@article {68512, title = {Oligodendrocyte precursor cells ingest axons in the mouse neocortex.}, journal = {Proceedings of the National Academies of Science of the U.S.A.}, volume = {119}, year = {2022}, month = {2022 Nov 29}, pages = {e2202580119}, abstract = {

Neurons in the developing brain undergo extensive structural refinement as nascent circuits adopt their mature form. This physical transformation of neurons is facilitated by the engulfment and degradation of axonal branches and synapses by surrounding glial cells, including microglia and astrocytes. However, the small size of phagocytic organelles and the complex, highly ramified morphology of glia have made it difficult to define the contribution of these and other glial cell types to this crucial process. Here, we used large-scale, serial section transmission\ electron microscopy (TEM) with computational volume segmentation to reconstruct the complete 3D morphologies of distinct glial types in the mouse visual cortex, providing unprecedented resolution of their morphology and composition. Unexpectedly, we discovered that the fine processes of oligodendrocyte precursor cells (OPCs), a population of abundant, highly dynamic glial progenitors, frequently surrounded small branches of axons. Numerous phagosomes and phagolysosomes (PLs) containing fragments of axons and vesicular structures were present inside their processes, suggesting that OPCs engage in axon pruning. Single-nucleus RNA sequencing from the developing mouse cortex revealed that OPCs express key phagocytic genes at this stage, as well as neuronal transcripts, consistent with active axon engulfment. Although microglia are thought to be responsible for the majority of synaptic pruning and structural refinement, PLs were ten times more abundant in OPCs than in microglia at this stage, and these structures were markedly less abundant in newly generated oligodendrocytes, suggesting that OPCs contribute substantially to the refinement of neuronal circuits during cortical development.

}, keywords = {Animals, Axons, Mice, Neocortex, Neurons, Oligodendrocyte Precursor Cells, Oligodendroglia}, issn = {1091-6490}, doi = {10.1073/pnas.2202580119}, author = {Buchanan, JoAnn and Elabbady, Leila and Collman, Forrest and Jorstad, Nikolas L and Bakken, Trygve E and Ott, Carolyn and Glatzer, Jenna and Bleckert, Adam A and Bodor, Agnes L and Brittain, Derrick and Bumbarger, Daniel J and Mahalingam, Gayathri and Seshamani, Sharmishtaa and Schneider-Mizell, Casey and Takeno, Marc M and Torres, Russel and Yin, Wenjing and Hodge, Rebecca D and Castro, Manuel and Dorkenwald, Sven and Ih, Dodam and Jordan, Chris S and Kemnitz, Nico and Lee, Kisuk and Lu, Ran and Macrina, Thomas and Mu, Shang and Popovych, Sergiy and Silversmith, William M and Tartavull, Ignacio and Turner, Nicholas L and Wilson, Alyssa M and Wong, William and Wu, Jingpeng and Zlateski, Aleksandar and Zung, Jonathan and Lippincott-Schwartz, Jennifer and Lein, Ed S and Seung, H Sebastian and Bergles, Dwight E and Reid, R Clay and da Costa, Nuno Ma{\c c}arico} } @article {68251, title = {A scalable and modular automated pipeline for stitching of large electron microscopy datasets.}, journal = {eLife}, volume = {11}, year = {2022}, month = {2022 Jul 26}, abstract = {

