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Dense connectomic reconstruction in layer 4 of the somatosensory cortex
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Abstract
Brain anatomy revealed in startling detail: The mammalian cerebral cortex is an enormously complex network of neuronal processes that are long and thin, branching, and extremely densely packed. This high packing density has made the reconstruction of cortical neuronal networks challenging. Motta et al. used advanced automated imaging and analysis tools to reconstruct with high spatial resolution the morphological features of 89 neurons and their connections in the mouse barrel cortex. The reconstruction covered an area more than two orders of magnitude larger than earlier neuroanatomical mapping attempts. This approach revealed information about the connectivity of inhibitory and excitatory synapses of corticocortical as well as excitatory thalamocortical connections. Science , this issue p. eaay3134
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“However, this characteristic is often misinterpreted as implying that synapses on dendritic shafts are mostly SS (Motta et al., 2019; Karimi et al., 2020; Loomba et al., 2022).”
From Unambiguous identification of asymmetric and symmetric synapses using volume electron microscopy (2024) · cites this paper as Motta et al., 2019
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Cites
Statistical significance for genomewide studies
Storey, J. D., & Tibshirani, R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1530509100 · Cited by 9,692
Friedman, J., Hastie, T., & Tibshirani, R. (2000) · The Annals of Statistics · doi:10.1214/aos/1016218223 · Cited by 6,884
Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs
Markram, H., Lübke, J., Frotscher, M., & Sakmann, B. (1997) · Science · doi:10.1126/science.275.5297.213 · Cited by 3,832
Structural basis of long-term potentiation in single dendritic spines
Matsuzaki, M., Honkura, N., Ellis-Davies, G. C. R., & Kasai, H. (2004) · Nature · doi:10.1038/nature02617 · Cited by 2,483
Globally optimal stitching of tiled 3D microscopic image acquisitions
Preibisch, S., Saalfeld, S., & Tomancak, P. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp184 · Cited by 2,452
Long-term in vivo imaging of experience-dependent synaptic plasticity in adult cortex
Trachtenberg, J. T., Chen, B. E., Knott, G. W., Feng, G., Sanes, J. R., Welker, E., & Svoboda, K. (2002) · Nature · doi:10.1038/nature01273 · Cited by 1,927
Serial Block-Face Scanning Electron Microscopy to Reconstruct Three-Dimensional Tissue Nanostructure
Denk, W., & Horstmann, H. (2004) · PLoS Biology · doi:10.1371/journal.pbio.0020329 · Cited by 1,644
Reconstruction and Simulation of Neocortical Microcircuitry
Markram, H., Muller, E., Ramaswamy, S., Reimann, M. W., Abdellah, M., Sanchez, C. A., Ailamaki, A., Alonso-Nanclares, L., Antille, N., Arsever, S., et al. (2015) · Cell · doi:10.1016/j.cell.2015.09.029 · Cited by 1,557
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
A place theory of sound localization.
Jeffress, L. A. (1948) · Journal of Comparative and Physiological Psychology · doi:10.1037/h0061495 · Cited by 1,484
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- D. Rolnick N. Shavit Morphological error detection in 3D segmentations. arXiv:1705.10882 [cs.CV] (30 May 2017). · arXiv
Cited by
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UCSF ChimeraX : Structure visualization for researchers, educators, and developers
