Amygdala interneuron subtypes control fear learning through disinhibition (original) (raw)

References

  1. LeDoux, J. E. Emotion circuits in the brain. Annu. Rev. Neurosci. 23, 155–184 (2000)
    CAS PubMed Google Scholar
  2. Davis, M. in The Amygdala (ed. Aggleton, J. P. ) 213–288 (Oxford Univ. Press, 2000)
    Google Scholar
  3. Fanselow, M. S. & Poulos, A. M. The neuroscience of mammalian associative learning. Annu. Rev. Psychol. 56, 207–234 (2005)
    PubMed Google Scholar
  4. Maren, S. & Quirk, G. J. Neuronal signalling of fear memory. Nature Rev. Neurosci. 5, 844–852 (2004)
    CAS Google Scholar
  5. Pape, H. C. & Paré, D. Plastic synaptic networks of the amygdala for the acquisition, expression, and extinction of conditioned fear. Physiol. Rev. 90, 419–463 (2010)
    CAS PubMed Google Scholar
  6. Waclaw, R. R., Ehrman, L. A., Pierani, A. & Campbell, K. Developmental origin of the neuronal subtypes that comprise the amygdalar fear circuit in the mouse. J. Neurosci. 30, 6944–6953 (2010)
    CAS PubMed PubMed Central Google Scholar
  7. Sah, P., Faber, E. S., Lopez De Armentia, M. & Power, J. The amygdaloid complex: anatomy and physiology. Physiol. Rev. 83, 803–834 (2003)
    CAS PubMed Google Scholar
  8. Ehrlich, I. et al. Amygdala inhibitory circuits and the control of fear memory. Neuron 62, 757–771 (2009)
    CAS PubMed Google Scholar
  9. Markram, H. et al. Interneurons of the neocortical inhibitory system. Nature Rev. Neurosci. 5, 793–807 (2004)
    CAS Google Scholar
  10. Somogyi, P. & Klausberger, T. Defined types of cortical interneurone structure space and spike timing in the hippocampus. J. Physiol. (Lond.) 562, 9–26 (2005)
    CAS Google Scholar
  11. Freund, T. F. & Buzsaki, G. Interneurons of the hippocampus. Hippocampus 6, 347–470 (1996)
    CAS PubMed Google Scholar
  12. Fishell, G. & Rudy, B. Mechanisms of inhibition within the telencephalon: “where the wild things are”. Annu. Rev. Neurosci. 34, 535–567 (2011)
    CAS PubMed PubMed Central Google Scholar
  13. Spampanato, J., Polepalli, J. & Sah, P. Interneurons in the basolateral amygdala. Neuropharmacology 60, 765–773 (2011)
    CAS PubMed Google Scholar
  14. Bienvenu, T. C., Busti, D., Magill, P. J., Ferraguti, F. & Capogna, M. Cell-type-specific recruitment of amygdala interneurons to hippocampal theta rhythm and noxious stimuli in vivo. Neuron 74, 1059–1074 (2012)
    CAS PubMed Google Scholar
  15. Heldt, S. A. & Ressler, K. J. Training-induced changes in the expression of GABAA-associated genes in the amygdala after the acquisition and extinction of Pavlovian fear. Eur. J. Neurosci. 26, 3631–3644 (2007)
    PubMed PubMed Central Google Scholar
  16. Chhatwal, J. P., Myers, K. M., Ressler, K. J. & Davis, M. Regulation of gephyrin and GABAA receptor binding within the amygdala after fear acquisition and extinction. J. Neurosci. 25, 502–506 (2005)
    CAS PubMed PubMed Central Google Scholar
  17. Harris, J. A. & Westbrook, R. F. Evidence that GABA transmission mediates context-specific extinction of learned fear. Psychopharmacology (Berl.) 140, 105–115 (1998)
    CAS Google Scholar
  18. Atallah, B. V., Bruns, W., Carandini, M. & Scanziani, M. Parvalbumin-expressing interneurons linearly transform cortical responses to visual stimuli. Neuron 73, 159–170 (2012)
    CAS PubMed PubMed Central Google Scholar
  19. Rudy, B., Fishell, G., Lee, S. & Hjerling-Leffler, J. Three groups of interneurons account for nearly 100% of neocortical GABAergic neurons. Dev. Neurobiol. 71, 45–61 (2011)
    PubMed PubMed Central Google Scholar
  20. Kawaguchi, Y. & Kubota, Y. GABAergic cell subtypes and their synaptic connections in rat frontal cortex. Cereb. Cortex 7, 476–486 (1997)
    CAS PubMed Google Scholar
