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Publications

Confound modelling in UK Biobank brain imaging

Alfaro-Almagro F. et al, (2020)

Guidelines for the content and format of PET brain data in publications and archives: A consensus paper.

Knudsen GM. et al, (2020), J Cereb Blood Flow Metab

Standardized Effect Sizes and Image-Based Meta-Analytical Approaches for fMRI Data

Bossier H. et al, (2019)

Spatial confidence sets for raw effect size images

Bowring A. et al, (2019), NeuroImage, 203

Variability in the analysis of a single neuroimaging dataset by many teams

Botvinik-Nezer R. et al, (2019)

Towards Algorithmic Analytics for Large-scale Datasets.

Bzdok D. et al, (2019), Nat Mach Intell, 1, 296 - 306

Effective degrees of freedom of the Pearson's correlation coefficient under autocorrelation.

Afyouni S. et al, (2019), Neuroimage

Structural variability in the human brain reflects fine-grained functional architecture at the population level.

Smith S. et al, (2019), J Neurosci

Spatial Confidence Sets for Raw Effect Size Images

Bowring A. et al, (2019)

Exploring the impact of analysis software on task fMRI results.

Bowring A. et al, (2019), Hum Brain Mapp

Cluster failure revisited: Impact of first level design and physiological noise on cluster false positive rates.

Eklund A. et al, (2019), Hum Brain Mapp, 40, 2017 - 2032

Genomic kinship construction to enhance genetic analyses in the human connectome project data.

Kochunov P. et al, (2019), Hum Brain Mapp, 40, 1677 - 1688

Reply to Chen et al.: Parametric methods for cluster inference perform worse for two-sided t-tests.

Eklund A. et al, (2019), Hum Brain Mapp, 40, 1689 - 1691

Stable between-subject statistical inference from unstable within-subject functional connectivity estimates.

Vidaurre D. et al, (2019), Hum Brain Mapp, 40, 1234 - 1243

Homogenizing estimates of heritability among SOLAR-eclipse, OpenMX, APACE, and FPHI software packages in neuroimaging data

Kochunov P. et al, (2019), Frontiers in Neuroinformatics, 13

Modelling the distribution of white matter hyperintensities due to ageing on MRI images using Bayesian inference.

Sundaresan V. et al, (2019), Neuroimage, 185, 434 - 445

Probabilistic TFCE: A generalized combination of cluster size and voxel intensity to increase statistical power.

Spisák T. et al, (2019), Neuroimage, 185, 12 - 26

A group analysis using the Multiregression Dynamic Models for fMRI networked time series.

Costa L. et al, (2019), J Stat Plan Inference, 198, 43 - 61

Accelerated estimation and permutation inference for ACE modeling

Chen X. et al, (2019), Human Brain Mapping

Bayesian log-Gaussian Cox process regression: applications to meta-analysis of neuroimaging working memory studies

Samartsidis P. et al, (2019), Journal of the Royal Statistical Society. Series C: Applied Statistics, 68, 217 - 234

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