Pettersen, E. F., Goddard, T. D., Huang, C. C., Meng, E. C., Couch, G. S., Croll, T. I., Morris, J. H., & Ferrin, T. E. (2020) · Protein Science · doi:10.1002/pro.3943 · Cited by 9,521
The connectome of an insect brain
Winding, M., Pedigo, B. D., Barnes, C. L., Patsolic, H. G., Park, Y., Kazimiers, T., Fushiki, A., Andrade, I. V., Khandelwal, A., Valdes-Aleman, J., et al. (2023) · Science · doi:10.1126/science.add9330 · Cited by 435
A petavoxel fragment of human cerebral cortex reconstructed at nanoscale resolution
Shapson-Coe, A., Januszewski, M., Berger, D. R., Pope, A., Wu, Y., Blakely, T., Schalek, R. L., Li, P. H., Wang, S., Maitin-Shepard, J., et al. (2024) · Science · doi:10.1126/science.adk4858 · Cited by 361
Synaptic Specificity, Recognition Molecules, and Assembly of Neural Circuits
Sanes, J. R., & Zipursky, S. L. (2020) · Cell · doi:10.1016/j.cell.2020.04.008 · Cited by 326
Quantitative magnetic resonance imaging of brain anatomy and in vivo histology
Weiskopf, N., Edwards, L., Helms, G., Mohammadi, S., & Kirilina, E. P. (2021) · Nature Reviews Physics · doi:10.1038/s42254-021-00326-1 · Cited by 321
Connectomic comparison of mouse and human cortex
Loomba, S., Straehle, J., Gangadharan, V., Heike, N., Khalifa, A., Motta, A., Ju, N., Sievers, M., Gempt, J., Meyer, H. S., et al. (2022) · Science · doi:10.1126/science.abo0924 · Cited by 242
Self-citationStructure and function of a neocortical synapse
Holler, S., Köstinger, G., Martin, K. A. C., Schuhknecht, G. F. P., & Stratford, K. J. (2021) · Nature · doi:10.1038/s41586-020-03134-2 · Cited by 249
A connectomic study of a petascale fragment of human cerebral cortex
Shapson-Coe, A., Januszewski, M., Berger, D. R., Pope, A., Wu, Y., Blakely, T., Schalek, R. L., Li, P. H., Wang, S., Maitin-Shepard, J., et al. (2021) · doi:10.1101/2021.05.29.446289 · Cited by 243
Peddie, C. J., Genoud, C., Kreshuk, A., Meechan, K., Micheva, K. D., Narayan, K., Pape, C., Parton, R. G., Schieber, N. L., Schwab, Y., et al. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-022-00131-9 · Cited by 250
Reconstruction of neocortex: Organelles, compartments, cells, circuits, and activity
Turner, N. L., Macrina, T., Bae, J. A., Yang, R., Wilson, A. M., Schneider-Mizell, C., Lee, K., Lu, R., Wu, J., Bodor, A. L., et al. (2022) · Cell · doi:10.1016/j.cell.2022.01.023 · Cited by 206
Functional connectomics spanning multiple areas of mouse visual cortex
The MICrONS Consortium, Bae, J. A., Baptiste, M., Bishop, C. A., Bodor, A. L., Brittain, D., Buchanan, J., Bumbarger, D. J., Castro, M. A., Celii, B., et al. (2021) · doi:10.1101/2021.07.28.454025 · Cited by 214
Aggarwal, A., Liu, R., Chen, Y., Ralowicz, A. J., Bergerson, S. J., Tomáška, F., Mohar, B., Hanson, T., Hasseman, J. P., Reep, D., et al. (2023) · Nature Methods · doi:10.1038/s41592-023-01863-6 · Cited by 192
Spine dynamics in the brain, mental disorders and artificial neural networks
Kasai, H., Ziv, N. E., Okazaki, H., Yagishita, S., & Toyoizumi, T. (2021) · Nature reviews. Neuroscience · doi:10.1038/s41583-021-00467-3 · Cited by 184
Jelescu, I. O., de Skowronski, A., Geffroy, F., Palombo, M., & Novikov, D. S. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119277 · Cited by 177
Automatic detection of synaptic partners in a whole-brain Drosophila electron microscopy data set
Buhmann, J., Sheridan, A., Malin-Mayor, C., Schlegel, P., Gerhard, S., Kazimiers, T., Krause, R., Nguyen, T. M., Heinrich, L., Lee, W.-C. A., et al. (2021) · Nature Methods · doi:10.1038/s41592-021-01183-7 · Cited by 175
