publications

2025

1. AdvSci

   Structural Diversity of Mitochondria in the Neuromuscular System across Development Revealed by 3D Electron Microscopy

   J. Alexander Bae, Myung-kyu Choi , Soungyub Ahn , Gwanho Ko , Daniel T. Choe , Hyunsoo Yim , Ken C. Nguyen , Jinseop S. Kim , David H. Hall , and Junho Lee

   Advanced Science, May 2025

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   Abstract As an animal matures, its neural circuit undergoes alterations, yet the developmental changes in intracellular organelles to facilitate these changes is less understood. Using 3D electron microscopy and deep learning, the study develops semi-automated methods for reconstructing mitochondria in C. elegans and collected mitochondria reconstructions from normal reproductive stages and dauer, enabling comparative study on mitochondria structure within the neuromuscular system. It is found that various mitochondria structural properties in neurons correlate with synaptic connections and these properties are preserved across development in different neural circuits. To test the necessity of these universal mitochondria properties, the study examines the behavior in drp-1 mutants with impaired mitochondria fission and discovers that it causes behavioral deficits. Moreover, it is observed that dauer neurons display distinctive mitochondrial features, and mitochondria in dauer muscles exhibit unique reticulum-like structure. It is proposed that these specialized mitochondria structures may serve as an adaptive mechanism to support stage-specific behavioral and physiological needs.

2. Nature

   Functional connectomics spanning multiple areas of mouse visual cortex

   The MICrONS Consortium , J. Alexander Bae, Mahaly Baptiste , Maya R. Baptiste , Caitlyn A. Bishop , Agnes L. Bodor , Derrick Brittain , Victoria Brooks , JoAnn Buchanan , Daniel J. Bumbarger , and 87 more authors

   Nature, Apr 2025

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   Understanding the brain requires understanding neurons’ functional responses to the circuit architecture shaping them. Here we introduce the MICrONS functional connectomics dataset with dense calcium imaging of around 75,000 neurons in primary visual cortex (VISp) and higher visual areas (VISrl, VISal and VISlm) in an awake mouse that is viewing natural and synthetic stimuli. These data are co-registered with an electron microscopy reconstruction containing more than 200,000 cells and 0.5 billion synapses. Proofreading of a subset of neurons yielded reconstructions that include complete dendritic trees as well the local and inter-areal axonal projections that map up to thousands of cell-to-cell connections per neuron. Released as an open-access resource, this dataset includes the tools for data retrieval and analysis1,2. Accompanying studies describe its use for comprehensive characterization of cell types3–6, a synaptic level connectivity diagram of a cortical column4, and uncovering cell-type-specific inhibitory connectivity that can be linked to gene expression data4,7. Functionally, we identify new computational principles of how information is integrated across visual space8, characterize novel types of neuronal invariances9 and bring structure and function together to uncover a general principle for connectivity between excitatory neurons within and across areas10,11.

3. Nature

   Perisomatic ultrastructure efficiently classifies cells in mouse cortex

   Leila Elabbady , Sharmishtaa Seshamani , Shang Mu , Gayathri Mahalingam , Casey M. Schneider-Mizell , Agnes L. Bodor , J. Alexander Bae, Derrick Brittain , JoAnn Buchanan , Daniel J. Bumbarger , and 28 more authors

   Nature, Apr 2025

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   Mammalian neocortex contains a highly diverse set of cell types. These cell types have been mapped systematically using a variety of molecular, electrophysiological and morphological approaches1–4. Each modality offers new perspectives on the variation of biological processes underlying cell-type specialization. Cellular-scale electron microscopy provides dense ultrastructural examination and an unbiased perspective on the subcellular organization of brain cells, including their synaptic connectivity and nanometre-scale morphology. In data that contain tens of thousands of neurons, most of which have incomplete reconstructions, identifying cell types becomes a clear challenge for analysis5. Here, to address this challenge, we present a systematic survey of the somatic region of all cells in a cubic millimetre of cortex using quantitative features obtained from electron microscopy. This analysis demonstrates that the perisomatic region is sufficient to identify cell types, including types defined primarily on the basis of their connectivity patterns. We then describe how this classification facilitates cell-type-specific connectivity characterization and locating cells with rare connectivity patterns in the dataset.

