Tag: Image processing

  • Scheffer, L. K. et al. A connectome and analysis of the adult Drosophila central brain. eLife 9, e57443 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Takemura, S.-Y. et al. A visual motion detection circuit suggested by Drosophila connectomics. Nature 500, 175–181 (2013).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Takemura, S., Nern, A., Chklovskii, D. B. & Scheffer, L. K. The comprehensive connectome of a neural substrate for ‘ON’ motion detection in Drosophila. eLife 6, e24394 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • MICrONS Consortium. Functional connectomics spanning multiple areas of mouse visual cortex. Preprint at bioRxiv https://doi.org/10.1101/2021.07.28.454025 (2021).

  • Shapson-Coe, A. et al. A petavoxel fragment of human cerebral cortex reconstructed at nanoscale resolution. Science 384, eadk4858 (2024).

  • Loomba, S. et al. Connectomic comparison of mouse and human cortex. Science 377, eabo0924 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Buhmann, J. et al. Automatic detection of synaptic partners in a whole-brain Drosophila electron microscopy data set. Nat. Methods 18, 771–774 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Dorkenwald, S. et al. FlyWire: online community for whole-brain connectomics. Nat. Methods 19, 119–128 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Zheng, Z. et al. A complete electron microscopy volume of the brain of adult Drosophila melanogaster. Cell 174, 730–743.e22 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Eckstein, N. et al. Neurotransmitter classification from electron microscopy images at synaptic sites in Drosophila melanogaster. Cell 187, 2574–2594.e23 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Matsliah, A. et al. Neuronal parts list and wiring diagram for a visual system. Nature https://doi.org/10.1038/s41586-024-07981-1 (2024).

  • Schlegel, P. et al. Whole-brain annotation and multi-connectome cell typing of Drosophila. Nature https://doi.org/10.1038/s41586-024-07686-5 (2024).

  • Ma, X., Hou, X., Edgecombe, G. D. & Strausfeld, N. J. Complex brain and optic lobes in an early Cambrian arthropod. Nature 490, 258–261 (2012).

  • Sporns, O., Tononi, G. & Kötter, R. The human connectome: a structural description of the human brain. PLoS Comput. Biol. 1, e42 (2005).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Costandi, M. Anti-connectome-ism. The Guardian (21 September 2012).

  • Lichtman, J. W. & Denk, W. The big and the small: challenges of imaging the brain’s circuits. Science 334, 618–623 (2011).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Briggman, K. L. & Bock, D. D. Volume electron microscopy for neuronal circuit reconstruction. Curr. Opin. Neurobiol. 22, 154–161 (2012).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Coen, P. et al. Dynamic sensory cues shape song structure in Drosophila. Nature 507, 233–237 (2014).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Fisher, Y. E. Flexible navigational computations in the Drosophila central complex. Curr. Opin. Neurobiol. 73, 102514 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Cognigni, P., Felsenberg, J. & Waddell, S. Do the right thing: neural network mechanisms of memory formation, expression and update in Drosophila. Curr. Opin. Neurobiol. 49, 51–58 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Schretter, C. E. et al. Cell types and neuronal circuitry underlying female aggression in Drosophila. eLife 9, e58942 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Deutsch, D. et al. The neural basis for a persistent internal state in Drosophila females. eLife 9, e59502 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Li, F. et al. The connectome of the adult Drosophila mushroom body provides insights into function. eLife 9, e62576 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hulse, B. K. et al. A connectome of the Drosophila central complex reveals network motifs suitable for flexible navigation and context-dependent action selection. eLife 10, e66039 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Baker, C. A. et al. Neural network organization for courtship-song feature detection in Drosophila. Curr. Biol. 32, 3317–3333.e7 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Schlegel, P., Bates, A. S., Stürner, T. & Jagannathan, S. R. Information flow, cell types and stereotypy in a full olfactory connectome. eLife 10, e66018 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Borst, A. & Helmstaedter, M. Common circuit design in fly and mammalian motion vision. Nat. Neurosci. 18, 1067–1076 (2015).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Farris, S. M. Are mushroom bodies cerebellum-like structures? Arthropod Struct. Dev. 40, 368–379 (2011).

