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Mapping of Brain Activity by Automated Volume Analysis of Immediate Early Genes
Open accessAbstract
Understanding how neural information is processed in physiological and pathological states would benefit from precise detection, localization, and quantification of the activity of all neurons across the entire brain, which has not, to date, been achieved in the mammalian brain. We introduce a pipeline for high-speed acquisition of brain activity at cellular resolution through profiling immediate early gene expression using immunostaining and light-sheet fluorescence imaging, followed by automated mapping and analysis of activity by an open-source software program we term ClearMap. We validate the pipeline first by analysis of brain regions activated in response to haloperidol. Next, we report new cortical regions downstream of whisker-evoked sensory processing during active exploration. Last, we combine activity mapping with axon tracing to uncover new brain regions differentially activated during parenting behavior. This pipeline is widely applicable to different experimental paradigms, including animal species for which transgenic activity reporters are not readily available.
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“Although several approaches for image analysis of light-sheet fluorescence microscopy (LSFM) datasets have been introduced in recent years6–10, these methods are typically limited to selected organs and do not provide integrated whole-body quantification of elongated structures.”
From A deep-learning framework reveals whole-body perturbations at cell level (2026) · cites this paper as [8]
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“However, tools for comprehensive, high-resolution analysis of disease-associated changes at the whole-body scale have been lacking.”
From A deep-learning framework reveals whole-body perturbations at cell level (2026) · cites this paper as [8]
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“However, methods that identify active cells through immediate early gene expression do not have the temporal resolution needed to capture alertness fluctuations on the order of seconds (Guenthner et al., 2013; Renier et al., 2016; Ye et al., 2016), precluding such a screen in mammals.”
From Ancestral Circuits for the Coordinated Modulation of Brain State (2017) · cites this paper as Renier et al., 2016
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Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Yushkevich, P. A., Piven, J., Hazlett, H. C., Smith, R. G., Ho, S., Gee, J. C., & Gerig, G. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.01.015 · Cited by 8,868
Elastix: a toolbox for intensity-based medical image registration
Klein, S., Staring, M., Murphy, K., Viergever, M. A., & Pluim, J. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2009.2035616 · Cited by 4,311
Induction of c-fos-like protein in spinal cord neurons following sensory stimulation
Hunt, S. P., Pini, A., & Evan, G. I. (1987) · Nature · doi:10.1038/328632a0 · Cited by 1,986
iDISCO: A Simple, Rapid Method to Immunolabel Large Tissue Samples for Volume Imaging
Renier, N., Wu, Z., Simon, D. J., Yang, J. H., Ariel, P., & Tessier‐Lavigne, M. (2014) · Cell · doi:10.1016/j.cell.2014.10.010 · Cited by 1,856
Self-citationWhole-Brain Imaging with Single-Cell Resolution Using Chemical Cocktails and Computational Analysis
Susaki, E. A., Tainaka, K., Perrin, D., Kishino, F., Tawara, T., Watanabe, T. M., Yokoyama, C., Onoe, H., Eguchi, M., Yamaguchi, S., et al. (2014) · Cell · doi:10.1016/j.cell.2014.03.042 · Cited by 1,352
Ilastik: Interactive learning and segmentation toolkit
Sommer, C., Straehle, C., Köthe, U., & Hamprecht, F. A. (2011) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2011.5872394 · Cited by 1,145
Richardson, D. S., & Lichtman, J. W. (2015) · Cell · doi:10.1016/j.cell.2015.06.067 · Cited by 1,095
Three-dimensional imaging of solvent-cleared organs using 3DISCO
Ertürk, A., Becker, K., Jährling, N., Mauch, C. P., Hojer, C. D., Egen, J. G., Hellal, F., Bradke, F., Sheng, M., & Dodt, H. (2012) · Nature Protocols · doi:10.1038/nprot.2012.119 · Cited by 997
Advanced CUBIC protocols for whole-brain and whole-body clearing and imaging
Susaki, E. A., Tainaka, K., Perrin, D., Yukinaga, H., Kuno, A., & Ueda, H. R. (2015) · Nature Protocols · doi:10.1038/nprot.2015.085 · Cited by 822
