The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
Intact-Brain Analyses Reveal Distinct Information Carried by SNc Dopamine Subcircuits
Open accessAbstract
Recent progress in understanding the diversity of midbrain dopamine neurons has highlighted the importance--and the challenges--of defining mammalian neuronal cell types. Although neurons may be best categorized using inclusive criteria spanning biophysical properties, wiring of inputs, wiring of outputs, and activity during behavior, linking all of these measurements to cell types within the intact brains of living mammals has been difficult. Here, using an array of intact-brain circuit interrogation tools, including CLARITY, COLM, optogenetics, viral tracing, and fiber photometry, we explore the diversity of dopamine neurons within the substantia nigra pars compacta (SNc). We identify two parallel nigrostriatal dopamine neuron subpopulations differing in biophysical properties, input wiring, output wiring to dorsomedial striatum (DMS) versus dorsolateral striatum (DLS), and natural activity patterns during free behavior. Our results reveal independently operating nigrostriatal information streams, with implications for understanding the logic of dopaminergic feedback circuits and the diversity of mammalian neuronal cell types.
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“Thus, the input-output architecture of the DR serotonin system differs from that of the locus coeruleus norepinephrine system with more homogeneous input and output (Schwarz et al., 2015), and resembles more closely those of the midbrain dopamine systems (Beier et al., 2015; Lerner et al., 2015), with biased input and segregated output.”
From Anatomically defined and functionally distinct dorsal raphe serotonin sub-systems (2018) · cites this paper as Lerner et al., 2015
In the citing paper
“Thus, a strong activation in response to the physical impact of an aversive stimulus in particularly sensitive neurons may supersede a depression reflecting the negative value, and this might appear as a categorical difference in aversive and motivational salience coding15,23,24,134.”
From Dopamine reward prediction-error signalling: A two-component response (2016) · cites this paper as [134]
In the citing paper
“Dopamine neurons in the lateral midbrain display greater burst activity in vivo28 and higher excitability in vitro29,30 than those in the medial midbrain, such as the ventral tegmental area (VTA), where canonical reward-processing dopamine neurons reside.”
From Dopamine dynamics as a regulatory mechanism for shifting between defensive and reward-seeking behaviors (2026) · cites this paper as [30]
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Citations
Cites
A neural substrate of prediction and reward
Schultz, W., Dayan, P., & Montague, P. R. (1997) · Science · doi:10.1126/science.275.5306.1593 · Cited by 9,388 · cited 2×
“Seminal early studies into the information encoded by midbrain DA neurons suggested that a key function of DA is to transmit reward prediction error signals (Mirenowicz and Schultz, 1996; Schultz et al., 1997; Waelti et al., 2001), a hypothesis concordant with temporal difference learning models (Montague et al.”
ResultUltrasensitive fluorescent proteins for imaging neuronal activity
Chen, T.-W., Wardill, T. J., Sun, Y., Pulver, S. R., Renninger, S. L., Baohan, A., Schreiter, E. R., Kerr, R. A., Orger, M. B., Jayaraman, V., et al. (2013) · Nature · doi:10.1038/nature12354 · Cited by 6,474 · cited 1×
“We expressed GCaMP6f (Chen et al., 2013) in DMS- or DLS-projecting SNc DA neurons by injecting CAV-cre into the DMS or the DLS, followed by injection of a cre-dependent GCaMP6f construct into the SNc.”
MethodLearning to predict by the methods of temporal differences
Sutton, R. S. (1988) · Machine Learning · doi:10.1007/bf00115009 · Cited by 5,148 · cited 2×
“…neurons suggested that a key function of DA is to transmit reward prediction error signals (Mirenowicz and Schultz, 1996; Schultz et al., 1997; Waelti et al., 2001), a hypothesis concordant with temporal difference learning models (Montague et al., 1996, 2004; Steinberg et al., 2013; Sutton, 1988).”
BackgroundDopamine in motivational control: rewarding, aversive, and alerting
Bromberg-Martin, E. S., Matsumoto, M., & Hikosaka, O. (2010) · Neuron · doi:10.1016/j.neuron.2010.11.022 · Cited by 2,396 · cited 4×
“…that the DA neurons, which increase their firing in response to these stimuli, may signal salience rather than value (Brischoux et al., 2009; Bromberg-Martin et al., 2010; Matsumoto and Hikosaka, 2009), though controversy on this point remains (Cohen et al., 2012; Fiorillo et al., 2013,…”
BackgroundInfluentialA framework for mesencephalic dopamine systems based on predictive Hebbian learning
Montague, P., Dayan, P., & Sejnowski, T. (1996) · The Journal of Neuroscience · doi:10.1523/jneurosci.16-05-01936.1996 · Cited by 2,066 · cited 1×
“…neurons suggested that a key function of DA is to transmit reward prediction error signals (Mirenowicz and Schultz, 1996; Schultz et al., 1997; Waelti et al., 2001), a hypothesis concordant with temporal difference learning models (Montague et al., 1996, 2004; Steinberg et al., 2013; Sutton, 1988).”
Structural and molecular interrogation of intact biological systems
Chung, K., Wallace, J., Kim, S.-Y., Kalyanasundaram, S., Andalman, A. S., Davidson, T. J., Mirzabekov, J. J., Zalocusky, K. A., Mattis, J., Denisin, A. K., et al. (2013) · Nature · doi:10.1038/nature12107 · Cited by 2,008 · cited 6×
“We began by demonstrating that novel circuit-tracing and -recording methodologies can be effectively combined with whole-brain CLARITY/COLM imaging (Chung et al., 2013; Tomer et al., 2014) to provide a uniquely informative overview of richly defined cell types and their global connectivity motifs…”
BackgroundMethodInfluentialSelf-citationHaber, S. N., Fudge, J. L., & McFarland, N. R. (2000) · The Journal of Neuroscience · doi:10.1523/jneurosci.20-06-02369.2000 · Cited by 1,610 · cited 1×
“This information notably enriches the ascending spiral model proposed by Haber et al. (2000), which could not make definitive statements about striatal inputs onto DAergic versus non-DAergic midbrain cells.”
