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Amygdala inputs to prefrontal cortex guide behavior amid conflicting cues of reward and punishment

Nature Neuroscience 20, 824–835 (2017) | Download Citation

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Abstract

Orchestrating appropriate behavioral responses in the face of competing signals that predict either rewards or threats in the environment is crucial for survival. The basolateral nucleus of the amygdala (BLA) and prelimbic (PL) medial prefrontal cortex have been implicated in reward-seeking and fear-related responses, but how information flows between these reciprocally connected structures to coordinate behavior is unknown. We recorded neuronal activity from the BLA and PL while rats performed a task wherein competing shock- and sucrose-predictive cues were simultaneously presented. The correlated firing primarily displayed a BLA→PL directionality during the shock-associated cue. Furthermore, BLA neurons optogenetically identified as projecting to PL more accurately predicted behavioral responses during competition than unidentified BLA neurons. Finally photostimulation of the BLA→PL projection increased freezing, whereas both chemogenetic and optogenetic inhibition reduced freezing. Therefore, the BLA→PL circuit is critical in governing the selection of behavioral responses in the face of competing signals.

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Acknowledgements

The authors thank S. Sorooshyari for discussion and assistance on Matlab programming, as well as C. Wildes, and the entire Tye laboratory for discussion and support on this project. A.B.-R. was supported by the Brain and Behavior Research Foundation (NARSAD Young Investigator Award) and NIMH (Research Supplement to Promote Diversity in Health-Related Sciences). E.Y.K. was supported by the Collaborative Clinical Neuroscience Fellowship and the American Brain Foundation Clinical Research Training Fellowship. W.A.R.-G. and P.A.P.-R. were supported by the MIT Summer Research Program, which received support from the Center for Brains, Minds and Machines (CBMM), NSF (STC Award CCF-1231216) and NIH (Endure Award 1R25-MH092912-01). E.H.N. was supported by the National Science Foundation Graduate Research Fellowship (NSF GRFP), the Integrative Neuronal Systems Training Fellowship (T32 GM007484) and the Training Program in the Neurobiology of Learning and Memory. A.C.F.-O. was supported by an institutional NRSA training grant (5T32GM007484-38). P.N. was supported by the Singleton, Leventhal and Whitaker fellowships. C.A.L. was supported by an NSF Graduate Research Fellowship, an Integrative Neuronal Systems Fellowship and the James R. Killian Fellowship. M.J.P., K.N.P. and M.A. were supported by the MIT Undergraduate Research Opportunities Program. K.K.A. was supported by the MIT Research Assistantship Program. K.M.T. is a New York Stem Cell Foundation - Robertson Investigator and McKnight Scholar, and this work was supported by funding from the JPB Foundation, PIIF, PNDRF, JFDP, Whitehall Foundation, Klingenstein Foundation, NARSAD Young Investigator Award, Alfred P Sloan Foundation, New York Stem Cell Foundation, McKnight Foundation, NIH R01-MH102441-01 (NIMH), R01-AA023305-01 (NIAAA) and NIH Director's New Innovator Award DP2-DK-102256-01 (NIDDK).

Author information

    • Anthony Burgos-Robles
    •  & Eyal Y Kimchi

    These authors contributed equally to this work.

Affiliations

  1. Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

    • Anthony Burgos-Robles
    • , Eyal Y Kimchi
    • , Ehsan M Izadmehr
    • , Mary Jane Porzenheim
    • , William A Ramos-Guasp
    • , Edward H Nieh
    • , Ada C Felix-Ortiz
    • , Praneeth Namburi
    • , Christopher A Leppla
    • , Kara N Presbrey
    • , Kavitha K Anandalingam
    • , Pablo A Pagan-Rivera
    • , Melodi Anahtar
    • , Anna Beyeler
    •  & Kay M Tye

  2. Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.

    • Eyal Y Kimchi

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Contributions

A.B.-R. and K.M.T. conceived and designed experiments. A.B.-R. and K.N.P. designed and constructed electrodes and optrodes for neural recordings. A.B.-R. performed surgeries to chronically implant electrodes and performed single-unit recordings. A.B.-R., C.A.L. and K.N.P. sorted extracellular waveforms. A.B.-R. and E.Y.K. analyzed electrophysiology data. E.Y.K. wrote the Matlab scripts for the support vector machine learning algorithms. E.M.I., M.J.P., K.N.P., K.K.A., P.A.P.-R. and M.A. built optical fibers. A.B.-R., E.M.I., M.J.P., W.A.R.-G., K.N.P. and M.A. performed animal training and analyzed behaviors from videos. A.B.-R., E.M.I., M.J.P., W.A.R.-G., A.C.F.-O., K.N.P., K.K.A. and M.A. performed histological assessment. E.H.N. assisted with programing of the neural recording workstation and wrote the Matlab script for quantification of animal movement. P.N. assisted with Med-PC programming for behavioral studies and wrote the Matlab scripts to analyze port entry data and waveform properties. A.B. performed ex vivo whole-cell patch-clamp electrophysiological recordings. A.B. and A.C.F.-O. assisted with figures. A.B.-R., E.Y.K. and K.M.T. made figures and wrote the manuscript. All authors contributed to the editing and revision of the final version of the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Kay M Tye.

Integrated supplementary information

Supplementary figures

  1. 1.

    Progression of port entry and freezing responses across training.

  2. 2.

    Histological reconstruction of neural recording sites in the BLA and PL.

  3. 3.

    Criteria used for the detection of significant correlations between the BLA and PL cells, and side-by-side results with multiple time bin sizes.

  4. 4.

    Representative raw cross-correlations demonstrating potential increased variability with smaller time bin widths.

  5. 5.

    BLA and PL exhibited less correlated activity during a habituation session prior to the acquisition of the reward and fear memories, as well as during a neutral cue after acquiring the memories.

  6. 6.

    Variations on cross-correlation lead/lag across counterbalanced cue conditions, event-biased populations, and putative projection neurons and interneurons.

  7. 7.

    CS-responsive populations in the BLA and PL, and cross-correlations across responsive and nonresponsive cells during reward–fear discrimination.

  8. 8.

    Histological assessment for the BLA→PL stimulation experiments and behavioral effects during a discrimination session with a neutral cue.

  9. 9.

    Validation of the ArchT viral construct and effects of optogenetically mediated inhibition of BLA inputs on the spontaneous firing of PL neurons.

  10. 10.

    Histological assessment for the BLA→PL inhibition experiments and behavioral effects during discrimination sessions with a neutral cue.

Supplementary information

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  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–10

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    Supplementary Methods Checklist

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DOI

https://doi.org/10.1038/nn.4553

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