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Sjöstedt, E., Zhong, W., Fagerberg, L., Karlsson, M., Mitsios, N., Adori, C., Oksvold, P., Edfors, F., Limiszewska, A., Hikmet, F., et al. (2020) · Science · doi:10.1126/science.aay5947 · Cited by 1,151
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Song, E., Zhang, C., Israelow, B., Lu-Culligan, A., Prado, A. V., Skriabine, S., Lu, P., Weizman, O.-E., Liu, F., Dai, Y., et al. (2020) · doi:10.1101/2020.06.25.169946 · Cited by 1,064
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Yap, E.-L., & Greenberg, M. E. (2018) · Neuron · doi:10.1016/j.neuron.2018.10.013 · Cited by 706
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DeNardo, L. A., Liu, C. D., Allen, W. E., Adams, E. L., Friedmann, D., Dadgar‐Kiani, E., Fu, L., Guenthner, C. J., Lee, J. H., Tessier‐Lavigne, M., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-018-0318-7 · Cited by 646
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Ueda, H. R., Ertürk, A., Chung, K., Gradinaru, V., Chédotal, A., Tomančák, P., & Keller, P. (2020) · Nature reviews. Neuroscience · doi:10.1038/s41583-019-0250-1 · Cited by 650
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Pan, C., Cai, R., Quacquarelli, F. P., Ghasemigharagoz, A., Lourbopoulos, A. I., Matryba, P., Plesnila, N., Dichgans, M., Hellal, F., & Ertürk, A. (2016) · Nature Methods · doi:10.1038/nmeth.3964 · Cited by 621
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Hrvatin, S., Hochbaum, D. R., Nagy, M. A., Cicconet, M., Robertson, K., Cheadle, L., Zilionis, R., Ratner, A., Borges-Monroy, R., Klein, A. M., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-017-0029-5 · Cited by 497
Cirelli, K. M., Carnathan, D. G., Nogal, B., Martin, J. T., Rodriguez, O. L., Upadhyay, A. A., Enemuo, C. A., Gebru, E. H., Choe, Y., Viviano, F., et al. (2019) · Cell · doi:10.1016/j.cell.2019.04.012 · Cited by 433
Cai, R., Pan, C., Ghasemigharagoz, A., Todorov, M. I., Förstera, B., Zhao, S., Bhatia, H. S., Parra-Damas, A., Mrowka, L., Theodorou, D., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-018-0301-3 · Cited by 470
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Belle, M., Godefroy, D., Couly, G. F., Malone, S. A., Collier, F., Giacobini, P., & Chédotal, A. (2017) · Cell · doi:10.1016/j.cell.2017.03.008 · Cited by 347
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Zhao, S., Todorov, M. I., Cai, R., -Maskari, R. A., Steinke, H., Kemter, E., Mai, H., Rong, Z., Warmer, M., Stanic, K., et al. (2020) · Cell · doi:10.1016/j.cell.2020.01.030 · Cited by 292
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Kathe, C., Skinnider, M. A., Hutson, T. H., Regazzi, N., Gautier, M., Demesmaeker, R., Komi, S., Ceto, S., James, N. D., Cho, N., et al. (2022) · Nature · doi:10.1038/s41586-022-05385-7 · Cited by 304
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Han, Y., Kebschull, J. M., Campbell, R. A. A., Cowan, D., Imhof, F., Zador, A. M., & Mrsic‐Flogel, T. D. (2018) · Nature · doi:10.1038/nature26159 · Cited by 319
Ren, J., Isakova, A., Friedmann, D., Zeng, J., Grutzner, S. M., Pun, A., Zhao, G. Q., Kolluru, S. S., Wang, R., Lin, R., et al. (2019) · eLife · doi:10.7554/elife.49424 · Cited by 304
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MethodSusaki, E. A., Shimizu, C., Kuno, A., Tainaka, K., Li, X., Nishi, K., Morishima, K., Ono, H., Ode, K. L., Saeki, Y., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15906-5 · Cited by 294
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Vetere, G., Kenney, J. W., Tran, L. M., Xia, F., Steadman, P. E., Parkinson, J., Josselyn, S. A., & Frankland, P. W. (2017) · Neuron · doi:10.1016/j.neuron.2017.03.037 · Cited by 293
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Chi, J., Wu, Z., Choi, C. H. J., Nguyen, L., Tegegne, S., Ackerman, S. E., Crane, A., Marchildon, F., Tessier-Lavigne, M., & Cohen, P. (2018) · Cell Metabolism · doi:10.1016/j.cmet.2017.12.011 · Cited by 285
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Kebschull, J. M., Richman, E. B., Ringach, N., Friedmann, D., Albarran, E., Kolluru, S. S., Jones, R. C., Allen, W. E., Wang, Y., Cho, S. W., et al. (2020) · Science · doi:10.1126/science.abd5059 · Cited by 262
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Roy, D. S., Park, Y.-G., Kim, M. E., Zhang, Y., Ogawa, S. K., DiNapoli, N., Gu, X., Cho, J. H., Choi, H., Kamentsky, L. D., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29384-4 · Cited by 253
