Brock A. Wester

The paper introduces a method to estimate proofreading progress in connectomic datasets using global graph invariants, enabling optimal stopping points for reconstruction quality.

The paper maps physical cell-cell contacts in mouse visual cortex, revealing a dense contactome that exceeds synaptic connectivity and highlights the role of astrocytes in shaping the neural microenvironment.

SmartEM accelerates electron microscopy image acquisition by using machine learning to intelligently allocate imaging time, achieving up to 7-fold speedup in connectomic samples.

The paper maps convergent synaptic motifs in the Drosophila Mushroom Body using connectome data, revealing their conserved architecture and potential role in associative learning.

NEURD automates proofreading and feature extraction from connectomics data to improve accuracy and accessibility of neural morphology analyses.

The paper presents a functional connectomics dataset of mouse visual cortex, combining calcium imaging with EM reconstruction to study neural circuits and their connectivity.

This paper introduces a free, browser-based tool for automated segmentation of synchrotron-scanned fossils, significantly reducing time and cost compared to manual methods.

EM-Compressor uses Variational Autoencoders to compress EM images for connectomics, reducing data size to 1/128th while preserving features needed for segmentation.

This paper uses a generative AI model to improve 3D tissue imaging by interpolating and restoring images, enhancing resolution and reducing artifacts across multiple modalities and species.

Vimo enables interactive visualization of neuronal connectivity motifs in large brain networks, allowing experts to sketch and analyze motif instances and chains in 3D.

This paper introduces a semi-automated pipeline for detecting mesh errors in neuronal EM data, improving the accuracy of connectomic reconstructions by highlighting potential inaccuracies near neuronal tips.

This paper introduces a motif discovery method for biological networks that identifies statistically significant subgraphs from single network examples, addressing challenges in computational cost and statistical significance testing.

The paper shows that mitotic lineage improves predictions of neural connections in C. elegans beyond cell type alone.

The paper provides a guide to the BRAIN Initiative Cell Census Network (BICCN) data ecosystem, its tools, and resources for analyzing brain cell types across species.

NeuVue is a framework for managing and prioritizing proofreading tasks in connectomic datasets, enabling scalable and coordinated error correction by over forty proofreaders.

The paper introduces a software framework for comparative connectomics to analyze large-scale, diverse brain connectome datasets efficiently.

DotMotif is an open-source tool that enables efficient subgraph isomorphism search and graph queries in connectomics data, demonstrated on simulated and real datasets.

The paper introduces a toolkit called intern to unify computational backends and abstract computer science challenges in neuroscience data analysis.

The paper introduces a data-visualization framework and Python library to simplify collaboration and visualization of complex datasets across research teams.

The paper introduces a cloud-native service for managing large-scale neuroscience datasets, enabling scalable storage, visualization, and sharing through an API and web interface.

The paper develops a scalable, reproducible framework for processing large neuroimaging datasets using open-source tools and standardized pipelines.