    Article 
    PubMed 

    Google Scholar
     

  • Pacheco, D. A., Thiberge, S. Y., Pnevmatikakis, E. & Murthy, M. Auditory activity is diverse and widespread throughout the central brain of Drosophila. Nat. Neurosci. 24, 93–104 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Brezovec, B. E. et al. Mapping the neural dynamics of locomotion across the Drosophila brain. Curr. Biol. 34, 710–726.e4 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • White, J. G., Southgate, E., Thomson, J. N. & Brenner, S. The structure of the nervous system of the nematode Caenorhabditis elegans. Philos. Trans. R. Soc. Lond. B 314, 1–340 (1986).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Cook, S. J. et al. Whole-animal connectomes of both Caenorhabditis elegans sexes. Nature 571, 63–71 (2019).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Winding, M. et al. The connectome of an insect brain. Science 379, eadd9330 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Shiu, P. K. et al. A Drosophila computational brain model reveals sensorimotor processing. Nature https://doi.org/10.1038/s41586-024-07763-9 (2024).

  • Eichler, K. et al. Somatotopic organization among parallel sensory pathways that promote a grooming sequence in Drosophila. eLife 12, RP87602 (2024).

  • Stürner, T. et al. Comparative connectomics of the descending and ascending neurons of the Drosophila nervous system: stereotypy and sexual dimorphism. Preprint at bioRxiv https://doi.org/10.1101/2024.06.04.596633 (2024).

  • Garner, D. et al. Connectome reconstruction predicts visual features used for navigation. Nature https://doi.org/10.1038/s41586-024-07967-z (2024).

  • Ganguly, I., Heckman, E. L., Litwin-Kumar, A., Clowney, E. J. & Behnia, R. Diversity of visual inputs to Kenyon cells of the Drosophila mushroom body. Nat. Commun. 15, 5698 (2024).

  • Pospisil, D. A. et al. The fly connectome reveals a path to the effectome. Nature https://doi.org/10.1038/s41586-024-07982-0 (2024).

  • Reinhard, N., Fukuda, A., Manoli, G., Derksen, E. & Saito, A. Synaptic and peptidergic connectomes of the Drosophila circadian clock. Preprint at bioRxiv https://doi.org/10.1101/2023.09.11.557222 (2023).

  • Christenson, M. P. et al. Hue selectivity from recurrent circuitry in Drosophila. Nat. Neurosci. 27, 1137–1147 (2024).

  • Lin, A. et al. Network statistics of the whole-brain connectome of Drosophila. Nature https://doi.org/10.1038/s41586-024-07968-y (2024).

  • Sapkal, N. et al. Neural circuit mechanisms underlying context-specific halting in Drosophila. Nature https://doi.org/10.1038/s41586-024-07854-7 (2024).

  • Seung, H. S. Predicting visual function by interpreting a neuronal wiring diagram. Nature https://doi.org/10.1038/s41586-024-07953-5 (2024).

  • Cornean, J. et al. Heterogeneity of synaptic connectivity in the fly visual system. Nat. Commun. 15, 1570 (2024).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cachero, S., Ostrovsky, A. D., Jai, Y. Y. & Dickson, B. J. Sexual dimorphism in the fly brain. Curr. Biol. 20, 1589–1601 (2010).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Murthy, M., Fiete, I. & Laurent, G. Testing odor response stereotypy in the Drosophila mushroom body. Neuron 59, 1009–1023 (2008).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zheng, Z. et al. Structured sampling of olfactory input by the fly mushroom body. Curr. Biol. 32, 3334–3349.e6 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lin, A. et al. Network statistics of the whole-brain connectome of Drosophila. Preprint at bioRxiv https://doi.org/10.1101/2023.07.29.551086 (2023).

  • Dorkenwald, S. et al. CAVE: Connectome Annotation Versioning Engine. Preprint at bioRxiv https://doi.org/10.1101/2023.07.26.550598 (2023).