4. Nature

   NEURD offers automated proofreading and feature extraction for connectomics

   Brendan Celii , Stelios Papadopoulos , Zhuokun Ding , Paul G. Fahey , Eric Wang , Christos Papadopoulos , Alexander B. Kunin , Saumil Patel , J. Alexander Bae, Agnes L. Bodor , and 48 more authors

   Nature, Apr 2025

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   We are in the era of millimetre-scale electron microscopy volumes collected at nanometre resolution1,2. Dense reconstruction of cellular compartments in these electron microscopy volumes has been enabled by recent advances in machine learning3–6. Automated segmentation methods produce exceptionally accurate reconstructions of cells, but post hoc proofreading is still required to generate large connectomes that are free of merge and split errors. The elaborate 3D meshes of neurons in these volumes contain detailed morphological information at multiple scales, from the diameter, shape and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting these features can require substantial effort to piece together existing tools into custom workflows. Here, building on existing open source software for mesh manipulation, we present Neural Decomposition (NEURD), a software package that decomposes meshed neurons into compact and extensively annotated graph representations. With these feature-rich graphs, we automate a variety of tasks such as state-of-the-art automated proofreading of merge errors, cell classification, spine detection, axonal-dendritic proximities and other annotations. These features enable many downstream analyses of neural morphology and connectivity, making these massive and complex datasets more accessible to neuroscience researchers.

5. Nature

   Inhibitory specificity from a connectomic census of mouse visual cortex

   Casey M. Schneider-Mizell , Agnes L. Bodor , Derrick Brittain , JoAnn Buchanan , Daniel J. Bumbarger , Leila Elabbady , Clare Gamlin , Daniel Kapner , Sam Kinn , Gayathri Mahalingam , and 31 more authors

   Nature, Apr 2025

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   Mammalian cortex features a vast diversity of neuronal cell types, each with characteristic anatomical, molecular and functional properties1. Synaptic connectivity shapes how each cell type participates in the cortical circuit, but mapping connectivity rules at the resolution of distinct cell types remains difficult. Here we used millimetre-scale volumetric electron microscopy2 to investigate the connectivity of all inhibitory neurons across a densely segmented neuronal population of 1,352 cells spanning all layers of mouse visual cortex, producing a wiring diagram of inhibition with more than 70,000 synapses. Inspired by classical neuroanatomy, we classified inhibitory neurons based on targeting of dendritic compartments and developed an excitatory neuron classification based on dendritic reconstructions with whole-cell maps of synaptic input. Single-cell connectivity showed a class of disinhibitory specialist that targets basket cells. Analysis of inhibitory connectivity onto excitatory neurons found widespread specificity, with many interneurons exhibiting differential targeting of spatially intermingled subpopulations. Inhibitory targeting was organized into ‘motif groups’, diverse sets of cells that collectively target both perisomatic and dendritic compartments of the same excitatory targets. Collectively, our analysis identified new organizing principles for cortical inhibition and will serve as a foundation for linking contemporary multimodal neuronal atlases with the cortical wiring diagram.

