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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]
In the citing paper
“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]
In the citing paper
“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
Neural Networks of the Mouse Neocortex
Zingg, B., Hintiryan, H., Gou, L., Song, M. Y., Bay, M., Bienkowski, M. S., Foster, N. N., Yamashita, S., Bowman, I., Toga, A. W., et al. (2014) · Cell · doi:10.1016/j.cell.2014.02.023 · Cited by 797
Intact-brain analyses reveal distinct information carried by SNc dopamine subcircuits
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., et al. (2015) · Cell · doi:10.1016/j.cell.2015.07.014 · Cited by 794 · cited 1×
“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).”
Brain-wide neuronal dynamics during motor adaptation in zebrafish
Ahrens, M. B., Li, J. M., Orger, M. B., Robson, D. N., Schier, A. F., Engert, F., & Portugues, R. (2012) · Nature · doi:10.1038/nature11057 · Cited by 729 · cited 1×
“Optical techniques, coupled with GCaMP calcium probes, provide live recording of neuronal activity over larger volumes, including activity of most neurons in Zebra fish larva brain (Ahrens et al., 2012), and several thousand neurons in the mouse cortex (Peron et al., 2015).”
Serial two-photon tomography for automated ex vivo mouse brain imaging
Ragan, T. M., Kadiri, L. R., Venkataraju, K. U., Bahlmann, K., Sutin, J. D. B., Taranda, J., Arganda‐Carreras, I., Kim, Y., Seung, H. S., & Osten, P. (2012) · Nature Methods · doi:10.1038/nmeth.1854 · Cited by 683
Self-citationActivity-dependent regulation of inhibitory synapse development by Npas4
Lin, Y., Bloodgood, B. L., Hauser, J. L., Lapan, A. D., Koon, A. C., Kim, T.-K., Hu, L. S., Malik, A. N., & Greenberg, M. E. (2008) · Nature · doi:10.1038/nature07319 · Cited by 639
Galanin neurons in the medial preoptic area govern parental behaviour
Wu, Z., Autry, A. E., Bergan, J. F., Watabe‐Uchida, M., & Dulac, C. G. (2014) · Nature · doi:10.1038/nature13307 · Cited by 610
Self-citationHippocampal Memory Traces Are Differentially Modulated by Experience, Time, and Adult Neurogenesis
Denny, C. A., Kheirbek, M. A., Alba, E. L., Tanaka, K. F., Brachman, R. A., Laughman, K. B., Tomm, N. K., Turi, G. F., Losonczy, A., & Hen, R. (2014) · Neuron · doi:10.1016/j.neuron.2014.05.018 · Cited by 599
Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease
Shamonin, D. P. (2013) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2013.00050 · Cited by 582
Whole-brain activity mapping onto a zebrafish brain atlas
Randlett, O., Wee, C. L., Naumann, E. A., Nnaemeka, O., Schoppik, D., Fitzgerald, J. E., Portugues, R., Lacoste, A. M. B., Riegler, C., Engert, F., et al. (2015) · Nature Methods · doi:10.1038/nmeth.3581 · Cited by 528
Dendritic Inhibition in the Hippocampus Supports Fear Learning
Lovett-Barron, M., Kaifosh, P., Kheirbek, M. A., Danielson, N., Zaremba, J. D., Reardon, T. R., Turi, G. F., Hen, R., Zemelman, B. V., & Losonczy, A. (2014) · Science · doi:10.1126/science.1247485 · Cited by 509
Neural control of maternal and paternal behaviors
Dulac, C., O’Connell, L. A., & Wu, Z. (2014) · Science · doi:10.1126/science.1253291 · Cited by 457
Self-citationSpiegel, I., Mardinly, A. R., Gabel, H. W., Bazinet, J. E., Couch, C. H., Tzeng, C. P., Harmin, D. A., & Greenberg, M. E. (2014) · Cell · doi:10.1016/j.cell.2014.03.058 · Cited by 416 · cited 1×
“Npas4 showed modest up-regulation of expression 30 min and 1 hr after stimulation (2.2 and 2.1 respectively) (Figure 2A and B), well within the induction window reported in vivo for the cortex (Lin et al., 2008; Spiegel et al., 2014).”
