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A hitchhiker’s guide to working with large, open-source neuroimaging datasets

Nature Human Behaviour volume 5, pages 185–193 (2021)Cite this article

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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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Fig. 1: A list of large, open-source datasets and open repositories.

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References

  1. Van Essen, D. C. et al. The WU-Minn Human Connectome Project: an overview. Neuroimage 80, 62–79 (2013).

    Article  PubMed  Google Scholar 

  2. Casey, B. J. et al. The Adolescent Brain Cognitive Development (ABCD) study: imaging acquisition across 21 sites. Dev. Cogn. Neurosci. 32, 43–54 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Miller, K. L. et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat. Neurosci. 19, 1523–1536 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Alexander, L. M. et al. An open resource for transdiagnostic research in pediatric mental health and learning disorders. Sci. Data 4, 170181 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Biswal, B. B. et al. Toward discovery science of human brain function. Proc. Natl. Acad. Sci. USA 107, 4734–4739 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Caspers, S. et al. Studying variability in human brain aging in a population-based German cohort-rationale and design of 1000BRAINS. Front. Aging Neurosci. 6, 149 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  7. HD-200 Consortium. The ADHD-200 Consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience. Front. Syst. Neurosci. 6, 62 (2012).

    Google Scholar 

  8. Das, S. et al. Cyberinfrastructure for open science at the Montreal Neurological Institute. Front. Neuroinform. 10, 53 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Das, S., Zijdenbos, A. P., Harlap, J., Vins, D. & Evans, A. C. LORIS: a web-based data management system for multi-center studies. Front. Neuroinform. 5, 37 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Di Martino, A. et al. Enhancing studies of the connectome in autism using the autism brain imaging data exchange II. Sci. Data 4, 170010 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Di Martino, A. et al. The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol. Psychiatry 19, 659–667 (2014).

    Article  PubMed  Google Scholar 

  12. Gorgolewski, K. J. et al. NeuroVault.org: a repository for sharing unthresholded statistical maps, parcellations, and atlases of the human brain. Neuroimage 124, 1242–1244 (2016). Pt B.

    Article  PubMed  Google Scholar 

  13. Holmes, A. J. et al. Brain Genomics Superstruct Project initial data release with structural, functional, and behavioral measures. Sci. Data 2, 150031 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  14. LaMontagne, P.J. et al. OASIS-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and Alzheimer disease. Preprint at medRxiv https://doi.org/10.1101/2019.12.13.19014902 (2019).

  15. Luo, X. Z., Kennedy, D. N. & Cohen, Z. Neuroimaging informatics tools and resources clearinghouse (NITRC) resource announcement. Neuroinformatics 7, 55–56 (2009).

    Article  PubMed  Google Scholar 

  16. Marek, K. et al. The Parkinson’s progression markers initiative (PPMI) - establishing a PD biomarker cohort. Ann. Clin. Transl. Neurol. 5, 1460–1477 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Marek, K. et al. Parkinson Progression Marker Initiative. The Parkinson Progression Marker Initiative (PPMI). Prog. Neurobiol. 95, 629–635 (2011).

    Article  PubMed Central  Google Scholar 

  18. Mennes, M., Biswal, B. B., Castellanos, F. X. & Milham, M. P. Making data sharing work: the FCP/INDI experience. Neuroimage 82, 683–691 (2013).

    Article  PubMed  Google Scholar 

  19. Mueller, S. G. et al. Ways toward an early diagnosis in Alzheimer’s disease: the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Alzheimers Dement. 1, 55–66 (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Nooner, K. B. et al. The NKI-Rockland Sample: a model for accelerating the pace of discovery science in psychiatry. Front. Neurosci. 6, 152 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Poldrack, R. A. et al. Toward open sharing of task-based fMRI data: the OpenfMRI project. Front. Neuroinform. 7, 12 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Poldrack, R. A. & Gorgolewski, K. J. OpenfMRI: Open sharing of task fMRI data. Neuroimage 144, 259–261 (2017). Pt B.

    Article  PubMed  Google Scholar 

  23. Satterthwaite, T. D. et al. Neuroimaging of the Philadelphia neurodevelopmental cohort. Neuroimage 86, 544–553 (2014).