Neuronal circuits in barrel cortex for whisker sensory perception
Staiger, J. F., & Petersen, C. C. H. (2021) · Physiological Reviews · doi:10.1152/physrev.00019.2019 · Cited by 165
Challenges for biophysical modeling of microstructure
Jelescu, I., Palombo, M., Bagnato, F. R., & Schilling, K. G. (2020) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2020.108861 · Cited by 166
neuPrint: an open access tool for EM connectomics
Plaza, S. M., Clements, J., Dolafi, T., Umayam, L., Neubarth, N. N., Scheffer, L. K., & Berg, S. (2022) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2022.896292 · Cited by 145
Oligodendrocyte precursor cells ingest axons in the mouse neocortex
Buchanan, J., Elabbady, L., Collman, F., Jorstad, N. L., Bakken, T. E., Ott, C. M., Glatzer, J. C., Bleckert, A., Bodor, Á. L., Brittain, D., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2202580119 · Cited by 162
Neuropathic pain caused by miswiring and abnormal end organ targeting
Gangadharan, V., Zheng, H., Taberner, F. J., Landry, J. J. M., Nees, T. A., Pistolic, J., Agarwal, N., Männich, D., Beneš, V., Helmstaedter, M. N., et al. (2022) · Nature · doi:10.1038/s41586-022-04777-z · Cited by 148
Self-citationWhole-body integration of gene expression and single-cell morphology
Vergara, H. M., Pape, C., Meechan, K., Zinchenko, V., Genoud, C., Wanner, A. A., Mutemi, K. N., Titze, B., Templin, R., Bertucci, P. Y., et al. (2021) · Cell · doi:10.1016/j.cell.2021.07.017 · Cited by 146
Vision-dependent specification of cell types and function in the developing cortex
Cheng, S., Butrus, S., Tan, L., Xu, R., Sagireddy, S., Trachtenberg, J. T., Shekhar, K., & Zipursky, S. L. (2022) · Cell · doi:10.1016/j.cell.2021.12.022 · Cited by 136
Binary and analog variation of synapses between cortical pyramidal neurons
Dorkenwald, S., Turner, N. L., Macrina, T., Lee, K., Lu, R., Wu, J., Bodor, Á. L., Bleckert, A., Brittain, D., Kemnitz, N., et al. (2022) · eLife · doi:10.7554/elife.76120 · Cited by 127
Diffusion time dependence, power‐law scaling, and exchange in gray matter
Olesen, J. L., Østergaard, L., Shemesh, N., & Jespersen, S. N. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.118976 · Cited by 125
A multi-scale brain map derived from whole-brain volumetric reconstructions
Brittin, C. A., Cook, S. J., Hall, D. H., Emmons, S. W., & Cohen, N. (2021) · Nature · doi:10.1038/s41586-021-03284-x · Cited by 116
McCafferty, C. L., Klumpe, S., Amaro, R. E., Kukulski, W., Collinson, L., & Engel, B. D. (2024) · Cell · doi:10.1016/j.cell.2024.01.005 · Cited by 113
MitoEM Dataset: Large-Scale 3D Mitochondria Instance Segmentation from EM Images
Wei, D., Lin, Z., Franco-Barranco, D., Wendt, N., Liu, X., Yin, W., Huang, X., Gupta, A., Jang, W.-D., Wang, X., et al. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-59722-1_7 · Cited by 105
Cell-type specific innervation of cortical pyramidal cells at their apical dendrites
Karimi, A., Odenthal, J., Drawitsch, F., Boergens, K. M., & Helmstaedter, M. (2020) · eLife · doi:10.7554/elife.46876 · Cited by 103
Self-citationPostnatal connectomic development of inhibition in mouse barrel cortex
Gour, A., Boergens, K. M., Heike, N., Hua, Y., Laserstein, P., Song, K., & Helmstaedter, M. N. (2020) · Science · doi:10.1126/science.abb4534 · Cited by 91
Self-citationStructure and function of axo-axonic inhibition
Schneider-Mizell, C. M., Bodor, A. L., Collman, F., Brittain, D., Bleckert, A. A., Dorkenwald, S., Turner, N. L., Macrina, T., Lee, K., Lu, R., et al. (2021) · eLife · doi:10.7554/elife.73783 · Cited by 115