  21. Woodruff, A. R. & Sah, P. Networks of parvalbumin-positive interneurons in the basolateral amygdala. J. Neurosci. 27, 553–563 (2007)
    CAS PubMed PubMed Central Google Scholar
  22. Rainnie, D. G., Mania, I., Mascagni, F. & McDonald, A. J. Physiological and morphological characterization of parvalbumin-containing interneurons of the rat basolateral amygdala. J. Comp. Neurol. 498, 142–161 (2006)
    PubMed Google Scholar
  23. Muller, J. F., Mascagni, F. & McDonald, A. J. Pyramidal cells of the rat basolateral amygdala: synaptology and innervation by parvalbumin-immunoreactive interneurons. J. Comp. Neurol. 494, 635–650 (2006)
    PubMed PubMed Central Google Scholar
  24. McDonald, A. J. & Betette, R. L. Parvalbumin-containing neurons in the rat basolateral amygdala: morphology and co-localization of Calbindin-D28k . Neuroscience 102, 413–425 (2001)
    CAS PubMed Google Scholar
  25. Muller, J. F., Mascagni, F. & McDonald, A. J. Coupled networks of parvalbumin-immunoreactive interneurons in the rat basolateral amygdala. J. Neurosci. 25, 7366–7376 (2005)
    CAS PubMed PubMed Central Google Scholar
  26. Lovett-Barron, M. et al. Regulation of neuronal input transformations by tunable dendritic inhibition. Nature Neurosci. 15, 423–430 (2012)
    CAS PubMed Google Scholar
  27. Muller, J. F., Mascagni, F. & McDonald, A. J. Postsynaptic targets of somatostatin-containing interneurons in the rat basolateral amygdala. J. Comp. Neurol. 500, 513–529 (2007)
    CAS PubMed Google Scholar
  28. Chiu, C. Q. et al. Compartmentalization of GABAergic inhibition by dendritic spines. Science 340, 759–762 (2013)
    ADS CAS PubMed PubMed Central Google Scholar
  29. Gentet, L. J. et al. Unique functional properties of somatostatin-expressing GABAergic neurons in mouse barrel cortex. Nature Neurosci. 15, 607–612 (2012)
    CAS PubMed Google Scholar
  30. Zhang, F., Aravanis, A. M., Adamantidis, A., de Lecea, L. & Deisseroth, K. Circuit-breakers: optical technologies for probing neural signals and systems. Nature Rev. Neurosci. 8, 577–581 (2007)
    CAS Google Scholar
  31. Hippenmeyer, S. et al. A developmental switch in the response of DRG neurons to ETS transcription factor signaling. PLoS Biol. 3, e159 (2005)
    PubMed PubMed Central Google Scholar
  32. Chow, B. Y. et al. High-performance genetically targetable optical neural silencing by light-driven proton pumps. Nature 463, 98–102 (2010)
    ADS CAS PubMed PubMed Central Google Scholar
  33. Lima, S. Q., Hromadka, T., Znamenskiy, P. & Zador, A. M. PINP: a new method of tagging neuronal populations for identification during in vivo electrophysiological recording. PLoS ONE 4, e6099 (2009)
    ADS PubMed PubMed Central Google Scholar
  34. Woodruff, A. R. & Sah, P. Inhibition and synchronization of basal amygdala principal neuron spiking by parvalbumin-positive interneurons. J. Neurophysiol. 98, 2956–2961 (2007)
    PubMed Google Scholar
  35. McDonald, A. J. Neuronal organization of the lateral and basolateral amygdaloid nuclei in the rat. J. Comp. Neurol. 222, 589–606 (1984)
    CAS PubMed Google Scholar
  36. Likhtik, E., Pelletier, J. G., Popescu, A. T. & Paré, D. Identification of basolateral amygdala projection cells and interneurons using extracellular recordings. J. Neurophysiol. 96, 3257–3265 (2006)
    PubMed Google Scholar
  37. Taniguchi, H. et al. A resource of Cre driver lines for genetic targeting of GABAergic neurons in cerebral cortex. Neuron 71, 995–1013 (2011)
    CAS PubMed PubMed Central Google Scholar
  38. Bissière, S., Humeau, Y. & Lüthi, A. Dopamine gates LTP induction in lateral amygdala by suppressing feedforward inhibition. Nature Neurosci. 6, 587–592 (2003)
    PubMed Google Scholar
