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Michał Januszewski

Michał Januszewski

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    Multi-Layered Maps of Neuropil with Segmentation Guided Contrastive Learning
    Sven Dorkenwald
    Daniel R. Berger
    Agnes L. Bodor
    Forrest Collman
    Casey M. Schneider-Mizell
    Nuno Maçarico da Costa
    Jeff W. Lichtman
    Nature Methods (2023)
    Preview abstract Maps of the nervous system that identify individual cells along with their type, subcellular components and connectivity have the potential to elucidate fundamental organizational principles of neural circuits. Nanometer-resolution imaging of brain tissue provides the necessary raw data, but inferring cellular and subcellular annotation layers is challenging. We present segmentation-guided contrastive learning of representations (SegCLR), a self-supervised machine learning technique that produces representations of cells directly from 3D imagery and segmentations. When applied to volumes of human and mouse cortex, SegCLR enables accurate classification of cellular subcompartments and achieves performance equivalent to a supervised approach while requiring 400-fold fewer labeled examples. SegCLR also enables inference of cell types from fragments as small as 10 μm, which enhances the utility of volumes in which many neurites are truncated at boundaries. Finally, SegCLR enables exploration of layer 5 pyramidal cell subtypes and automated large-scale analysis of synaptic partners in mouse visual cortex. View details
    SyConn2: dense synaptic connectivity inference for volume electron microscopy
    Philipp J. Schubert
    Sven Dorkenwald
    Jonathan Klimesch
    Fabian Svara
    Andrei Mancu
    Hashir Ahmad
    Michale S. Fee
    Joergen Kornfeld
    Nature Methods, vol. 19 (2022), 1367–1370
    Preview abstract The ability to acquire ever larger datasets of brain tissue using volume electron microscopy leads to an increasing demand for the automated extraction of connectomic information. We introduce SyConn2, an open-source connectome analysis toolkit, which works with both on-site high-performance compute environments and rentable cloud computing clusters. SyConn2 was tested on connectomic datasets with more than 10 million synapses, provides a web-based visualization interface and makes these data amenable to complex anatomical and neuronal connectivity queries. View details
    Visual recognition of social signals by a tectothalamic neural circuit
    Johannes M. Kappel
    Dominique Förster
    Katja Slangewal
    Inbal Shainer
    Fabian Svara
    Joseph C. Donovan
    Shachar Sherman
    Herwig Baier
    Johannes Larsch
    Nature, vol. 608 (2022), 146–152
    Preview abstract Social affiliation emerges from individual-level behavioural rules that are driven by conspecific signals. Long-distance attraction and short-distance repulsion, for example, are rules that jointly set a preferred interanimal distance in swarms. However, little is known about their perceptual mechanisms and executive neural circuits. Here we trace the neuronal response to self-like biological motion, a visual trigger for affiliation in developing zebrafish. Unbiased activity mapping and targeted volumetric two-photon calcium imaging revealed 21 activity hotspots distributed throughout the brain as well as clustered biological-motion-tuned neurons in a multimodal, socially activated nucleus of the dorsal thalamus. Individual dorsal thalamus neurons encode local acceleration of visual stimuli mimicking typical fish kinetics but are insensitive to global or continuous motion. Electron microscopic reconstruction of dorsal thalamus neurons revealed synaptic input from the optic tectum and projections into hypothalamic areas with conserved social function. Ablation of the optic tectum or dorsal thalamus selectively disrupted social attraction without affecting short-distance repulsion. This tectothalamic pathway thus serves visual recognition of conspecifics, and dissociates neuronal control of attraction from repulsion during social affiliation, revealing a circuit underpinning collective behaviour. View details
    Automated synapse-level reconstruction of neural circuits in the larval zebrafish brain
    Fabian Svara
    Dominique Förster
    Fumi Kubo
    Marco dal Maschio
    Philipp Schubert
    Jörgen Kornfeld
    Adrian Wanner
    Winfried Denk
    Herwig Baier
    Nature Methods, vol. 19 (2022), 1357–1366
