An Integrated Toolkit for Extensible and Reproducible Neuroscience

Matelsky, Jordan; Rodríguez, Luis M.; Xenes, Daniel; Gion, Timothy; Hider, Robert; Wester, Brock A.; Gray-Roncal, William

doi:10.1109/embc46164.2021.9630199

Cited by 5 publications

(4 citation statements)

References 22 publications

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“…We wrote an experiment generator script that output configuration files for image data blocks in JSON format. These configuration files contained the coordinates and specifications for pulling data blocks from BossDB using the intern Python library [25]. Our model was then run on each data block to obtain bounding box results ( Fig.…”

Section: Methodsmentioning

confidence: 99%

In search of a brain microbiome: A machine learning search pipeline for electron microscopy images of brain tissue

Matelsky

Shih

Hijazi

et al. 2022

Preprint

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The healthy human brain has long been considered a sterile environment, with the blood brain barrier preventing the formation of a bacterial brain microbiome. Recent electron microscopy (EM) imaging of brain tissue has, however, provided the first preliminary evidence of bacteria in otherwise healthy brain slices. Whether due to contamination, disease, or a previously unknown relationship of bacteria to healthy brain tissue, novel tools are needed to detect and search for bacteria in nanoscale, volumetric EM images. While computer vision tools are widely used in cell segmentation and object detection problems in EM imaging, no bacteria detection tool or dataset exists. Overcoming the rarity of training data, this work presents the first pipeline for training a bacteria detection network for EM images, leveraging existing deep networks for object detection. A deployment and proofreading pipeline is presented, along with characterization of deployment to public EM image datasets. While bacteria in healthy brain tissue were not discovered in this work, this tool presents an opportunity for large scale bacteria search in EM imaging for both scientific discovery and experimental quality control, and serves more generally as a framework for sparse object detection in large imagery datasets.

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Section: Methodsmentioning

confidence: 99%

In search of a brain microbiome: A machine learning search pipeline for electron microscopy images of brain tissue

Matelsky

Shih

Hijazi

et al. 2022

Preprint

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“…For normal reproductive stages, we used electron microscopy (EM) images published by Witviet et al (Witvliet et al 2021) (L1: Dataset 2, L2: Dataset 5, L3: Dataset 6, Adult: Dataset 8). The raw images were acquired from Brain Observatory Storage Service and Database (BossDB) using intern library (Hider et al 2022;Matelsky et al 2021). For dauer stage, same EM dataset from Yim et al (Yim et al 2024) was used.…”

Section: Author Contributionsmentioning

confidence: 99%

“…For normal reproductive stages, we used volumetrically segmented cell reconstructions published by (Witvliet et al 2021) (L1: Dataset 2, L2: Dataset 5, L3: Dataset 6, Adult: Dataset 8). The segmentation images were acquired from BossDB using intern library (Hider et al 2022;Matelsky et al 2021). For body wall muscle segmentation, an annotator painted body wall muscles in every 8th section.…”

Section: Author Contributionsmentioning

confidence: 99%

Structural diversity of mitochondria in the neuromuscular system across development

Bae,

Choi,

Ahn

et al. 2024

Preprint

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As an animal matures, its neural circuit undergoes alterations, leading to changes in both neuronal morphology and the connectivity between neurons. However, less is known about the mitochondrial structure changes across development to facilitate these changes, while mitochondria are highly dynamic organelles related to neuronal development. Here, we attempt to answer this question with a model organismC. elegansusing 3D electron microscopy (EM). We developed semi-automated methods for reconstructing mitochondria inC. elegansEM images using deep learning. Consequently, we collected mitochondria reconstructions from normal reproductive stages and dauer, enabling comparative study on mitochondrial morphology and spatial organization within the neuromuscular system across different stages. We have identified that the mitochondria structural properties in neurons are correlated with synaptic properties. Neuronal compartments have distinct roles, leading axonal mitochondria to differ morphologically from dendritic mitochondria. We tested this by analyzing behavior in animals with a mutation indrp-1, required for proper mitochondrial fission, confirming that compartment-specific mitochondrial morphology is vital for effective functioning of synapses. Given that these functions are essential throughout development, the structural properties of mitochondria are preserved across development. We report that dauer inter- and motor neurons, predominantly cholinergic and glutamatergic, show distinctive mitochondrial structure and increased mitochondria density. In addition, mitochondria in dauer body wall muscles exhibit distinctive reticulum-like structure. We propose that the stage-specific mitochondrial structure observed inC. elegansdauer may constitute an adaptive mechanism to support stage-specific behavioral and physiological characteristics.

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“…In addition, they do not disclose every step of the processing pipeline, such as spike sorting, which can significantly affect the results (Magland et al, 2020). Some of the technologies they use, such as cloud computing services (e.g., CodeOcean) and sophisticated databases and APIs (Hider et al, 2022; Matelsky et al, 2021; Sanchez et al, 2022), can be cost-prohibitive or require specialized software engineering expertise that is beyond the reach of most labs. Furthermore, many of these existing efforts are focused on the needs of specific projects, data types, and behavioral paradigms, limiting their scope.…”

Section: Introductionmentioning

confidence: 99%

Spyglass: a framework for reproducible and shareable neuroscience research

Lee,

Denovellis,

et al. 2024

Preprint

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Sharing data and reproducing scientific results are essential for progress in neuroscience, but the community lacks the tools to do this easily for large datasets and results obtained from intricate, multi-step analysis procedures. To address this issue, we created Spyglass, an open-source software framework designed to promote the shareability and reproducibility of data analysis in neuroscience. Spyglass integrates standardized formats with reliable open-source tools, offering a comprehensive solution for managing neurophysiological and behavioral data. It provides well-defined and reproducible pipelines for analyzing electrophysiology data, including core functions like spike sorting. In addition, Spyglass simplifies collaboration by enabling the sharing of final and intermediate results across custom, complex, multi-step pipelines as well as web-based visualizations. Here we demonstrate these features and showcase the potential of Spyglass to enable findable, accessible, interoperable, and reusable (FAIR) data management and analysis by applying advanced state space decoding algorithms to publicly available data from multiple laboratories.

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An Integrated Toolkit for Extensible and Reproducible Neuroscience

Cited by 5 publications

References 22 publications

In search of a brain microbiome: A machine learning search pipeline for electron microscopy images of brain tissue

In search of a brain microbiome: A machine learning search pipeline for electron microscopy images of brain tissue

Structural diversity of mitochondria in the neuromuscular system across development

Spyglass: a framework for reproducible and shareable neuroscience research

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