Bio‐Inspired 3D Artificial Neuromorphic Circuits
Liu, X., Wang, F., Su, J., Zhou, Y., & Ramakrishna, S. (2022) · Advanced Functional Materials · doi:10.1002/adfm.202113050 · Cited by 82
Light-microscopy-based connectomic reconstruction of mammalian brain tissue
Tavakoli, M. R., Lyudchik, J., Januszewski, M., Vistunou, V., Dueñas, N. A., Vorlaufer, J., Sommer, C., Kreuzinger, C., Oliveira, B., Cenameri, A., et al. (2025) · Nature · doi:10.1038/s41586-025-08985-1 · Cited by 91
Synaptic wiring motifs in posterior parietal cortex support decision-making
Kuan, A. T., Bondanelli, G., Driscoll, L. N., Han, J., Kim, M., Hildebrand, D. G. C., Graham, B. J., Wilson, D. E., Thomas, L. A., Panzeri, S., et al. (2024) · Nature · doi:10.1038/s41586-024-07088-7 · Cited by 81
The fractal brain: scale-invariance in structure and dynamics
Grosu, G. F., Hopp, A. V., Moca, V. V., Bârzan, H., Ciuparu, A., Ercsey-Ravasz, M., Winkel, M., Linde, H., & Muresan, R. C. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac363 · Cited by 87
CAVE: Connectome Annotation Versioning Engine
Dorkenwald, S., Schneider-Mizell, C. M., Brittain, D., Halageri, A., Jordan, C. A., Kemnitz, N., Castro, M. A., Silversmith, W., Maitin-Shepard, J., Troidl, J., et al. (2025) · Nature Methods · doi:10.1038/s41592-024-02426-z · Cited by 85
The impact of neuron morphology on cortical network architecture
Udvary, D., Harth, P., Macke, J. H., Hege, H., de Kock, C. P. J., Sakmann, B. J., & Oberlaender, M. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110677 · Cited by 76
Semi-Supervised Neuron Segmentation via Reinforced Consistency Learning
Huang, W., Chen, C., Xiong, Z.-W., Zhang, Y.-Y., Chen, X., Sun, X., & Wu, F. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3176050 · Cited by 79
Inhibitory specificity from a connectomic census of mouse visual cortex
Schneider-Mizell, C. M., Bodor, Á. L., Brittain, D., Buchanan, J., Bumbarger, D. J., Elabbady, L., Gamlin, C. R., Kapner, D., Kinn, S., Mahalingam, G., et al. (2025) · Nature · doi:10.1038/s41586-024-07780-8 · Cited by 154
Local shape descriptors for neuron segmentation
Sheridan, A., Nguyen, T., Deb, D., Lee, W.-C. A., Saalfeld, S., Turaga, S. C., Manor, U., & Funke, J. (2022) · Nature Methods · doi:10.1038/s41592-022-01711-z · Cited by 76
Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortexPreprint
Schneider-Mizell, C. M., Bodor, Á. L., Brittain, D., Buchanan, J., Bumbarger, D. J., Elabbady, L., Gamlin, C. R., Kapner, D., Kinn, S., Mahalingam, G., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.23.525290 · Cited by 70
Emerging memristive neurons for neuromorphic computing and sensing
Li, Z., Tang, W., Zhang, B., Yang, R., & Miao, X. (2023) · Science and Technology of Advanced Materials · doi:10.1080/14686996.2023.2188878 · Cited by 75
Imig, C., López-Murcia, F. J., Maus, L., García-Plaza, I. H., Mortensen, L. S., Schwark, M., Schwarze, V., Angibaud, J., Nägerl, U. V., Taschenberger, H., et al. (2020) · Neuron · doi:10.1016/j.neuron.2020.09.004 · Cited by 66
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Clements, J., Dolafi, T., Umayam, L., Neubarth, N. L., Berg, S., Scheffer, L. K., & Plaza, S. M. (2020) · doi:10.1101/2020.01.16.909465 · Cited by 65
Ultrastructural analysis of dendritic spine necks reveals a continuum of spine morphologies
Ofer, N., Berger, D. R., Kasthuri, N., Lichtman, J. W., & Yuste, R. (2021) · Developmental Neurobiology · doi:10.1002/dneu.22829 · Cited by 65
Binary and analog variation of synapses between cortical pyramidal neurons
