T
Thomas E. Nichols
Researcher at University of Oxford
Publications - 448
Citations - 71043
Thomas E. Nichols is an academic researcher from University of Oxford. The author has contributed to research in topics: Population & Medicine. The author has an hindex of 88, co-authored 411 publications receiving 58970 citations. Previous affiliations of Thomas E. Nichols include University of Pittsburgh & Imperial College London.
Papers
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Journal ArticleDOI
Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data.
Stephen M. Smith,Mark Jenkinson,Heidi Johansen-Berg,Daniel Rueckert,Thomas E. Nichols,Clare E. Mackay,Kate E. Watkins,Olga Ciccarelli,M Z Cader,Paul M. Matthews,Timothy E.J. Behrens +10 more
TL;DR: TBSS aims to improve the sensitivity, objectivity and interpretability of analysis of multi-subject diffusion imaging studies by solving the question of how to align FA images from multiple subjects in a way that allows for valid conclusions to be drawn from the subsequent voxelwise analysis.
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Nonparametric permutation tests for functional neuroimaging: A primer with examples
TL;DR: The standard nonparametric randomization and permutation testing ideas are developed at an accessible level, using practical examples from functional neuroimaging, and the extensions for multiple comparisons described.
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Thresholding of statistical maps in functional neuroimaging using the false discovery rate.
TL;DR: This paper introduces to the neuroscience literature statistical procedures for controlling the false discovery rate (FDR) and demonstrates this approach using both simulations and functional magnetic resonance imaging data from two simple experiments.
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Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference
TL;DR: A new method is proposed which attempts to keep the sensitivity benefits of cluster-based thresholding (and indeed the general concept of "clusters" of signal), while avoiding (or at least minimising) these problems, and is referred to as "threshold-free cluster enhancement" (TFCE).
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Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates
TL;DR: It is found that the most common software packages for fMRI analysis (SPM, FSL, AFNI) can result in false-positive rates of up to 70%.