6. Nature

   Functional connectomics reveals general wiring rule in mouse visual cortex

   Zhuokun Ding , Paul G. Fahey , Stelios Papadopoulos , Eric Y. Wang , Brendan Celii , Christos Papadopoulos , Andersen Chang , Alexander B. Kunin , Dat Tran , Jiakun Fu , and 53 more authors

   Nature, Apr 2025

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   Understanding the relationship between circuit connectivity and function is crucial for uncovering how the brain computes. In mouse primary visual cortex, excitatory neurons with similar response properties are more likely to be synaptically connected1–8; however, broader connectivity rules remain unknown. Here we leverage the millimetre-scale MICrONS dataset to analyse synaptic connectivity and functional properties of neurons across cortical layers and areas. Our results reveal that neurons with similar response properties are preferentially connected within and across layers and areas—including feedback connections—supporting the universality of ‘like-to-like’ connectivity across the visual hierarchy. Using a validated digital twin model, we separated neuronal tuning into feature (what neurons respond to) and spatial (receptive field location) components. We found that only the feature component predicts fine-scale synaptic connections beyond what could be explained by the proximity of axons and dendrites. We also discovered a higher-order rule whereby postsynaptic neuron cohorts downstream of presynaptic cells show greater functional similarity than predicted by a pairwise like-to-like rule. Recurrent neural networks trained on a simple classification task develop connectivity patterns that mirror both pairwise and higher-order rules, with magnitudes similar to those in MICrONS data. Ablation studies in these recurrent neural networks reveal that disrupting like-to-like connections impairs performance more than disrupting random connections. These findings suggest that these connectivity principles may have a functional role in sensory processing and learning, highlighting shared principles between biological and artificial systems.

7. Nature

   Connectomics of predicted Sst transcriptomic types in mouse visual cortex

   Clare R. Gamlin , Casey M. Schneider-Mizell , Matthew Mallory , Leila Elabbady , Nathan Gouwens , Grace Williams , Alice Mukora , Rachel Dalley , Agnes L. Bodor , Derrick Brittain , and 41 more authors

   Nature, Apr 2025

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   Neural circuit function is shaped both by the cell types that comprise the circuit and the connections between them1. Neural cell types have previously been defined by morphology2,3, electrophysiology4, transcriptomic expression5,6, connectivity7–9 or a combination of such modalities10–12. The Patch-seq technique enables the characterization of morphology, electrophysiology and transcriptomic properties from individual cells13–15. These properties were integrated to define 28 inhibitory, morpho-electric-transcriptomic (MET) cell types in mouse visual cortex16, which do not include synaptic connectivity. Conversely, large-scale electron microscopy (EM) enables morphological reconstruction and a near-complete description of a neuron’s local synaptic connectivity, but does not include transcriptomic or electrophysiological information. Here, we leveraged morphological information from Patch-seq to predict the transcriptomically defined cell subclass and/or MET-type of inhibitory neurons within a large-scale EM dataset. We further analysed Martinotti cells—a somatostatin (Sst)-positive17 morphological cell type18,19—which were classified successfully into Sst MET-types with distinct axon myelination and synaptic output connectivity patterns. We demonstrate that morphological features can be used to link cell types across experimental modalities, enabling further comparison of connectivity to gene expression and electrophysiology. We observe unique connectivity rules for predicted Sst cell types.

8. NatMet

   CAVE: Connectome Annotation Versioning Engine

   Sven Dorkenwald , Casey M. Schneider-Mizell , Derrick Brittain , Akhilesh Halageri , Chris Jordan , Nico Kemnitz , Manual A. Castro , William Silversmith , Jeremy Maitin-Shephard , Jakob Troidl , and 32 more authors

   Nature Methods, May 2025

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   Advances in electron microscopy, image segmentation and computational infrastructure have given rise to large-scale and richly annotated connectomic datasets, which are increasingly shared across communities. To enable collaboration, users need to be able to concurrently create annotations and correct errors in the automated segmentation by proofreading. In large datasets, every proofreading edit relabels cell identities of millions of voxels and thousands of annotations like synapses. For analysis, users require immediate and reproducible access to this changing and expanding data landscape. Here we present the Connectome Annotation Versioning Engine (CAVE), a computational infrastructure that provides scalable solutions for proofreading and flexible annotation support for fast analysis queries at arbitrary time points. Deployed as a suite of web services, CAVE empowers distributed communities to perform reproducible connectome analysis in up to petascale datasets (~1\thinspacemm3) while proofreading and annotating is ongoing.