Dorkenwald, S., Turner, N. L., Macrina, T., Lee, K., Lu, R., Wu, J., Bodor, A. L., Bleckert, A. A., Brittain, D., Kemnitz, N., et al. (2019) · doi:10.1101/2019.12.29.890319 · Cited by 130
“We also considered the possibility of correlations in pairs of synapses sharing the same presynaptic cell but not the same postsynaptic cell, or pairs of synapses sharing the same postsynaptic cell but not the same presynaptic cell (Bartol et al., 2015; Kasthuri et al., 2015; Dvorkin and Ziv, 2016; Bloss et al., 2018;…”
BackgroundResultInfluentialLee, C. T., Laughlin, J. G., Beaumelle, N. A. D. L., Amaro, R. E., McCammon, J. A., Ramamoorthi, R., Holst, M., & Rangamani, P. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007756 · Cited by 61
Light microscopy based approach for mapping connectivity with molecular specificity
Shen, F. Y., Harrington, M. M., Walker, L. A., Cheng, H. P. J., Boyden, E. S., & Cai, D. (2020) · Nature Communications · doi:10.1038/s41467-020-18422-8 · Cited by 57
Petascale neural circuit reconstruction: automated methods
Macrina, T., Lee, K., Lu, R., Turner, N. L., Wu, J., Popovych, S., Silversmith, W., Kemnitz, N., Bae, J. A., Castro, M. A., et al. (2021) · doi:10.1101/2021.08.04.455162 · Cited by 55
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Uytiepo, M., Zhu, Y., Bushong, E. A., Chou, K., Polli, F. S., Zhao, E., Keun-young Kim, A., Luu, D., Chang, L., Yang, D. U., et al. (2025) · Science · doi:10.1126/science.ado8316 · Cited by 55
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Uzel, K., Kato, S., & Zimmer, M. (2022) · Current Biology · doi:10.1016/j.cub.2022.06.039 · Cited by 54
Wang, C., Zhang, T., Chen, X., He, S., Li, S., & Wu, S. (2023) · eLife · doi:10.7554/elife.86365 · Cited by 61
A single-cell transcriptomic atlas of complete insect nervous systems across multiple life stages
Corrales, M., Cocanougher, B., Kohn, A. B., Wittenbach, J. D., Long, X., Lemire, A. L., Cardona, A., Singer, R. H., Moroz, L. L., & Zlatic, M. (2022) · Neural Development · doi:10.1186/s13064-022-00164-6 · Cited by 55
Lu, C., Chen, K., Qiu, H., Chen, X., Chen, G., Qi, X., & Jiang, H. (2024) · Nature Communications · doi:10.1038/s41467-024-49125-z · Cited by 55
Bosch, C., Ackels, T., Pacureanu, A., Zhang, Y., Peddie, C. J., Berning, M., Rzepka, N., Zdora, M., Whiteley, I., Storm, M., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30199-6 · Cited by 52
Self-citationThe present and the future of microstructure MRI: From a paradigm shift to normal science
Novikov, D. S. (2020) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2020.108947 · Cited by 52
Dense 4D nanoscale reconstruction of living brain tissue
Velicky, P., Miguel, E., Michalska, J. M., Lyudchik, J., Wei, D., Lin, Z., Watson, J. F., Troidl, J., Beyer, J., Ben‐Simon, Y., et al. (2023) · Nature Methods · doi:10.1038/s41592-023-01936-6 · Cited by 43
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Mancini, M., Casamitjana, A., Peter, L., Robinson, E., Crampsie, S., Thomas, D. L., Holton, J. L., Jaunmuktane, Z., & Iglesias, J. E. (2020) · Scientific Reports · doi:10.1038/s41598-020-69163-z · Cited by 50
Bodea, S., & Westmeyer, G. G. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.655247 · Cited by 45
Primate Neuronal Connections are Sparse as Compared to Mouse
Wildenberg, G. A., Rosen, M. C., Lundell, J., Paukner, D., Freedman, D. J., & Kasthuri, N. (2020) · Cell Reports · doi:10.1016/j.celrep.2021.109709 · Cited by 49
Dow, E. R., Khan, N. C., Chen, K. M., Mishra, K., Perera, C., Narala, R., Basina, M., Dang, J., Kim, M., Levine, M., et al. (2023) · Ophthalmology Science · doi:10.1016/j.xops.2023.100330 · Cited by 40