  39. Humeau, Y. & Lüthi, A. Dendritic calcium spikes induce bi-directional synaptic plasticity in the lateral amygdala. Neuropharmacology 52, 234–243 (2007)
    CAS PubMed Google Scholar
  40. Murayama, M. et al. Dendritic encoding of sensory stimuli controlled by deep cortical interneurons. Nature 457, 1137–1141 (2009)
    ADS CAS PubMed Google Scholar
  41. Han, J. H. et al. Neuronal competition and selection during memory formation. Science 316, 457–460 (2007)
    ADS CAS PubMed Google Scholar
  42. Zhou, Y. et al. CREB regulates excitability and the allocation of memory to subsets of neurons in the amygdala. Nature Neurosci. 12, 1438–1443 (2009)
    CAS PubMed Google Scholar
  43. Woodson, W., Farb, C. R. & Ledoux, J. E. Afferents from the auditory thalamus synapse on inhibitory interneurons in the lateral nucleus of the amygdala. Synapse 38, 124–137 (2000)
    CAS PubMed Google Scholar
  44. Letzkus, J. J. et al. A disinhibitory microcircuit for associative fear learning in the auditory cortex. Nature 480, 331–335 (2011)
    ADS CAS PubMed Google Scholar
  45. Farb, C. R. & LeDoux, J. E. NMDA and AMPA receptors in the lateral nucleus of the amygdala are postsynaptic to auditory thalamic afferents. Synapse 27, 106–121 (1997)
    CAS PubMed Google Scholar
  46. McDonald, A. J. Cortical pathways to the mammalian amygdala. Prog. Neurobiol. 55, 257–332 (1998)
    CAS PubMed Google Scholar
  47. Jiang, X., Wang, G., Lee, A. J., Stornetta, R. L. & Zhu, J. J. The organization of two new cortical interneuronal circuits. Nature Neurosci. 16, 210–218 (2013)
    CAS PubMed Google Scholar
  48. Lisman, J., Yasuda, R. & Raghavachari, S. Mechanisms of CaMKII action in long-term potentiation. Nature Rev. Neurosci. 13, 169–182 (2012)
    CAS Google Scholar
  49. Klausberger, T. & Somogyi, P. Neuronal diversity and temporal dynamics: the unity of hippocampal circuit operations. Science 321, 53–57 (2008)
    ADS CAS PubMed PubMed Central Google Scholar
  50. Kvitsiani, D. et al. Distinct behavioural and network correlates of two interneuron types in prefrontal cortex. Nature 498, 363–366 (2013)
    ADS CAS PubMed PubMed Central Google Scholar
  51. Tang, W. et al. Faithful expression of multiple proteins via 2A-peptide self-processing: a versatile and reliable method for manipulating brain circuits. J. Neurosci. 29, 8621–8629 (2009)
    CAS PubMed PubMed Central Google Scholar
  52. Gallistel, C. R., Fairhurst, S. & Balsam, P. The learning curve: implications of a quantitative analysis. Proc. Natl Acad. Sci. USA 101, 13124–13131 (2004)
    ADS CAS PubMed PubMed Central Google Scholar
  53. Herry, C. et al. Switching on and off fear by distinct neuronal circuits. Nature 454, 600–606 (2008)
    ADS CAS PubMed Google Scholar
  54. Legendy, C. R. & Salcman, M. Bursts and recurrences of bursts in the spike trains of spontaneously active striate cortex neurons. J. Neurophysiol. 53, 926–939 (1985)
    CAS PubMed Google Scholar
  55. McDonald, A. J. & Mascagni, F. Immunohistochemical characterization of somatostatin containing interneurons in the rat basolateral amygdala. Brain Res. 943, 237–244 (2002)
    CAS PubMed Google Scholar
  56. Pivetta, C., Esposito, M. S., Sigrist, M. & Arber, S. Motor-circuit communication matrix from spinal cord to brainstem neurons revealed by developmental origin. Cell 156, 537–548 (2014)
    CAS PubMed Google Scholar
  57. Cuntz, H., Forstner, F., Borst, A. & Hausser, M. One rule to grow them all: a general theory of neuronal branching and its practical application. PLoS Comp. Biol. 6, e1000877 (2010)
    ADS MathSciNet Google Scholar
  58. Hill, M. N. et al. Disruption of fatty acid amide hydrolase activity prevents the effects of chronic stress on anxiety and amygdalar microstructure. Mol. Psychiatry 18, 1125–1135 (2013)
    CAS PubMed Google Scholar