InfluentialDopamine neurons projecting to the posterior striatum form an anatomically distinct subclass
Menegas, W., Bergan, J. F., Ogawa, S. K., Isogai, Y., Venkataraju, K. U., Osten, P., Uchida, N., & Watabe‐Uchida, M. (2015) · eLife · doi:10.7554/elife.10032 · Cited by 335
Self-citationA Cellular Resolution Map of Barrel Cortex Activity during Tactile Behavior
Peron, S., Freeman, J., Iyer, V., Guo, C., & Svoboda, K. (2015) · Neuron · doi:10.1016/j.neuron.2015.03.027 · Cited by 334
Mapping Social Behavior-Induced Brain Activation at Cellular Resolution in the Mouse
Kim, Y., Venkataraju, K. U., Pradhan, K., Mende, C., Taranda, J., Turaga, S. C., Arganda‐Carreras, I., Ng, L., Hawrylycz, M., Rockland, K. S., et al. (2014) · Cell Reports · doi:10.1016/j.celrep.2014.12.014 · Cited by 311
Self-citationSensory Integration in Mouse Insular Cortex Reflects GABA Circuit Maturation
Gogolla, N., Takesian, A. E., Feng, G., Fagiolini, M., & Hensch, T. K. (2014) · Neuron · doi:10.1016/j.neuron.2014.06.033 · Cited by 310
Large-scale, high-density (up to 512 channels) recording of local circuits in behaving animals
Berényi, A., Somogyvári, Z., Nagy, A. J., Roux, L., Long, J. D., Fujisawa, S., Stark, E., Leonardo, A. M., Harris, T. D., & Buzsáki, G. (2013) · Journal of Neurophysiology · doi:10.1152/jn.00785.2013 · Cited by 305
Keller, P., & Ahrens, M. B. (2015) · Neuron · doi:10.1016/j.neuron.2014.12.039 · Cited by 251
Wang, K. H., Majewska, A. K., Schummers, J., Farley, B. J., Hu, C., Sur, M., & Tonegawa, S. (2006) · Cell · doi:10.1016/j.cell.2006.06.038 · Cited by 243
Time-contingent change in infanticide and parental behavior induced by ejaculation in male mice
Saal, F. S. V. (1985) · Physiology & Behavior · doi:10.1016/0031-9384(85)90069-1 · Cited by 209
Binding of typical and atypical antipsychotic agents to transiently expressed 5-HT1C receptors.
Roth, B. L., Ciaranello, R. D., & Meltzer, H. Y. (1992) · Journal of Pharmacology and Experimental Therapeutics · doi:10.1016/s0022-3565(25)11452-3 · Cited by 199
Tachikawa, K. S., Yoshihara, Y., & Kuroda, K. O. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.2364-12.2013 · Cited by 192
Fluorescent-Protein Stabilization and High-Resolution Imaging of Cleared, Intact Mouse Brains
Schwarz, M. K., Scherbarth, A., Sprengel, R., Engelhardt, J., Theer, P., & Giese, G. (2015) · PLoS ONE · doi:10.1371/journal.pone.0124650 · Cited by 183
Auditory, Somatosensory, and Multisensory Insular Cortex in the Rat
Rodgers, K. M., Benison, A. M., Klein, A., & Barth, D. S. (2008) · Cerebral Cortex · doi:10.1093/cercor/bhn054 · Cited by 164
Neural Correlates of Competing Fear Behaviors Evoked by an Innately Aversive Stimulus
Mongeau, R., Miller, G. A., Chiang, E., & Anderson, D. J. (2003) · Journal of Neuroscience · doi:10.1523/jneurosci.23-09-03855.2003 · Cited by 142
Deep brain optical measurements of cell type–specific neural activity in behaving mice
Cui, G., Jun, S. B., Jin, X., Luo, G., Pham, M. D., Lovinger, D. M., Vogel, S. S., & Costa, R. M. (2014) · Nature Protocols · doi:10.1038/nprot.2014.080 · Cited by 134
Subnetwork-Specific Homeostatic Plasticity in Mouse Visual Cortex In Vivo
Barnes, S. J., Sammons, R. P., Jacobsen, R. I., Mackie, J., Keller, G. B., & Keck, T. (2015) · Neuron · doi:10.1016/j.neuron.2015.05.010 · Cited by 123
Henry, B., Sumner, B. E. H., Cruise, L., Slattery, D. A., Hill, D. R., & Shahid, M. (2004) · Psychopharmacology · doi:10.1007/s00213-003-1579-7 · Cited by 78
Distinct Sensory Requirements for Unimodal and Cross-Modal Homeostatic Synaptic Plasticity
He, K., Petrus, E., Gammon, N., & Lee, H. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.1424-12.2012 · Cited by 58
Identification of Optogenetically Activated Striatal Medium Spiny Neurons by Npas4 Expression
Bepari, A. K., Sano, H., Tamamaki, N., Nambu, A., Tanaka, K. F., & Takebayashi, H. (2012) · PLoS ONE · doi:10.1371/journal.pone.0052783 · Cited by 27
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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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