Michał Januszewski
Authored Publications
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Connectome-driven neural inventory of a complete visual system
Harald Hess
C Shan Xu
Stuart Berg
Gary Huang
John Bogovic
Christopher Knecht
Christopher Ordish
Stephan Preibisch
Jan Funke
Ken Hayworth
Stephen Plaza
Shin-ya Takemura
Umayam Lowell
Stephan Saalfeld
William Katz
Gerry Rubin
Michael B. Reiser
Nature (2025)
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Vision provides animals with detailed information about their surroundings, conveying diverse features such as color, form, and movement across the visual scene. Computing these parallel spatial features requires a large and diverse network of neurons, such that in animals as distant as flies and humans, visual regions comprise half the brain’s volume. These visual brain regions often reveal remarkable structure-function relationships, with neurons organized along spatial maps with shapes that directly relate to their roles in visual processing. To unravel the stunning diversity of a complex visual system, a careful mapping of the neural architecture matched to tools for targeted exploration of that circuitry is essential. Here, we report a new connectome of the right optic lobe from a male Drosophila central nervous system FIB-SEM volume and a comprehensive inventory of the fly’s visual neurons. We developed a computational framework to quantify the anatomy of visual neurons, establishing a basis for interpreting how their shapes relate to spatial vision. By integrating this analysis with connectivity information, neurotransmitter identity, and expert curation, we classified the ~53,000 neurons into 727 types, about half of which are systematically described and named for the first time. Finally, we share an extensive collection of split-GAL4 lines matched to our neuron type catalog. Together, this comprehensive set of tools and data unlock new possibilities for systematic investigations of vision in Drosophila, a foundation for a deeper understanding of sensory processing.
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ZAPBench: a benchmark for whole-brain activity prediction in zebrafish
Alex Immer
Alex Bo-Yuan Chen
Mariela Petkova
Nirmala Iyer
Luuk Hesselink
Aparna Dev
Gudrun Ihrke
Woohyun Park
Alyson Petruncio
Aubrey Weigel
Wyatt Korff
Florian Engert
Jeff W. Lichtman
Misha Ahrens
International Conference on Learning Representations (ICLR) (2025)
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Data-driven benchmarks have led to significant progress in key scientific modeling domains including weather and structural biology. Here, we present the Zebrafish Activity Prediction Benchmark (ZAPBench), which quantitatively measures progress on the problem of predicting cellular-resolution neural activity throughout an entire vertebrate brain. The benchmark is based on a novel dataset containing 4d light-sheet microscopy recordings of more than 70,000 neurons in a larval zebrafish brain, along with motion stabilized and voxel-level cell segmentations of these data that facilitate development of a variety of forecasting methods. Initial results from a selection of time series and volumetric video modeling approaches achieve better performance than naive baseline methods, but also show room for further improvement. The specific brain used in the activity recording is also undergoing synaptic-level anatomical mapping, which will enable future integration of detailed structural information into ZAP forecasting methods.
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ZAPBench: A Benchmark for Whole-Brain Activity Prediction in Zebrafish
Alexander Immer
Alex Bo-Yuan Chen
Mariela D. Petkova
Nirmala A. Iyer
Luuk Willem Hesselink
Aparna Dev
Gudrun Ihrke
Woohyun Park
Alyson Petruncio
Aubrey Weigel
Wyatt Korff
Florian Engert
Jeff W. Lichtman
Misha B. Ahrens
International Conference on Learning Representations (ICLR) (2025)
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Data-driven benchmarks have led to significant progress in key scientific modeling domains including weather and structural biology. Here, we present the Zebrafish Activity Prediction Benchmark (ZAPBench), which quantitatively measures progress on the problem of predicting cellular-resolution neural activity throughout an entire vertebrate brain. The benchmark is based on a novel dataset containing 4d light-sheet microscopy recordings of more than 70,000 neurons in a larval zebrafish brain, along with motion stabilized and voxel-level cell segmentations of these data that facilitate development of a variety of forecasting methods. Initial results from a selection of time series and volumetric video modeling approaches achieve better performance than naive baseline methods, but also show room for further improvement. The specific brain used in the activity recording is also undergoing synaptic-level anatomical mapping, which will enable future integration of detailed structural information into ZAP forecasting methods.
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Light-microscopy-based dense connectomic reconstruction of mammalian brain tissue
Mojtaba R. Tavakoli
Julia Lyudchik
Vitali Vistunou
Nathalie Agudelo Duenas
Jakob Vorlaufer
Christoph Sommer
Caroline Kreuzinger
Barbara de Souza Oliveira
Alban Cenameri
Gaia Novarino
Johann Danzl
Nature (2025)
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The information-processing capability of the brain’s cellular network depends on the physical wiring pattern between neurons and their molecular and functional characteristics. Charting neurons and resolving the individual synaptic connections requires volumetric imaging at nanoscale resolution and comprehensive cellular contrast. Light microscopy is uniquely positioned to visualize specific molecules but dense, synapse-level circuit reconstruction by light microscopy has been out of reach due to limitations in resolution, contrast, and volumetric imaging capability. Here we developed light-microscopy based connectomics (LICONN). We integrated hydrogel embedding and expansion with comprehensive deep-learning based segmentation and analysis of connectivity, thus directly incorporating molecular information in synapse-level brain tissue reconstructions. LICONN will allow synapse-level brain tissue phenotyping in biological experiments in a readily adoptable manner.
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A petavoxel fragment of human cerebral cortex reconstructed at nanoscale resolution
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
Science (2024)
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To fully understand how the human brain works, knowledge of its structure at high resolution is needed. Presented here is a computationally intensive reconstruction of the ultrastructure of a cubic millimeter of human temporal cortex that was surgically removed to gain access to an underlying epileptic focus. It contains about 57,000 cells, about 230 millimeters of blood vessels, and about 150 million synapses and comprises 1.4 petabytes. Our analysis showed that glia outnumber neurons 2:1, oligodendrocytes were the most common cell, deep layer excitatory neurons could be classified on the basis of dendritic orientation, and among thousands of weak connections to each neuron, there exist rare powerful axonal inputs of up to 50 synapses. Further studies using this resource may bring valuable insights into the mysteries of the human brain.
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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)
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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.
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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, 19 (2022), 1357–1366
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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.
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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, 19 (2022), 1367–1370
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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.
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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, 608 (2022), 146–152
Denoising-based Image Compression for Connectomics
Alex Shapson-Coe
Richard L. Schalek
Johannes Ballé
Jeff W. Lichtman
bioRxiv (2021)
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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.
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