Download references

Acknowledgements

We thank all members of the Lüthi laboratory and E. Schuman for discussions and critical comments on the manuscript. We would like to thank J. Lüdke, P. Argast and P. Buchmann for technical assistance. We further thank the Facility for Imaging and Microscopy at the FMI, in particular S. Bourke and M. Kirschmann, for their assistance in image acquisition and analysis. We also thank K. Deisseroth, E. Boyden, J. Huang, R. Sprengel and S. Arber for sharing materials and mouse lines. This work was supported by the Novartis Research Foundation, by the National Center of Competences in Research: ‘SYNAPSY — The Synaptic Bases of Mental Diseases’ (financed by the Swiss National Science Foundation) as well as by a SNSF core grant to A.L. S.B.E.W. is supported by a Schering Foundation Fellowship. J.J.L. is supported by a Swiss National Science Foundation Ambizione Fellowship. J.G. and G.A.J. are supported by EMBO Long-Term Fellowships and Marie Curie Action Fellowships.

Author information

Author notes

  1. Johannes J. Letzkus
    Present address: Present address: Max-Planck Institute for Brain Research, 60438 Frankfurt, Germany.,
  2. Steffen B. E. Wolff, Jan Gründemann, Johannes J. Letzkus and Andreas Lüthi: These authors contributed equally to this work.

Authors and Affiliations

  1. Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, CH-4058 Basel, Switzerland,
    Steffen B. E. Wolff, Jan Gründemann, Philip Tovote, Sabine Krabbe, Gilad A. Jacobson, Christian Müller, Rainer W. Friedrich, Johannes J. Letzkus & Andreas Lüthi
  2. University of Basel, 4000 Basel, Switzerland,
    Steffen B. E. Wolff
  3. INSERM U862, Neurocentre Magendie, 146 rue Leo Saignat, 33077 Bordeaux, France,
    Cyril Herry
  4. Hertie Institute for Clinical Brain Research, 72076 Tübingen, Germany,
    Ingrid Ehrlich

Authors

  1. Steffen B. E. Wolff
    You can also search for this author inPubMed Google Scholar
  2. Jan Gründemann
    You can also search for this author inPubMed Google Scholar
  3. Philip Tovote
    You can also search for this author inPubMed Google Scholar
  4. Sabine Krabbe
    You can also search for this author inPubMed Google Scholar
  5. Gilad A. Jacobson
    You can also search for this author inPubMed Google Scholar
  6. Christian Müller
    You can also search for this author inPubMed Google Scholar
  7. Cyril Herry
    You can also search for this author inPubMed Google Scholar
  8. Ingrid Ehrlich
    You can also search for this author inPubMed Google Scholar
  9. Rainer W. Friedrich
    You can also search for this author inPubMed Google Scholar
  10. Johannes J. Letzkus
    You can also search for this author inPubMed Google Scholar
  11. Andreas Lüthi
    You can also search for this author inPubMed Google Scholar

Contributions

S.B.E.W. initiated the project, performed most experiments and data analysis, and wrote the manuscript. J.G. performed and analysed in vitro experiments and immunohistochemistry. P.T. performed and analysed immunohistochemistry. S.K. performed in vitro experiments and analysed subcellular targeting experiments. G.A.J. and R.W.F. performed and helped with data analysis. C.M. provided technical assistance. C.H. and I.E. helped to establish optogenetic manipulations and single-unit recordings. S.B.E.W., J.J.L. and A.L. conceived the project and wrote the manuscript. All authors contributed to the experimental design and interpretation, and commented on the manuscript.