  • Schüz, A. & Palm, G. Density of neurons and synapses in the cerebral cortex of the mouse. J. Comp. Neurol. 286, 442–455 (1989).

    Article 
    PubMed 

    Google Scholar
     

  • Dorkenwald, S. et al. Automated synaptic connectivity inference for volume electron microscopy. Nat. Methods 14, 435–442 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Schneider-Mizell, C. M. et al. Quantitative neuroanatomy for connectomics in Drosophila. eLife 5, e12059 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Phelps, J. S. et al. Reconstruction of motor control circuits in adult Drosophila using automated transmission electron microscopy. Cell 184, 759–774.e18 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Takemura, S.-Y. et al. A connectome of the male Drosophila ventral nerve cord. eLife 13, RP97769 (2024).

  • Marin, E. C. et al. Systematic annotation of a complete adult male Drosophila nerve cord connectome reveals principles of functional organisation. Preprint at bioRxiv https://doi.org/10.1101/2023.06.05.543407 (2023).

  • Kim, H. et al. Wiring patterns from auditory sensory neurons to the escape and song-relay pathways in fruit flies. J. Comp. Neurol. 528, 2068–2098 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sterne, G. R., Otsuna, H., Dickson, B. J. & Scott, K. Classification and genetic targeting of cell types in the primary taste and premotor center of the adult Drosophila brain. eLife 10, e71679 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Azevedo, A. et al. Connectomic reconstruction of a female Drosophila ventral nerve cord. Nature 631, 360–368 (2024).

  • Wu, M. et al. Visual projection neurons in the Drosophila lobula link feature detection to distinct behavioral programs. eLife 5, e21022 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Otsuna, H. & Ito, K. Systematic analysis of the visual projection neurons of Drosophila melanogaster. I. Lobula-specific pathways. J. Comp. Neurol. 497, 928–958 (2006).

    Article 
    PubMed 

    Google Scholar
     

  • Zhao, A. et al. A comprehensive neuroanatomical survey of the Drosophila lobula plate tangential neurons with predictions for their optic flow sensitivity. Preprint at bioRxiv https://doi.org/10.1101/2023.10.16.562634 (2023).

  • Repérant, J. et al. The centrifugal visual system of vertebrates: a comparative analysis of its functional anatomical organization. Brain Res. Rev. 52, 1–57 (2006).

    Article 
    PubMed 

    Google Scholar
     

  • Karuppudurai, T. et al. A hard-wired glutamatergic circuit pools and relays UV signals to mediate spectral preference in Drosophila. Neuron 81, 603–615 (2014).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Meinertzhagen, I. A. Of what use is connectomics? A personal perspective on the Drosophila connectome. J. Exp. Biol. 221, jeb164954 (2018).

    Article 
    PubMed 

    Google Scholar
     

  • Chklovskii, D. B. Synaptic connectivity and neuronal morphology: two sides of the same coin. Neuron 43, 609–617 (2004).

    CAS 
    PubMed 

    Google Scholar
     

  • Kremer, M. C., Jung, C., Batelli, S., Rubin, G. M. & Gaul, U. The glia of the adult Drosophila nervous system. Glia 65, 606–638 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ohyama, T. et al. A multilevel multimodal circuit enhances action selection in Drosophila. Nature 520, 633–639 (2015).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Hong, E. J. & Wilson, R. I. Simultaneous encoding of odors by channels with diverse sensitivity to inhibition. Neuron 85, 573–589 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Meier, M. & Borst, A. Extreme compartmentalization in a Drosophila amacrine cell. Curr. Biol. 29, 1545–1550.e2 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Croset, V., Treiber, C. D. & Waddell, S. Cellular diversity in the Drosophila midbrain revealed by single-cell transcriptomics. eLife 7, e34550 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Molina-Obando, S. et al. ON selectivity in the Drosophila visual system is a multisynaptic process involving both glutamatergic and GABAergic inhibition. eLife 8, e49373 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Liu, W. W. & Wilson, R. I. Glutamate is an inhibitory neurotransmitter in the Drosophila olfactory system. Proc. Natl Acad. Sci. USA 110, 10294–10299 (2013).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zingg, B. et al. Neural networks of the mouse neocortex. Cell 156, 1096–1111 (2014).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Oh, S. W. et al. A mesoscale connectome of the mouse brain. Nature 508, 207–214 (2014).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Markov, N. T. et al. A weighted and directed interareal connectivity matrix for macaque cerebral cortex. Cereb. Cortex 24, 17–36 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Meissner, G. W. et al. A searchable image resource of Drosophila GAL4 driver expression patterns with single neuron resolution. eLife 12, e80660 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chiang, A.-S. et al. Three-dimensional reconstruction of brain-wide wiring networks in Drosophila at single-cell resolution. Curr. Biol. 21, 1–11 (2011).

