Confound modelling in UK Biobank brain imaging

Dealing with confounds is an essential step in large cohort studies to address problems such as unexplained variance and spurious correlations. UK Biobank is a powerful resource for studying associations between imaging and non-imaging measures such as lifestyle factors and health outcomes, in part because of the large subject numbers. However, the resulting high statistical power also raises the sensitivity to confound effects, which therefore have to be carefully considered. In this work we describe a set of possible confounds (including non-linear effects and interactions that researchers may wish to consider for their studies using such data). We include descriptions of how we can estimate the confounds, and study the extent to which each of these confounds affects the data, and the spurious correlations that may arise if they are not controlled. Finally, we discuss several issues that future studies should consider when dealing with confounds.

[1]  Cecilia M. Lindgren,et al.  Adjusting for Confounding in Unsupervised Latent Representations of Images , 2018, ArXiv.

[2]  Theo G. M. van Erp,et al.  Multisite reliability of MR-based functional connectivity , 2017, NeuroImage.

[3]  Ludovica Griffanti,et al.  Hand classification of fMRI ICA noise components , 2017, NeuroImage.

[4]  D. Rubin,et al.  The central role of the propensity score in observational studies for causal effects , 1983 .

[5]  Ludovica Griffanti,et al.  Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank , 2017, NeuroImage.

[6]  Eduard T. Klapwijk,et al.  Qoala-T: A supervised-learning tool for quality control of FreeSurfer segmented MRI data , 2019, NeuroImage.

[7]  Jessica A. Turner,et al.  Exploration of scanning effects in multi-site structural MRI studies , 2014, Journal of Neuroscience Methods.

[8]  Stephen M. Smith,et al.  Temporal Autocorrelation in Univariate Linear Modeling of FMRI Data , 2001, NeuroImage.

[9]  Steen Moeller,et al.  ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging , 2014, NeuroImage.

[10]  Sébastien Ourselin,et al.  Head size, age and gender adjustment in MRI studies: a necessary nuisance? , 2010, NeuroImage.

[11]  Nicholas J. Buser,et al.  Variations in Structural MRI Quality Significantly Impact Commonly-Used Measures of Brain Anatomy , 2019, bioRxiv.

[12]  Tom Johnstone,et al.  Motion correction and the use of motion covariates in multiple‐subject fMRI analysis , 2006, Human brain mapping.

[13]  Jiji Zhang,et al.  On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias , 2008, Artif. Intell..

[14]  Stefan Klöppel,et al.  Correction of inter-scanner and within-subject variance in structural MRI based automated diagnosing , 2014, NeuroImage.

[15]  Tal Yarkoni,et al.  Statistically Controlling for Confounding Constructs Is Harder than You Think , 2016, PloS one.

[16]  J. Pearl.  Causal inference in statistics: An overview , 2009 .

[17]  F. Dekker,et al.  Confounding: what it is and how to deal with it. , 2008, Kidney international.

[18]  Simon B. Eickhoff,et al.  An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data , 2013, NeuroImage.

[19]  Terence P. Speed,et al.  A comparison of normalization methods for high density oligonucleotide array data based on variance and bias , 2003, Bioinform..

[20]  Sean P. Fitzgibbon,et al.  Automated quality control for within and between studies diffusion MRI data using a non-parametric framework for movement and distortion correction , 2019, NeuroImage.

[21]  J. Haxby,et al.  Functional Magnetic Resonance Imaging of the Brain , 1995, Annals of Internal Medicine.

[22]  Karl J. Friston,et al.  Movement‐Related effects in fMRI time‐series , 1996, Magnetic resonance in medicine.

[23]  E. Feczko,et al.  Motion‐related artifacts in structural brain images revealed with independent estimates of in‐scanner head motion , 2016, Human brain mapping.

[24]  Timothy O. Laumann,et al.  Methods to detect, characterize, and remove motion artifact in resting state fMRI , 2014, NeuroImage.

[25]  Bryon A. Mueller,et al.  A Survey of the Sources of Noise in fMRI , 2012, Psychometrika.

[26]  Thomas E. Nichols,et al.  Statistical Challenges in “Big Data” Human Neuroimaging , 2018, Neuron.

[27]  Stamatios N. Sotiropoulos,et al.  Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR images , 2016, NeuroImage.

[28]  H. Steven Scholte,et al.  How to control for confounds in decoding analyses of neuroimaging data , 2018, NeuroImage.

[29]  Thomas E. Nichols,et al.  Discovering markers of healthy aging: a prospective study in a Danish male birth cohort , 2019, Aging.

[30]  Kevin Murphy,et al.  Resting-state fMRI confounds and cleanup , 2013, NeuroImage.

[31]  Mark W. Woolrich,et al.  Investigations into within- and between-subject resting-state amplitude variations , 2017, NeuroImage.

[32]  K. Linkenkaer-Hansen,et al.  Resting-State fMRI Functional Connectivity Is Associated with Sleepiness, Imagery, and Discontinuity of Mind , 2015, PloS one.

[33]  Julian D. Karch,et al.  Identifying predictors of within-person variance in MRI-based brain volume estimates , 2019, NeuroImage.

[34]  Janaina Mourão Miranda,et al.  Predictive modelling using neuroimaging data in the presence of confounds , 2017, NeuroImage.

[35]  N. K. Focke,et al.  Multi-site voxel-based morphometry — Not quite there yet , 2011, NeuroImage.

[36]  Christopher Rorden,et al.  Image Processing and Quality Control for the first 10,000 Brain Imaging Datasets from UK Biobank , 2017 .

[37]  Ian J Deary,et al.  Reliability and validity of the UK Biobank cognitive tests , 2019, PloS one.

[38]  Cristina Granziera,et al.  Effects of MRI scan acceleration on brain volume measurement consistency , 2012, Journal of magnetic resonance imaging : JMRI.

[39]  Soroosh Afyouni,et al.  Insight and inference for DVARS , 2017, NeuroImage.

[40]  J. Dukart,et al.  Age Correction in Dementia – Matching to a Healthy Brain , 2011, PloS one.

[41]  Kevin Murphy,et al.  Towards a consensus regarding global signal regression for resting state functional connectivity MRI , 2017, NeuroImage.

[42]  J. Marchini,et al.  Genome-wide association studies of brain imaging phenotypes in UK Biobank , 2018, Nature.

[43]  M. Dylan Tisdall,et al.  Quantitative assessment of structural image quality , 2018, NeuroImage.

[44]  Marisa O. Hollinshead,et al.  Identification of common variants associated with human hippocampal and intracranial volumes , 2012, Nature Genetics.

[45]  Andrew Dienstfrey,et al.  Assessing effects of scanner upgrades for clinical studies , 2019, Journal of magnetic resonance imaging : JMRI.

[46]  Stephen M. Smith,et al.  Accurate, Robust, and Automated Longitudinal and Cross-Sectional Brain Change Analysis , 2002, NeuroImage.

[47]  P. Matthews,et al.  Multimodal population brain imaging in the UK Biobank prospective epidemiological study , 2016, Nature Neuroscience.

[48]  Hui Zhang,et al.  Towards a comprehensive framework for movement and distortion correction of diffusion MR images: Within volume movement , 2017, NeuroImage.