How innovations in methodology offer new prospects for volume electron microscopy
Kievits, A. J., Lane, R. I., Carroll, E. C., & Hoogenboom, J. P. (2022) · Journal of Microscopy · doi:10.1111/jmi.13134 · Cited by 44
Brain wiring determinants uncovered by integrating connectomes and transcriptomes
Yoo, J., Dombrovski, M., Mirshahidi, P., Nern, A., LoCascio, S. A., Zipursky, S. L., & Kurmangaliyev, Y. Z. (2023) · Current Biology · doi:10.1016/j.cub.2023.08.020 · Cited by 39
Goulas, A., Damicelli, F., & Hilgetag, C. C. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.07.011 · Cited by 42
Zhang, X., Man, Y., Zhuang, X., Shen, J., Zhang, Y., Cui, Y., Yu, M., Xing, J., Wang, G., Lian, N., et al. (2021) · Science China Life Sciences · doi:10.1007/s11427-020-1910-1 · Cited by 38
CAVE: Connectome Annotation Versioning Engine
Dorkenwald, S., Schneider-Mizell, C. M., Brittain, D., Halageri, A., Jordan, C., Kemnitz, N., Castro, M. A., Silversmith, W., Maitin-Shephard, J., Troidl, J., et al. (2023) · doi:10.1101/2023.07.26.550598 · Cited by 79
“When combined with neuronal reconstructions, these datasets enable new analyses of richly annotated connectomes 16–22 .”
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Briscoe, J., & Marín, Ó. (2020) · Science · doi:10.1126/science.aaz8627 · Cited by 38
Bidel, F., Meirovitch, Y., Schalek, R. L., Lu, X., Pavarino, E. C., Yang, F., Peleg, A., Wu, Y., Shomrat, T., Berger, D. R., et al. (2023) · eLife · doi:10.7554/elife.84257 · Cited by 36
Chandelier cell anatomy and function reveal a variably distributed but common signalPreprint
Schneider-Mizell, C. M., Bodor, Á. L., Collman, F., Brittain, D., Bleckert, A., Dorkenwald, S., Turner, N. L., Macrina, T., Lee, K., Lu, R., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.31.018952 · Cited by 37
Multiscale and multimodal reconstruction of cortical structure and functionPreprint
Turner, N. L., Macrina, T., Bae, J. A., Yang, R., Wilson, A. M., Schneider-Mizell, C. M., Lee, K., Lu, R., Wu, J., Bodor, Á. L., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.10.14.338681 · Cited by 37
Layer-specific anatomical and physiological features of the retina’s neurovascular unit
Grimes, W. N., Berson, D. M., Sabnis, A., Hoon, M., Sinha, R., Tian, H., & Diamond, J. S. (2024) · Current Biology · doi:10.1016/j.cub.2024.11.023 · Cited by 46
Dense Circuit Reconstruction to Understand Neuronal Computation: Focus on Zebrafish
Friedrich, R. W., & Wanner, A. A. (2021) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-110220-013050 · Cited by 33
Furuta, T., Yamauchi, K., Okamoto, S., Takahashi, M., Kakuta, S., Ishida, Y., Takenaka, A., Yoshida, A., Uchiyama, Y., Koike, M., et al. (2021) · iScience · doi:10.1016/j.isci.2021.103601 · Cited by 35
Wildenberg, G. A., Sorokina, A. M., Koranda, J. L., Monical, A., Heer, C., Sheffield, M. E., Zhuang, X., McGehee, D., & Kasthuri, N. (2021) · eLife · doi:10.7554/elife.71981 · Cited by 39
Multi-layered maps of neuropil with segmentation-guided contrastive learning
Dorkenwald, S., Li, P. H., Januszewski, M., Berger, D. R., Maitin-Shepard, J., Bodor, A. L., Collman, F., Schneider-Mizell, C. M., da Costa, N. M., Lichtman, J. W., et al. (2023) · Nature Methods · doi:10.1038/s41592-023-02059-8 · Cited by 30
Baguley, D., & Norman, M. (2001) · PubMed · Cited by 34
Song, K., Feng, Z., & Helmstaedter, M. N. (2023) · Nature Methods · doi:10.1038/s41592-023-01866-3 · Cited by 31