    Preview abstract Dense reconstruction of synaptic connectivity requires high-resolution electron microscopy images of entire brains and tools to efficiently trace neuronal wires across the volume. To generate such a resource, we sectioned and imaged a larval zebrafish brain by serial block-face electron microscopy at a voxel size of 14 × 14 × 25 nm3. We segmented the resulting dataset with the flood-filling network algorithm, automated the detection of chemical synapses and validated the results by comparisons to transmission electron microscopic images and light-microscopic reconstructions. Neurons and their connections are stored in the form of a queryable and expandable digital address book. We reconstructed a network of 208 neurons involved in visual motion processing, most of them located in the pretectum, which had been functionally characterized in the same specimen by two-photon calcium imaging. Moreover, we mapped all 407 presynaptic and postsynaptic partners of two superficial interneurons in the tectum. The resource developed here serves as a foundation for synaptic-resolution circuit analyses in the zebrafish nervous system. View details
    A connectomic study of a petascale fragment of human cerebral cortex
    Alex Shapson-Coe
    Daniel R. Berger
    Yuelong Wu
    Richard L. Schalek
    Shuohong Wang
    Neha Karlupia
    Sven Dorkenwald
    Evelina Sjostedt
    Dongil Lee
    Luke Bailey
    Angerica Fitzmaurice
    Rohin Kar
    Benjamin Field
    Hank Wu
    Julian Wagner-Carena
    David Aley
    Joanna Lau
    Zudi Lin
    Donglai Wei
    Hanspeter Pfister
    Adi Peleg
    Jeff W. Lichtman
    bioRxiv (2021)
    Preview abstract We acquired a rapidly preserved human surgical sample from the temporal lobe of the cerebral cortex. We stained a 1 mm3 volume with heavy metals, embedded it in resin, cut more than 5000 slices at ∼30 nm and imaged these sections using a high-speed multibeam scanning electron microscope. We used computational methods to render the three-dimensional structure containing 57,216 cells, hundreds of millions of neurites and 133.7 million synaptic connections. The 1.4 petabyte electron microscopy volume, the segmented cells, cell parts, blood vessels, myelin, inhibitory and excitatory synapses, and 104 manually proofread cells are available to peruse online. Many interesting and unusual features were evident in this dataset. Glia outnumbered neurons 2:1 and oligodendrocytes were the most common cell type in the volume. Excitatory spiny neurons comprised 69% of the neuronal population, and excitatory synapses also were in the majority (76%). The synaptic drive onto spiny neurons was biased more strongly toward excitation (70%) than was the case for inhibitory interneurons (48%). Despite incompleteness of the automated segmentation caused by split and merge errors, we could automatically generate (and then validate) connections between most of the excitatory and inhibitory neuron types both within and between layers. In studying these neurons we found that deep layer excitatory cell types can be classified into new subsets, based on structural and connectivity differences, and that chandelier interneurons not only innervate excitatory neuron initial segments as previously described, but also each other’s initial segments. Furthermore, among the thousands of weak connections established on each neuron, there exist rarer highly powerful axonal inputs that establish multi-synaptic contacts (up to ∼20 synapses) with target neurons. Our analysis indicates that these strong inputs are specific, and allow small numbers of axons to have an outsized role in the activity of some of their postsynaptic partners. View details
    Preview abstract Connectomic reconstruction of neural circuits relies on nanometer resolution microscopy which produces on the order of a petabyte of imagery for each cubic millimeter of brain tissue. The cost of storing such data is a significant barrier to broadening the use of connectomic approaches and scaling to even larger volumes. We present an image compression approach that uses machine learning-based denoising and standard image codecs to compress raw electron microscopy imagery of neuropil up to 17-fold with negligible loss of reconstruction accuracy. View details
    Preview abstract Recent advances in 3d electron microscopy are yielding ever larger reconstructions of brain tissue, encompassing thousands of individual neurons interconnected by millions of synapses. Interpreting reconstructions at this scale demands advances in the automated analysis of neuronal morphologies, for example by identifying morphological and functional subcompartments within neurons. We present a method that for the first time uses full 3d input (voxels) to automatically classify reconstructed neuron fragments as axon, dendrite, or somal subcompartments. Based on 3d convolutional neural networks, this method achieves a mean f1-score of 0.972, exceeding the previous state of the art of 0.955. The resulting predictions can support multiple analysis and proofreading applications. In particular, we leverage finely localized subcompartment predictions for automated detection and correction of merge errors in the volume reconstruction, successfully detecting 90.6% of inter-class merge errors with a false positive rate of only 2.7%. View details
