The Neurodata Without Borders ecosystem for neurophysiological data science

Rübel, Oliver; Tritt, Andrew; Ly, Ryan; Dichter, Benjamin; Ghosh, Samik; Niu, Lawerence; Baker, Pamela; Soltész, Iván; Ng, Lydia; Svoboda, Karel; Frank, L. Matthew; Bouchard, Kristofer E.

doi:10.7554/elife.78362

Cited by 62 publications

(52 citation statements)

References 45 publications

(74 reference statements)

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“…Code detailing all the analysis in this manuscript will be made available upon publication (https://github.com/denisecailab). Calcium imaging data used in this manuscript will be made available using the Neurodata Without Borders framework to seamlessly share data across institutions 72 . Statistical significance was assessed with two-tailed paired and unpaired t- tests, as well as one-way or two-way ANOVAs where appropriate.…”

Section: Miniscope Data Processing and Data Alignmentmentioning

confidence: 99%

Aversive experience drives offline ensemble reactivation to link memories across days

Zaki

Pennington

Morales-Rodriguez

et al. 2023

Preprint

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Memories are encoded in neural ensembles during learning and stabilized by post-learning reactivation. Integrating recent experiences into existing memories ensures that memories contain the most recently available information, but how neural ensembles accomplish this critical process remains unknown. Here we show that in mice, a strong aversive experience drives the offline ensemble reactivation of not only the recent aversive memory but also a neutral memory formed two days prior, spreading the fear from the recent aversive memory to the previous neutral memory. We find that fear specifically spreads retrospectively, but not prospectively, to neutral memories across days. Consistent with prior studies, we find reactivation of the recent aversive memory ensemble during the offline period following learning. However, a strong aversive experience also increases co-reactivation of the aversive and neutral memory ensembles during the offline period. Finally, inhibiting hippocampal reactivation during this offline period abolishes the spread of fear from the aversive experience to the neutral memory. Taken together, these results demonstrate that strong aversive experience can drive retrospective memory integration through the offline co-reactivation of recent memory ensembles with memory ensembles formed days prior, providing a neural mechanism by which memories can be integrated across days.

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Section: Miniscope Data Processing and Data Alignmentmentioning

confidence: 99%

Aversive experience drives offline ensemble reactivation to link memories across days

Zaki

Pennington

Morales-Rodriguez

et al. 2023

Preprint

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“…By combining microscopy, RNA sequencing, and electrophysiological recording for individual neurons, multi-modal datasets can be developed with the potential to reveal relationships between neuronal function, structure, and gene expression (1). Multi-modal single-cell datasets are increasingly available to researchers, in part due to efforts by the Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative to support the development and storage of such datasets in freely accessible repositories such as the Neuroscience Multi-Omic Archive (2) for genomic data and Distributed Archives for Neurophysiology Data Integration (3,4) for neurophysiology data, including electrophysiology. While multi-modal single-cell data offers great potential for improving understanding of brain organization and function, new methods are required for integration and analysis of the data (5).…”

Section: Introductionmentioning

confidence: 99%

MANGEM: a web app for Multimodal Analysis of Neuronal Gene expression, Electrophysiology and Morphology

Olson

Kalafut

Wang

2023

Preprint

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Single-cell techniques have enabled the acquisition of multi-modal data, particularly for neurons, to characterize cellular functions. Patch-seq, for example, combines patch-clamp recording, cell imaging, and single-cell RNA-seq to obtain electrophysiology, morphology, and gene expression data from a single neuron. While these multi-modal data offer potential insights into neuronal functions, they can be heterogeneous and noisy. To address this, machine-learning methods have been used to align cells from different modalities onto a low-dimensional latent space, revealing multi-modal cell clusters. However, the use of those methods can be challenging for biologists and neuroscientists without computational expertise and also requires suitable computing infrastructure for computationally expensive methods. To address these issues, we developed a cloud-based web application, MANGEM (Multimodal Analysis of Neuronal Gene expression, Electrophysiology, and Morphology) at https://ctc.waisman.wisc.edu/mangem. MANGEM provides a step-by-step accessible and user-friendly interface to machine-learning alignment methods of neuronal multi-modal data while enabling real-time visualization of characteristics of raw and aligned cells. It can be run asynchronously for large-scale data alignment, provides users with various downstream analyses of aligned cells and visualizes the analytic results such as identifying multi-modal cell clusters of cells and detecting correlated genes with electrophysiological and morphological features. We demonstrated the usage of MANGEM by aligning Patch-seq multimodal data of neuronal cells in the mouse visual cortex.

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“…Individual laboratories can release data compatible with the same API by 'uploading and forgetting' a zip of ONE files to a site such as FigShare, for users to download (instructions at https://github.com/int-brain-lab/ONE/ blob/main/docs/Open_Neurophysiology_Environment_Filename_ Convention.pdf). Users can also access data via Neurodata Without Borders 13,14 using software that translates from the ONE standard (https://github.com/catalystneuro/IBL-to-nwb; Supplementary Table 1), or through DataJoint 15 . A comparison of these and other sharing systems is in Supplementary Note 4.…”

mentioning

confidence: 99%

“…Because Alyx is accessed through http, this works despite different universities' diverse firewall policies, and allows monitoring, logging and restarting all preprocessing tasks. Higher-level analyses are automatically run on newly preprocessed data using DataJoint 13 , which runs automated analyses and places the results on a website, including summaries of behavioral performance, allowing scientists to monitor training progress, and basic analyses of spike trains. While manual curation of the full dataset will be required before public release, an illustrative curated…”

mentioning

confidence: 99%

A modular architecture for organizing, processing and sharing neurophysiology data

Acerbi¹,

Aguillon-Rodriguez²,

Ahmadi³

et al. 2023

Nat Methods

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Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

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The Neurodata Without Borders ecosystem for neurophysiological data science

Cited by 62 publications

References 45 publications

Aversive experience drives offline ensemble reactivation to link memories across days

Aversive experience drives offline ensemble reactivation to link memories across days

MANGEM: a web app for Multimodal Analysis of Neuronal Gene expression, Electrophysiology and Morphology

A modular architecture for organizing, processing and sharing neurophysiology data

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