The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments

Gorgolewski, Krzysztof J.; Auer, Tibor; Calhoun, Vince D.; Craddock, R. Cameron; Das, Samir; Duff, Eugene P.; Flandin, Guillaume; Ghosh, Samik; Glatard, Tristan; Halchenko, Yaroslav O.; Handwerker, Daniel A.; Hanke, Michael; Keator, David B.; Li, Xiangrui; Michael, Zachary; Maumet, Camille; Nichols, B. Nolan; Nichols, Thomas E.; Pellman, John; Poline, Jean‐Baptiste; Rokem, Ariel; Schaefer, Gunnar; Sochat, Vanessa; Triplett, William; Turner, Jessica A.; Varoquaux, Gaël; Poldrack, Russell A.

doi:10.1038/sdata.2016.44

Cited by 1,422 publications

(1,186 citation statements)

References 24 publications

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“…Software, software version, and even operating system are all variables that should ideally remain fixed to obtain consistent results. Teams can make use of recently standardized protocols to simplify data management and analysis, such as the Brain Imaging Data Structure (BIDS; Gorgolewski et al, 2016), to ensure they (and others) can access and understand their results and address these variables over the longer term.…”

Section: Discussionmentioning

confidence: 99%

Presurgical language fMRI: Technical practices in epilepsy surgical planning

Benjamin¹,

Dhingra²,

et al. 2018

Human Brain Mapping

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Little is known about how language functional MRI (fMRI) is executed in clinical practice in spite of its widespread use. Here we comprehensively documented its execution in surgical planning in epilepsy. A questionnaire focusing on cognitive design, image acquisition, analysis and interpretation, and practical considerations was developed. Individuals responsible for collecting, analyzing, and interpreting clinical language fMRI data at 63 epilepsy surgical programs responded. The central finding was of marked heterogeneity in all aspects of fMRI. Most programs use multiple tasks, with a fifth routinely using 2, 3, 4, or 5 tasks with a modal run duration of 5 min. Variants of over 15 protocols are in routine use with forms of noun–verb generation, verbal fluency, and semantic decision‐making used most often. Nearly all aspects of data acquisition and analysis vary markedly. Neither of the two best‐validated protocols was used by more than 10% of respondents. Preprocessing steps are broadly consistent across sites, language‐related blood flow is most often identified using general linear modeling (76% of respondents), and statistical thresholding typically varies by patient (79%). The software SPM is most often used. fMRI programs inconsistently include input from experts with all required skills (imaging, cognitive assessment, MR physics, statistical analysis, and brain–behavior relationships). These data highlight marked gaps between the evidence supporting fMRI and its clinical application. Teams performing language fMRI may benefit from evaluating practice with reference to the best‐validated protocols to date and ensuring individuals trained in all aspects of fMRI are involved to optimize patient care.

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Section: Discussionmentioning

confidence: 99%

Presurgical language fMRI: Technical practices in epilepsy surgical planning

Benjamin¹,

Dhingra²,

et al. 2018

Human Brain Mapping

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“…It started with a meeting at Stanford in Spring 2015, followed by follow-up meetings at the OHBM and INCF annual meetings the same year, with a first release candidate and public call for comments in September 2015. Initially, BIDS focused on MRI data (structural, diffusion and functional, see Gorgolewski et al, 2016 ), but now encompasses many modalities including MEEG. BIDS offers a systematic way to organize data into folders using dedicated names, in association with text files, either as tabulated separated value file (.tsv) or JavaScript Object Notation file (.json)) to store metadata.…”

Section: Bids Meg and Eegmentioning

confidence: 99%

Best Practices in Data Analysis and Sharing in Neuroimaging using MEEG

Pernet¹,

Garrido²,

Gramfort³

et al. 2018

Preprint

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Neuroimaging methods, including magnetoencephalography and electroencephalography (MEEG), allow non-invasive collection of neural data in healthy people and in individuals with neurological or psychiatric disorders, with the aim of advancing the understanding of brain function in health and disease. Currently, scientific practice is undergoing a tremendous change, aiming to improve both research reproducibility and transparency in data collection, documentation and analysis, and in manuscript review. To advance the practice of open science, the Organization for Human Brain Mapping originally created the Committee on Best Practice in Data Analysis and Sharing (COBIDAS), which produced a report for MRI-based data. This effort continues with the OHBM's creation of a COBIDAS MEEG committee whose task is to create a similar document that describes best practice recommendations for MEEG data. The document has been drafted by OHBM experts in MEEG, with input from the brain imaging community including OHBM members who volunteered to help with this effort and executive committee members of the International Federation for Clinical Neurophysiology. This document outlines the principles of open and reproducible research in MEEG, and also applies these principles to specific research practices. Not all MEEG data practice is described in this document. Instead, we present information that we believe should be reported to fully understand and potentially replicate any study. We describe both good practice and reporting standards that ensure data collection and analysis will adhere to the highest standards. We also deal with issues of nomenclature and terminology which, due to the relatively long history of MEEG, have come about as different subfields emerged.

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“…For example, for neuroimaging data, does the fi le layout follow the Brain Imaging Data Structure (Gorgolewski et al, 2016) format?…”

Section: Availability Of Datamentioning

confidence: 99%