    An anatomical substrate of credit assignment in reinforcement learning
    Jorgen Kornfeld
    Michale S. Fee
    Philipp Schubert
    Winfried Denk
    bioRxiv (2020)
    Preview abstract How is experience used to improve performance? In both biological and artificial systems, the optimization of parameters that affect behavior requires a process that determines whether a parameter affects the outcome and then modifies the parameter accordingly. Central to the recent bloom of artificial intelligence has been the error-backpropagation algorithm(Rumelhart, Hinton, and Williams 1986) , which computationally retraces the signal from the output to each synapse (weight) and allows a large number of parameters to be optimized in parallel at high learning rates. Biological systems, however, lack an obvious mechanism to retrace the signal path. Here we show, by combining high-throughput volume electron microscopy (Denk and Horstmann 2004) and automated connectomic analysis(Januszewski et al. 2018; Dorkenwald et al. 2017; Schubert et al. 2019) , that the synaptic architecture of songbird basal ganglia supports a form of local credit assessment proposed in a model of songbird reinforcement learning (M. S. Fee and Goldberg 2011). We show that three of this model’s major predictions hold true: first, cortical axons that encode exploratory motor variability terminate predominantly on dendritic shafts of spiny neurons. Second, cortical axons that encode timing seek out spines, which enable calcium-based coincidence detection (R. Yuste and Denk 1995) and appear to be capable of creating and storing eligibility traces (Yagishita et al. 2014). Third, synapse pairs that presynaptically share a cortical timing axon and post-synaptically a medium spiny dendrite are substantially more similar in size than expected, indicating a history of Hebbian plasticity (Bartol et al. 2015; Kasthuri et al. 2015) . Combined with numerical simulations these data provide strong evidence for a model of basal ganglia learning with a biologically plausible credit assignment mechanism. View details
    A connectome and analysis of the adult Drosophila central brain
    Louis K Scheffer
    C Shan Xu
    Zhiyuan Lu
    Shin-ya Takemura
    Kenneth J Hayworth
    Gary B Huang
    Kazunori Shinomiya
    Stuart Berg
    Jody Clements
    Philip M Hubbard
    William T Katz
    Lowell Umayam
    Ting Zhao
    David Ackerman
    John Bogovic
    Tom Dolafi
    Dagmar Kainmueller
    Takashi Kawase
    Khaled A Khairy
    Larry Lindsey
    Nicole Neubarth
    Donald J Olbris
    Hideo Otsuna
    Eric T Trautman
    Masayoshi Ito
    Alexander S Bates
    Jens Goldammer
    Tanya Wolff
    Robert Svirskas
    Philipp Schlegel
    Erika Neace
    Christopher J Knecht
    Chelsea X Alvarado
    Dennis A Bailey
    Samantha Ballinger
    Jolanta A Borycz
    Brandon S Canino
    Natasha Cheatham
    Michael Cook
    Marisa Dreher
    Octave Duclos
    Bryon Eubanks
    Kelli Fairbanks
    Samantha Finley
    Nora Forknall
    Audrey Francis
    Gary Patrick Hopkins
    Emily M Joyce
    SungJin Kim
    Nicole A Kirk
    Julie Kovalyak
    Shirley Lauchie
    Alanna Lohff
    Charli Maldonado
    Emily A Manley
    Sari McLin
    Caroline Mooney
    Miatta Ndama
    Omotara Ogundeyi
    Nneoma Okeoma
    Christopher Ordish
    Nicholas Padilla
    Christopher M Patrick
    Tyler Paterson
    Elliott E Phillips
    Emily M Phillips
    Neha Rampally
    Caitlin Ribeiro
    Madelaine K Robertson
    Jon Thomson Rymer
    Sean M Ryan
    Megan Sammons
    Anne K Scott
    Ashley L Scott
    Aya Shinomiya
    Claire Smith
    Kelsey Smith
    Natalie L Smith
    Margaret A Sobeski
    Alia Suleiman
    Jackie Swift
    Satoko Takemura
    Iris Talebi
    Dorota Tarnogorska
    Emily Tenshaw
    Temour Tokhi
    John J Walsh
    Tansy Yang
    Jane Anne Horne
    Feng Li
    Ruchi Parekh
    Patricia K Rivlin
    Vivek Jayaraman
    Marta Costa
    Gregory SXE Jefferis
    Kei Ito
    Stephan Saalfeld
    Reed George
    Ian Meinertzhagen
    Gerald M Rubin
    Harald F Hess
    Stephen M Plaza
    eLife, vol. 9 (2020)
    Preview abstract The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the fly's brain. View details