9. NatComm

   An unsupervised map of excitatory neuron dendritic morphology in the mouse visual cortex

   Marissa A. Weis , Stelios Papadopoulos , Laura Hansel , Timo Lüddecke , Brendan Celii , Paul G. Fahey , Eric Y. Wang , J. Alexander Bae, Agnes L. Bodor , Derrick Brittain , and 38 more authors

   Nature Communications, Apr 2025

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   Neurons in the neocortex exhibit astonishing morphological diversity, which is critical for properly wiring neural circuits and giving neurons their functional properties. However, the organizational principles underlying this morphological diversity remain an open question. Here, we took a data-driven approach using graph-based machine learning methods to obtain a low-dimensional morphological “bar code” describing more than 30,000 excitatory neurons in mouse visual areas V1, AL, and RL that were reconstructed from the millimeter scale MICrONS serial-section electron microscopy volume. Contrary to previous classifications into discrete morphological types (m-types), our data-driven approach suggests that the morphological landscape of cortical excitatory neurons is better described as a continuum, with a few notable exceptions in layers 5 and 6. Dendritic morphologies in layers 2–3 exhibited a trend towards a decreasing width of the dendritic arbor and a smaller tuft with increasing cortical depth. Inter-area differences were most evident in layer 4, where V1 contained more atufted neurons than higher visual areas. Moreover, we discovered neurons in V1 on the border to layer 5, which avoided deeper layers with their dendrites. In summary, we suggest that excitatory neurons’ morphological diversity is better understood by considering axes of variation than using distinct m-types.

2024

1. NatComm

   Comparative connectomics of dauer reveals developmental plasticity

   Hyunsoo Yim* , Daniel T Choe* , J Alexander Bae*, Myung-Kyu Choi , Hae-Mook Kang , Ken C Q Nguyen , Soungyub Ahn , Sang-Kyu Bahn , Heeseung Yang , David H Hall , and 2 more authors

   Nature Communications, Apr 2024

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   A fundamental question in neurodevelopmental biology is how flexibly the nervous system changes during development. To address this, we reconstructed the chemical connectome of dauer, an alternative developmental stage of nematodes with distinct behavioral characteristics, by volumetric reconstruction and automated synapse detection using deep learning. With the basic architecture of the nervous system preserved, structural changes in neurons, large or small, were closely associated with connectivity changes, which in turn evoked dauer-specific behaviors such as nictation. Graph theoretical analyses revealed significant dauer-specific rewiring of sensory neuron connectivity and increased clustering within motor neurons in the dauer connectome. We suggest that the nervous system in the nematode has evolved to respond to harsh environments by developing a quantitatively and qualitatively differentiated connectome.

2. NatComm

   Petascale pipeline for precise alignment of images from serial section electron microscopy

   Sergiy Popovych , Thomas Macrina , Nico Kemnitz , Manuel Castro , Barak Nehoran , Zhen Jia , J Alexander Bae, Eric Mitchell , Shang Mu , Eric T Trautman , and 3 more authors

   Nature Communications, Jan 2024

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   The reconstruction of neural circuits from serial section electron microscopy (ssEM) images is being accelerated by automatic image segmentation methods. Segmentation accuracy is often limited by the preceding step of aligning 2D section images to create a 3D image stack. Precise and robust alignment in the presence of image artifacts is challenging, especially as datasets are attaining the petascale. We present a computational pipeline for aligning ssEM images with several key elements. Self-supervised convolutional nets are trained via metric learning to encode and align image pairs, and they are used to initialize iterative fine-tuning of alignment. A procedure called vector voting increases robustness to image artifacts or missing image data. For speedup the series is divided into blocks that are distributed to computational workers for alignment. The blocks are aligned to each other by composing transformations with decay, which achieves a global alignment without resorting to a time-consuming global optimization. We apply our pipeline to a whole fly brain dataset, and show improved accuracy relative to prior state of the art. We also demonstrate that our pipeline scales to a cubic millimeter of mouse visual cortex. Our pipeline is publicly available through two open source Python packages.