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Fürth, D., Vaissière, T., Tzortzi, O., Xuan, Y., Märtin, A., Lazaridis, I., Spigolon, G., Fisone, G., Tomer, R., Deisseroth, K., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-017-0027-7 · Cited by 215
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Lin, R., Feng, Q., Li, P., Zhou, P., Wang, R., Liu, Z., Wang, Z., Qi, X., Tang, N., Shao, F., et al. (2018) · Nature Methods · doi:10.1038/nmeth.4611 · Cited by 125
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Hardaway, J. A., Halladay, L. R., Mazzone, C. M., Pati, D., Bloodgood, D. W., Kim, M., Jensen, J., DiBerto, J. F., Boyt, K. M., Shiddapur, A., et al. (2019) · Neuron · doi:10.1016/j.neuron.2019.03.037 · Cited by 138
Davoudian, P. A., Shao, L.-X., & Kwan, A. C. (2023) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.2c00637 · Cited by 134
Cell-type-specific and projection-specific brain-wide reconstruction of single neurons
Lin, R., Wang, R., Yuan, J., Feng, Q., Zhou, Y., Zeng, S., Ren, M., Jiang, S., Ni, H., Zhou, C., et al. (2018) · Nature Methods · doi:10.1038/s41592-018-0184-y · Cited by 132
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Ariel, P. (2017) · The International Journal of Biochemistry & Cell Biology · doi:10.1016/j.biocel.2016.12.009 · Cited by 134
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Bhatia, H. S., Brunner, A.-D., Öztürk, F., Kapoor, S., Rong, Z., Mai, H., Thielert, M., Ali, M., Al-Maskari, R., Paetzold, J. C., et al. (2022) · Cell · doi:10.1016/j.cell.2022.11.021 · Cited by 129
Whole-brain wiring diagram of oxytocin system in adult mice
Son, S., Manjila, S. B., Newmaster, K. T., Wu, Y.-T., Vanselow, D. J., Ciarletta, M., Anthony, T. E., Cheng, K. C., & Kim, Y. (2022) · The Journal of Neuroscience · doi:10.1523/jneurosci.0307-22.2022 · Cited by 125
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Pisano, T. J., Dhanerawala, Z. M., Kislin, M., Bakshinskaya, D. E., Engel, E. A., Hansen, E. J., Hoag, A. T., Lee, J., de Oude, N. L., Venkataraju, K. U., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109721 · Cited by 135
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Li, Y., Su, Y. P., Guo, M. Y., Han, X., Liu, J., Vishwasrao, H. D., Li, X., Christensen, R. P., Sengupta, T., Moyle, M. W., et al. (2022) · Nature Methods · doi:10.1038/s41592-022-01652-7 · Cited by 124
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Seiriki, K., Kasai, A., Hashimoto, T., Schulze, W., Niu, M., Yamaguchi, S., Nakazawa, T., Inoue, K., Uezono, S., Takada, M., et al. (2017) · Neuron · doi:10.1016/j.neuron.2017.05.017 · Cited by 126
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Keyes, P. C., Adams, E. L., Chen, Z., Bi, L., Nachtrab, G. T., Wang, V. J., Tessier‐Lavigne, M., Zhu, Y., & Chen, X. (2020) · Neuron · doi:10.1016/j.neuron.2020.06.028 · Cited by 127
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Hanna, P., Dacey, M. J., Brennan, J. A., Moss, A., Robbins, S., Achanta, S., Biscola, N. P., Swid, M. A., Rajendran, P. S., Mori, S., et al. (2021) · Circulation Research · doi:10.1161/circresaha.120.318458 · Cited by 126
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Gómez‐Gaviro, M. V., Sanderson, D., Ripoll, J., & Desco, M. (2020) · iScience · doi:10.1016/j.isci.2020.101432 · Cited by 121
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Rattray, N. J. W., Deziel, N. C., Wallach, J. D., Khan, S. A., Vasiliou, V., Ioannidis, J. P. A., & Johnson, C. H. (2018) · Human Genomics · doi:10.1186/s40246-018-0134-x · Cited by 121
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To, K., Fei, L., Pett, J. P., Roberts, K., Blain, R., Polański, K., Li, T., Yayon, N., He, P., Xu, C., et al. (2024) · Nature · doi:10.1038/s41586-024-08189-z · Cited by 123
Adori, C., Daraio, T., Kuiper, R., Barde, S., Horvathova, L., Yoshitake, T., Ihnatko, R., Valladolid-Acebes, I., Vercruysse, P., Wellendorf, A. M., et al. (2021) · Science Advances · doi:10.1126/sciadv.abg5733 · Cited by 105
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Sograte‐Idrissi, S., Schlichthaerle, T., Duque-Afonso, C. J., Alevra, M., Strauss, S., Moser, T., Jungmann, R., Rizzoli, S. O., & Opazo, F. (2020) · Nanoscale · doi:10.1039/d0nr00227e · Cited by 100
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Chi, J., Crane, A., Wu, Z., & Cohen, P. (2018) · Journal of Visualized Experiments · doi:10.3791/58271 · Cited by 110
