Standardizing the intrinsic brain: Towards robust measurement of inter-individual variation in 1000 functional connectomes

By Chaogan Yan, R. Craddock, Xi-Nian Zuo, Yufeng Zang, Michael P. Milham. NeuroImage, 2013

445 citations recorded by the source corpus.

As researchers increase their efforts to characterize variations in the functional connectome across studies and individuals, concerns about the many sources of nuisance variation present and their impact on resting state fMRI (R-fMRI) measures continue to grow. Although substantial within-site variation can exist, efforts to aggregate data across multiple sites such as the 1000 Functional Connectomes Project (FCP) and International Neuroimaging Data-sharing Initiative (INDI) datasets amplify these concerns. The present work draws upon standardization approaches commonly used in the microarray gene expression literature, and to a lesser extent recent imaging studies, and compares them with respect to their impact on relationships between common R-fMRI measures and nuisance variables (e.g., imaging site, motion), as well as phenotypic variables of interest (age, sex). Standardization approaches differed with regard to whether they were applied post-hoc vs. during pre-processing, and at the individual vs. group level; additionally they varied in whether they addressed additive effects vs. additive + multiplicative effects, and were parametric vs. non-parametric. While all standardization approaches were effective at reducing undesirable relationships with nuisance variables, post-hoc approaches were generally more effective than global signal regression (GSR). Across approaches, correction for additive effects (global mean) appeared to be more important than for multiplicative effects (global SD) for all R-fMRI measures, with the exception of amplitude of low frequency fluctuations (ALFF). Group-level post-hoc standardizations for mean-centering and variance-standardization were found to be advantageous in their ability to avoid the introduction of artifactual relationships with standardization parameters; though results between individual and group-level post-hoc approaches were highly similar overall. While post-hoc standardization procedures drastically increased test–retest (TRT) reliability for ALFF, modest reductions were observed for other measures after post-hoc standardizations—a phenomena likely attributable to the separation of voxel-wise from global differences among subjects (global mean and SD demonstrated moderate TRT reliability for these measures). Finally, the present work calls into question previous observations of increased anatomical specificity for GSR over mean centering, and draws attention to the near equivalence of global and gray matter signal regression.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/b363ec340d788c87a919ff0e92fca59e1f7955a5

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Citations in the corpus

The Neuro Bureau ADHD-200 Preprocessed Repository (2016, background): “average pairwise correlation between all regions in the brain (GCOR) [86], or the whole-brain average of the feature under inquiry [83], have all been shown to be effective for dealing with inter-site variation.”
Recent progress and outstanding issues in motion correction in resting state fMRI (2014, unknown): “For example Van Dijk et al. (2012) calculated the root-meansquare of the derivatives of the translational realignment estimates, whereas Power et al. (2012) summed the absolute values of the derivatives of the translational and rotational realignment estimates (after converting rotational estimates ”
Benchmarking of participant‐level confound regression strategies for the control of motion artifact in studies of functional connectivity (2017, methodology): “…(2017), http://dx.doi.org/10.1016/j.neuroimage.2017.03.020 In response to this challenge, there has been a recent proliferation of participant-level confound regression and censoring methods aimed at mitigating the impact of motion on functional connectivity (Yan et al., 2013a; Power et al., 2015).”
Multi-Site Diagnostic Classification of Schizophrenia Using Discriminant Deep Learning with Functional Connectivity MRI (2018, unknown): “S2) (Yan et al., 2013).”
Deriving reproducible biomarkers from multi‐site resting‐state data: An Autism‐based example (2016, background): “Beyond challenges due to inter-site data heterogeneity, choices in the functional-connectivity data-processing pipeline further add to the variability of results [28, 27, 29].”
DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging (2016, methodology): “Keywords: data processing; quality control; resting-state fMRI; standardization; statistical analysis”