    Article  PubMed  Google Scholar 

  24. Scott, A. et al. COINS: an innovative informatics and neuroimaging tool suite built for large heterogeneous datasets. Front. Neuroinform. 5, 33 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Shafto, M. A. et al. The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study protocol: a cross-sectional, lifespan, multidisciplinary examination of healthy cognitive ageing. BMC Neurol. 14, 204 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Snoek, L. et al. The Amsterdam Open MRI Collection, a set of multimodal MRI datasets for individual difference analyses. Preprint at bioRxiv https://doi.org/10.1101/2020.06.16.155317 (2020).

  27. Taylor, J. R. et al. The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample. Neuroimage 144, 262–269 (2017). Pt B.

    Article  PubMed  Google Scholar 

  28. Zuo, X. N. et al. An open science resource for establishing reliability and reproducibility in functional connectomics. Sci. Data 1, 140049 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Southard, E. E. On the topographical distribution of cortex lesions and anomalies in dementia praecox, with some account of their functional significance. Am. J. Insanity 71, 603–671 (1915).

    Google Scholar 

  30. Smith, S. M. & Nichols, T. E. Statistical challenges in “Big Data” human neuroimaging. Neuron 97, 263–268 (2018).

    Article  CAS  PubMed  Google Scholar 

  31. Noble, S., Scheinost, D. & Constable, R. T. Cluster failure or power failure? Evaluating sensitivity in cluster-level inference. Neuroimage 209, 116468 (2020).

    Article  PubMed  Google Scholar 

  32. Bzdok, D., Nichols, T. E. & Smith, S. M. Towards algorithmic analytics for large-scale datasets. Nat. Mach. Intell. 1, 296–306 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Bzdok, D. & Yeo, B. T. T. Inference in the age of big data: Future perspectives on neuroscience. Neuroimage 155, 549–564 (2017).

    Article  PubMed  Google Scholar 

  34. Fan, J., Han, F. & Liu, H. Challenges of big data analysis. Natl. Sci. Rev. 1, 293–314 (2014).

    Article  PubMed  Google Scholar 

  35. Sandu, A. L., Paillère Martinot, M. L., Artiges, E. & Martinot, J. L. 1910s′ brains revisited. Cortical complexity in early 20th century patients with intellectual disability or with dementia praecox. Acta Psychiatr. Scand. 130, 227–237 (2014).

    Article  PubMed  Google Scholar 

  36. Brakewood, B. & Poldrack, R. A. The ethics of secondary data analysis: considering the application of Belmont principles to the sharing of neuroimaging data. Neuroimage 82, 671–676 (2013).

    Article  PubMed  Google Scholar 

  37. Meyer, M. N. Practical tips for ethical data sharing. Adv. Methods Pract. Psychol. Sci. 1, 131–144 (2018).

    Article  Google Scholar 

  38. White, T., Blok, E. & Calhoun, V.D. Data sharing and privacy issues in neuroimaging research: opportunities, obstacles, challenges, and monsters under the bed. Hum. Brain Map. https://doi.org/10.1002/hbm.25120 (2020).

  39. Nichols, T. E. et al. Best practices in data analysis and sharing in neuroimaging using MRI. Nat. Neurosci. 20, 299–303 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Poline, J. B. et al. Data sharing in neuroimaging research. Front. Neuroinform. 6, 9 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Barron, D.S. & Fox, P.T. BrainMap Database as a Resource for Computational Modeling. in Brain Mapping: An Encyclopedic Reference (ed. Toga, A. W.) 1, 675–683 (Elsevier, 2015).

  42. Poldrack, R. A. & Gorgolewski, K. J. Making big data open: data sharing in neuroimaging. Nat. Neurosci. 17, 1510–1517 (2014).

    Article  CAS  PubMed  Google Scholar 

  43. Hagler, D. J. Jr. et al. Image processing and analysis methods for the Adolescent Brain Cognitive Development Study. Neuroimage 202, 116091 (2019).

