Hans Knutsson
Radiology, Nuclear Medicine and Imaging · 1996–2016
A researcher in the Science corpus: 3 papers; trusts 17 researchers and is trusted by 19 researchers here, as inferred from the citation graph.
Subfields
- Radiology, Nuclear Medicine and Imaging — 22 citations into it; #1,066 of 3,265 by trust here, #1,462 by citations
- Cognitive Neuroscience — 20 citations into it; #4,309 of 10,852 by trust here, #4,716 by citations
- Statistics and Probability — 7 citations into it
Whom Hans Knutsson trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Thomas E. Nichols
- Russell A. Poldrack
- John Ashburner
- Karl J. Friston
- Matthew D. Lieberman
- Stephen M. Smith
Who trusts Hans Knutsson overall
Papers from Hans Knutsson's perspective
Ordered by how strongly the researchers Hans Knutsson trusts endorse each paper. Same corpus, a different order for every researcher.
- Voxel-based morphometry--the methods.
- Multilevel linear modelling for FMRI group analysis using Bayesian inference
- Controlling the familywise error rate in functional neuroimaging: a comparative review (Statistical Methods in Medical Research)
- Power failure: why small sample size undermines the reliability of neuroscience (Nature Reviews Neuroscience)
- Permutation inference for the general linear model (NeuroImage)
- Nonparametric Permutation Tests for Functional Neuroimaging
- Dynamic functional connectivity: Promise, issues, and interpretations (NeuroImage)
- Making big data open: data sharing in neuroimaging (Nature Neuroscience)
- NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain (NeuroImage)
- Type I and Type II error concerns in fMRI research: re-balancing the scale. (Social Cognitive and Affective Neuroscience)
Their papers
- Q-space trajectory imaging for multidimensional diffusion MRI of the human brain (2016) — 355 citations
- Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates (2015) — 3,333 citations
- Reinforcement Learning Trees (1996)