found that PL projections to the PVT arise almost exclusively from L6 of PL, consistent with the literature^20,21 (Extended Data Fig. 1c, f, h). Next, we sought to investigate how PL input to the PVT contributes to goal-oriented behavior. For this, we used a genetically encoded calcium sensor combined with fiber photometry and monitored the activity of PL terminals innervating the PVT while mice performed a two-way signaled active avoidance behavior task (2AA)²² (Fig. 1; Extended Data Fig. 2) (See Methods). In this task, mice must shuttle to the adjacent compartment of a shuttle-box during the presentation of a warning signal (WS) to both terminate the WS and avoid a noxious stimulus (i.e., footshock; FS). Following training, animals learned to avoid on most trials (Avoidance trials) (Fig. 1b). Failure to avoid during the WS was observed in a small proportion of trials in well-trained mice. In these trials, animals shuttled at FS onset—typically within the first second—, and the FS was immediately terminated upon shuttling (Escape trials) (Fig. 1b). As such, we categorized trials into either ‘Avoidances’ or ‘Escapes’ and independently assessed their associated calcium responses during fiber photometry recordings. Our findings revealed that PL–PVT terminals were bidirectionally modulated during the 2AA task (Fig. 1b-d; Extended Data Fig. 2). Specifically, while terminals were activated at WS onset for both trial types, the initiation of shuttle responses was associated with a robust suppression of activity in the PL–PVT pathway (Fig. 1c, d). These response properties contrast with those of PVT neurons projecting to the nucleus accumbens (NAc), a pathway where threat representations are outcome dependent (i.e., WS signal responses are selectively observed for avoidance trials) and shuttle responses associated with large calcium transients²³ (Fig. 1g-l). As such, our findings suggest that direct PL–PVT projections may not mediate the differential recruitment of PVT neurons associated with avoidance responses. In agreement with our prediction, when inactivating PL–PVT projections using optogenetics (unilaterally, to prevent behavioral deficits) and simultaneously recording PVT–NAc neurons with fiber photometry, we did not detect any impact of manipulating the cortical input on task-related dynamics (e.g., WS, shuttle initiation) in PVT–NAc neurons (Fig. 1h-l) (Extended Data Fig. 3). Importantly, however, this manipulation reliably decreased spontaneous activity within the PVT–NAc pathway (Fig. 1i, j; Extended Data 3c), and bilateral optogenetic silencing of PL–PVT input during the presentation of the WS (but not during the inter-trial interval, ITI) yielded deficits in avoidance, escape, and freezing behavior (Fig. 1e, f; Extended Data Fig. 4; Supplementary Fig. 1, 2). These results indicate that: 1) intact PL projections to the PVT are necessary for the appropriate execution of defensive responses to learned stimuli, and 2) these projections alone are not likely to orchestrate the selection process that confers avoidance behavior adaptive advantage over escaping and/or freezing behavior following active avoidance training.

The lacking influence of PL-derived afferents on task-related dynamics within PVT neurons is rather unexpected, given the mPFC’s position as a major source of top-down control of dorsal midline and medial thalamic nuclei^18,24. Similarly, the observation that shuttle initiation coincides with inactivation of PL–PVT projections is at odds with the notion that active avoidance behavior requires engagement of PVT output to the NAc²³ (Fig. 1g-l). The seemingly contradictory nature of these findings raises important questions as to how inhibitory responses in PL orchestrate adaptive defensive behaviors via the PVT. One possibility is that the PL influences the PVT indirectly via the thalamic reticular nucleus (TRN), a sheet of inhibitory cells that surrounds and constitutes a major source of inhibition to the dorsal thalamus^25,26. The TRN is divided into two sectors which can be distinguished on the bases of gene expression, anatomical distribution, connectional features, and physiological properties^27–30. The ‘core’ sector of the TRN contains neurons that express genes such as Spp1 and Calb1 and project to first-order thalamic nuclei. In contrast, neurons within the TRN ‘shell’ express Ecel1 and Sst and innervate higher-order nuclei^29,30 (but see³¹). Importantly, unlike the core, the shell of the TRN receives input from L5 of the cortex²¹ (Extended Data Fig. 1b, d-h), and the strength of the resulting L5-TRN^shell synapses is larger than those of L6-TRN^shell synapses²⁷. Using retrograde labeling methods, we confirmed that the anteroventral portion of the TRN (avTRN) contains predominantly Ecel1⁺ neurons that project to the PVT (Fig. 2a, b). Interestingly, however, both retrograde and anterograde labeling demonstrated that most PVT-projecting avTRN neurons express Gad2 but not parvalbumin, a prototypical TRN marker³² (Fig. 2c; Extended Data Fig. 5) (but see³³). Next, using patch clamp electrophysiology in acute brain slices in combination with optogenetic stimulation of either PL or avTRN input to the PVT, we found that while PL-evoked excitatory postsynaptic currents recorded in PVT–NAc neurons were typically small, avTRN-evoked inhibitory postsynaptic currents were about an order of magnitude larger (Fig. 2d; Extended Data Fig. 5). Of note, in a separate set of experiments we confirmed that input from both PL and avTRN converge onto individual PVT neurons (Fig. 2e). Moreover, optogenetic stimulation of PL in vivo, elicited predominantly inhibitory responses in PVT–NAc neurons, and this inhibition was mediated at least in part by the TRN (Fig. 3; Supplementary Fig. 3). Collectively, our findings suggest that the TRN might be a conduit via which strong cortical inputs arising in L5 impact threat representations within the midline thalamus. Below, we empirically test this prediction.