Self-citationHomophilic wiring principles underpin neuronal network topology in vitro
Akarca, D., Dunn, A. W., Hornauer, P., Ronchi, S., Michele, F., Wang, C., Terrigno, M., Jagasia, R., Vértes, P. E., Mierau, S. B., et al. (2025) · eLife · doi:10.7554/elife.85300 · Cited by 31
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Sievers, M., Motta, A., Schmidt, M., Yener, Y., Loomba, S., Song, K., Bruett, J., & Helmstaedter, M. N. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.22.586254 · Cited by 32
Self-citationRoboEM: automated 3D flight tracing for synaptic-resolution connectomics
Schmidt, M., Motta, A., Sievers, M., & Helmstaedter, M. N. (2024) · Nature Methods · doi:10.1038/s41592-024-02226-5 · Cited by 30
Self-citationAn anatomical substrate of credit assignment in reinforcement learningPreprint
Kornfeld, J. M. R., Wang, Y., Januszewski, M., Rother, A., Schubert, P., Goldman, M., Jain, V., Denk, W. T., & Fee, M. S. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.02.18.954354 · Cited by 30
Lappalainen, J. K., Tschopp, F. D., Prakhya, S., McGill, M., Nern, A., Shinomiya, K., Takemura, S.-Y., Gruntman, E., Macke, J. H., & Turaga, S. C. (2023) · doi:10.1101/2023.03.11.532232 · Cited by 31
The future of cerebral organoids in drug discovery
Salick, M. R., Lubeck, E., Riesselman, A., & Kaykas, A. (2021) · Seminars in Cell & Developmental Biology · doi:10.1016/j.semcdb.2020.05.024 · Cited by 29
Ren, Y., Chong, B., Xu, W., Zhang, Z., Liu, L., Wu, Y., Liu, Y., Jiang, H., Liang, X., Wu, H., et al. (2022) · The Innovation · doi:10.1016/j.xinn.2022.100334 · Cited by 28
An interactive deep learning-based approach reveals mitochondrial cristae topologies
Suga, S., Nakamura, K., Nakanishi, Y., Humbel, B. M., Kawai, H., & Hirabayashi, Y. (2023) · PLoS Biology · doi:10.1371/journal.pbio.3002246 · Cited by 30
Oligodendrocyte precursor cells prune axons in the mouse neocortexPreprint
Buchanan, J., Elabbady, L., Collman, F., Jorstad, N. L., Bakken, T. E., Ott, C. M., Glatzer, J. C., Bleckert, A., Bodor, Á. L., Brittan, D., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.29.446047 · Cited by 28
New light on cortical neuropeptides and synaptic network plasticity
Smith, S. J., Hawrylycz, M., Rossier, J., & Sümbül, U. (2020) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2020.04.002 · Cited by 26
An ultrastructural connectomic analysis of a higher‐order thalamocortical circuit in the mouse
Sampathkumar, V., Miller-Hansen, A. J., Sherman, S. M., & Kasthuri, N. (2020) · European Journal of Neuroscience · doi:10.1111/ejn.15092 · Cited by 7
Current Progress and Challenges in Large-Scale 3D Mitochondria Instance Segmentation
Franco-Barranco, D., Lin, Z., Jang, W.-D., Wang, X., Shen, Q., Yin, W., Fan, Y., Li, M., Chen, C., Xiong, Z., et al. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3320497 · Cited by 27
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van Albada, S. J., Morales-Gregorio, A., Dickscheid, T., Goulas, A., Bakker, R., Bludau, S., Palm, G., Hilgetag, C. C., & Diesmann, M. (2021) · Advances in experimental medicine and biology · doi:10.1007/978-3-030-89439-9_9 · Cited by 29
Advanced Deep Networks for 3d Mitochondria Instance Segmentation
Li, M., Chen, C. Q., Liu, X., Huang, W., Zhang, Y., & Xiong, Z. (2022) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi52829.2022.9761477 · Cited by 27
Three-dimensional SEM, TEM, and STEM for analysis of large-scale biological systems
Radulović, S., Sunkara, S., Rachel, R., & Leitinger, G. (2022) · Histochemistry and Cell Biology · doi:10.1007/s00418-022-02117-w · Cited by 27
