ArticleNature human behaviour2021
A hitchhiker's guide to working with large, open-source neuroimaging datasets.
Article in Nature human behaviour, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning and Prediction in Fetal, Infant, and Toddler Neuroimaging: A Review and Primer.Biological psychiatry · 2023Pooled it
- External validation improves generalizability, replicability and reproducibility in predictive models for neuroimaging.Nature methods · 2026Review
- Multi-Centre Reproducibility of DTI and NODDI in White Matter Tracts Segmented Using TractFinder Across Three MRI Scanners of the Same Model.Human brain mapping · 2026Article
- A comprehensive EEG dataset for investigating visual touch perception.Scientific data · 2026Article
- Fractal analysis of brain shape formation predicts age and genetic similarity in human newborns.Nature neuroscience · 2026Article
- When Brain Models Aren't Universal: Benchmarking of Ethnic Bias in MRI-Based Cognitive Prediction Across Modalities.bioRxiv : the preprint server for biology · 2025Article
- Topographic Variation in Human Neurotransmitter Receptor Densities Explains Differences in Intracranial EEG Spectra.Human brain mapping · 2025Article
- DICOM datasets for reproducible neuroimaging research across manufacturers and software versions.Scientific data · 2025Article
- Improving predictability, reliability, and generalizability of brain-wide associations for cognitive abilities via multimodal stacking.PNAS nexus · 2025Article
- Enhanced structural brain connectivity analyses using high diffusion-weighting strengths.Brain structure & function · 2025Article
- Mapping ADHD Heterogeneity and Biotypes through Topological Deviations in Morphometric Similarity Networks.medRxiv : the preprint server for health sciences · 2025Article
- BrainScape: An open-source framework for integrating and preprocessing anatomical MRI datasets.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- HED LANG - A Hierarchical Event Descriptors library extension for annotation of language cognition experiments.Scientific data · 2024Article
- Psychiatric neuroimaging at a crossroads: Insights from psychiatric genetics.Developmental cognitive neuroscience · 2024Review
- Principles of intensive human neuroimaging.Trends in neurosciences · 2024Article
- Gradients of Brain Organization: Smooth Sailing from Methods Development to User Community.Neuroinformatics · 2024Review
- Power and reproducibility in the external validation of brain-phenotype predictions.Nature human behaviour · 2024Article
- Human brain state dynamics are highly reproducible and associated with neural and behavioral features.PLoS biology · 2024Article
- Brain age has limited utility as a biomarker for capturing fluid cognition in older individuals.eLife · 2024Article
- Human brain state dynamics reflect individual neuro-phenotypes.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
Large datasets that enable researchers to perform investigations with unprecedented rigor are growing increasingly common in neuroimaging. Due to the simultaneous increasing popularity of open science, these state-of-the-art datasets are more accessible than ever to researchers around the world. While analysis of these samples has pushed the field forward, they pose a new set of challenges that might cause difficulties for novice users. Here we offer practical tips for working with large datasets from the end-user's perspective. We cover all aspects of the data lifecycle: from what to consider when downloading and storing the data to tips on how to become acquainted with a dataset one did not collect and what to share when communicating results. This manuscript serves as a practical guide one can use when working with large neuroimaging datasets, thus dissolving barriers to scientific discovery.
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Registered trials
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.