3. Nature

   Neuronal wiring diagram of an adult brain

   Sven Dorkenwald , Arie Matsliah , Amy R Sterling , Philipp Schlegel , Szi-Chieh Yu , Claire E McKellar , Albert Lin , Marta Costa , Katharina Eichler , Yijie Yin , and 37 more authors

   Nature, Oct 2024

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   Connections between neurons can be mapped by acquiring and analysing electron microscopic brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative1–6, but nevertheless inadequate for understanding brain function more globally. Here we present a neuronal wiring diagram of a whole brain containing 5 \times 107 chemical synapses7 between 139,255 neurons reconstructed from an adult female Drosophila melanogaster8,9. The resource also incorporates annotations of cell classes and types, nerves, hemilineages and predictions of neurotransmitter identities10–12. Data products are available for download, programmatic access and interactive browsing and have been made interoperable with other fly data resources. We derive a projectome—a map of projections between regions—from the connectome and report on tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine and descending neurons) across both hemispheres and between the central brain and the optic lobes. Tracing from a subset of photoreceptors to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviours. The technologies and open ecosystem reported here set the stage for future large-scale connectome projects in other species.

2022

1. Cell

   Reconstruction of neocortex: Organelles, compartments, cells, circuits, and activity

   Nicholas L. Turner* , Thomas Macrina* , J. Alexander Bae*, Runzhe Yang* , Alyssa M. Wilson* , Casey Schneider-Mizell* , Kisuk Lee* , Ran Lu* , Jingpeng Wu* , Agnes L. Bodor* , and 35 more authors

   Cell, Oct 2022

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   We assembled a semi-automated reconstruction of L2/3 mouse primary visual cortex from ∼250 × 140 × 90 μm3 of electron microscopic images, including pyramidal and non-pyramidal neurons, astrocytes, microglia, oligodendrocytes and precursors, pericytes, vasculature, nuclei, mitochondria, and synapses. Visual responses of a subset of pyramidal cells are included. The data are publicly available, along with tools for programmatic and three-dimensional interactive access. Brief vignettes illustrate the breadth of potential applications relating structure to function in cortical circuits and neuronal cell biology. Mitochondria and synapse organization are characterized as a function of path length from the soma. Pyramidal connectivity motif frequencies are predicted accurately using a configuration model of random graphs. Pyramidal cells receiving more connections from nearby cells exhibit stronger and more reliable visual responses. Sample code shows data access and analysis.

2. NatMet

   FlyWire: online community for whole-brain connectomics

   Sven Dorkenwald , Claire E McKellar , Thomas Macrina , Nico Kemnitz , Kisuk Lee , Ran Lu , Jingpeng Wu , Sergiy Popovych , Eric Mitchell , Barak Nehoran , and 33 more authors

   Nature Methods, Oct 2022

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   Due to advances in automated image acquisition and analysis, whole-brain connectomes with 100,000 or more neurons are on the horizon. Proofreading of whole-brain automated reconstructions will require many person-years of effort, due to the huge volumes of data involved. Here we present FlyWire, an online community for proofreading neural circuits in a Drosophila melanogaster brain and explain how its computational and social structures are organized to scale up to whole-brain connectomics. Browser-based three-dimensional interactive segmentation by collaborative editing of a spatially chunked supervoxel graph makes it possible to distribute proofreading to individuals located virtually anywhere in the world. Information in the edit history is programmatically accessible for a variety of uses such as estimating proofreading accuracy or building incentive systems. An open community accelerates proofreading by recruiting more participants and accelerates scientific discovery by requiring information sharing. We demonstrate how FlyWire enables circuit analysis by reconstructing and analyzing the connectome of mechanosensory neurons.

