Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

Continue

Publications

Large-scale GWAS reveals genetic architecture of brain white matter microstructure and genetic overlap with cognitive and mental health trait

NICHOLS T., (2019), Molecular Psychiatry

Spatial confidence sets for raw effect size images.

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

Exploring the impact of analysis software on task fMRI results.

Bowring A. et al, (2019), Human brain mapping, 40, 3362 - 3384

Towards Algorithmic Analytics for Large-scale Datasets.

Bzdok D. et al, (2019), Nature machine intelligence, 1, 296 - 306

Structural Variability in the Human Brain Reflects Fine-Grained Functional Architecture at the Population Level.

Smith S. et al, (2019), The Journal of neuroscience : the official journal of the Society for Neuroscience, 39, 6136 - 6149

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

Afyouni S. et al, (2019), NeuroImage, 199, 609 - 625

Spatial Confidence Sets for Raw Effect Size Images

Bowring A. et al, (2019)

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

Eklund A. et al, (2019), Human brain mapping, 40, 2017 - 2032

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

Kochunov P. et al, (2019), Human brain mapping, 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), Human brain mapping, 40, 1689 - 1691

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

Vidaurre D. et al, (2019), Human brain mapping, 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

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

Costa L. et al, (2019), Journal of statistical planning and 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

Beyond Bonferroni revisited: concerns over inflated false positive research findings in the fields of conservation genetics, biology, and medicine

White T. et al, (2019), Conservation Genetics

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

Comparison of heritability estimates on resting state fMRI connectivity phenotypes using the ENIGMA analysis pipeline.

Adhikari BM. et al, (2018), Human brain mapping, 39, 4893 - 4902

Extending the Human Connectome Project across ages: Imaging protocols for the Lifespan Development and Aging projects.

Harms MP. et al, (2018), NeuroImage, 183, 972 - 984

Load More

Include forthcoming?