    Article  CAS  PubMed  Google Scholar 

  44. Gordon, E. M. et al. Generation and evaluation of a cortical area parcellation from resting-state correlations. Cereb. Cortex 26, 288–303 (2016).

    Article  PubMed  Google Scholar 

  45. Botvinik-Nezer, R. et al. Variability in the analysis of a single neuroimaging dataset by many teams. Nature 582, 84–88 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Ciric, R. et al. Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity. Neuroimage 154, 174–187 (2017).

    Article  PubMed  Google Scholar 

  47. Dadi, K. et al. Benchmarking functional connectome-based predictive models for resting-state fMRI. Neuroimage 192, 115–134 (2019).

    Article  PubMed  Google Scholar 

  48. Gorgolewski, K. J. et al. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci. Data 3, 160044 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Bennett, L. M. & Gadlin, H. Collaboration and team science: from theory to practice. J. Investig. Med. 60, 768–775 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Lake, E. M. R. et al. The functional brain organization of an individual allows prediction of measures of social abilities transdiagnostically in autism and attention-deficit/hyperactivity disorder. Biol. Psychiatry 86, 315–326 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Pomponio, R. et al. Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan. Neuroimage 208, 116450 (2020).

    Article  PubMed  Google Scholar 

  52. Sripada, C. et al. Prediction of neurocognition in youth from resting state fMRI. Mol. Psychiatry https://doi.org/10.1038/s41380-019-0481-6 (2019).

  53. Fortin, J. P. et al. Harmonization of cortical thickness measurements across scanners and sites. Neuroimage 167, 104–120 (2018).

    Article  PubMed  Google Scholar 

  54. Fortin, J. P. et al. Harmonization of multi-site diffusion tensor imaging data. Neuroimage 161, 149–170 (2017).

    Article  PubMed  Google Scholar 

  55. Yamashita, A. et al. Harmonization of resting-state functional MRI data across multiple imaging sites via the separation of site differences into sampling bias and measurement bias. PLoS Biol. 17, e3000042 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Yu, M. et al. Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data. Hum. Brain Mapp. 39, 4213–4227 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Pinto, M. S. et al. Harmonization of brain diffusion MRI: concepts and methods. Front. Neurosci. 14, 396 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Orban, C., Kong, R., Li, J., Chee, M. W. L. & Yeo, B. T. T. Time of day is associated with paradoxical reductions in global signal fluctuation and functional connectivity. PLoS Biol. 18, e3000602 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Noble, S. et al. Multisite reliability of MR-based functional connectivity. Neuroimage 146, 959–970 (2017).

    Article  PubMed  Google Scholar 

  60. Marek, S. et al. Identifying reproducible individual differences in childhood functional brain networks: an ABCD study. Dev. Cogn. Neurosci. 40, 100706 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Alfaro-Almagro, F. et al. Confound modelling in UK Biobank brain imaging. Neuroimage 224, 117002 (2021).

    Article  PubMed  Google Scholar 

  62. Esteban, O. et al. MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites. PLoS One 12, e0184661 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. Bissett, P.G., Hagen, M.P. & Poldrack, R.A. A cautionary note on stop-signal data from the Adolescent Brain Cognitive Development [ABCD] study. Preprint at bioRxiv https://doi.org/10.1101/2020.05.08.084707(2020).

  64. Barch, D. M. et al. Function in the human connectome: task-fMRI and individual differences in behavior. Neuroimage 80, 169–189 (2013).

    Article  PubMed  Google Scholar 

  65. Gur, R. C. et al. Age group and sex differences in performance on a computerized neurocognitive battery in children age 8-21. Neuropsychology 26, 251–265 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Fischbach, G. D. & Lord, C. The Simons Simplex Collection: a resource for identification of autism genetic risk factors. Neuron 68, 192–195 (2010).

    Article  CAS  PubMed  Google Scholar 

  67. Lord, C. et al. A multisite study of the clinical diagnosis of different autism spectrum disorders. Arch. Gen. Psychiatry 69, 306–313 (2012).