Serial-section electronmicroscopy (ssEM) is themethod of choice for studyingmacroscopic biological samples at extremely high resolution in three dimensions. In the nervous system, nanometer-scale images are necessary to reconstruct dense neural wiring diagrams in the brain, so called connectomes. In order to use this data, consisting of up to 10 individual EM images, it must be assembled into a volume, requiring seamless 2D stitching from each physical section followed by 3D alignment of the stitched sections. The high throughput of ssEM necessitates 2D stitching to be done at the pace of imaging, which currently produces tens of terabytes per day. To achieve this, we present a modular volume assembly software pipeline ASAP (Assembly Stitching and Alignment Pipeline) that is scalable to datasets containing petabytes of data and parallelized to work in a distributed computational environment. The pipeline is built on top of the Render (27) services used in the volume assembly of the brain of adult Drosophilamelanogaster (30). It achieves high throughput by operating on themeta-data and transformations of each image stored in a database, thus eliminating the need to render intermediate output. ASAP ismodular, allowing for easy incorporation of new algorithms without significant changes in the workflow. The entire software pipeline includes a complete set of tools for stitching, automated quality control, 3D section alignment, and final rendering of the assembled volume to disk. ASAP has been deployed for continuous stitching of several large-scale datasets of the mouse visual cortex and human brain samples including one cubic millimeter of mouse visual cortex (28; 8) at speeds that exceed imaging. The pipeline also has multi-channel processing capabilities and can be applied to fluorescence and multi-modal datasets like array tomography.

}, issn = {2050-084X}, doi = {10.7554/eLife.76534}, author = {Mahalingam, Gayathri and Torres, Russel and Kapner, Daniel and Trautman, Eric T and Fliss, Tim and Seshamani, Shamishtaa and Perlman, Eric and Young, Rob and Kinn, Samuel and Buchanan, JoAnn and Takeno, Marc M and Yin, Wenjing and Bumbarger, Daniel J and Gwinn, Ryder P and Nyhus, Julie and Lein, Ed and Smith, Steven J and Reid, R Clay and Khairy, Khaled A and Saalfeld, Stephan and Collman, Forrest and Macarico da Costa, Nuno} } @article {66008, title = {Survey of spiking in the mouse visual system reveals functional hierarchy.}, journal = {Nature}, volume = {592(7852):86-92}, year = {2021}, month = {2021 Jan 20}, pages = {86-92}, abstract = {

The anatomy of the mammalian visual system, from the retina to the neocortex, is organized hierarchically. However, direct observation of cellular-level functional interactions across this hierarchy is lacking due to the challenge of simultaneously recording activity across numerous regions. Here we describe a large, open dataset-part of the Allen Brain Observatory-that surveys spiking from tens of thousands of units in six cortical and two thalamic regions in the brains of mice responding to a battery of visual stimuli. Using cross-correlation analysis, we reveal that the organization of inter-area functional connectivity during visual stimulation mirrors the anatomical hierarchy from the Allen Mouse Brain Connectivity Atlas. We find that four classical hierarchical measures-response latency, receptive-field size, phase-locking to drifting gratings and response decay timescale-are all correlated with the hierarchy. Moreover, recordings obtained during a visual task reveal that the correlation between neural activity and behavioural choice also increases along the hierarchy. Our study provides a foundation for understanding coding and signal propagation across hierarchically organized cortical and thalamic visual areas.

}, issn = {1476-4687}, doi = {10.1038/s41586-020-03171-x}, author = {Siegle, Joshua H and Jia, Xiaoxuan and Durand, Severine and Gale, Sam and Bennett, Corbett and Graddis, Nile and Heller, Greggory and Ramirez, Tamina K and Choi, Hannah and Luviano, Jennifer A and Groblewski, Peter A and Ahmed, Ruweida and Arkhipov, Anton and Bernard, Amy and Billeh, Yazan N and Brown, Dillan and Buice, Michael A and Cain, Nicolas and Caldejon, Shiella and Casal, Linzy and Cho, Andrew and Chvilicek, Maggie and Cox, Timothy C and Dai, Kael and Denman, Daniel J and de Vries, Saskia E J and Dietzman, Roald and Esposito, Luke and Farrell, Colin and Feng, David and Galbraith, John and Garrett, Marina and Gelfand, Emily C and Hancock, Nicole and Harris, Julie A and Howard, Robert and Hu, Brian and Hytnen, Ross and Iyer, Ramakrishnan and Jessett, Erika and Johnson, Katelyn and Kato, India and Kiggins, Justin and Lambert, Sophie and Lecoq, Jerome and Ledochowitsch, Peter and Lee, Jung Hoon and Leon, Arielle and Li, Yang and Liang, Elizabeth and Long, Fuhui and Mace, Kyla and Melchior, Jose and Millman, Daniel and Mollenkopf, Tyler and Nayan, Chelsea and Ng, Lydia and Ngo, Kiet and Nguyen, Thuyahn and Nicovich, Philip R and North, Kat and Ocker, Gabriel Koch and Ollerenshaw, Doug and Oliver, Michael and Pachitariu, Marius and Perkins, Jed and Reding, Melissa and Reid, David and Robertson, Miranda and Ronellenfitch, Kara and Seid, Sam and Slaughterbeck, Cliff and Stoecklin, Michelle and Sullivan, David and Sutton, Ben and Swapp, Jackie and Thompson, Carol and Turner, Kristen and Wakeman, Wayne and Whitesell, Jennifer D and Williams, Derric and Williford, Ali and Young, Rob and Zeng, Hongkui and Naylor, Sarah and Phillips, John W and Reid, R Clay and Mihalas, Stefan and Olsen, Shawn R and Koch, Christof} } @article {65738, title = {The mind of a mouse.}, journal = {Cell}, volume = {182}, year = {2020}, month = {2020 Sep 17}, pages = {1372-1376}, abstract = {

