Evidence map›Paper›PMID 31653867›Full record

ArticleScientific data2019

Integrated open-source software for multiscale electrophysiology.

Konstantinos Nasiotis, Martin Cousineau, François Tadel, Adrien Peyrache, Richard M Leahy, Christopher C Pack, Sylvain Baillet

Abstract read
In one paragraph

Article in Scientific data, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Konstantinos NasiotisMontreal Neurological Institute, McGill University, Montreal, QC, Canada. konstantinos.nasiotis@mail.mcgill.ca.ORCID http://orcid.org/0000-0002-4083-2608
Martin CousineauMontreal Neurological Institute, McGill University, Montreal, QC, Canada.
François TadelGrenoble Institute of Neuroscience, Grenoble, France.
Adrien PeyracheMontreal Neurological Institute, McGill University, Montreal, QC, Canada.ORCID http://orcid.org/0000-0001-9708-309X
Richard M LeahySignal & Image Processing Institute, University of Southern California, Los Angeles, USA.
Christopher C PackMontreal Neurological Institute, McGill University, Montreal, QC, Canada.
Sylvain BailletMontreal Neurological Institute, McGill University, Montreal, QC, Canada.ORCID http://orcid.org/0000-0002-6762-5713

Funding

BrainStorm: Highly Extensible Software for Advanced Electrophysiology and MEG/EEG ImagingR01EB026299 · NIBIB · UNIVERSITY OF SOUTHERN CALIFORNIA · PI LEAHY, RICHARD M · 2018 to 2025
$5.4M
Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada (NSERC Canadian Network for Research and Innovation in Machining Technology) 341534-2012Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada (NSERC Canadian Network for Research and Innovation in Machining Technology) 436355-13NIBIB NIH HHS R01 EB026299
6 · The paper itself

Abstract

The methods for electrophysiology in neuroscience have evolved tremendously over the recent years with a growing emphasis on dense-array signal recordings. Such increased complexity and augmented wealth in the volume of data recorded, have not been accompanied by efforts to streamline and facilitate access to processing methods, which too are susceptible to grow in sophistication. Moreover, unsuccessful attempts to reproduce peer-reviewed publications indicate a problem of transparency in science. This growing problem could be tackled by unrestricted access to methods that promote research transparency and data sharing, ensuring the reproducibility of published results. Here, we provide a free, extensive, open-source software that provides data-analysis, data-management and multi-modality integration solutions for invasive neurophysiology. Users can perform their entire analysis through a user-friendly environment without the need of programming skills, in a tractable (logged) way. This work contributes to open-science, analysis standardization, transparency and reproducibility in invasive neurophysiology.

Indexed as

SoftwareDatasets as TopicElectrophysiologyHumansInformation DisseminationReproducibility of Results

Identifiers

PMID31653867
PMCPMC6814804

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.