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The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Statistical Challenges in “Big Data” Human Neuroimaging

Open access
Smith, Stephen M., Nichols, Thomas E.·2018·Neuron

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Research works printed but not linked: 13

  1. Andersson, 2016, An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging, Neuroimage, 125, 1063, 10.1016/j.neuroimage.2015.10.019 · DOI · Paper page
  2. Bearden, 2017, Emerging global initiatives in neurogenetics: the enhancing neuroimaging genetics through meta-analysis (ENIGMA) consortium, Neuron, 94, 232, 10.1016/j.neuron.2017.03.033 · DOI
  3. Duff, 2015, Learning to identify CNS drug action and efficacy using multistudy fMRI data, Sci. Transl. Med., 7, 274ra16, 10.1126/scitranslmed.3008438 · DOI
  4. Insel, 2010, Research domain criteria (RDoC): toward a new classification framework for research on mental disorders, Am. J. Psychiatry, 167, 748, 10.1176/appi.ajp.2010.09091379 · DOI · Paper page
  5. Le Floch, 2012, Significant correlation between a set of genetic polymorphisms and a functional brain network revealed by feature selection and sparse Partial Least Squares, Neuroimage, 63, 11, 10.1016/j.neuroimage.2012.06.061 · DOI
  6. Mueller, 2005, Ways toward an early diagnosis in Alzheimer’s disease: the Alzheimer’s Disease Neuroimaging Initiative (ADNI), Alzheimers Dement., 1, 55, 10.1016/j.jalz.2005.06.003 · DOI · Paper page
  7. Munafò, 2017, Collider scope: when selection bias can substantially influence observed associations, Int. J. Epidemiol.
  8. Rosenbaum, 1983, The central role of the propensity score in observational studies for causal effects, Biometrika, 70, 41, 10.1093/biomet/70.1.41 · DOI · Paper page
  9. Salimi-Khorshidi, 2014, Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers, Neuroimage, 90, 449, 10.1016/j.neuroimage.2013.11.046 · DOI · Paper page
  10. Van Essen, 2013, The WU-Minn Human Connectome Project: an overview, Neuroimage, 80, 62, 10.1016/j.neuroimage.2013.05.041 · DOI · Paper page
  11. Winkler, 2015, Multi-level block permutation, Neuroimage, 123, 253, 10.1016/j.neuroimage.2015.05.092 · DOI
  12. Winkler, 2016, Faster permutation inference in brain imaging, Neuroimage, 141, 502, 10.1016/j.neuroimage.2016.05.068 · DOI · Paper page
  13. Winkler, 2016, Non-parametric combination and related permutation tests for neuroimaging, Hum. Brain Mapp., 37, 1486, 10.1002/hbm.23115 · DOI · Paper page

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Metadata

Authors
Smith, S. M., & Nichols, T. E.
Venue
Neuron
Publisher
Elsevier BV
Published
2018-01
Volume
97
Issue
2
Pages
263-268
ISSN
0896-6273 (ISSN-L)
OpenAlex
W2782556780
Cited by
330
References
5
FWCI
24.48
Type
journal article
Language
en
Open access
bronze
Licence
Funders
  • Wellcome Trust
  • UK Medical Research Council
  • NIH

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CoChat. (2026). Citation check: Statistical Challenges in “Big Data” Human Neuroimaging. https://app.cochat.ai/papers/10.1016/j.neuron.2017.12.018
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Smith, S. M., & Nichols, T. E. (2018). Statistical Challenges in “Big Data” Human Neuroimaging. Neuron. https://doi.org/10.1016/j.neuron.2017.12.018

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.