    Article 
    MathSciNet 
    CAS 
    PubMed 

    Google Scholar
     

  • Kasthuri, N. & Lichtman, J. W. The rise of the ‘projectome’. Nat. Methods 4, 307–308 (2007).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Ito, K. et al. A systematic nomenclature for the insect brain. Neuron 81, 755–765 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Fischbach, K.-F. & Dittrich, A. P. M. The optic lobe of Drosophila melanogaster. I. A Golgi analysis of wild-type structure. Cell Tissue Res. 258, 441–475 (1989).

    Article 

    Google Scholar
     

  • Nern, A., Pfeiffer, B. D. & Rubin, G. M. Optimized tools for multicolor stochastic labeling reveal diverse stereotyped cell arrangements in the fly visual system. Proc. Natl Acad. Sci. USA 112, E2967–E2976 (2015).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bae, J. A. et al. Digital museum of retinal ganglion cells with dense anatomy and physiology. Cell 173, 1293–1306.e19 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Shinomiya, K., Nern, A., Meinertzhagen, I. A., Plaza, S. M. & Reiser, M. B. Neuronal circuits integrating visual motion information in Drosophila melanogaster. Curr. Biol. 32, 3529–3544.e2 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Lappalainen, J. K. et al. Connectome-constrained networks predict neural activity across the fly visual system. Nature https://doi.org/10.1038/s41586-024-07939-3 (2024).

  • Snell, N. J. et al. Complex representation of taste quality by second-order gustatory neurons in Drosophila. Curr. Biol. 32, 3758–3772.e4 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Vogt, K. et al. Direct neural pathways convey distinct visual information to Drosophila mushroom bodies. eLife 5, e14009 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mu, S. et al. 3D reconstruction of cell nuclei in a full Drosophila brain. Preprint at bioRxiv https://doi.org/10.1101/2021.11.04.467197 (2021).

  • Hofbauer, A. & Buchner, E. Does Drosophila have seven eyes? Naturwissenschaften 76, 335–336 (1989).

    Article 
    ADS 

    Google Scholar
     

  • Hu, K. G., Reichert, H. & Stark, W. S. Electrophysiological characterization of Drosophila ocelli. J. Comp. Physiol. 126, 15–24 (1978).

    Article 

    Google Scholar
     

  • Stark, W. S., Sapp, R. & Carlson, S. D. Ultrastructure of the ocellar visual system in normal and mutant Drosophila melanogaster. J. Neurogenet. 5, 127–153 (1989).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Stange, G., Stowe, S., Chahl, J. S. & Massaro, A. Anisotropic imaging in the dragonfly median ocellus: a matched filter for horizon detection. J. Comp. Physiol. A 188, 455–467 (2002).

    Article 
    CAS 

    Google Scholar
     

  • Cheong, H. S. J. et al. Transforming descending input into behavior: the organization of premotor circuits in the Drosophila male adult nerve cord connectome. Preprint at bioRxiv https://doi.org/10.1101/2023.06.07.543976 (2023).