Large scientific projects in genomics and astronomy are influential not because they answer any single question but because they enable investigation of continuously arising new questions from the same data-rich sources. Advances in automated mapping of the brain{\textquoteright}s synaptic connections (connectomics) suggest that the complicated circuits underlying brain function are ripe for analysis. We discuss benefits of mapping a mouse brain at the level of synapses.

}, issn = {1097-4172}, doi = {10.1016/j.cell.2020.08.010}, author = {Abbott, Larry F and Bock, Davi D and Callaway, Edward M and Denk, Winfried and Dulac, Catherine and Fairhall, Adrienne L and Fiete, Ila and Harris, Kristen M and Helmstaedter, Moritz and Jain, Viren and Kasthuri, Narayanan and LeCun, Yann and Lichtman, Jeff W and Littlewood, Peter B and Luo, Liqun and Maunsell, John H R and Reid, R Clay and Rosen, Bruce R and Rubin, Gerald M and Sejnowski, Terrence J and Seung, H Sebastian and Svoboda, Karel and Tank, David W and Tsao, Doris and Van Essen, David C} } @article {49518, title = {Visual physiology of the layer 4 cortical circuit in silico.}, journal = {PLoS Computational Biology}, volume = {14}, year = {2018}, month = {2018 Nov 12}, pages = {e1006535}, abstract = {

Despite advances in experimental techniques and accumulation of large datasets concerning the composition and properties of the cortex, quantitative modeling of cortical circuits under in-vivo-like conditions remains challenging. Here we report and publicly release a biophysically detailed circuit model of layer 4 in the mouse primary visual cortex, receiving thalamo-cortical visual inputs. The 45,000-neuron model was subjected to a battery of visual stimuli, and results were compared to published work and new in vivo experiments. Simulations reproduced a variety of observations, including effects of optogenetic perturbations. Critical to the agreement between responses in silico and in vivo were the rules of functional synaptic connectivity between neurons. Interestingly, after extreme simplification the model still performed satisfactorily on many measurements, although quantitative agreement with experiments suffered. These results emphasize the importance of functional rules of cortical wiring and enable a next generation of data-driven models of in vivo neural activity and computations.

}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1006535}, author = {Arkhipov, Anton and Gouwens, Nathan W and Billeh, Yazan N and Gratiy, Sergey and Iyer, Ramakrishnan and Wei, Ziqiang and Xu, Zihao and Abbasi-Asl, Reza and Berg, Jim and Buice, Michael and Cain, Nicholas and da Costa, Nuno and de Vries, Saskia and Denman, Daniel and Durand, Severine and Feng, David and Jarsky, Tim and Lecoq, Jerome and Lee, Brian and Li, Lu and Mihalas, Stefan and Ocker, Gabriel K and Olsen, Shawn R and Reid, R Clay and Soler-Llavina, Gilberto and Sorensen, Staci A and Wang, Quanxin and Waters, Jack and Scanziani, Massimo and Koch, Christof} } @article {6904, title = {The Open Connectome Project Data Cluster: Scalable analysis and vision for high-throughput neuroscience.}, journal = {Scientific and Statistical Database Management: International Conference, SSDBM ... : Proceedings. International Conference on Scientific and Statistical Database Management}, year = {2013}, month = {2013 Jun 18}, abstract = {

