{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T16:07:06Z","timestamp":1753891626243,"version":"3.41.2"},"reference-count":17,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,4,25]],"date-time":"2023-04-25T00:00:00Z","timestamp":1682380800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["U19 NS107464"],"award-info":[{"award-number":["U19 NS107464"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neuroinform."],"abstract":"<jats:p>Multiphoton calcium imaging is one of the most powerful tools in modern neuroscience. However, multiphoton data require significant pre-processing of images and post-processing of extracted signals. As a result, many algorithms and pipelines have been developed for the analysis of multiphoton data, particularly two-photon imaging data. Most current studies use one of several algorithms and pipelines that are published and publicly available, and add customized upstream and downstream analysis elements to fit the needs of individual researchers. The vast differences in algorithm choices, parameter settings, pipeline composition, and data sources combine to make collaboration difficult, and raise questions about the reproducibility and robustness of experimental results. We present our solution, called NeuroWRAP (<jats:ext-link>www.neurowrap.org<\/jats:ext-link>), which is a tool that wraps multiple published algorithms together, and enables integration of custom algorithms. It enables development of collaborative, shareable custom workflows and reproducible data analysis for multiphoton calcium imaging data enabling easy collaboration between researchers. NeuroWRAP implements an approach to evaluate the sensitivity and robustness of the configured pipelines. When this sensitivity analysis is applied to a crucial step of image analysis, cell segmentation, we find a substantial difference between two popular workflows, CaImAn and Suite2p. NeuroWRAP harnesses this difference by introducing consensus analysis, utilizing two workflows in conjunction to significantly increase the trustworthiness and robustness of cell segmentation results.<\/jats:p>","DOI":"10.3389\/fninf.2023.1082111","type":"journal-article","created":{"date-parts":[[2023,4,25]],"date-time":"2023-04-25T04:28:12Z","timestamp":1682396892000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["NeuroWRAP: integrating, validating, and sharing neurodata analysis workflows"],"prefix":"10.3389","volume":"17","author":[{"given":"Zac","family":"Bowen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gudjon","family":"Magnusson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Madeline","family":"Diep","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ujjwal","family":"Ayyangar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aleksandr","family":"Smirnov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patrick O.","family":"Kanold","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wolfgang","family":"Losert","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2023,4,25]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1038\/s41586-020-2314-9","article-title":"Variability in the analysis of a single neuroimaging dataset by many teams.","volume":"582","author":"Botvinik-Nezer","year":"2020","journal-title":"Nature"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1101\/2020.01.02.893198","article-title":"EZcalcium: Open source toolbox for analysis of calcium imaging data.","author":"Cantu","year":"2020","journal-title":"Biorxiv"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1016\/j.celrep.2022.110878","article-title":"Sequential transmission of task-relevant information in cortical neuronal networks.","volume":"39","author":"Francis","year":"2022","journal-title":"Cell Rep."},{"key":"B4","doi-asserted-by":"publisher","first-page":"885","DOI":"10.1016\/j.neuron.2018.01.019","article-title":"Small networks encode decision-making in primary auditory cortex.","volume":"97","author":"Francis","year":"2018","journal-title":"Neuron"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.38173","article-title":"CaImAn an open source tool for scalable calcium imaging data analysis.","volume":"8","author":"Giovannucci","year":"2019","journal-title":"Elife"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1101\/193383","article-title":"Onacid: Online analysis of calcium imaging data in real time.","author":"Giovannucci","year":"2017","journal-title":"Biorxiv"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2016.44","article-title":"The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments.","volume":"3","author":"Gorgolewski","year":"2016","journal-title":"Sci. Data"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2011.00013","article-title":"Nipype: A flexible, lightweight and extensible neuroimaging data processing framework in python.","volume":"5","author":"Gorgolewski","year":"2011","journal-title":"Front. Neuroinform."},{"key":"B9","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1364\/OL.33.000156","article-title":"Efficient subpixel image registration algorithms.","volume":"33","author":"Guizar-Sicairos","year":"2008","journal-title":"Opt. Lett."},{"key":"B10","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1007\/s10827-018-0702-z","article-title":"Replicability or reproducibility? On the replication crisis in computational neuroscience and sharing only relevant detail.","volume":"45","author":"Mi\u0142kowski","year":"2018","journal-title":"J. Comput. Neurosci."},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1101\/061507","article-title":"Suite2p: Beyond 10,000 neurons with standard two-photon microscopy.","author":"Pachitariu","year":"2017","journal-title":"Biorxiv"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2017.00076","article-title":"Reproducibility vs. replicability: A brief history of a confused terminology.","volume":"11","author":"Plesser","year":"2018","journal-title":"Front. Neuroinform."},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1101\/2021.03.13.435173","article-title":"The neurodata without borders ecosystem for neurophysiological data science.","author":"R\u00fcbel","year":"2021","journal-title":"Biorxiv"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-29180-0","article-title":"A deep-learning approach for online cell identification and trace extraction in functional two-photon calcium imaging","volume":"13","author":"Sit\u00e1","year":"2022","journal-title":"Nat. Commun"},{"key":"B15","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1016\/j.neuron.2015.10.025","article-title":"Neurodata without borders: Creating a common data format for neurophysiology.","volume":"88","author":"Teeters","year":"2015","journal-title":"Neuron"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1006064","article-title":"Porcupine: A visual pipeline tool for neuroimaging analysis.","volume":"14","author":"van Mourik","year":"2018","journal-title":"PLoS Comput. Biol."},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.28728","article-title":"Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data.","volume":"7","author":"Zhou","year":"2018","journal-title":"Elife"}],"container-title":["Frontiers in Neuroinformatics"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fninf.2023.1082111\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,25]],"date-time":"2023-04-25T04:28:16Z","timestamp":1682396896000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fninf.2023.1082111\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,25]]},"references-count":17,"alternative-id":["10.3389\/fninf.2023.1082111"],"URL":"https:\/\/doi.org\/10.3389\/fninf.2023.1082111","relation":{},"ISSN":["1662-5196"],"issn-type":[{"type":"electronic","value":"1662-5196"}],"subject":[],"published":{"date-parts":[[2023,4,25]]},"article-number":"1082111"}}