Corresponding authors

Correspondence toJohannes J. Letzkus or Andreas Lüthi.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Fibre and electrode placement sites and single-unit recording quality.

a, Position of electrode tips for single-unit recordings and of bilateral optic fibres for all PV–Cre and SOM–Cre animals used. b, Left, superimposed waveforms of three single units recorded on the same channel in the BLA of a PV–Cre animal. Right, spikes from individual units were sorted into clusters, using 3D principal component analysis. c, Recording quality was evaluated by calculation of J3 and Davies Bouldin validity index (DB) statistics for PV+ and SOM+ interneurons. To obtain control values two clusters were selected in the central noise cloud in channels without units. High J3 and low DB values indicate good isolation of single units. d, Left, principal component (PC) space cylinders to determine the stability of clustered waveforms during long-term recordings. Straight cylinders indicate stable recordings of the same set of single units. Right, superimposed waveforms recorded during the different sessions, used for the calculation of the PC space cylinders. e, For a quantitative evaluation of spike shape similarity over different recording days, we calculated linear correlation values for PV+ and SOM+ interneurons. As a control, we calculated the r values for waveforms of different random neurons. The maximum r value across recording sessions was used to quantify similarity (r = 1 indicates identical spike shapes). All PV+ and SOM+ interneurons had r values above 0.95, but only about 4% of the random comparisons. Values are mean ± s.e.m. *P < 0.05. Statistical analysis in Methods.

Extended Data Figure 2 Effects on freezing by baseline optogenetic stimulation and freezing for all behavioural groups.

a, Effects of optogenetic manipulations of PV+ or SOM+ cell activity on baseline freezing levels. Illumination patterns resemble stimulations during the CS in the conditioning paradigms (10 s of 300-ms blue light pulses at 0.9 Hz or 10 s continuous yellow light). b, Freezing values during fear retrieval for all behavioural groups with optogenetic PV+ or SOM+ cell manipulations during the CS, US or CS+US. Freezing levels are compared between the internal control CS1 and CS2 which was paired with optogenetic stimulation during the conditioning.

Extended Data Figure 3 Optogenetic identification and manipulation of PV+ and SOM+ interneurons.

a, b, Top left: ex vivo whole-cell patch clamp and cell-attached recordings of ChR2–mCherry-expressing PV+ (a) and SOM+ (b) BLA interneurons in amygdala slices. Top right: distinct firing patterns of PV+ (a) and SOM+ (b) cells in response to depolarizing somatic current injection. Bottom: PV+ (a) and SOM+ (b) cells fire brief bursts of action potentials in response to short blue light stimulation (468 nm, 5 ms, 10 mW) and show sustained firing during prolonged stimulation (468 nm, 300 ms, 10 mW, cell-attached recordings). c, Single-unit recordings of optogenetically identified PV+ (green) and SOM+ (red) interneurons upon 300-ms stimulation with blue (ChR2, top) or yellow (ARCH, bottom) light in behaving animals. Left: _Z_-scored activity. Right: firing frequency. d, Left: comparison of spontaneous and light-evoked superimposed average spike waveforms of all PV+ interneurons identified by optogenetic activation. Middle: linear correlations between spontaneous and light-evoked spikes were calculated for individual optogenetically identified PV+ interneurons. Only cells with r values above 0.95 were considered as directly light-activated. Right: jitter of first light-evoked spikes in identified PV+ cells. e, Change point analysis for determination of the latency of light-evoked activity changes. The cumulative sum of the activity of a neuron was calculated and the change point was determined. Shown is an example of ARCH-mediated inhibition for the entire illumination period (left) and at the light onset (right). The arrow indicates the change point. f, Expression of ChR2, co-expressed with Venus (green), is restricted to SOM+ interneurons (red). g, Specificity of opsin-expression. Left: examples of opsin expression in a PV–Cre (top) and a SOM–Cre (bottom) animal with immunohistochemistry (IHC) for the respective other interneuron marker. Right: quantification of co-localization of opsin-expressing and IHC-labelled cells. h, Light-induced changes in activity of SOM+ interneurons upon illumination with either blue or yellow light for activation of ChR2 or ARCH, respectively. i, Latencies and Z scores of light-induced activity changes in optogenetically identified SOM+ interneurons. j, Left: comparison of average waveforms of spontaneous and light-evoked spikes in SOM+ interneurons. Middle: linear correlations between spontaneous and light-evoked spikes in optogenetically identified SOM+ interneurons. Right: jitter of first light-evoked spikes in identified SOM+ cells.