We describe a scalable database cluster for the spatial analysis and annotation of high-throughput brain imaging data, initially for 3-d electron microscopy image stacks, but for time-series and multi-channel data as well. The system was designed primarily for workloads that build connectomes- neural connectivity maps of the brain-using the parallel execution of computer vision algorithms on high-performance compute clusters. These services and open-science data sets are publicly available at openconnecto.me. The system design inherits much from NoSQL scale-out and data-intensive computing architectures. We distribute data to cluster nodes by partitioning a spatial index. We direct I/O to different systems-reads to parallel disk arrays and writes to solid-state storage-to avoid I/O interference and maximize throughput. All programming interfaces are RESTful Web services, which are simple and stateless, improving scalability and usability. We include a performance evaluation of the production system, highlighting the effec-tiveness of spatial data organization.

}, doi = {10.1145/2484838.2484870}, author = {Burns, Randal and Roncal, William Gray and Kleissas, Dean and Lillaney, Kunal and Manavalan, Priya and Perlman, Eric and Berger, Daniel R. and Bock, Davi D and Chung, Kwanghun and Grosenick, Logan and Kasthuri, Narayanan and Weiler, Nicholas C and Deisseroth, Karl and Kazhdan, Michael and Lichtman, Jeff and Reid, R Clay and Smith, Stephen J and Szalay, Alexander S and Vogelstein, Joshua T and Vogelstein, R Jacob} } @article {4326, title = {Large-scale automated histology in the pursuit of connectomes.}, journal = {The Journal of Neuroscience: The Official Journal of the Society for Neuroscience}, volume = {31}, year = {2011}, month = {2011 Nov 9}, pages = {16125-38}, abstract = {

How does the brain compute? Answering this question necessitates neuronal connectomes, annotated graphs of all synaptic connections within defined brain areas. Further, understanding the energetics of the brain{\textquoteright}s computations requires vascular graphs. The assembly of a connectome requires sensitive hardware tools to measure neuronal and neurovascular features in all three dimensions, as well as software and machine learning for data analysis and visualization. We present the state of the art on the reconstruction of circuits and vasculature that link brain anatomy and function. Analysis at the scale of tens of nanometers yields connections between identified neurons, while analysis at the micrometer scale yields probabilistic rules of connection between neurons and exact vascular connectivity.

}, issn = {1529-2401}, doi = {10.1523/JNEUROSCI.4077-11.2011}, author = {Kleinfeld, David and Bharioke, Arjun and Blinder, Pablo and Bock, Davi D and Briggman, Kevin L and Chklovskii, Dmitri B and Denk, Winfried and Helmstaedter, Moritz and Kaufhold, John P and Lee, Wei-Chung Allen and Meyer, Hanno S and Micheva, Kristina D and Oberlaender, Marcel and Prohaska, Steffen and Reid, R Clay and Smith, Stephen J and Takemura, Shinya and Tsai, Philbert S and Sakmann, Bert} } @article {2164, title = {Network anatomy and in vivo physiology of visual cortical neurons.}, journal = {Nature}, volume = {471}, year = {2011}, month = {2011 Mar 10}, pages = {177-82}, abstract = {

In the cerebral cortex, local circuits consist of tens of thousands of neurons, each of which makes thousands of synaptic connections. Perhaps the biggest impediment to understanding these networks is that we have no wiring diagrams of their interconnections. Even if we had a partial or complete wiring diagram, however, understanding the network would also require information about each neuron{\textquoteright}s function. Here we show that the relationship between structure and function can be studied in the cortex with a combination of in vivo physiology and network anatomy. We used two-photon calcium imaging to characterize a functional property{\textendash}the preferred stimulus orientation{\textendash}of a group of neurons in the mouse primary visual cortex. Large-scale electron microscopy of serial thin sections was then used to trace a portion of these neurons{\textquoteright} local network. Consistent with a prediction from recent physiological experiments, inhibitory interneurons received convergent anatomical input from nearby excitatory neurons with a broad range of preferred orientations, although weak biases could not be rejected.

