OpenfMRI: Open sharing of task fMRI data

Poldrack, Russell A.; Gorgolewski, Krzysztof J.

doi:10.1016/j.neuroimage.2015.05.073

Cited by 146 publications

(102 citation statements)

References 11 publications

Supporting

Mentioning

102

Contrasting

Order By: Relevance

“…Several data-sharing platforms are currently available (see [146]), for example openfMRI [147]. For the accumulation of data across sites, standardized procedures must however be implemented.…”

Section: Further Considerations For a Fmri Science Of Individual Diffmentioning

confidence: 99%

Building a Science of Individual Differences from fMRI

Dubois

Adolphs

2016

Trends in Cognitive Sciences

630

482

View full text Add to dashboard Cite

To date, fMRI research has been concerned primarily with evincing generic principles of brain function through averaging data from multiple subjects. Given rapid developments in both hardware and analysis tools, the field is now poised to study fMRI-derived measures in individual subjects, and to relate these to psychological traits or genetic variations. We discuss issues of validity and reliability that arise when the focus shifts to individual subjects and that are widely applicable across imaging modalities. We also emphasize that individual assessment of neural function with fMRI presents specific challenges and necessitates careful consideration of anatomical and vascular between-subject variability, sources of within-subject variability, and statistical power.

show abstract

Section: Further Considerations For a Fmri Science Of Individual Diffmentioning

confidence: 99%

Building a Science of Individual Differences from fMRI

Dubois

Adolphs

2016

Trends in Cognitive Sciences

630

482

View full text Add to dashboard Cite

show abstract

“…Comprehensive guidelines for reporting an fMRI study (Poldrack et al, 2008), as well as the principles of open and reproducible research for neuroimaging (Nichols et al, 2016) have been proposed, and have been accompanied by the development of a number of databases (Van Horn and Ishai, 2007; Poldrack and Gorgolewski, 2014; Poldrack and Poline, 2015). Specific examples of such data pools include OpenfMRI (Poldrack and Gorgolewski, 2015), ConnectomeDB (Hodge et al, 2016), Neuroinformatics Database (NiDB) (Book et al, 2016), or NeuroVault.org (Gorgolewski et al, 2016b). Effective communication of the results of fMRI investigations requires that the information has been organized and described in a clear and straightforward manner, using an unambiguous ontology (formal description of all terms and syntax) (Burns and Turner, 2013; Poldrack and Yarkoni, 2016) and format (Gorgolewski et al, 2016a).…”

Section: Report and Interpretation Of Resultsmentioning

confidence: 99%

A Hitchhiker's Guide to Functional Magnetic Resonance Imaging

et al. 2016

View full text Add to dashboard Cite

Functional Magnetic Resonance Imaging (fMRI) studies have become increasingly popular both with clinicians and researchers as they are capable of providing unique insights into brain functions. However, multiple technical considerations (ranging from specifics of paradigm design to imaging artifacts, complex protocol definition, and multitude of processing and methods of analysis, as well as intrinsic methodological limitations) must be considered and addressed in order to optimize fMRI analysis and to arrive at the most accurate and grounded interpretation of the data. In practice, the researcher/clinician must choose, from many available options, the most suitable software tool for each stage of the fMRI analysis pipeline. Herein we provide a straightforward guide designed to address, for each of the major stages, the techniques, and tools involved in the process. We have developed this guide both to help those new to the technique to overcome the most critical difficulties in its use, as well as to serve as a resource for the neuroimaging community.

show abstract

“…In addition to multisite studies, depositing EEG data in repositories can facilitate sharing and combining of data sets to hopefully improve statistical power. A few repositories currently exist for such purpose and include the OpenfMRI database (https://www.openfmri.org/; Poldrack et al, ; Poldrack & Gorgolewski, ) and the Patient Repository for EEG Data + Computational Tools (PRED + CT; http://predict.cs.unm.edu/; Cavanagh, Napolitano, Wu, & Mueen, ). Furthermore, funding agencies have started to require depositing data to repositories.…”

Section: Discussionmentioning

confidence: 99%

Methodological reporting behavior, sample sizes, and statistical power in studies of event‐related potentials: Barriers to reproducibility and replicability

et al. 2019

View full text Add to dashboard Cite

Methodological reporting guidelines for studies of ERPs were updated in Psychophysiology in 2014. These guidelines facilitate the communication of key methodological parameters (e.g., preprocessing steps). Failing to report key parameters represents a barrier to replication efforts, and difficulty with replicability increases in the presence of small sample sizes and low statistical power. We assessed whether guidelines are followed and estimated the average sample size and power in recent research. Reporting behavior, sample sizes, and statistical designs were coded for 150 randomly sampled articles from five high‐impact journals that frequently published ERP research from 2011 to 2017. An average of 63% of guidelines were reported, and reporting behavior was similar across journals, suggesting that gaps in reporting is a shortcoming of the field rather than any specific journal. Publication of the guidelines article had no impact on reporting behavior, suggesting that editors and peer reviewers are not enforcing these recommendations. The average sample size per group was 21. Statistical power was conservatively estimated as .72‒.98 for a large effect size, .35‒.73 for a medium effect, and .10‒.18 for a small effect. These findings indicate that failing to report key guidelines is ubiquitous and that ERP studies are primarily powered to detect large effects. Such low power and insufficient following of reporting guidelines represent substantial barriers to replication efforts. The methodological transparency and replicability of studies can be improved by the open sharing of processing code and experimental tasks and by a priori sample size calculations to ensure adequately powered studies.

show abstract

OpenfMRI: Open sharing of task fMRI data

Abstract: OpenfMRI is a repository for the open sharing of task-based fMRI data. Here we outline its goals, architecture, and current status of the repository, as well as outlining future plans for the project.

Cited by 146 publications

References 11 publications

Building a Science of Individual Differences from fMRI

Building a Science of Individual Differences from fMRI

A Hitchhiker's Guide to Functional Magnetic Resonance Imaging

Methodological reporting behavior, sample sizes, and statistical power in studies of event‐related potentials: Barriers to reproducibility and replicability

Contact Info

Product

Resources

About