Open and reproducible neuroimaging: From study inception to publication

Niso, Guiomar; Botvinik‐Nezer, Rotem; Appelhoff, Stefan; Vega, Alejandro de la; Estéban, Oscar; Etzel, Joset A.; Finc, Karolina; Ganz, Melanie; Gau, Rémi; Halchenko, Yaroslav; Herholz, Peer; Karakuzu, Agâh; Keator, David; Markiewicz, Christopher J.; Maumet, Camille; Pernet, Cyril; Pestilli, Franco; Queder, Nazek; Schmitt, Tina; Sójka, Weronika; Wagner, Adina; Whitaker, Kirstie; Rieger, Jochem W.

doi:10.1016/j.neuroimage.2022.119623

Cited by 42 publications

(37 citation statements)

References 223 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some other domains might be underrepresented like cellular molecular neuroscience (11%) or neuroinformatics (14%). The sample size of the survey is comparable to other surveys in this area and similarity of results indicate representativeness ( Niso et al, 2022 ). The low rate of shared data can be explained by the fact that scientists do not want to or cannot share the data or that there are at least barriers that ultimately lead to the data not being shared.…”

Section: Discussionsupporting

confidence: 65%

“…However, the proportion of scientific data that is actually openly shared within the neuroscientific community remains low ( Watson, 2022 ). The lack of sharing properly annotated data and tools contributes to the poor reproducibility of research results, known as “the reproducibility crisis,” that hinders the growth of knowledge and innovation on the one hand and leads to inefficient use of resources on the other hand ( Baker, 2016 ; Stodden et al, 2016 ; Poldrack et al, 2019 ; Crook et al, 2020 ; Loss et al, 2021 ; Niso et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Research Data Management and Data Sharing for Reproducible Research—Results of a Community Survey of the German National Research Data Infrastructure Initiative Neuroscience

Klingner

Denker

Grün

et al. 2023

eNeuro

View full text Add to dashboard Cite

Science is changing: the volume and complexity of data is increasing, the number of studies is growing and the goal of achieving reproducible results requires new solutions for scientific data management. In the field of neuroscience, the German National Research Data Infrastructure (NFDI-Neuro) initiative aims to develop sustainable solutions for research data management (RDM). To obtain an understanding of the present RDM situation in the neuroscience community, NFDI-Neuro conducted a comprehensive survey amongst the neuroscience community. Here, we report and analyse the results of the survey. We focused the survey and our analysis on current needs, challenges, and opinions about RDM.The German neuroscience community perceives barriers with respect to RDM and data sharing mainly linked to (1) lack of data and metadata standards, (2) lack of community adopted provenance tracking methods, 3) lack of secure and privacy preserving research infrastructure for sensitive data (4) lack of RDM literacy and (5) lack of resources (time, personnel, money) for proper RDM. However, an overwhelming majority of community members (91%) indicated that they would be willing to share their data with other researchers and are interested to increase their RDM skills. Taking advantage of this willingness and overcoming the existing barriers requires the systematic development of standards, tools, and infrastructure, the provision of training, education, and support, as well as additional resources for RDM to the research community and a constant dialogue with relevant stakeholders including policy makers to leverage of a culture change through adapted incentivisation and regulation.Significance StatementA comprehensive survey amongst the neuroscience community in Germany determined the current needs, challenges, and opinions with respect to standardized research data management (RDM). The Neuroscience community perceives a lack of standards for data and metadata, a lack of provenance tracking and versioning of data, a lack of protected digital research infrastructure for sensitive data and a lack of education and resources for proper RDM. However, an overwhelming majority of community members indicated that they would be willing to share their data with other researchers and are interested to increase their RDM skills. Thus, the survey results suggest that training, the provision of standards, tools, infrastructure, and resources for RDM holds the potential to significantly facilitate reproducible research in neuroscience.

show abstract

Section: Discussionsupporting

confidence: 65%

Section: Introductionmentioning

confidence: 99%

Research Data Management and Data Sharing for Reproducible Research—Results of a Community Survey of the German National Research Data Infrastructure Initiative Neuroscience