    Article  PubMed  Google Scholar 

  68. Greene, A. S., Gao, S., Scheinost, D. & Constable, R. T. Task-induced brain state manipulation improves prediction of individual traits. Nat. Commun. 9, 2807 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  69. Duncan, N. W. & Northoff, G. Overview of potential procedural and participant-related confounds for neuroimaging of the resting state. J. Psychiatry Neurosci. 38, 84–96 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Pervaiz, U., Vidaurre, D., Woolrich, M. W. & Smith, S. M. Optimising network modelling methods for fMRI. Neuroimage 211, 116604 (2020).

    Article  PubMed  Google Scholar 

  71. Rao, A., Monteiro, J. M. & Mourao-Miranda, J. Predictive modelling using neuroimaging data in the presence of confounds. Neuroimage 150, 23–49 (2017).

    Article  PubMed  Google Scholar 

  72. Snoek, L., Miletić, S. & Scholte, H. S. How to control for confounds in decoding analyses of neuroimaging data. Neuroimage 184, 741–760 (2019).

    Article  PubMed  Google Scholar 

  73. Milham, M. P. et al. Assessment of the impact of shared brain imaging data on the scientific literature. Nat. Commun. 9, 2818 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  74. Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    Google Scholar 

  75. Lombardo, M. V., Lai, M. C. & Baron-Cohen, S. Big data approaches to decomposing heterogeneity across the autism spectrum. Mol. Psychiatry 24, 1435–1450 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Button, K. S. et al. Power failure: why small sample size undermines the reliability of neuroscience. Nat. Rev. Neurosci. 14, 365–376 (2013).

    Article  CAS  PubMed  Google Scholar 

  77. Szucs, D. & Ioannidis, J. P. Empirical assessment of published effect sizes and power in the recent cognitive neuroscience and psychology literature. PLoS Biol. 15, e2000797 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  78. Wasserstein, R. L., Schirm, A. L. & Lazar, N. A. Moving to a world beyond “P < 0.05”. Am. Stat. 73 Suppl. 1, 1–19 (2019).

    Article  Google Scholar 

  79. Kaplan, R. M., Chambers, D. A. & Glasgow, R. E. Big data and large sample size: a cautionary note on the potential for bias. Clin. Transl. Sci. 7, 342–346 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  80. Bzdok, D. & Ioannidis, J. P. A. Exploration, inference, and prediction in neuroscience and biomedicine. Trends Neurosci. 42, 251–262 (2019).

    Article  CAS  PubMed  Google Scholar 

  81. Chen, G., Taylor, P. A. & Cox, R. W. Is the statistic value all we should care about in neuroimaging? Neuroimage 147, 952–959 (2017).

    Article  PubMed  Google Scholar 

  82. Szucs, D. & Ioannidis, J. P. A. When null hypothesis significance testing is unsuitable for research: a reassessment. Front. Hum. Neurosci. 11, 390 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  83. Wasserstein, R. L. & Lazar, N. A. The ASA’s statement on P-values: context, process, and purpose. Am. Stat. 70, 129–133 (2016).

    Article  Google Scholar 

  84. Earp, B. D. The need for reporting negative results - a 90 year update. J. Clin. Transl. Res. 3, 344–347 (2017). Suppl 2.

    PubMed  PubMed Central  Google Scholar 

  85. Easterbrook, P. J., Berlin, J. A., Gopalan, R. & Matthews, D. R. Publication bias in clinical research. Lancet 337, 867–872 (1991).

    Article  CAS  PubMed  Google Scholar 

  86. Greenwald, A. G. Consequences of prejudice against the null hypothesis. Psychol. Bull. 82, 1–20 (1975).

    Article  Google Scholar 

  87. Heger, M. Editor’s inaugural issue foreword: perspectives on translational and clinical research. J. Clin. Transl. Res. 1, 1–5 (2015).

    PubMed  PubMed Central  Google Scholar 

  88. Pautasso, M. Worsening file-drawer problem in the abstracts of natural, medical and social science databases. Scientometrics 85, 193–202 (2010).

    Article  Google Scholar 

  89. Rosenthal, R. The file drawer problem and tolerance for null results. Psychol. Bull. 86, 638–641 (1979).

    Article  Google Scholar 

  90. Thompson, W. H., Wright, J., Bissett, P. G. & Poldrack, R. A. Dataset decay and the problem of sequential analyses on open datasets. eLife 9, e53498 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  91. Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. J. Mach. Learn. Res. 11, 2079–2107 (2010).