}, keywords = {Animals, Calcium Signaling, Electron, Fluorescence, Interneurons, Male, Mice, Microscopy, Microtomy, Nerve Net, Neural Inhibition, Neurons, Pyramidal Cells, Synapses, Transmission, Visual Cortex}, issn = {1476-4687}, doi = {10.1038/nature09802}, author = {Bock, Davi D and Lee, Wei-Chung Allen and Kerlin, Aaron M and Andermann, Mark L and Hood, Greg and Wetzel, Arthur W and Yurgenson, Sergey and Soucy, Edward R and Kim, Hyon Suk and Reid, R Clay} } @article {2165, title = {Accelerating feature based registration using the Johnson-Lindenstrauss Lemma.}, journal = {Medical Image Computing and Computer-Assisted Intervention: MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention}, volume = {12}, year = {2009}, month = {2009}, pages = {632-9}, abstract = {

We introduce an efficient search strategy to substantially accelerate feature based registration. Previous feature based registration algorithms often use truncated search strategies in order to achieve small computation times. Our new accelerated search strategy is based on the realization that the search for corresponding features can be dramatically accelerated by utilizing Johnson-Lindenstrauss dimension reduction. Order of magnitude calculations for the search strategy we propose here indicate that the algorithm proposed is more than a million times faster than previously utilized naive search strategies, and this advantage in speed is directly translated into an advantage in accuracy as the fast speed enables more comparisons to be made in the same amount of time. We describe the accelerated scheme together with a full complexity analysis. The registration algorithm was applied to large transmission electron microscopy (TEM) images of neural ultrastructure. Our experiments demonstrate that our algorithm enables alignment of TEM images with increased accuracy and efficiency compared to previous algorithms.

}, keywords = {Algorithms, Animals, Artificial Intelligence, Automated, Computer-Assisted, Electron, Ferrets, Geniculate Bodies, Image Enhancement, Image Interpretation, Imaging, Microscopy, Pattern Recognition, Reproducibility of Results, Sensitivity and Specificity, Subtraction Technique, Three-Dimensional}, author = {Akselrod-Ballin, Ayelet and Bock, Davi and Reid, R Clay and Warfield, Simon K} } @article {2167, title = {Alignment of large image series using cubic B-splines tessellation: application to transmission electron microscopy data.}, journal = {Medical Image Computing and Computer-Assisted Intervention: MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention}, volume = {10}, year = {2007}, month = {2007}, pages = {710-7}, abstract = {

3D reconstruction from serial 2D microscopy images depends on non-linear alignment of serial sections. For some structures, such as the neuronal circuitry of the brain, very large images at very high resolution are necessary to permit reconstruction. These very large images prevent the direct use of classical registration methods. We propose in this work a method to deal with the non-linear alignment of arbitrarily large 2D images using the finite support properties of cubic B-splines. After initial affine alignment, each large image is split into a grid of smaller overlapping sub-images, which are individually registered using cubic B-splines transformations. Inside the overlapping regions between neighboring sub-images, the coefficients of the knots controlling the B-splines deformations are blended, to create a virtual large grid of knots for the whole image. The sub-images are resampled individually, using the new coefficients, and assembled together into a final large aligned image. We evaluated the method on a series of large transmission electron microscopy images and our results indicate significant improvements compared to both manual and affine alignment.

}, keywords = {Algorithms, Artificial Intelligence, Automated, Computer-Assisted, Electron, Image Enhancement, Image Interpretation, Imaging, Microscopy, Pattern Recognition, Reproducibility of Results, Sensitivity and Specificity, Subtraction Technique, Three-Dimensional, Transmission}, author = {Dauguet, Julien and Bock, Davi and Reid, R Clay and Warfield, Simon K} }