Extended Data Figure 4 Physiological properties of PV+ and SOM+ interneurons and of non-light-responsive cells.

a, Distribution of physiological properties for optogenetically identified PV+ and SOM+ interneurons and for non-light-responsive cells. Spike half-width and spontaneous activity were compared for identified interneurons and for a population of simultaneously recorded non-light-responsive cells, which is mainly comprised of principal cells, but could also include unidentified interneurons. Squares indicate average values for the three groups. SOM+ interneurons cannot be separated from the non-light-responsive cells whereas PV+ cells are, on average, different from the other groups. b, Comparison of normalized spike waveforms of PV+ and SOM+ interneurons and non-light-responsive cells. c, Linear correlations of the spike waveforms between the different groups. d, Comparison of spike half-width and spontaneous activity of PV+ and SOM+ interneurons and non-light-responsive cells. Note the similarity of SOM+ interneurons with non-light-responsive cells. Values are mean ± s.e.m. *P < 0.05. Statistical analysis in Methods.

Extended Data Figure 5 US and CS responses in PV+ and SOM+ interneurons in the BLA.

a–d, Z-scored population activity (left) and firing frequency (right) for identified PV+ (green) and SOM+ (red) cells in response to the US (a, b) or the CS (c, d). Shown are the population responses for all cells (top graphs). The graphs below show population responses that were separated according to whether the cells showed inhibitory or excitatory responses to the stimuli. Cells were included in the stimulus inhibited group or stimulus excited group if their average _Z_-scored stimulus response was <0 or >0, respectively.

Extended Data Figure 6 Effects of optogenetic manipulations of PV+ interneuron activity on footshock responses of putative principal neurons.

a, Footshock-evoked responses in footshock-excited putative principal neurons with (blue or yellow) and without (black) optogenetic stimulation of PV+ interneurons. Cells are split based on being excited either exclusively during the footshock alone, exclusively during the footshock+light or during both conditions. b, Cumulative probability distributions of the average Z score of individual putative principal neurons during the footshock without (black) and with optogenetic manipulations of PV+ interneurons. c, Cumulative probability distributions of the change index (CI) as a measure for the light-induced changes in footshock responses for the different optogenetic interneuron manipulations. The change index is calculated as CI = (Z score(footshock+light)−Z score(footshock))/(Z score(footshock+light)+Z score(footshock)) (all Z scores are the averages of the shock response during the entire footshock). Shown are the distributions for the footshock-excited cells (black), for the footshock+light excited cells (blue or yellow), for cells which are excited in both conditions (green) and the weighted sum (grey). d, Left, comparisons of cumulative probability distributions for the differences in average footshock Z score during the entire footshock ((footshock+light) – footshock) in putative principal neurons for PV+ interneuron activation (blue) and inhibition (yellow). Right, comparisons of cumulative probability distributions for the change index of the footshock responses of putative principal neurons upon optogenetic activation (blue) or inhibition (yellow) of PV+ interneurons. e, Inhibition of BLA principal neurons during the US impairs fear acquisition. Principal neurons were selectively inhibited by expression of ARCH under the control of the CaMKII promoter and illumination with yellow light during the US. Values are mean ± s.e.m. **P < 0.01. Statistical analysis in Methods.

Extended Data Figure 7 Inhibitory inputs from PV+ to SOM+ as well as to unidentified GABAergic neurons.

a, Slice patch-clamp experiments. Left: BLA location of PV+ interneurons expressing ChR2–mCherry in acute slices of PV–Cre::GAD–EGFP animals. Right: whole-cell recording (middle) of neuron expressing GAD (top) but not ChR2 (bottom). (Original magnification: left: ×5, right: ×60.) b, Optogenetic stimulation of PV+ neurons resulted in inhibitory postsynaptic currents (IPSCs) in GAD–EGFP+ neurons (n = 26, 5-ms blue light). IPSCs reverse at about 0 mV (voltage clamp Cl− reversal potential, n = 4). c, Left: fractions of patched and identified SOM+ cells with and without detected PV+ input. Owing to potential wash-out of the antigen (SOM) during the whole-cell recordings, these data probably represent a solid lower bound rather than the actual connectivity rate (see Methods). Right: IPSC amplitude, peak latency after blue light onset and 10–90% rise time of PV inputs on all measured GABAergic (PV–GAD, n = 26) and somatostatin-positive (PV–SOM, n = 3) neurons, respectively. 1 SOM+ cell with PV inputs was tested with a current clamp internal at −40 mV and thus does not contribute to the statistics. d, e, Suppression of spikes in a SOM+ cell by optogenetic activation of PV+ cells. d, individual trials of somatic current injection into a SOM+ cell with and without PV+ activation. Red traces show spike failures on PV+ stimulation. e, overlay of current injections with spiking success and failures without (top) and with (bottom) simultaneous PV+ cell activation.

