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Distinct behavioural and network correlates of two interneuron types in prefrontal cortex
Open accessAbstract
Neurons in the prefrontal cortex exhibit diverse behavioural correlates, an observation that has been attributed to cell-type diversity. To link identified neuron types with network and behavioural functions, we recorded from the two largest genetically defined inhibitory interneuron classes, the perisomatically targeting parvalbumin (PV) and the dendritically targeting somatostatin (SOM) neurons in anterior cingulate cortex of mice performing a reward foraging task. Here we show that PV and a subtype of SOM neurons form functionally homogeneous populations showing a double dissociation between both their inhibitory effects and behavioural correlates. Out of several events pertaining to behaviour, a subtype of SOM neurons selectively responded at reward approach, whereas PV neurons responded at reward leaving and encoded preceding stay duration. These behavioural correlates of PV and SOM neurons defined a behavioural epoch and a decision variable important for foraging (whether to stay or to leave), a crucial function attributed to the anterior cingulate cortex. Furthermore, PV neurons could fire in millisecond synchrony, exerting fast and powerful inhibition on principal cell firing, whereas the inhibitory effect of SOM neurons on firing output was weak and more variable, consistent with the idea that they respectively control the outputs of, and inputs to, principal neurons. These results suggest a connection between the circuit-level function of different interneuron types in regulating the flow of information and the behavioural functions served by the cortical circuits. Moreover, these observations bolster the hope that functional response diversity during behaviour can in part be explained by cell-type diversity.
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In the citing paper
“Extracellular recordings in the anterior cingulate cortex of mice performing a reward foraging task showed that a subgroup of optogenetically-identified Sst INs with narrow spikes fired preferentially at reward approach, whereas PV INs showed an increase firing when the mouse was leaving the reward location that was correlated with stay duration in the reward zone (Kvitsiani et al., 2013).”
From GABAergic Interneurons in the Neocortex: From Cellular Properties to Circuits (2016) · cites this paper as Kvitsiani et al., 2013
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“PV+ interneurons, which could fire in millisecond synchrony, exert fast and powerful inhibition on principal neuron firing, whereas SST+ interneurons exert weak and more variable inhibitory effects on firing output in behaving mice 29.”
From Mechanisms of synaptic transmission dysregulation in the prefrontal cortex: pathophysiological implications (2022) · cites this paper as [29]
In the citing paper
“In mice performing a reward foraging task, SST+ interneurons in ACC are found to selectively respond at reward approach, whereas PV+ interneurons respond at reward leaving 29.”
From Mechanisms of synaptic transmission dysregulation in the prefrontal cortex: pathophysiological implications (2022) · cites this paper as [29]
In the citing paper
“These elements can be task-dependently recruited into larger brain networks for systems-level information processing and computation, representing their distal behavioural and cognitive functions59,60.”
From The diversity of GABAergic neurons and neural communication elements (2019) · cites this paper as [59]
In the citing paper
“This finding supports the idea that local regions such as dACC can contain multiple, distinct neural population codes25 that encode distinct mental processes.”
From Separate neural representations for physical pain and social rejection (2014) · cites this paper as [25]
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Cites
Estimating the Number of Clusters in a Data Set Via the Gap Statistic
Tibshirani, R., Walther, G., & Hastie, T. (2001) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00293 · Cited by 6,205
ASH, R. B. (1966) · Cited by 3,990
Interneurons of the neocortical inhibitory system
Markram, H., Toledo-Rodriguez, M., Wang, Y., Gupta, A., Silberberg, G., & Wu, C. (2004) · Nature Reviews Neuroscience · doi:10.1038/nrn1519 · Cited by 2,954
Parvalbumin neurons and gamma rhythms enhance cortical circuit performance
Sohal, V. S., Zhang, F., Yizhar, O., & Deisseroth, K. (2009) · Nature · doi:10.1038/nature07991 · Cited by 2,606
Driving fast-spiking cells induces gamma rhythm and controls sensory responses
Cardin, J. A., Carlén, M., Meletis, K., Knoblich, U., Zhang, F., Deisseroth, K., Tsai, L.-H., & Moore, C. I. (2009) · Nature · doi:10.1038/nature08002 · Cited by 2,603
A resource of Cre driver lines for genetic targeting of GABAergic neurons in cerebral cortex
