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Publications

Exploring the impact of analysis software on task fMRI results.

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

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

Towards algorithmic analytics for large-scale datasets

Bzdok D. et al, (2019), Nature Machine Intelligence, 1, 296 - 306

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

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), 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

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

The Lifespan Human Connectome Project in Development: A large-scale study of brain connectivity development in 5-21 year olds.

Somerville LH. et al, (2018), NeuroImage, 183, 456 - 468

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

ARE INFARCTS IN STROKE PATIENTS WITH NEWLY DIAGNOSED ATRIAL FIBRILLATION TRIGGER OF THE ARRHYTHMIA OR ONLY TARGET OF EMBOLISM?

Rizos T. et al, (2018), INTERNATIONAL JOURNAL OF STROKE, 11, 69 - 69

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