BackgroundNatural neural projection dynamics underlying social behavior
Gunaydin, L. A., Grosenick, L., Finkelstein, J. C., Kauvar, I. V., Fenno, L. E., Adhikari, A., Lammel, S., Mirzabekov, J. J., Airan, R. D., Zalocusky, K. A., et al. (2014) · Cell · doi:10.1016/j.cell.2014.05.017 · Cited by 1,389 · cited 1×
“…monitored activity from projection-defined SNc DA neurons during behavior using fiber photometry, a method for collecting population intracellular [Ca2+] fluorescent signals from a genetically encoded Ca2+ indicator such as GCaMP through a single chronic fiber optic implant (Gunaydin et al., 2014).”
MethodTwo types of dopamine neuron distinctly convey positive and negative motivational signals
Matsumoto, M., & Hikosaka, O. (2009) · Nature · doi:10.1038/nature08028 · Cited by 1,378 · cited 5×
“Another study used recordings in awake monkeys to demonstrate a correlation between the depth at which a DA neuron was recorded along the electrode (indicating a more ventromedial location in the midbrain) and the likelihood that it would decrease rather than increase its firing in response to an aversive cue (Matsumo…”
BackgroundResultInfluentialNeuron-type-specific signals for reward and punishment in the ventral tegmental area
Cohen, J. Y., Haesler, S., Vong, L., Lowell, B. B., & Uchida, N. (2012) · Nature · doi:10.1038/nature10754 · Cited by 1,318
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Single-trial neural dynamics are dominated by richly varied movements
Musall, S., Kaufman, M. T., Juavinett, A., Gluf, S., & Churchland, A. K. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0502-4 · Cited by 1,102
Circuit architecture of VTA dopamine neurons revealed by systematic input-output mapping
Beier, K. T., Steinberg, E. E., DeLoach, K. E., Xie, S., Miyamichi, K., Schwarz, L., Gao, X. J., Kremer, E. J., Malenka, R. C., & Luo, L. (2015) · Cell · doi:10.1016/j.cell.2015.07.015 · Cited by 1,068
“A similar input-output architecture also applies to SNc-DA circuitry (Lerner et al., 2015 [in this issue of Cell]).”
Ultrafast neuronal imaging of dopamine dynamics with designed genetically encoded sensors
Patriarchi, T., Cho, J. R., Merten, K., Howe, M. W., Marley, A., Xiong, W.-H., Folk, R. W., Broussard, G. J., Liang, R., Jang, M. J., et al. (2018) · Science · doi:10.1126/science.aat4422 · Cited by 1,088
“In vivo imaging with fiber photometry Fiber photometry was performed as in (28, 63, 64).”
BackgroundMethodDopamine reward prediction-error signalling: A two-component response
Schultz, W. (2016) · Nature Reviews Neuroscience · doi:10.1038/nrn.2015.26 · Cited by 1,036
Mapping of Brain Activity by Automated Volume Analysis of Immediate Early Genes
Renier, N., Adams, E. L., Kirst, C., Wu, Z., Azevedo, R., Kohl, J., Autry, A. E., Kadiri, L., Umadevi Venkataraju, K., Zhou, Y., et al. (2016) · Cell · doi:10.1016/j.cell.2016.05.007 · Cited by 918
“Those techniques are now starting to enable the streamlined analysis of axonal projections or cell positions in intact mouse brains in a manual or semi-automated way (Lerner et al., 2015; Menegas et al., 2015; Schwarz et al., 2015).”
Metadata
- Authors
- Lerner, T. N., Shilyansky, C., Davidson, T. J., Evans, K. E., Beier, K. T., Zalocusky, K. A., Crow, A. K., Malenka, R. C., Luo, L., Tomer, R., & Deisseroth, K.
- Affiliations
- Stanford University (US)
- Stanford Medicine (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Deisseroth, Karl
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2015-07
- Volume
- 162
- Issue
- 3
- Pages
- 635-647
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC4790813
- OpenAlex
- W1875506666
- Cited by
- 794
- References
- 55
- Citations by year
- 2026: 52 · 2025: 90 · 2024: 87 · 2023: 77 · 2022: 85 · 2021: 77 · 2020: 70 · 2019: 71 · 2018: 70 · 2017: 51 · 2016: 51 · 2015: 13
- FWCI
- 28.63
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Howard Hughes Medical Institute
- H. L. Snyder Medical Foundation
- National Institutes of Health (5f31mh105151-02, 1f32mh105053-01)
- Defense Advanced Research Projects Agency
- National Institute of Mental Health (1F31MH105151-01, F32 MH105053, 1F32MH105053-01, P50MH086403, F31 MH105151)
- National Institute on Drug Abuse (F32DA038913, R37DA035377)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Health Sciences › Medicine › Clinical Neurology › Deep Brain Stimulation and Movement Disorders
- Keywords
- substantia nigra pars compacta, dopaminergic neurons, neuronal cell types, nigrostriatal pathway, dorsomedial striatum, dorsolateral striatum, striatal output, neuronal heterogeneity, clarity, optogenetics, viral tracing, fiber photometry
- MeSH terms
- Animals, Brain Mapping, Dopamine, Mice, Inbred C57BL, Neural Pathways, Neurons, Reward, Shock, Substantia Nigra, Mice
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