Self-citationGuillot, J., Dominici, C., Lucchesi, A., Nguyen, H., Puget, A., Hocine, M., Rangel‐Sosa, M. M., Simic, M., Nigri, J., Guillaumond, F., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29659-w · Cited by 114
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Brohawn, S. G., Wang, W., Schwarz, J. R., Handler, A., Campbell, E. B., & MacKinnon, R. (2019) · eLife · doi:10.7554/elife.50403 · Cited by 107
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Weiss, K. R., Voigt, F. F., Shepherd, D. P., & Huisken, J. (2021) · Nature Protocols · doi:10.1038/s41596-021-00502-8 · Cited by 108
Goubran, M., Leuze, C. W., Hsueh, B., Aswendt, M., Li, Y., Tian, Q., Cheng, M., Crow, A., Steinberg, G. K., McNab, J. A., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-13374-0 · Cited by 109
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Tyson, A. L., Rousseau, C. V., Niedworok, C. J., Keshavarzi, S., Tsitoura, C., Cossell, L., Strom, M., & Margrie, T. W. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009074 · Cited by 101
Salinas, C. B. G., Lu, T. T.-H., Gabery, S., Marstal, K., Alanentalo, T., Mercer, A. J., Cornea, A., Conradsen, K., Hecksher-Sørensen, J., Dahl, A. B., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-28496-6 · Cited by 95
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Molbay, M., Kolabas, Z. I., Todorov, M. I., Ohn, T., & Ertürk, A. (2021) · Molecular Systems Biology · doi:10.15252/msb.20209807 · Cited by 96
Stratoulias, V., Ruiz, R., Kanatani, S., Osman, A. M., Keane, L., Armengol, J. A., Rodríguez-Moreno, A., Murgoci, A.-N., García-Domínguez, I., Alonso-Bellido, I., et al. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01326-3 · Cited by 96
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Schneeberger, M., Parolari, L., Banerjee, T. D., Bhave, V. M., Wang, P., Patel, B., Topilko, T., Wu, Z., Choi, C. H. J., Yu, X., et al. (2019) · Cell · doi:10.1016/j.cell.2019.05.048 · Cited by 99
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Yamashita, T., Vavladeli, A., Pala, A., Galan, K., Crochet, S., Petersen, S. S. A., & Petersen, C. C. H. (2018) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2018.00033 · Cited by 100
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Friedmann, D., Pun, A., Adams, E. L., Lui, J. H., Kebschull, J. M., Grutzner, S. M., Castagnola, C., Tessier‐Lavigne, M., & Luo, L. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1918465117 · Cited by 97
Self-citationNeuroscience in the third dimension: shedding new light on the brain with tissue clearing
Vigouroux, R. J., Belle, M., & Chédotal, A. (2017) · Molecular Brain · doi:10.1186/s13041-017-0314-y · Cited by 99
Metadata
- Authors
- Renier, N., Adams, E. L., Kirst, C., Wu, Z., Azevedo, R., Kohl, J., Autry, A. E., Kadiri, L., Umadevi Venkataraju, K., Zhou, Y., Wang, V. X., Tang, C. Y., Olsen, O., Dulac, C., Osten, P., & Tessier-Lavigne, M.
- Affiliations
- Rockefeller University (US)
- Howard Hughes Medical Institute (US)
- Certerra (United States) (US)
- Icahn School of Medicine at Mount Sinai (US)
- Cold Spring Harbor Laboratory (US)
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2016-06
- Volume
- 165
- Issue
- 7
- Pages
- 1789-1802
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC4912438
- OpenAlex
- W2408110716
- Cited by
- 918
- References
- 40
- Citations by year
- 2026: 80 · 2025: 115 · 2024: 114 · 2023: 99 · 2022: 94 · 2021: 120 · 2020: 107 · 2019: 83 · 2018: 58 · 2017: 36 · 2016: 10
- FWCI
- 39.15
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- Funders
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (F32HD078040, 1R01HD082131-01A1)
- National Institutes of Health (F32HD078040, 1R01HD082131-01A1)
- Howard Hughes Medical Institute (F32HD078040, 1R01HD082131-01A1)
- Rockefeller University (F32HD078040, 1R01HD082131-01A1)
- National Alliance for Research on Schizophrenia and Depression (F32HD078040, 1R01HD082131-01A1)
- EMBO (F32HD078040, 1R01HD082131-01A1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Advanced Fluorescence Microscopy Techniques
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Keywords
- brain activity mapping, immediate early gene expression, neuronal activity imaging, immunostaining, haloperidol, cortical activation, axon tracing, parental behavior, cellular resolution imaging
- MeSH terms
- Animals, Behavior, Animal, Brain, Exploratory Behavior, Haloperidol, Immunohistochemistry, Mice, Inbred C57BL, Antipsychotic Agents, Genes, Immediate-Early, Mice, Neuroimaging
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