Taniguchi, H., He, M., Wu, P., Kim, S., Paik, R., Sugino, K., Kvitsani, D., Fu, Y., Lu, J., Lin, Y., et al. (2011) · Neuron · doi:10.1016/j.neuron.2011.07.026 · Cited by 1,828
Self-citationNeural correlations, population coding and computation
Averbeck, B. B., Latham, P. E., & Pouget, A. (2006) · Nature reviews. Neuroscience · doi:10.1038/nrn1888 · Cited by 1,743
How Inhibition Shapes Cortical Activity
Isaacson, J. S., & Scanziani, M. (2011) · Neuron · doi:10.1016/j.neuron.2011.09.027 · Cited by 1,678
Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex
The Petilla Interneuron Nomenclature Group (PING) (2008) · Nature Reviews Neuroscience · doi:10.1038/nrn2402 · Cited by 1,491
Brain-state- and cell-type-specific firing of hippocampal interneurons in vivo
Klausberger, T., Magill, P. J., Márton, L. F., Roberts, J. D. B., Cobden, P. M., Buzsáki, G., & Somogyi, P. (2003) · Nature · doi:10.1038/nature01374 · Cited by 1,345
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Li, Y., Xu, J., Ju, H., Xiao, Y., Chen, H., Lv, J., Shao, T., Bai, J., Zhang, Y., Wang, L., et al. (2013) · Molecular BioSystems · doi:10.1039/c3mb70270g · Cited by 10
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Chapeton, J. I., Gala, R., & Stepanyants, A. (2015) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2015.00074 · Cited by 11
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Wang, X., Liu, P., Ma, L., & Wang, F. (2019) · Neuroreport · doi:10.1097/wnr.0000000000001254 · Cited by 10
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Bogdańska-Chomczyk, E., Równiak, M., Huang, A. C. W., & Kozłowska, A. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1359237 · Cited by 10
Ding, M., Tomsick, P. L., Young, R. A., & Jadhav, S. P. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.1670-24.2025 · Cited by 14
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Zhanmu, O., Yang, Y., Feng, B., Wang, H., Li, H., Zhou, H., Ge, W., Wan, K.-X., Wang, S.-X., Zhang, K. J., et al. (2024) · Acta Pharmacologica Sinica · doi:10.1038/s41401-024-01426-1 · Cited by 8
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Rose, T. R., de Velasco, E. M. F., Vo, B. N., Tipps, M. E., & Wickman, K. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.2606-20.2020 · Cited by 9
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Pan, X., Fan, H., Wang, R., & Sakagami, M. (2014) · Science China Technological Sciences · doi:10.1007/s11431-014-5561-x · Cited by 5
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Naka, A., Veit, J., Shababo, B., Chance, R. K., Risso, D., Stafford, D. A., Snyder, B. J., Egladyous, A., Chu, D., Sridharan, S., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/456574 · Cited by 6
Russo, S., Dimwamwa, E., & Stanley, G. B. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.0094-25.2025 · Cited by 4
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Cui, W., Shen, C., Xiong, W., & Mei, L. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.115628 · Cited by 4
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Anderson, K. M., Collins, M. A., Chin, R., Ge, T., Rosenberg, M. D., & Holmes, A. J. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/481036 · Cited by 4
Ghezzi, F., Baruchin, L. J., Ha, N. T., Shah-Ostrowski, M. J., Chama, A. G. C., Stacey, J. A., & Butt, S. J. B. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.21.568139 · Cited by 4
Ding, M., Tomsick, P. L., Young, R. A., & Jadhav, S. P. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.09.611811 · Cited by 4
Yevoo, P., Fontanini, A., & Maffei, A. (2025) · Current Biology · doi:10.1016/j.cub.2025.01.035 · Cited by 4
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Boroujeni, K. B., Balaram, P., Tiesinga, P. H. E., & Womelsdorf, T. (2025) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2025.102873 · Cited by 4
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Metadata
- Authors
- Kvitsiani, D., Ranade, S., Hangya, B., Taniguchi, H., Huang, J. Z., & Kepecs, A.
- Affiliations
- Cold Spring Harbor Laboratory (US)
- Venue
- Nature
- Published
- 2013-05-26
- Volume
- 498
- Issue
- 7454
- Pages
- 363-366
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC4349584
- OpenAlex
- W2012509205
- Cited by
- 516
- References
- 49
- Citations by year
- 2026: 20 · 2025: 49 · 2024: 45 · 2023: 39 · 2022: 36 · 2021: 36 · 2020: 43 · 2019: 48 · 2018: 34 · 2017: 39 · 2016: 41 · 2015: 35 · 2014: 37 · 2013: 14
- FWCI
- 23.13
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Institute of Neurological Disorders and Stroke (R01NS075531)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Keywords
- parvalbumin interneurons, somatostatin interneurons, inhibitory interneurons, anterior cingulate cortex, prefrontal cortex, neuronal synchronization, decision variables, stay duration, cortical circuits
- MeSH terms
- Animals, Feeding Behavior, Interneurons, Male, Neural Pathways, Parvalbumins, Reward, Somatostatin, Prefrontal Cortex, Mice, Single-Cell Analysis, Optogenetics
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