3. FrontNeuroinf

   RealNeuralNetworks.jl: An Integrated Julia Package for Skeletonization, Morphological Analysis, and Synaptic Connectivity Analysis of Terabyte-Scale 3D Neural Segmentations

   Jingpeng Wu , Nicholas Turner , J Alexander Bae, Ashwin Vishwanathan , and H Sebastian Seung

   Front. Neuroinform., Mar 2022

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   Benefiting from the rapid development of electron microscopy imaging and deep learning technologies, an increasing number of brain image datasets with segmentation and synapse detection are published. Most of the automated segmentation methods label voxels rather than producing neuron skeletons directly. A further skeletonization step is necessary for quantitative morphological analysis. Currently, several tools are published for skeletonization as well as morphological and synaptic connectivity analysis using different computer languages and environments. Recently the Julia programming language, notable for elegant syntax and high performance, has gained rapid adoption in the scientific computing community. Here, we present a Julia package, called RealNeuralNetworks.jl, for efficient sparse skeletonization, morphological analysis, and synaptic connectivity analysis. Based on a large-scale Zebrafish segmentation dataset, we illustrate the software features by performing distributed skeletonization in Google Cloud, clustering the neurons using the NBLAST algorithm, combining morphological similarity and synaptic connectivity to study their relationship. We demonstrate that RealNeuralNetworks.jl is suitable for use in terabyte-scale electron microscopy image segmentation datasets.

2021

1. MICCAI

   AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions

   Donglai Wei , Kisuk Lee , Hanyu Li , Ran Lu , J. Alexander Bae, Zequan Liu , Lifu Zhang , Márcia Santos , Zudi Lin , Thomas Uram , and 6 more authors

   In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 , Mar 2021

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   Electron microscopy (EM) enables the reconstruction of neural circuits at the level of individual synapses, which has been transformative for scientific discoveries. However, due to the complex morphology, an accurate reconstruction of cortical axons has become a major challenge. Worse still, there is no publicly available large-scale EM dataset from the cortex that provides dense ground truth segmentation for axons, making it difficult to develop and evaluate large-scale axon reconstruction methods. To address this, we introduce the AxonEM dataset, which consists of two 30 x 30 x 30 μm3 EM image volumes from the human and mouse cortex, respectively. We thoroughly proofread over 18,000 axon instances to provide dense 3D axon instance segmentation, enabling large-scale evaluation of axon reconstruction methods. In addition, we densely annotate nine ground truth subvolumes for training, per each data volume. With this, we reproduce two published state-of-the-art methods and provide their evaluation results as a baseline. We publicly release our code and data at https://connectomics-bazaar.github.io/proj/AxonEM/index.html to foster the development of advanced methods.

2020

1. ISBI

   Caesar: Segment-Wise Alignment Method for Solving Discontinuous Deformations

   S Popovych , J Alexander Bae, and H S Seung

   2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Mar 2020

2018

1. Cell

   Digital Museum of Retinal Ganglion Cells with Dense Anatomy and Physiology

   J. Alexander Bae*, Shang Mu* , Jinseop S. Kim* , Nicholas L. Turner* , Ignacio Tartavull , Nico Kemnitz , Chris S. Jordan , Alex D. Norton , William M. Silversmith , Rachel Prentki , and 8 more authors

   Cell, Mar 2018

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   When 3D electron microscopy and calcium imaging are used to investigate the structure and function of neural circuits, the resulting datasets pose new challenges of visualization and interpretation. Here, we present a new kind of digital resource that encompasses almost 400 ganglion cells from a single patch of mouse retina. An online "museum" provides a 3D interactive view of each cell’s anatomy, as well as graphs of its visual responses. The resource reveals two aspects of the retina’s inner plexiform layer: an arbor segregation principle governing structure along the light axis and a density conservation principle governing structure in the tangential plane. Structure is related to visual function; ganglion cells with arbors near the layer of ganglion cell somas are more sustained in their visual responses on average. Our methods are potentially applicable to dense maps of neuronal anatomy and physiology in other parts of the nervous system.