To investigate whether the synaptic organization of the cortico-reticulo-thalamic circuit described here supports task-related neuronal dynamics in the PVT, we first used fiber photometry to assess the activity of PL cells projecting to the avTRN during the 2AA task. In trained mice, we found that like PL–PVT projections, PL–avTRN cells were bidirectionally modulated during the avoidance task (Fig. 4a-c; Extended Data Fig. 6). However, unlike PL–PVT projections, threat associations in the PL–avTRN pathway appeared to be outcome dependent, with WS onset-related responses observed for avoidance trials only (Fig. 4c). Because a significant proportion of L6 avTRN-projecting PL neurons appear to send collaterals to PVT (Extended Data Fig. 1), and PL–PVT projections do not display outcome-dependent WS responses (Fig. 1b, c) our findings suggest that L5 PL neurons are responsible for selective recruitment of the avTRN during avoidance trials. Accordingly, when specifically monitoring the activity of projections from L5 PL neurons to the avTRN, we observed similar WS-related dynamics (Extended Data Fig. 7). Collectively, our findings suggest that recruitment of PL–avTRN neurons promotes avoidance decisions. In agreement with this view, brief optogenetic stimulation of the PL–avTRN pathway (2s) at the WS onset improved active avoidance (Fig. 4e, f; Supplementary Data Fig. 4). In addition to differences in threat representation between the PL–PVT and PL–avTRN pathways, inhibitory responses in PL–avTRN neurons emerged ~1s prior to the onset of avoidance but not escape initiation (Fig. 4c). Of note, optogenetic manipulations of PL–avTRN projections throughout the duration of the WS significantly attenuated avoidance responses (Fig. 4g, h; Extended Data Fig. 6i-k). Together, our findings suggest that the PL–avTRN pathway plays a critical role in guiding avoidance decisions when threat cues emerge.

To determine whether the avTRN relays PL-derived information to the dorsal midline thalamus to influence behavioral decisions, we first used the TRIO method³⁴ to assess whether PL directly innervates PVT-projecting avTRN neurons. Using this approach, we found that avTRN–PVT neurons receive input from both L5 and L6 neurons of the PL (Supplementary Fig. 5). Next, we used fiber photometry to monitor the activity of avTRN–PVTterminals while mice performed the 2AA task (Fig. 5). Consistent with L5 PL–avTRN neuron dynamics, and unlike the PL–PVT pathway, avTRN–PVT terminals displayed excitatory responses to WS onset that were outcome dependent (i.e., significantly observed for avoidance but not escape trials) (Fig. 5a-c). Furthermore, inhibitory responses were associated with shuttle initiation for avoidances only, and these inhibitory responses emerged ahead of the onset of behavior (Fig. 5d; Extended Data Fig. 8a-f). Altogether, our findings support the idea that the avTRN–PVT pathway underlies the selection and execution of avoidance responses. In agreement with this view, unilateral optogenetic inactivation of avTRN terminals significantly attenuated WS and shuttle-related responses in PVT–NAc neurons (Fig. 5g-l; Extended Data Fig, 8), and bilateral optogenetic manipulations of the avTRN–PVT pathway impaired avoidance behavior (Fig. 5e, f; Extended Data Fig. 8). Of note, similar to PL–avTRN projections, brief optogenetic stimulation of the avTRN–PVT pathway at shuttle onset facilitated avoidance (Extended Data Fig. 8n-q).

The observations described above support a model wherein L5 PL-mediated recruitment of the avTRN at WS onset and subsequent inactivation of their thalamic projections may precipitate avoidance-related engagement of the PVT via disinhibition. In partial support of this model, using a recently developed genetically encoded sensor alongside fiber photometry we monitored changes in GABA release in the PVT during active avoidance. Our findings revealed that shuttle initiation is associated with decreased GABAergic inhibition of PVT–NAc cells (Extended Data Fig. 9). PVT neurons are known to display ionic conductances that make them amenable to engaging in post-inhibitory rebound³⁵. In agreement with the avTRN’s capacity to elicit post-inhibitory rebound responses in the PVT, optogenetic silencing of avTRN–PVT terminals during 2AA training elicited transient excitation in PVT–NAc neurons at light onset (Extended Data Fig. 10). Moreover, optogenetic stimulation of avTRN–PVT terminals in vivo resulted in robust suppression and subsequent rebound activity in PVT–NAc neurons (NAc) at light offset (Extended Data Fig. 10). Taken together these experiments suggest that biphasic responses that emerge in PL when threats are signaled, subserve a process by which cortically driven engagement and subsequent disengagement of avTRN neurons triggers instrumental defensive actions.

Here we identify an unexpected cortico-reticulo-thalamic architecture that promotes goal-oriented actions. Specifically, our findings reveal that in contrast with models of cortico-thalamic circuit organization drawn from sensory systems, prefrontal projections to a higher-order region of the dorsal midline thalamus involve both a weak modulatory input from L6 and an atypical strong input from L5 that is entirely channeled through the TRN. Moreover, our data suggest that this indirect pathway shapes avoidance behavior through TRN-mediated post-inhibitory rebound activity in thalamo-striatal neurons.

Our finding that the majority of TRN neurons innervating the PVT do not express parvalbumin is at first surprising, considering that most work in the limbic TRN focuses on parvalbumin-expressing populations^26,36,37. Of note, while the TRN is mainly comprised of parvalbumin-expressing neurons³², functionally and anatomically distinct classes of TRN cells lacking parvalbumin expression have been identified³³. Here we propose that parvalbumin-negative Ecel1 neurons of the avTRN represent a novel reticular subtype that innervates the dorsal midline thalamus to shape limbic-related processes such as emotion and motivation.

An extensive body of literature supports a critical role of PL in fear-related processes^5,38–44. PL is required for fear memory retrieval and drives both conditioned freezing and active avoidance behavior in rodent species^5,40,42,45. However, while population dynamics within the dorsomedial prefrontal cortex (dmPFC) (which includes PL) are thought to drive the selection of defensive strategies, recent reports that outcome-dependent threat representations (i.e., cue responses) are not seen in the PL appear counterintuitive to this notion⁴². Here we find that outcome-dependent threat representations can be observed in PL neurons that project to the avTRN, suggesting that this might be a unique feature associated with this cortical stream of information. In conclusion, our collective results suggest that cue-promoted and cortically-driven intra-thalamic disinhibitory dynamics might belong to a general mechanism by which the PL promotes the selection of adaptive goal-directed behaviors.

Life Sciences Reporting Summary

Additional information on experimental design and materials used in our study is available in the Life Sciences Reporting Summary.

Data availability

All the data that support the findings presented in this study are available from the corresponding author upon reasonable request. Source Data are provided with this paper and are publicly available at the following repository: pending.

Code availability

R code used to analyze active avoidance behavior and photometric signal is available at the following repository: https://github.com/Penzolab/Data-analysis-of-Two-way-active-avoidance-task.git.