  • Suver, M. P., Huda, A., Iwasaki, N., Safarik, S. & Dickinson, M. H. An array of descending visual interneurons encoding self-motion in Drosophila. J. Neurosci. 36, 11768–11780 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Haag, J., Wertz, A. & Borst, A. Integration of lobula plate output signals by DNOVS1, an identified premotor descending neuron. J. Neurosci. 27, 1992–2000 (2007).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kim, A. J., Fenk, L. M., Lyu, C. & Maimon, G. Quantitative predictions orchestrate visual signaling in Drosophila. Cell 168, 280–294.e12 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Braitenberg, V. Vehicles: Experiments in Synthetic Psychology (MIT Press, 1984).

  • Seung, H. S. Connectome: How the Brain’s Wiring Makes Us Who We Are (Houghton Mifflin Harcourt, 2012).

  • Davis, F. P. et al. A genetic, genomic, and computational resource for exploring neural circuit function. eLife 9, e50901 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Groschner, L. N., Malis, J. G., Zuidinga, B. & Borst, A. A biophysical account of multiplication by a single neuron. Nature 603, 119–123 (2022).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ammer, G. et al. Multilevel visual motion opponency in Drosophila. Nat. Neurosci. 26, 1894–1905 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Knott, G., Marchman, H., Wall, D. & Lich, B. Serial section scanning electron microscopy of adult brain tissue using focused ion beam milling. J. Neurosci. 28, 2959–2964 (2008).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Xu, C. S. et al. Enhanced FIB-SEM systems for large-volume 3D imaging. eLife 6, e25916 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hayworth, K. J. et al. Gas cluster ion beam SEM for imaging of large tissue samples with 10 nm isotropic resolution. Nat. Methods 17, 68–71 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Denk, W. & Horstmann, H. Serial block-face scanning electron microscopy to reconstruct three-dimensional tissue nanostructure. PLoS Biol. 2, e329 (2004).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Leighton, S. B. SEM images of block faces, cut by a miniature microtome within the SEM – a technical note. Scan. Electron Microsc. 1981, 73–76 (1981).


    Google Scholar
     

  • Macrina, T. et al. Petascale neural circuit reconstruction: automated methods. Preprint at bioRxiv https://doi.org/10.1101/2021.08.04.455162 (2021).

  • Popovych, S. et al. Petascale pipeline for precise alignment of images from serial section electron microscopy. Nat. Commun. 15, 289 (2024).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Januszewski, M. et al. High-precision automated reconstruction of neurons with flood-filling networks. Nat. Methods 15, 605–610 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Jain, V. et al. Supervised learning of image restoration with convolutional networks. In Proc. 2007 IEEE 11th International Conference on Computer Vision 636–643 (IEEE, 2007).

  • Turaga, S. C. et al. Convolutional networks can learn to generate affinity graphs for image segmentation. Neural Comput. 22, 511–538 (2010).

    Article 
    PubMed 

    Google Scholar
     

  • Maitin-Shepard, J. Neuroglancer. https://github.com/google/neuroglancer (2020).

  • Verasztó, C. et al. Whole-animal connectome and cell-type complement of the three-segmented Platynereis dumerilii larva. Preprint at bioRxiv https://doi.org/10.1101/2020.08.21.260984 (2020).

  • Schoofs, A. et al. Serotonergic reinforcement of a complete swallowing circuit. Preprint at bioRxiv https://doi.org/10.1101/2023.05.26.542464 (2023).

  • Ngai, J. BRAIN 2.0: transforming neuroscience. Cell 185, 4–8 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Jefferis, G., Collinson, L., Bosch, C., Costa, M. & Schlegel, P. Scaling up Connectomics: the road to a whole mouse brain connectome (Wellcome, 2023).

  • Collins, F. S., Morgan, M. & Patrinos, A. The Human Genome Project: lessons from large-scale biology. Science 300, 286–290 (2003).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Heinrich, L., Funke, J., Pape, C., Nunez-Iglesias, J. & Saalfeld, S. Synaptic cleft segmentation in non-isotropic volume electron microscopy of the complete Drosophila brain. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 (eds Frangi, A. F. et al.) 317–325 (Springer, 2018).