Connectomic analysis of thalamus-driven disinhibition in cortical layer 4
Hua, Y., Loomba, S., Pawlak, V., Voit, K.-M., Laserstein, P., Boergens, K. M., Wallace, D. J., Kerr, J. N. D., & Helmstaedter, M. N. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111476 · Cited by 25
Self-citationPredicting modular functions and neural coding of behavior from a synaptic wiring diagram
Vishwanathan, A., Sood, A., Wu, J., Ramirez, A. D., Yang, R., Kemnitz, N., Ih, D., Turner, N., Lee, K., Tartavull, I., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01784-3 · Cited by 36
Neuronal diversity and stereotypy at multiple scales through whole brain morphometry
Liu, Y., Jiang, S., Li, Y., Zhao, S., Yun, Z., Zhao, Z.-H., Zhang, L., Wang, G., Chen, X., Manubens-Gil, L., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-54745-6 · Cited by 26
High-throughput segmentation of unmyelinated axons by deep learning
Plebani, E., Biscola, N. P., Havton, L. A., Rajwa, B. P., Shemonti, A. S., Jaffey, D., Powley, T. L., Keast, J. R., Lu, K., & Dündar, M. (2022) · Scientific Reports · doi:10.1038/s41598-022-04854-3 · Cited by 24
Wildenberg, G. A., Sorokina, A., Koranda, J. L., Monical, A., Heer, C. M., Sheffield, M. E. J., Zhuang, X., McGehee, D. S., & Kasthuri, B. (2021) · University of Chicago · doi:10.6082/232sj-vk702 · Cited by 27
Upscaling X-ray nanoimaging to macroscopic specimens
Du, M., Di, Z. W., Gürsoy, D., Xian, R. P., Kozorovitskiy, Y., & Jacobsen, C. J. (2021) · Journal of Applied Crystallography · doi:10.1107/s1600576721000194 · Cited by 24
Perisomatic ultrastructure efficiently classifies cells in mouse cortex
Elabbady, L., Seshamani, S., Mu, S., Mahalingam, G., Schneider-Mizell, C. M., Bodor, Á. L., Bae, J. A., Brittain, D., Buchanan, J., Bumbarger, D. J., et al. (2025) · Nature · doi:10.1038/s41586-024-07765-7 · Cited by 45
Deistler, M., Kadhim, K. L., Pals, M., Beck, J., Huang, Z., Gloeckler, M., Lappalainen, J. K., Schröder, C., Berens, P., Gonçalves, P. J., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02895-w · Cited by 38
Zhu, Y., Uytiepo, M., Bushong, E. A., Haberl, M. G., Beutter, E., Scheiwe, F., Zhang, W., Chang, L., Luu, D., Chui, B., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2021.108953 · Cited by 22
Metadata
- Authors
- Motta, A., Berning, M., Boergens, K. M., Staffler, B., Beining, M., Loomba, S., Hennig, P., Wissler, H., & Helmstaedter, M.
- Affiliations
- Max Planck Institute for Brain Research (DE)
- Max Planck Society (DE)
- Corresponding author
- Motta, Alessandro
- Berning, Manuel
- Boergens, Kevin M.
- Staffler, Benedikt
- Wissler, Heiko
- ORCID
- Venue
- Science
- Published
- 2019-11-29
- Volume
- 366
- Issue
- 6469
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2982268628
- Cited by
- 333
- References
- 101
- Citations by year
- 2026: 23 · 2025: 37 · 2024: 62 · 2023: 55 · 2022: 60 · 2021: 52 · 2020: 40 · 2019: 2
- FWCI
- 18.45
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Science (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- Max-Planck-Gesellschaft
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Keywords
- connectome reconstruction, barrel cortex, layer 4, somatosensory cortex, mice, Hebbian synaptic plasticity, long-term potentiation, neural circuits, 3D electron microscopy
- MeSH terms
- Animals, Axons, Male, Mice, Inbred C57BL, Microscopy, Electron, Neurons, Somatosensory Cortex, Synapses, Neuropil, Imaging, Three-Dimensional, Mice, Connectome
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