ACKNOWLEDGEMENTS

We thank the members of the Unit on the Neurobiology of Affective Memory (NIMH) who offered scientific feedback. In addition, we thank the NIMH IRP Rodent Behavioral Core for their support with the development of the custom apparatus for the 2AA task, and the NIMH IRP Systems Neuroscience Imaging Resource for assistance with microscopy tools used for histological assessment. Lastly, we thank Drs. Fabricio Do Monte, Michael Halassa, and Briana Carroll for offering scientific and writing feedback. This work was supported by the NIMH Intramural Research Program (1ZIAMH002950, to M.A.P.)

AUTHOR CONTRIBUTIONS

J.M. and J.J.O. performed all experiments. M.K. assisted with anatomical studies. Y.L. assisted with RNAscope experiments. M.K., Y.L., and I.K. assisted with histological procedures. J.M., J.J.O., and M.K. analyzed the data. J.M., J.J.O., and M.A.P. designed the study, interpreted results, and wrote the paper.

COMPETING INTERESTS

The authors declare no competing interests.

Wimmer, R. D. et al. Thalamic control of sensory selection in divided attention. Nature 526, 705–709 (2015).
Destexhe, A., Contreras, D. & Steriade, M. Mechanisms Underlying the Synchronizing Action of Corticothalamic Feedback Through Inhibition of Thalamic Relay Cells. J Neurophysiol 79, 999–1016 (1998).
Steriade, M., Contreras, D., Amzica, F. & Timofeev, I. Synchronization of fast (30-40 Hz) spontaneous oscillations in intrathalamic and thalamocortical networks. The Journal of Neuroscience 16, 2788–2808 (1996).
Olsen, S. R., Bortone, D. S., Adesnik, H. & Scanziani, M. Gain control by layer six in cortical circuits of vision. Nature 483, 47–52 (2012).
Sierra-Mercado, D., Padilla-Coreano, N. & Quirk, G. J. Dissociable Roles of Prelimbic and Infralimbic Cortices, Ventral Hippocampus, and Basolateral Amygdala in the Expression and Extinction of Conditioned Fear. Neuropsychopharmacology 36, 529–538 (2011).
Diehl, M. M. et al. Active avoidance requires inhibitory signaling in the rodent prelimbic prefrontal cortex. Elife 7, (2018).
Fernandez-Leon, J. A. et al. Neural correlates and determinants of approach–avoidance conflict in the prelimbic prefrontal cortex. Elife 10, (2021).
Sangha, S., Robinson, P. D., Greba, Q., Davies, D. A. & Howland, J. G. Alterations in Reward, Fear and Safety Cue Discrimination after Inactivation of the Rat Prelimbic and Infralimbic Cortices. Neuropsychopharmacology 39, 2405–2413 (2014).
Briggs, F. & Usrey, W. M. Emerging views of corticothalamic function. Curr Opin Neurobiol 18, 403–407 (2008).
Cudeiro, J. & Sillito, A. M. Looking back: corticothalamic feedback and early visual processing. Trends Neurosci 29, 298–306 (2006).
Miller, E. K. The prefontral cortex and cognitive control. Nat Rev Neurosci 1, 59–65 (2000).
Friedman, N. P. & Robbins, T. W. The role of prefrontal cortex in cognitive control and executive function. Neuropsychopharmacology 47, 72–89 (2022).
Schmitt, L. I. et al. Thalamic amplification of cortical connectivity sustains attentional control. Nature 545, 219–223 (2017).
Bolkan, S. S. et al. Thalamic projections sustain prefrontal activity during working memory maintenance. Nat Neurosci 20, 987–996 (2017).
Otis, J. M. et al. Prefrontal cortex output circuits guide reward seeking through divergent cue encoding. Nature 543, 103–107 (2017).
Do-Monte, F. H., Quiñones-Laracuente, K. & Quirk, G. J. A temporal shift in the circuits mediating retrieval of fear memory. Nature 519, 460–3 (2015).
Quiñones-Laracuente, K., Vega-Medina, A. & Quirk, G. J. Time-Dependent Recruitment of Prelimbic Prefrontal Circuits for Retrieval of Fear Memory. Front Behav Neurosci 15, (2021).
Campus, P. et al. The paraventricular thalamus is a critical mediator of top-down control of cue-motivated behavior in rats. Elife 8, (2019).
Harris, J. A. et al. Hierarchical organization of cortical and thalamic connectivity. Nature 575, 195–202 (2019).
Li, S. & Kirouac, G. J. Sources of inputs to the anterior and posterior aspects of the paraventricular nucleus of the thalamus. Brain Struct Funct 217, 257–273 (2012).
Gao, C. et al. Two genetically, anatomically and functionally distinct cell types segregate across anteroposterior axis of paraventricular thalamus. Nat Neurosci 23, 217–228 (2020).
LeDoux, J. E., Moscarello, J., Sears, R. & Campese, V. The birth, death and resurrection of avoidance: a reconceptualization of a troubled paradigm. Mol Psychiatry 22, 24–36 (2017).
Ma, J. et al. Divergent projections of the paraventricular nucleus of the thalamus mediate the selection of passive and active defensive behaviors. Nat Neurosci 24, 1429–1440 (2021).
Lucantonio, F. et al. Aversive stimuli bias corticothalamic responses to motivationally significant cues. Elife 10, (2021).
Pinault, D. The thalamic reticular nucleus: structure, function and concept. Brain Res Brain Res Rev 46, 1–31 (2004).
Halassa, M. M. & Acsády, L. Thalamic Inhibition: Diverse Sources, Diverse Scales. Trends Neurosci 39, 680–693 (2016).
Hádinger, N. et al. Region-selective control of the thalamic reticular nucleus via cortical layer 5 pyramidal cells. Nat Neurosci 26, 116–130 (2023).
Carroll, B. J., Sampathkumar, V., Kasthuri, N. & Sherman, S. M. Layer 5 of cortex innervates the thalamic reticular nucleus in mice. Proceedings of the National Academy of Sciences 119, (2022).
Martinez-Garcia, R. I. et al. Two dynamically distinct circuits drive inhibition in the sensory thalamus. Nature 583, 813–818 (2020).
Li, Y. et al. Distinct subnetworks of the thalamic reticular nucleus. Nature 583, 819–824 (2020).
Hoseini, M. S. et al. Gamma rhythms and visual information in mouse V1 specifically modulated by somatostatin+ neurons in reticular thalamus. Elife 10, (2021).
Hou, G., Smith, A. G. & Zhang, Z.-W. Lack of Intrinsic GABAergic Connections in the Thalamic Reticular Nucleus of the Mouse. Journal of Neuroscience 36, 7246–7252 (2016).
Clemente-Perez, A. et al. Distinct Thalamic Reticular Cell Types Differentially Modulate Normal and Pathological Cortical Rhythms. Cell Rep 19, 2130–2142 (2017).
Schwarz, L. A. et al. Viral-genetic tracing of the input–output organization of a central noradrenaline circuit. Nature 524, 88–92 (2015).
Kolaj, M., Zhang, L., Hermes, M. L. H. J. & Renaud, L. P. Intrinsic properties and neuropharmacology of midline paraventricular thalamic nucleus neurons. Front Behav Neurosci 8, 132 (2014).
Dong, P. et al. A novel cortico-intrathalamic circuit for flight behavior. Nat Neurosci 22, 941–949 (2019).
Lee, J.-H. et al. The rostroventral part of the thalamic reticular nucleus modulates fear extinction. Nat Commun 10, 4637 (2019).
Courtin, J. et al. Prefrontal parvalbumin interneurons shape neuronal activity to drive fear expression. Nature 505, 92–96 (2014).
Burgos-Robles, A., Vidal-Gonzalez, I. & Quirk, G. J. Sustained Conditioned Responses in Prelimbic Prefrontal Neurons Are Correlated with Fear Expression and Extinction Failure. Journal of Neuroscience 29, 8474–8482 (2009).
Bravo-Rivera, C., Roman-Ortiz, C., Brignoni-Perez, E., Sotres-Bayon, F. & Quirk, G. J. Neural Structures Mediating Expression and Extinction of Platform-Mediated Avoidance. Journal of Neuroscience 34, 9736–9742 (2014).
Cummings, K. A. & Clem, R. L. Prefrontal somatostatin interneurons encode fear memory. Nat Neurosci 23, 61–74 (2020).
Jercog, D. et al. Dynamical prefrontal population coding during defensive behaviours. Nature 595, 690–694 (2021).
Marek, R., Xu, L., Sullivan, R. K. P. & Sah, P. Excitatory connections between the prelimbic and infralimbic medial prefrontal cortex show a role for the prelimbic cortex in fear extinction. Nat Neurosci 21, 654–658 (2018).
Burgos-Robles, A. et al. Amygdala inputs to prefrontal cortex guide behavior amid conflicting cues of reward and punishment. Nat Neurosci 20, 824–835 (2017).
Corcoran, K. A. & Quirk, G. J. Activity in prelimbic cortex is necessary for the expression of learned, but not innate, fears. J Neurosci 27, 840–4 (2007).