  • Bates, A. S. et al. The natverse, a versatile toolbox for combining and analysing neuroanatomical data. eLife 9, e53350 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mitchell, E., Keselj, S., Popovych, S., Buniatyan, D. & Seung, H. S. Siamese encoding and alignment by multiscale learning with self-supervision. Preprint at https://doi.org/10.48550/arXiv.1904.02643 (2019).

  • Lee, K., Zung, J., Li, P., Jain, V. & Seung, H. S. Superhuman accuracy on the SNEMI3D Connectomics Challenge. Preprint at https://doi.org/10.48550/arXiv.1706.00120 (2017).

  • Lu, R., Zlateski, A. & Seung, H. S. Large-scale image segmentation based on distributed clustering algorithms. Preprint at https://doi.org/10.48550/arXiv.2106.10795 (2021).

  • Lapraz, F. et al. Asymmetric activity of NetrinB controls laterality of the Drosophila brain. Nat. Commun. 14, 1052 (2023).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Dorkenwald, S. et al. Binary and analog variation of synapses between cortical pyramidal neurons. eLife 11, e76120 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kim, J. S. et al. Space-time wiring specificity supports direction selectivity in the retina. Nature 509, 331–336 (2014).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Brittin, C. A., Cook, S. J., Hall, D. H., Emmons, S. W. & Cohen, N. A multi-scale brain map derived from whole-brain volumetric reconstructions. Nature 591, 105–110 (2021).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Sato, M., Bitter, I., Bender, M. A., Kaufman, A. E. & Nakajima, M. TEASAR: tree-structure extraction algorithm for accurate and robust skeletons. In Proc. 8th Pacific Conference on Computer Graphics and Applications (eds Barsky, B. A. A. et al.) (IEEE, 2000); https://doi.org/10.1109/PCCGA.2000.883951.

  • Schlegel, P. et al. navis-org/navis: version 1.5.0. Zenodo https://doi.org/10.5281/ZENODO.8191725 (2023).

  • McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at https://doi.org/10.48550/arXiv.1802.03426 (2018).

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  • A pathology foundation model for cancer diagnosis and prognosis prediction

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  • Van der Laak, J., Litjens, G. & Ciompi, F. Deep learning in histopathology: the path to the clinic. Nat. Med. 27, 775–784 (2021).

    Article 
    PubMed 

    Google Scholar
     

  • Shmatko, A., Ghaffari Laleh, N., Gerstung, M. & Kather, J. N. Artificial intelligence in histopathology: enhancing cancer research and clinical oncology. Nat. Cancer 3, 1026–1038 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Song, A. H. et al. Artificial intelligence for digital and computational pathology. Nat. Rev. Bioeng. 1, 930–949 (2023).

    Article 

    Google Scholar
     

  • Campanella, G. et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat. Med. 25, 1301–1309 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bejnordi, B. E. et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318, 2199–2210 (2017).

