Statistical Challenges in "Big Data" Human Neuroimaging.

By Stephen M. Smith, Thomas E. Nichols. Neuron, 2018

319 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Health, Toxicology and Mutagenesis, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/d607e767e66b06f42b0abf9e5ede45291b1b58aa

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Paul M. Thompson

In Radiology, Nuclear Medicine and Imaging

1. G. Bragi Walters
2. Kári Stefánsson
3. Hreinn Stefansson
4. Daniel Fannar Gudbjartsson
5. Thorgeir E. Thorgeirsson
6. M. O. Ulfarsson
7. Paul M. Thompson
8. Lotta Maria Ellingsen

Citations in the corpus

• The OpenNeuro resource for sharing of neuroscience data (2021, unknown): “Discussions of ‘big data’ in neuroimaging ( Poldrack and Gorgolewski, 2014 ; Smith and Nichols, 2018 ) have largely focused on datasets including large numbers of individuals.”
• Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data (2018, background): “Interestingly, the issue of “leaking” spurious variability of confounders after correction has been already reported in a number of multi-centric studies, and is matter of ongoing research [14, 15].”
• Brain age prediction using deep learning uncovers associated sequence variants (2019, background): “Confound effects are a problem for big imaging studies due to the huge number of imaging artifacts that can potentially in fl uence both imaging and non-imaging variables of interest 32 .”