Mice

All procedures were performed in accordance with the Guide for the Care and Use of Laboratory Animals and were approved by the National Institute of Mental Health (NIMH) Animal Care and Use Committee. Mice used in this study were group housed under a 12-h light/dark cycle (6:00–18:00 light), at temperatures of 70–74 °F and 40–65% humidity, with food and water available ad libitum. After surgery, mice were singly housed. C57BL/6NJ (005304), Gad2-Cre (019022), and Pv-Cre (017320) were obtained from The Jackson Laboratory. Drd2-Cre (founder line ER44), Rbp4-Cre (founder line KL100), and Syt6-Cre (founder line KI148, 109, or 130) were obtained from GENSAT/MMRRC. Both male and female mice 8–16 weeks of age were used for all experiments. Animals were randomly allocated to the different experimental conditions reported in this study.

Viral vectors

AAV2-CaMKII-eNpHR3.0-mCherry (Deisseroth), AAV2-Ef1α-DIO-hChR2(H134R)-EYFP (Deisseroth), AAV5-CaMKII-ChR2(H134R)-EYFP (Deisseroth), AAV2-CaMKII-EYFP (Deisseroth), AAV2-Ef1α-DIO-eNpHR3.0-mCherry (Deisseroth), and AAV2-syn-FLEX-ChrimsonR-tdTomato (Boyden) were produced by the Vector Core of the University of North Carolina. AAV9-hSyn-Flex-GCaMP8s-WPRE (plasmid no. 162377), AAV9-syn-ChrimsonR -tdTomato (plasmid no. 59171), AAV2-CaMKII-mCherry (plasmid no. 114469), AAV2(retro)-CAG-iCre (plasmid no. 81070), AAV2-hSyn-DIO-hM4Di-mCherry (plasmid no. 44362), AAV2-hSyn-DIO-mCherry (plasmid no. 50459), AAV9-syn-FLEX-jGCaMP7s-WPRE (plasmid no. 104491), and AAV1-hSyn-FLEX-iGABASnFR (plasmid no. 112163) were purchased from Addgene. CAV-Cre was produced by the Institute of Molecular Genetics of Montpellier (Montpellier, France). AAV9-EF1a-FLEX-TVA-mCherry (Addgene, plasmid no. 38044) and AAV9-CAG-FLEX-RG (Addgene, plasmid no. 38043) were produced by Vigene Biosciences. EnvA-SAD-ΔG-eGFP (Addgene, plasmid no. 32635) was produced by the Viral Vector Core of the Salk Institute for Biological Studies. All viral vectors were stored in aliquots at -80°C until use.

Stereotaxic surgery.

All viral injections were performed using previously described procedures¹ and an AngleTwo stereotaxic device (Leica Biosystems) at the following stereotaxic coordinates: PL, -1.90 mm from bregma, ±0.55 mm lateral from midline and -2.30 mm vertical from cortical surface; avTRN, -0.70 mm from bregma, ±1.00 mm lateral from midline and -4.20 mm vertical from cortical surface; pPVT, -1.60 mm from bregma, 0.06 mm lateral from midline and -3.30 mm vertical from cortical surface, 6.12° angle for both fiber photometry and optogenetics; NAc, 1.70 mm from bregma, ±0.60 mm lateral from midline and -4.80 mm vertical from cortical surface.

For fiber photometry and optogenetic experiments, an optical fiber (400 µm for photometry, Doric Lenses; 200 µm for optogenetics, ThorLabs) was implanted 200-300 µm above the target of interest and immediately following viral injections, and cemented using Metabond Cement System (Parkell) and Jet Brand dental acrylic (Lang Dental Manufacturing).