    Google Scholar 

  92. Dietterich, T. Overfitting and undercomputing in machine learning. ACM Comp. Surv. 27, 326–327 (1995).

    Article  Google Scholar 

  93. Reunanen, J. Overfitting in making comparisons between variable selection methods. J. Mach. Learn. Res. 3, 1371–1382 (2003).

    Google Scholar 

  94. Thompson, P. M. et al. Alzheimer’s Disease Neuroimaging Initiative, EPIGEN Consortium, IMAGEN Consortium, Saguenay Youth Study (SYS) Group. The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behav. 8, 153–182 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  95. Pierce, H. H., Dev, A., Statham, E. & Bierer, B. E. Credit data generators for data reuse. Nature 570, 30–32 (2019).

    Article  CAS  PubMed  Google Scholar 

  96. Weston, S. J., Ritchie, S. J., Rohrer, J. M. & Przybylski, A. K. Recommendations for increasing the transparency of analysis of preexisting data sets. Adv. Methods Pract. Psychol. Sci. 2, 214–227 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  97. Milham, M. P. & Klein, A. Be the change you seek in science. BMC Biol. 17, 27 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  98. Nowogrodzki, A. Eleven tips for working with large data sets. Nature 577, 439–440 (2020).

    Article  CAS  PubMed  Google Scholar 

  99. Van Essen, D. C. et al. The Brain Analysis Library of Spatial Maps and Atlases (BALSA) database. Neuroimage 144, 270–274 (2017). Pt B.

    Article  PubMed  Google Scholar 

  100. Niso, G. et al. OMEGA: the open MEG archive. Neuroimage 124, 1182–1187 (2016). Pt B.

    Article  PubMed  Google Scholar 

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Acknowledgements

The authors acknowledge funding from the following NIH grants: C.H. and A.S.G., T32GM007205; S.N., K00MH122372; K.L., R01MH111424 and P50MH115716; D.S.B., T32 MH019961 and R25 MH071584; and D.S., R24 MH114805. The funders had no role in the conception or writing of this manuscript.

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Authors and Affiliations

  1. Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA

    Corey Horien, Abigail S. Greene, R. Todd Constable & Dustin Scheinost

  2. MD/PhD program, Yale School of Medicine, New Haven, CT, USA

    Corey Horien & Abigail S. Greene

  3. Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA

    Stephanie Noble, Kangjoo Lee, Xilin Shen, Evelyn M. R. Lake, R. Todd Constable & Dustin Scheinost

  4. Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA

    Daniel S. Barron

  5. Department of Biomedical Engineering, Yale University, New Haven, CT, USA

    Siyuan Gao, David O’Connor, Mehraveh Salehi, R. Todd Constable & Dustin Scheinost

  6. Summary Analytics Inc., Seattle, WA, USA

    Mehraveh Salehi

  7. Deparment of Computer Science, Yale University, New Haven, CT, USA

    Javid Dadashkarimi

  8. Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA

    R. Todd Constable

  9. Deparment of Statistics & Data Science, Yale University, New Haven, CT, USA

    Dustin Scheinost

  10. Child Study Center, Yale School of Medicine, New Haven, CT, USA

    Dustin Scheinost

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  1. Corey Horien
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Contributions

C.H. wrote the first draft of the manuscript. C.H., S.N., A.S.G., K.L., D.S.B., S.G., D.O’C., M.S., J.D., X.S., E.M.R.L., R.T.C. and D.S. contributed to the conceptualization, writing and editing of the manuscript. C.H., S.N., A.S.G., K.L., D.S.B., S.G., D.O’C., M.S., J.D., X.S., E.M.R.L., R.T.C. and D.S. read and approved the final draft.

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Correspondence to Corey Horien or Dustin Scheinost.

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Horien, C., Noble, S., Greene, A.S. et al. A hitchhiker’s guide to working with large, open-source neuroimaging datasets. Nat Hum Behav 5, 185–193 (2021). https://doi.org/10.1038/s41562-020-01005-4

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