    Article 

    Google Scholar
     

  • Lu, M. Y. et al. Data-efficient and weakly supervised computational pathology on whole-slide images. Nat. Biomed. Eng. 5, 555–570 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Coudray, N. et al. Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning. Nat. Med. 24, 1559–1567 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Nasrallah, M. P. et al. Machine learning for cryosection pathology predicts the 2021 WHO classification of glioma. Med 4, 526–540 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Tsai, P.-C. et al. Histopathology images predict multi-omics aberrations and prognoses in colorectal cancer patients. Nat. Commun. 14, 2102 (2023).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yu, K.-H. et al. Classifying non-small cell lung cancer types and transcriptomic subtypes using convolutional neural networks. J. Am. Med. Inform. Assoc. 27, 757–769 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yu, K.-H. et al. Association of omics features with histopathology patterns in lung adenocarcinoma. Cell Syst. 5, 620–627 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chen, R. J. et al. Pan-cancer integrative histology-genomic analysis via multimodal deep learning. Cancer Cell 40, 865–878 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Marostica, E. et al. Development of a histopathology informatics pipeline for classification and prediction of clinical outcomes in subtypes of renal cell carcinoma. Clin. Cancer Res. 27, 2868–2878 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Yu, K.-H. et al. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat. Commun. 7, 12474 (2016).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Vanguri, R. S. et al. Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer. Nat. Cancer 3, 1151–1164 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yu, K.-H. et al. Deciphering serous ovarian carcinoma histopathology and platinum response by convolutional neural networks. BMC Med. 18, 236 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Foersch, S. et al. Multistain deep learning for prediction of prognosis and therapy response in colorectal cancer. Nat. Med. 29, 430–439 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Kather, J. N. et al. Pan-cancer image-based detection of clinically actionable genetic alterations. Nat. Cancer 1, 789–799 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Echle, A. et al. Deep learning in cancer pathology: a new generation of clinical biomarkers. Br. J. Cancer 124, 686–696 (2021).

    Article 
    PubMed 

    Google Scholar
     

  • Ektefaie, Y. et al. Integrative multiomics-histopathology analysis for breast cancer classification. NPJ Breast Cancer 7, 147 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yu, K.-H., Beam, A. L. & Kohane, I. S. Artificial intelligence in healthcare. Nat. Biomed. Eng. 2, 719–731 (2018).

    Article 
    PubMed 

    Google Scholar
     

  • Krishnan, R., Rajpurkar, P. & Topol, E. J. Self-supervised learning in medicine and healthcare. Nat. Biomed. Eng. 6, 1346–1352 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Zhou, Y. et al. A foundation model for generalizable disease detection from retinal images. Nature 622, 156–1638 (2023).

  • Chen, C. et al. Fast and scalable search of whole-slide images via self-supervised deep learning. Nat. Biomed. Eng. 6, 1420–1434 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wang, X. et al. RetCCL: clustering-guided contrastive learning for whole-slide image retrieval. Med. Image Anal. 83, 102645 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Chen, R. J. et al. Towards a general-purpose foundation model for computational pathology. Nat. Med. 30, 850–862 (2024).

  • Wagner, S. J. et al. Transformer-based biomarker prediction from colorectal cancer histology: a large-scale multicentric study. Cancer Cell 41, 1650–1661 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual–language foundation model for pathology image analysis using medical twitter. Nat. Med. 29, 2307–2316 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Lu, M. Y. et al. A visual-language foundation model for computational pathology. Nat. Med. 30, 863–874 (2024).

  • Wang, X. et al. Transformer-based unsupervised contrastive learning for histopathological image classification. Med. Image Anal. 81, 102559 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Koziarski, M. et al. Diagset: a dataset for prostate cancer histopathological image classification. Sci. Rep. 14, 6780 (2024).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yu, G. et al. Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images. Nat. Commun. 12, 6311 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Loménie, N. et al. Can AI predict epithelial lesion categories via automated analysis of cervical biopsies: the TissueNet challenge? J. Pathol. Inform. 13, 100149 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ilse, M., Tomczak, J. & Welling, M. Attention-based deep multiple instance learning. In Proc. 35th International Conference on Machine Learning (eds Dy, J. & Krause, A.) 2127–2136 (PMLR, 2018).

  • Li, B., Li, Y. & Eliceiri, K. W. Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning. In Proc. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition 14313–14323 (IEEE, 2021).

  • Fu, Y. et al. Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis. Nat. Cancer 1, 800–810 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Petrini, I. et al. A specific missense mutation in GTF2I occurs at high frequency in thymic epithelial tumors. Nat. Genet. 46, 844–849 (2014).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Carbone, M. et al. Biological mechanisms and clinical significance of BAP1 mutations in human cancer. Cancer Discov. 10, 1103–1120 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chakravarty, D. et al. OncoKB: a precision oncology knowledge base. JCO Precision Oncology 1, 1–16 (2017).