For retrograde tracing of TRN-projecting and PVT-projecting PL cells CTB-488 and CTB-555 (1.0% in PBS, Thermo Fisher Scientific) were injected into the TRN (0.5 µL) and PVT (1.0 µL), respectively, and allowed 4 d for retrograde transport. For retrograde tracing of PVT-projecting TRN cells unconjugated CTB (List Labs Product No. 104) was injected into the PVT (1.0 µL). For retrograde labeling of PVT-projecting TRN cells for RNAscope, retrobeads (LumaFluor, Inc.) were injected into the PVT and allowed 7 d for retrograde transport.

After all surgical procedures, animals were returned to their home cages and placed on a heating pad for 24 h for post-surgical recovery and monitoring. Animals received subcutaneous injections with Metacam (meloxicam, 1–2 mg kg^-1) for analgesia and anti-inflammatory purposes. Mice without correct targeting of optical fibers, tracers, or vectors were excluded from this study.

Fiber photometry

Fiber photometry was performed as previously described². Briefly, mice were allowed to habituate to the fiber patch cord in their home cage for approximately 5 min before each behavior test. GCaMP fluorescence and isosbestic autofluorescence signals were excited by the fiber photometry system (Doric Lenses) using two sinusoidally modulated LEDs (473 nm at 211 Hz and 405 nm at 531 Hz) controlled by a standalone driver (DC4100, ThorLabs). Both LEDs were combined via a commercial Mini Cube fiber photometry apparatus (Doric Lenses) into a fiber patch cord (400-µm core, 0.48 NA) connected to the brain implant in each mouse. The light intensity at the interface between the fiber tip and the animal was adjusted from 10 µW to 20 µW (but was constant throughout each test session for each mouse). An RZ5P fiber photometry acquisition system with Synapse software (Tucker-Davis Technologies) collected and saved real-time demodulated emission signals and behavior-relevant TTL inputs. For each trial, GCaMP signals (F_{473 nm}) were compared with autofluorescence signals (F_{405 nm}) to control for movement and bleaching artifacts. Signal data were de-trended by first applying a least-squares linear fit to produce F_{fitted 405 nm}, and dF/F was calculated as (F_{473 nm} – F_{fitted 405 nm})/F_{fitted 405 nm}. All GCaMP signal data are presented as the z-score of the dF/F from baseline (pre-WS) segments.

Two-way active avoidance (2AA)

Mice were trained on the 2AA task as previously described³. Briefly, the behavioral apparatus consisted of a custom-built shuttle box (18 cm × 36 cm × 30 cm) that contained two identical chambers separated by a hurdle (17.5 cm × 6 cm). The hurdle projected 3 cm above the floor and allowed mice easy access to both chambers. The floor consisted of electrifiable metal rods (H10-11M-TC-SF, Coulbourn Instruments) and was connected to a shock generator (H13-15, Coulbourn Instruments). Before each subject was trained/tested, the shuttle box was wiped clean with 70% ethanol. The mouse’s behavior was captured with a USB camera during each session. A speaker located on the top of the shuttle box (50 cm high) was used to deliver the WS. Subjects’ movement and TTLs of WS, US and optogenetic stimulation were recorded by ANY-maze version 5 (Stoelting).

After a 5-min habituation period, mice were trained with daily sessions of 2AA, each consisting of 30 presentations of the WS (4 kHz, 75dB, lasting up to 15 s each). Trials in which subjects failed to shuttle to the adjacent chamber before the termination of the WS resulted in the presentation of the US (0.6 mA foot shock lasting up to 15 s each) until subjects escaped to the opposite chamber (escape trials). No subject failed to escape the US. For trials in which subjects shuttled to the opposite chamber during the WS, the WS was abruptly terminated, and the US was also prevented (avoidance trials). The inter-trial interval (ITI) was 30 s. Avoidance rate was calculated as the percentage of the number of avoidance trials over the total number of trials. For optogenetic and fiber photometry experiments fiber patch cords were attached every session of training. For all experiments, subjects that did not reach 30% avoidance rates by Day 5 were excluded from data analysis.

Fiber photometry during 2AA. In Fig. 1a-d, Fig. 4a-c, Extended Data Fig. 2 and Extended Data Fig. 6a-d, mice were subjected to five 2AA sessions (one session per day), and the GCaMP signal was collected on Day 5 as described above. In Fig. 5a-d, Extended Data Fig. 7, Extended Data Fig. 8a-f, and Extended Data Fig. 9, mice were subjected to five 2AA sessions (one session per day), and the GCaMP or iGABASnFR signal was collected on Days 4 and 5 as described above.

Optogenetic manipulations during 2AA. In Fig. 1e-f, Fig. 4g-h, Fig. 5e-f, Extended Data Fig. 4, Extended Data Fig. 6g-k, Extended Data Fig. 8g-i, Supplementary Fig. 1, Supplementary Fig. 4b-c and Supplementary Fig. 6a-e, mice were subjected to five 2AA sessions (one session per day), and on Day 4 a yellow light (Ce:YAG + LED Driver, Doric) was presented coinciding with the WS. In Fig. 4e-f, Extended Data Fig. 6e-f and Supplementary Fig. 4a, mice were subjected to five 2AA sessions (one session per day), and on Day 4 the yellow light was presented at the onset of the WS but terminated after 2s. In Supplementary Fig. 2, mice were subjected to five 2AA sessions (one session per day), and on Day 4 a yellow light (Ce:YAG + LED Driver, Doric) was presented coinciding with the ITI. For all mice the light intensity at the at the interface between the fiber tip and the brain implant was ~10 mW. For optogenetic excitation (ChR) the light (10 Hz, 20% duty cycle) was presented at the certain duration as each experiment. For optogenetic inhibition (Halo) the light was presented continuously for the certain duration as each experiment. In Extended Data Fig. 8n-q and Supplementary Fig. 6f-j, mice were subjected to five 2AA sessions (one session per day), and on Day 4 the yellow light (~10 mW, 50Hz, 10% duty) was presented at the onset of the WS but terminated 2s later.

Fiber photometry with optogenetic manipulations during 2AA. In Fig. 1g-l, Fig. 5g-l, Extended Data Fig. 3, and Extended Data Fig. 8j-m, mice were subjected to seven 2AA sessions (one session per day), the GCaMP signal was collected on Days 4-7 and optogenetic inhibition was performed on Days 4 and 5 with constant light presentation that began 5s prior to WS onset and culminated 5s after the offset of the WS.

