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Abstract
Estimates of functional connectivity using resting state functional Magnetic Resonance Imaging (rs-fMRI) are acutely sensitive to artifacts and large scale nuisance variation. As a result much effort is dedicated to preprocessing rs-fMRI data and using diagnostic measures to identify bad scans. One such diagnostic measure is DVARS, the spatial root mean square of the data after temporal differencing. A limitation of DVARS however is the lack of concrete interpretation of the absolute values of DVARS, and finding a threshold to distinguish bad scans from good. In this work we describe a sum of squares decomposition of the entire 4D dataset that shows DVARS to be just one of three sources of variation we refer to as D-var (closely linked to DVARS), S-var and E-var. D-var and S-var partition the sum of squares at adjacent time points, while E-var accounts for edge effects; each can be used to make spatial and temporal summary diagnostic measures. Extending the partitioning to global (and non-global) signal leads to a rs-fMRI DSE table, which decomposes the total and global variability into fast (D-var), slow (S-var) and edge (E-var) components. We find expected values for each component under nominal models, showing how D-var (and thus DVARS) scales with overall variability and is diminished by temporal autocorrelation. Finally we propose a null sampling distribution for DVARS-squared and robust methods to estimate this null model, allowing computation of DVARS p-values. We propose that these diagnostic time series, images, p-values and DSE table will provide a succinct summary of the quality of a rs-fMRI dataset that will support comparisons of datasets over preprocessing steps and between subjects.
Highlights
Functional connectivity obtained with resting state functional magnetic resonance imaging is typically computed by correlation coefficients between different brain regions, or with a multivariate decomposition like Independent Components Analysis (Cole et al, 2010)
The direct estimates of the μ0 based on the DVARS2t time series perform best on this clean, artifact-free data, while μ0 estimated on variance of the differenced data degrades with increasing heterogeneity
We have provided a formal context for the diagnostic measure DVARS, showing DVARS2t to be part of a decomposition of sum-ofsquares at each successive scan pair and over the whole 4D data
Summary
Functional connectivity obtained with resting state functional magnetic resonance imaging (rs-fMRI) is typically computed by correlation coefficients between different brain regions, or with a multivariate decomposition like Independent Components Analysis (Cole et al, 2010). Both approaches can be corrupted by artifacts due to head motion or physiological effects, and much effort is dedicated to preprocessing rs-fMRI data and using diagnostic measure to identify bad scans. The emergence of the large scale data sets such as the Human Connectome Project (HCP) (>1k subjects) and the UK Biobank (>10k subjects) further motivates the need for automated, yet reliable, quantitative techniques to control and improve the data quality
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