    Article 

    Google Scholar
     

  • Louis, D. N. et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro-Oncology 23, 1231–1251 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Roetzer-Pejrimovsky, T. et al. The Digital Brain Tumour Atlas, an open histopathology resource. Sci. Data 9, 55 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kim, K. et al. PAIP 2020: microsatellite instability prediction in colorectal cancer. Med. Image Anal. 89, 102886 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Amin, M. B. et al. The Eighth Edition AJCC Cancer Staging Manual: continuing to build a bridge from a population-based to a more “personalized” approach to cancer staging. CA Cancer J. Clin. 67, 93–99 (2017).

    Article 
    PubMed 

    Google Scholar
     

  • Achiam, J. et al. GPT-4 technical report. Preprint at https://doi.org/10.48550/arXiv.2303.08774 (2023).

  • Team, G. et al. Gemini: a family of highly capable multimodal models. Preprint at https://doi.org/10.48550/arXiv.2312.11805 (2023).

  • Azizi, S. et al. Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging. Nat. Biomed. Eng. 7, 756–779 (2023).

  • Cancer Genome Atlas Research Network, J. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45, 1113–1120 (2013).

    Article 

    Google Scholar
     

  • Lonsdale, J. et al. The genotype-tissue expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).

    Article 
    CAS 

    Google Scholar
     

  • Bulten, W. et al. Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge. Nat. Med. 28, 154–163 (2022).

  • Yacob, F. et al. Weakly supervised detection and classification of basal cell carcinoma using graph-transformer on whole slide images. Sci Rep. 13, 7555 (2023).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Xu, F. et al. Predicting axillary lymph node metastasis in early breast cancer using deep learning on primary tumor biopsy slides. Front. Oncol. 11, 4133 (2021).

    Article 

    Google Scholar
     

  • Weitz, P. et al. A multi-stain breast cancer histological whole-slide-image data set from routine diagnostics. Sci. Data 10, 562 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wang, C.-W. et al. Histopathological whole slide image dataset for classification of treatment effectiveness to ovarian cancer. Sci. Data 9, 25 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Radford, A. et al. Learning transferable visual models from natural language supervision. In Proc. 38th International Conference on Machine Learning (eds Meila, M. & Zhang, T.) 8748–8763 (PMLR, 2021).

  • Otsu, N. A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. Syst. 9, 62–66 (1979).

    Article 

    Google Scholar
     

  • Kingma, D. P. & Ba, J. L. Adam: a method for stochastic optimization. In Proc. 3rd International Conference on Learning Representations (eds Bengio, Y. & LeCun, Y.) (ICLR, 2015).

  • Loshchilov, I. & Hutter, F. SGDR: stochastic gradient descent with warm restarts. In Proc. 5th International Conference on Learning Representations 1769–1784 (ICLR, 2017).

  • Stadler, C. B. et al. Proactive construction of an annotated imaging database for artificial intelligence training. J. Digit. Imaging 34, 105–115 (2021).

    Article 
    PubMed 

    Google Scholar
     

  • Lu, M. Y. et al. AI-based pathology predicts origins for cancers of unknown primary. Nature 594, 106–110 (2021).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Black, A. et al. PLCO: evolution of an epidemiologic resource and opportunities for future studies. Rev. Recent Clin. Trials 10, 238–245 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Shao, Z. et al. TransMIL: Transformer based correlated multiple instance learning for whole slide image classification. Adv. Neural Inf. Process. Syst. 34, 2136–2147 (2021).


    Google Scholar
     

  • Liang, J. et al. Deep learning supported discovery of biomarkers for clinical prognosis of liver cancer. Nat. Mach. Intell. 5, 408–420 (2023).

    Article 

    Google Scholar
     

  • Courtiol, P. et al. Deep learning-based classification of mesothelioma improves prediction of patient outcome. Nat. Med. 25, 1519–1525 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Graham, S. et al. Hover-Net: simultaneous segmentation and classification of nuclei in multi-tissue histology images. Med. Image Anal. 58, 101563 (2019).

    Article 
    PubMed 

    Google Scholar
     

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