Data analysis and behavioral tracking for 2AA

Analysis of the 2AA behavioral tracking was done as previously described³. We performed post hoc position tracking of the animal’s nose and body center from video in the software TopScan (CleverSys). WS and US times from ANY-maze and raw video tracking position values from TopScan were exported, and analysis was performed with custom routines in the R statistical computing environment (R Core Team 2019, R Foundation). Missing positions up to ten successive frames were linearly interpolated with custom routines in R. For imaging sessions, video tracking and ANY-maze TTL pulse timestamps were zero corrected to align behavioral and calcium signal timestamps. Next, calcium signals and/or position frames during US and WS were flagged by matching the relevant timestamps to TTL pulse times from ANY-maze, and the frame-by-frame distance traveled for the nose and body center was calculated for the tracking data. To minimize the effects of noise in the tracking data, we calculated the 40% quantile of the frame-by-frame distance traveled by the animal’s nose and body center for each session; in all cases, this yielded a distance value of 0 or 1 mm. This quantile value served as a movement threshold—that is, an inter-frame distance traveled less than or equal to the quantile value was considered non-movement. We then created a binary vector, and frames with coincident immobility of the nose and center body were set to 1. Changepoint analysis⁴ (R package version 2.2.2 (https://CRAN.R-project.org/package=changepoint)), with a minimum segment length of 30 video frames, was then applied to this vector. This approach allowed us to statistically determine when transitions to (and from) coincident periods of non-movement of nose and body occurred, which were used as a proxy for freezing behavior. Next, each sustained bout of non-movement was isolated, and we probed whether there was any movement that lasted for >5 consecutive video frames. If such movement did occur, we truncated the bout of immobility at the start of movement. Finally, immobility bouts with a duration ≥1 s were considered freezing.

We isolated freezing events (see freeze detection section above) that occurred during the WS as WS freezing and those that occurred during ITI as ITI freezing. For each trial, we calculated the time interval between the moment of animal crossing the hurdle and the WS onset during trials where the animal avoided the footshock, named latency to avoid. WS, ITI freezing or latency to avoid were average within session and then within each group and plotted as mean ±s.e.m.

For fiber photometry, GCaMP data were normalized as dF/F. Next, we used the behavioral flags calculated from the video tracking to create average peri-event time histograms (PETHs) time-locked to the onset of the behavior events of interest, including WS onset, highest movement velocity during the WS (Max Velocity), escape or avoidance movement onset (Shuttle Initiate), escape or avoidance moment (Shuttle) and freezing onset during the WS (WS Freezing). All trials in each session were separated into avoidance and escape trials as described above. For each trial type, the z-score from 10 s before to 30 s after WS onset was plotted in heat maps for all trials in test sessions. The mean of all recorded activity for each trial type was plotted below the corresponding heat map. We calculated and plotted the AUC of the z-scores before WS onset as a baseline, during the WS, and post WS period. We isolated the peri-event calcium signal by a certain time window (2 sec for the onset of the WS and 5 s for all other behavioral events) from avoidance and escape trials separately, then we calculated z-scores based on pre-event signal for each trial and area under the curve (AUC) of the z-score from 1 s bins throughout each behavior event. Lastly, we plotted the mean of signal transitions and AUC for each event type from each trial type and compared the differences of AUCs of 1) adjacent seconds in same trial type and 2) same second between trial types or groups. All 2AA photometric signals and behavioral performance were analyzed blinded. All codes are available at the following repository: https://github.com/Penzolab/Data-analysis-of-Two-way-active-avoidance-task.git.

Fiber photometry with optogenetic and chemogenetic manipulations

In Fig. 3 and Supplementary Fig. 3, mice with mCherry or DREADDs (Gi) unilaterally expressed in the TRN and channelrhodopsin (ChR2) or YFP unilaterally expressed in the PL were injected with either clozapine N-oxide (CNO) or saline (Sal) and subjected to 15 trials of optogenetic stimulation (~10mW, 10 Hz, 20% duty cycle, 15 s) with 30s ITI while simultaneously recording GCaMP signal as described above in NAc-projecting pPVT cells. Both CNO (10mg/kg; Enzo Life Sciences) and Sal injections were given to all mice 30 min prior to recording GCaMP signal and the injection order was counterbalanced on separate days.

Monosynaptic rabies tracing of inputs to PVT-projecting TRN cells

To limit monosynaptic rabies tracing to PVT-projecting neurons of the TRN, CAV-Cre virus was unilaterally injected into the PVT (1.0 µl) of C57BL/6NJ mice. Within the same surgical procedure, a virus mixture of AAV9-EF1a-FLEX-TVA-mCherry and AAV9-CAG-FLEX-RG at a 1:1 ratio was injected into the avTRN (0.6 µl), followed by an injection of the pseudotyped rabies virus EnvA-SAD-ΔG-eGFP (1.2 ul) in the same location of avTRN 2 weeks later. Mouse brain tissues were collected and subjected to analysis 1 week later. Representative images in Supplementary Fig. 5 were scanned through Zeiss 780 confocal.

Histology and immunofluorescence

Animals were deeply anesthetized with euthanasia solution (Vet One) and transcardially perfused with PBS (pH 7.4, 4 °C), followed by paraformaldehyde (PFA) solution (4% in PBS, 4 °C). After extraction, brains were post-fixed in 4% PFA at 4 °C for a minimum of 2 h and subsequently cryoprotected by transferring to a 30% PBS-buffered sucrose solution until brains were saturated (for over 24 h). Coronal brain sections (50 μm) were cut using a freezing microtome (SM 2010R, Leica). For immunofluorescence staining, brain sections were incubated in PBS (pH 7.4) with 10% normal goat serum and 0.1% Triton X-100 (Sigma-Aldrich) for 1 h and then incubated using the following antibody: anti-PV (1:1000, rabbit, Swant, PV 27) (overnight, at 4 °C); anti-CTB (1:##, goat, List Labs, Product No. 703)( 48 h, at 4 °C). After washing, Alexa Fluor-conjugated secondary antibodies (1:500, goat anti-mouse, Thermos Fisher Scientific A-11001; 1:500, goat anti-rabbit, Thermo Fisher Scientific A-21245). Finally, sections were subsequently mounted onto glass slides for imaging (LSM 780 laser scanning confocal microscope, Carl Zeiss). Image analysis and cell counting were performed using ImageJ software (Fiji, version 1.52p). Optical fiber placements for all mice included in this study are presented in Figs 2a, 3c, 4b, 5c, 6b and 7c and Extended Data Figs. 1a, 4b,l, 5b, 6b, 8b,m and 9a.