Klingner

Denker

Grün

et al. 2023

eNeuro

View full text Add to dashboard Cite

show abstract

“…For that reason, a PDF report is generated for each recording, in which the intermediate steps of the preprocessing (Figure 2) and the extracted EEG features (Figure 3) are visualized. These visualizations can serve as quality control checkpoints and help to detect shorter or corrupted files, misalignment of electrodes or missing data through fast visual inspection 25 . Along with their visualization, the preprocessed data and the extracted features of each recording are saved to separate files that can be later used for statistical group analysis and/or biomarker discovery.…”

Section: Ease Of Use Transparency and Interpretabilitymentioning

confidence: 99%

DISCOVER-EEG: an open, fully automated EEG pipeline for biomarker discovery in clinical neuroscience

Ávila

Bott

Tiemann

et al. 2023

Preprint

View full text Add to dashboard Cite

Biomarker discovery in neurological and psychiatric disorders critically depends on reproducible and transparent methods applied to large-scale datasets. Electroencephalography (EEG) is a promising tool for identifying biomarkers. However, recording, preprocessing and analysis of EEG data is time-consuming and mostly subjective. Therefore, we developed DISCOVER-EEG, an open and fully automated pipeline that enables easy and fast preprocessing, analysis and visualization of resting state EEG data. Data in the standard EEG-BIDS structure are automatically preprocessed and physiologically meaningful features of brain function (including oscillatory power, connectivity and network characteristics) are extracted and visualized using two open-source and widely used Matlab toolboxes (EEGlab and FieldTrip). We exemplify the use of the pipeline for biomarker discovery in healthy ageing in the LEMON dataset, containing 212 healthy participants. We demonstrate its utility to speed up biomarker discovery in a clinical setting with a new dataset containing 74 patients with chronic pain. Thus, the DISCOVER-EEG pipeline facilitates the aggregation, reuse and analysis of large EEG datasets, promoting open and reproducible research on brain function.

show abstract

“…preregistration of data quality control procedures and acceptable thresholds could also decrease the risk of biases influencing decisionmaking, while at the same time reducing questionable research practices more generally (Niso et al, 2022). However this has not yet been widely adopted in the neuroimaging community (Borghi and Gulick, 2018).…”

Section: Figurementioning

confidence: 99%

Inter-rater reliability of functional MRI data quality control assessments: A standardised protocol and practical guide using pyfMRIqc

Williams

Hedger²,

McNabb³

et al. 2023

Front. Neurosci.

View full text Add to dashboard Cite

Quality control is a critical step in the processing and analysis of functional magnetic resonance imaging data. Its purpose is to remove problematic data that could otherwise lead to downstream errors in the analysis and reporting of results. The manual inspection of data can be a laborious and error-prone process that is susceptible to human error. The development of automated tools aims to mitigate these issues. One such tool is pyfMRIqc, which we previously developed as a user-friendly method for assessing data quality. Yet, these methods still generate output that requires subjective interpretations about whether the quality of a given dataset meets an acceptable standard for further analysis. Here we present a quality control protocol using pyfMRIqc and assess the inter-rater reliability of four independent raters using this protocol for data from the fMRI Open QC project (https://osf.io/qaesm/). Data were classified by raters as either “include,” “uncertain,” or “exclude.” There was moderate to substantial agreement between raters for “include” and “exclude,” but little to no agreement for “uncertain.” In most cases only a single rater used the “uncertain” classification for a given participant’s data, with the remaining raters showing agreement for “include”/“exclude” decisions in all but one case. We suggest several approaches to increase rater agreement and reduce disagreement for “uncertain” cases, aiding classification consistency.

show abstract

Open and reproducible neuroimaging: From study inception to publication

Cited by 42 publications

References 223 publications

Research Data Management and Data Sharing for Reproducible Research—Results of a Community Survey of the German National Research Data Infrastructure Initiative Neuroscience

Research Data Management and Data Sharing for Reproducible Research—Results of a Community Survey of the German National Research Data Infrastructure Initiative Neuroscience

DISCOVER-EEG: an open, fully automated EEG pipeline for biomarker discovery in clinical neuroscience

Inter-rater reliability of functional MRI data quality control assessments: A standardised protocol and practical guide using pyfMRIqc

Contact Info

Product

Resources

About