    Preview abstract Reconstruction of neural circuitry at single-synapse resolution is an attractive target for improving understanding of the nervous system in health and disease. Serial section transmission electron microscopy (ssTEM) is among the most prolific imaging methods employed in pursuit of such reconstructions. We demonstrate how Flood-Filling Networks (FFNs) can be used to computationally segment a forty-teravoxel whole-brain Drosophila ssTEM volume. To compensate for data irregularities and imperfect global alignment, FFNs were combined with procedures that locally re-align serial sections as well as dynamically adjust and synthesize image content. The proposed approach produced a largely merger-free segmentation of the entire ssTEM Drosophila brain, which we make freely available. As compared to manual tracing using an efficient skeletonization strategy, the segmentation enabled circuit reconstruction and analysis workflows that were an order of magnitude faster. View details
    Preview abstract Reconstruction of neural circuits from volume electron microscopy data requires the tracing of cells in their entirety, including all their neurites. Automated approaches have been developed for tracing, but their error rates are too high to generate reliable circuit diagrams without extensive human proofreading. We present flood-filling networks, a method for automated segmentation that, similar to most previous efforts, uses convolutional neural networks, but contains in addition a recurrent pathway that allows the iterative optimization and extension of individual neuronal processes. We used flood-filling networks to trace neurons in a dataset obtained by serial block-face electron microscopy of a zebra finch brain. Using our method, we achieved a mean error-free neurite path length of 1.1 mm, and we observed only four mergers in a test set with a path length of 97 mm. The performance of flood-filling networks was an order of magnitude better than that of previous approaches applied to this dataset, although with substantially increased computational costs. View details
    Preview abstract Algorithmic reconstruction of neurons from volume electron microscopy data traditionally requires training machine learning models on dataset-specific ground truth annotations that are expensive and tedious to acquire. We enhanced the training procedure of an unsupervised image-to-image translation method with additional components derived from an automated neuron segmentation approach. We show that this method, Segmentation-Enhanced CycleGAN, enables near perfect reconstruction accuracy on a benchmark connectomics segmentation dataset despite operating in a “zero-shot” setting in which only volumetric labels from a different volume imaging method were used. View details
    Preview abstract We introduce a new machine learning approach for image segmentation that uses a neural network to model the conditional energy of a segmentation given an image. Our approach, combinatorial energy learning for image segmentation (CELIS) places a particular emphasis on modeling the inherent combinatorial nature of dense image segmentation problems. We propose efficient algorithms for learning deep neural networks to model the energy function, and for local optimization of this energy in the space of supervoxel agglomerations. We extensively evaluate our method on a publicly available 3-D microscopy dataset with 25 billion voxels of ground truth data. On an 11 billion voxel test set, we find that our method improves volumetric reconstruction accuracy by more than 20% as compared to two state-of-the-art baseline methods: graph-based segmentation of the output of a 3-D convolutional neural network trained to predict boundaries, as well as a random forest classifier trained to agglomerate supervoxels that were generated by a 3-D convolutional neural network. View details
    Preview abstract State-of-the-art image segmentation algorithms generally consist of at least two successive and distinct computations: a boundary detection process that uses local image information to classify image locations as boundaries between objects, followed by a pixel grouping step such as watershed or connected components that clusters pixels into segments. Prior work has varied the complexity and approach employed in these two steps, including the incorporation of multi-layer neural networks to perform boundary prediction, and the use of global optimizations during pixel clustering. We propose a unified and end-to-end trainable machine learning approach, flood-filling networks, in which a recurrent 3d convolutional network directly produces individual segments from a raw image. The proposed approach robustly segments images with an unknown and variable number of objects as well as highly variable object sizes. We demonstrate the approach on a challenging 3d image segmentation task, connectomic reconstruction from volume electron microscopy data, on which flood-filling neural networks substantially improve accuracy over other state-of-the-art methods. The proposed approach can replace complex multi-step segmentation pipelines with a single neural network that is learned end-to-end. View details
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