Sample preparation and ISH procedure for RNAscope. Fresh-frozen brains from adult male C57BL/6NJ mice (8-12 weeks old) were sectioned at a thickness of 16 µm using a Cryostat (Leica Biosystems). Sections were collected onto Superfrost Plus slides (Daigger Scientific), immediately placed on dry ice and subsequently transferred to a -80 °C freezer. The Spp1 and Ecel1 mRNA signal was detected using the RNAscope fluorescent kit (Advanced Cell Diagnostics). Specifically, slides with sections covering the entire anteroposterior spread of the PVT were removed from the -80 °C freezer, fixed with freshly prepared ice-chilled 4% PFA for 15 min at 4 °C and then dehydrated using a series of ethanol solutions at different concentrations (5 min each, room temperature): 1 50%, 1 70% and 2 100%. Next, sections were treated with Protease IV (Advanced Cell Diagnostics) at room temperature for 30 min. Slides were then washed with PBS twice (1 min each) and dried for 5 min at room temperature, and sections were circled with an ImmEdge Hydrophobic Barrier PAP Pen (Vector Laboratories). Hybridization was performed on a HybEZ oven for 2 h at 40 °c using a Spp1 or Ecel1 probe (Advanced Cell Diagnostics). After this, the slides were washed twice with washing buffer (2 min each), then incubated with Hybridize Amp 1-FL for 30 min, Hybridize Amp 2-FL for 15 min, Hybridize Amp 3-FL for 30 min and Hybridize Amp 4-FL for 15 min. Next, the slides were washed twice with washing buffer (2 min each) and coverslips added using Diamond Prolong antifade mounting medium with DAPI (Thermo Fisher Scientific).

Signal detection and analysis for RNAscope. Dried slides were examined on an LSM 780 laser scanning confocal microscope (Carl Zeiss) using 20X objective 24 h after the amplification procedure. Signal was subsequently quantified with CellProfiler 3.1.8 using a freely available pipeline (macros) for RNAscope⁵. A protocol with a step-by-step description of how to implement this pipeline for analyzing RNAscope data was recently published⁶. All RNAscope data were analyzed in a blinded manner.

Whole-cell patch-clamp slice electrophysiology

For electrophysiological experiments, slices were prepared as previously described⁷^. Briefly, mice were anesthetized with isoflurane and transcardially perfused with an ice-cold NMDG cutting solution (92 mM N-Methyl-D-glucamine, 2.5 mM KCl, 1.25 mM NaH₂PO₄, 10 mM MgSO₄, 0.5 mM CaCl₂, 30 mM NaHCO₃, 20 mM glucose, 20 mM HEPES, 2 mM thiourea, 5 mM Na-ascorbate, 3 mM Na-pyruvate, at 7.3-7.4 pH gassed with 95% O₂ and 5% CO₂). Coronal sections (300-µm thick) containing the avTRN or PVT were cut in the ice-cold NMDG cutting solution using a VT1200S automated vibrating-blade microtome (Leica Biosystems), and were subsequently transferred to a heated incubation chamber containing the NMDG cutting solution at 34-35 °C. After approximately 12 min, slices were transferred to a room temperature (20-24°C) holding chamber containing a HEPES-modified artificial cerebrospinal fluid (92 nM NaCl, 2.5 mM KCl, 1.25 mM NaH₂PO₄, 2 mM MgSO₄, 2 mM CaCl₂, 30 mM NaHCO₃, 25 mM glucose, 20 mM HEPES, 2 mM thiourea, 5 mM Na-ascorbate, 3 mM Na-pyruvate, at 7.3 pH, gassed with 95% O₂ and 5% CO₂) and remained in the holding chamber until needed. For recordings, slices were transferred to the recording chamber and constantly supplied with a room-temperature ACSF (118 mM NaCl, 2.5 mM KCl, 26.2 mM NaHCO₃, 1 mM NaH₂PO₄, 20 mM glucose, 2 mM MgCl₂, and 2 mM CaCl₂, at pH 7.4, gassed with 95% O2 and 5% CO2).

Tetrodotoxin (TTX, Cat. No. 1078), 4-aminopyridine (4AP, Cat. No. 0940), 2,3-Dioxo-6-nitro-1,2,3,4-tetrahydrobenzo[f]quinoxaline-7-sulfonamide (NBQX, Cat. No. 0373), D-2-amino-5-phosphovalerate (APV, Cat. No 0106), picrotoxin (PTX, Cat. No. 1128) were obtained from Tocris Bio-Techne, all other salts were obtained from MilleporeSigma.

Whole-cell patch-clamp recordings from avTRN and/or PVT neurons were obtained with a Multiclamp 700B amplifier (Molecular Devices). Recordings were done under visual guidance using an Olympus BX51 microscope with transmitted light illumination. Recordings were made in ACSF and pharmacological antagonists were added to the ACSF and bath applied. All recordings were made with borosilicate glass pipettes with tip resistance of 3-6 MΩ. Access resistance was monitored throughout all recordings and recordings where access resistance increased above 30 MΩ were not included in analyses.

Optogenetically evoked synaptic responses were achieved by shining a blue LED (470 nm, pE-300^white, CoolLED) over acute slices to drive ChR2-expressing terminals. All recordings were done in voltage-clamp configuration. Cells were kept at a holding potential of -70 mV and recorded using a Cs-based internal solution containing 117 mM Cs methanesulphate, 10 mM HEPES, 2.5 mM MgCl₂, 2 mM Na₂-ATP, 0.4 mM Na₂-GTP, 10 mM Na₂-phosphocreatine, 0.6 mM EGTA, 5 mM QX-314 at pH 7.2 and 288-290 mOSM. Retrogradely-labeled cells (CTB 555) were identified bases on their red fluorescence using a green LED (pE-300^white, CoolLED). To isolate monosynaptic responses, recordings were done in the presence of TTX and 4AP.