Extended Data Figure 8 PV+ and SOM+ interneurons contact different compartments of pyramidal neurons.

a, Maximum intensity projection (MIP) showing a biocytin-filled principal neuron (grey) and conditional expression of the presynaptic marker synaptophysin–GFP (Syn–GFP, red) in SOM+ interneurons in the BLA. b, Soma of the same principal neuron (grey) with expression of synaptophysin–GFP (red) selectively in SOM+ interneurons (MIP). Arrowhead points to 1 out of 7 identified synaptic contacts at the soma revealed by 3D analysis of confocal images. c, Dendritic branch located 200 μm distant from the soma (MIP, yellow rectangle in a). Arrowheads mark synaptic contacts of SOM+ interneurons with the principal neuron dendrite identified in 3D analysis. d, Single focal planes of contacts labelled in b, c. e, Biocytin-filled principal neuron and conditional synaptophysin–GFP expression (green) in PV+ interneurons in the BLA (MIP). f, Soma of the same principal neuron with presynaptic synaptophysin–GFP expression selectively in PV+ interneurons (MIP). Arrowheads point to 3 out of 53 synaptic contacts from PV+ interneurons identified in 3D analysis at this soma. g, Dendrite located 200 μm distant from the soma (MIP, yellow rectangle in e). Arrowhead marks a synaptic contact from PV+ interneurons. h, Single section images of appositions labelled in f, g. Scale bars: 100 μm (a, e), 5 μm (b, f), 2 μm (c, g) and 0.5 μm (d, h). i, Quantification of synaptic contacts from SOM+ and PV+ interneurons with the soma of principal neurons in the BLA. j, Number of extrapolated synaptic appositions from SOM+ and PV+ interneurons onto the estimated dendritic tree of principal neurons. k, Normalization of dendritic versus somatic appositions shows a highly significant difference between postsynaptic targeting of SOM+ and PV+ interneurons. l, Distance dependency of synaptic inputs from SOM+ (red, n = 8) and PV+ interneurons (green, n = 7) onto principal neuron dendrites. SOM+ interneurons preferentially target distal dendrites of principal neurons. All values are mean ± s.e.m. *P < 0.05, **P < 0.01, ***P < 0.001. Statistical analysis in Methods.

Extended Data Figure 9 Effects of optogenetic manipulations of PV+ or SOM+ interneuron activity on tone-evoked responses in putative principal neurons.

a, Tone-evoked responses in tone-excited putative principal neurons with (blue or yellow) and without (black) optogenetic stimulation of PV+ or SOM+ interneurons. Cells are split based on being tone excited either exclusively during the tone alone, exclusively during tone+light or in both conditions. b, Cumulative probability distributions of the change index (CI) as a measure for the light-induced changes in tone responses for the different optogenetic interneuron manipulations. The change index is calculated as CI = (Z score(tone+light) − Z score(tone))/(Z score(tone+light) + Z score(tone)) (all Z scores are the averages of the tone response during the pip). Shown are the distributions for the tone-excited cells (black), for the tone+light excited cells (blue or yellow), for cells which are excited in both conditions (green) and the weighted sum (grey). c, Comparisons of cumulative probability distributions for the change index of the tone responses of putative principal neurons upon optogenetic stimulation of PV+ or SOM+ cells.

Extended Data Figure 10 Effects of optogenetic manipulations of PV+ or SOM+ interneurons on spontaneous activity and burst firing of putative principal neurons.

a, Effects of optogenetic activation (top) or inhibition (bottom) of PV+ (grey) or SOM+ (black) cells on the spontaneous activity of putative principal neurons. b, Effects of optogenetic manipulations of PV+ or SOM+ cells on the burst frequency (top) and burst duration (bottom) in putative principal neurons. c, Comparisons of cumulative probability distributions for light-induced changes in spontaneous activity in putative principal neurons on optogenetic stimulation of PV+ or SOM+ interneurons. d, Comparisons of cumulative probability distributions for light-induced changes in burst rate in putative principal neurons on optogenetic stimulation of PV+ or SOM+ interneurons. Values are mean ± s.e.m. *P < 0.05, ***P < 0.001. Statistical analysis in Methods.

PowerPoint slides

Rights and permissions

About this article

Cite this article

Wolff, S., Gründemann, J., Tovote, P. et al. Amygdala interneuron subtypes control fear learning through disinhibition.Nature 509, 453–458 (2014). https://doi.org/10.1038/nature13258

Download citation