For dual opsin stimulation experiments, two distinct excitatory opsins were selectively expressed in PL and TRN. Opsins for each target were counterbalanced across subjects. For 5 mice (8 slices) AAV2-syn-FLEX-ChrimsonR-tdTomato was injected in the TRN and AAV5-CaMKII-ChR2(H134R)-EYFP was injected into in the PL. For 2 mice (4 slices) AAV9-syn-ChrimsonR-tdTomato was injected into the PL and AAV2-Ef1α-DIO-hChR2(H134R)-EYFP was injected into the TRN. Recordings were done in voltage clamp mode and using Cs-based internal solution as described above, with TTX and 4AP present in the bath. The holding potential was varied between -70 mV or 0 mV to isolate PL-mediated EPSCs and TRN-mediated IPSCs respectively. A blue light was used to excite ChR2 and a fiber optic cable from a red laser was attached to the recording chamber (630 nm, Opto Engine LLC) to excite ChrimsonR. Given that red-shifted opsins like ChrimsonR can be excited by blue-shifted light (i.e., 470 nm)[NO_PRINTED_FORM], and that the two inputs onto PVT neurons were either excitatory or inhibitory, we implemented a protocol that minimized potential contamination of postsynaptic currents driven by blue light activation of ChrimsonR. First, cells were held at a membrane potential necessary to isolate the postsynaptic current (PSCs) dependent on the location of ChrimsonR expression (e.g., 0 mV for ChrimsonR expression in TRN). After a baseline recording was established, the appropriate antagonists were bath applied to block the PSCs (e.g., PTX). Once the current was reliably blocked, the holding potential was changed to isolate the PSC driven by the neurons expressing ChR2 (e.g., -70 mV for ChR2 expression in PL).

Statistical analysis and reproducibility

All data were plotted and analyzed with OriginPro version 2016 and version 2018 (OriginLab) and GraphPad Prism (version 8.0.1, GraphPad Software). All data are presented as mean ± s.e.m. There were no assumptions or corrections made before data analysis. Differences between two groups were tested with a two-tailed Student’s t-test; differences among multiple groups were examined with analysis of variance (ANOVA, one-way and two-way repeated-measures) followed by two-stage linear step-up procedure of Benjamini, Krieger and Yekutieli; P < 0.05 was considered significant. The sample sizes used in our study, such as the numbers of animals, are typically the same or exceed those estimated by power analysis (power = 0.80, α= 0.05). For tracing experiments, the sample size is 2–5 mice. For fiber photometry experiments alone, the sample size is 4–6 mice. For fiber photometry experiments with optogenetic and/or chemogenetic malipulations, the sample size is 3–4 mice. For optogenetic behavior experiments, the sample size is 6–13 mice. For ex vivo electrophysiology experiments, the sample size was 3-8 mice and 1-3 slices per mouse. All experiments were replicated at least once, and similar results were obtained. All experiments were randomized. For all behavior experiments, investigators were blinded to allocation during experiments. Data distribution was assumed to be normal, but this was not formally tested.

REFERENCES IN METHODS:

1. Penzo, M. A. et al. The paraventricular thalamus controls a central amygdala fear circuit. Nature 519, 455–459 (2015).

2. Beas, B. S. et al. The locus coeruleus drives disinhibition in the midline thalamus via a dopaminergic mechanism. Nat Neurosci 21, 963–973 (2018).

3. Ma, J. et al. Divergent projections of the paraventricular nucleus of the thalamus mediate the selection of passive and active defensive behaviors. Nat Neurosci 24, 1429–1440 (2021).

4. Haynes, K., Fearnhead, P. & Eckley, I. A. A computationally efficient nonparametric approach for changepoint detection. Stat Comput 27, 1293–1305 (2017).

5. Erben, L., He, M.-X., Laeremans, A., Park, E. & Buonanno, A. A Novel Ultrasensitive In Situ Hybridization Approach to Detect Short Sequences and Splice Variants with Cellular Resolution. Mol Neurobiol 55, 6169–6181 (2018).

6. Erben, L. & Buonanno, A. Detection and Quantification of Multiple RNA Sequences Using Emerging Ultrasensitive Fluorescent In Situ Hybridization Techniques. Curr Protoc Neurosci 87, e63 (2019).

7. Ting, J. T., Daigle, T. L., Chen, Q. & Feng, G. Acute Brain Slice Methods for Adult and Aging Animals: Application of Targeted Patch Clamp Analysis and Optogenetics. in 221–242 (2014). doi:10.1007/978-1-4939-1096-0_14.

There is NO Competing Interest.

SupplementaryFigure1RelatedtooptogeneticpartofFigure1.tif
Extended Data Supplementary Figure 1
SupplementaryFigure2OptomanipulationofPLpPVTpathwayduringtheITI.tif
Extended Data Supplementary Figure 2
SupplementaryFigure3RelatedtoFigure3.tif
Extended Data Supplementary Figure 3
SupplementaryFigure4RelatedtoFigure4.tif
Extended Data Supplementary Figure 4
SupplementaryFigure5RabiestracingofPLTRNpPVTpathway.tif
Extended Data Supplementary Figure 5
SupplementaryFigure6RelatedtooptogeneticpartofFigure5.tif
Extended Data Supplementary Figure 6
ExtendedDataFigure1AnatomyofPLprojections.tif
Extended Data Figure 1
ExtendedDataFigure2RelatedtophotometrypartofPLpPVTpathwayinFigure1.tif
Extended Data Figure 2
ExtendedDataFigure3RelatedtophotometrypartofpPVTactivityinFigure1.tif
Extended Data Figure 3
ExtendedDataFigure4RelatedtooptogeneticpartofFigure1.tif
Extended Data Figure 4
ExtendedDataFigure5AnatomyofTRNpPVTpathway.tif
Extended Data Figure 5
ExtendedDataFigure6RelatedtoFigure4.tif
Extended Data Figure 6
ExtendedDataFigure7PLRbp4TRNpathwayinAA.tif
Extended Data Figure 7
ExtendedDataFigure8RelatedtoFigure5.tif
Extended Data Figure 8
ExtendedDataFigure9pPVTGABAsensorinAA.tif
Extended Data Figure 9
ExtendedDataFigure10ReboundofpPVTactivity.tif
Extended Data Figure 10
Legends.docx

Download PDF

Journal Publication

published 04 Aug, 2024

Read the published version in Nature Communications →

Version 1

posted

You are reading this latest preprint version

Non-canonical cortico-thalamic dynamics gate avoidance decisions

Status:

Journal Publication

Version 1

Abstract

Figures

Main

Discussion

Declarations

References

Methods

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1