Abstract
In this work fMRI BOLD datasets are shown to contain slice-dependent non-stationarities. A model containing slice-dependent, non-stationary signal power is proposed to address time-varying signal power during BOLD data acquisition. The impact of non-stationary power on functional MRI connectivity is analytically derived, establishing that pairwise connectivity estimates are scaled by a function of the time-varying signal power, with magnitude upper bound by 1, and that the variance of sample correlation is increased, thereby inducing spurious connectivity. Consequently, we make the observation that time-varying power during acquisition of BOLD timeseries has the propensity to diminish connectivity estimates. To ameliorate the impact of non-stationary signal power, a simple correction for slice-dependent non-stationarity is proposed. Our correction is analytically shown to restore both signal stationarity and, subsequently, the integrity of connectivity estimates. Theoretical results are corroborated with empirical evidence demonstrating the utility of our correction. In addition, slice-dependent non-stationary variance is experimentally determined to be optimally characterized by an inverse Gamma distribution. The resulting distribution of a voxel's signal intensity is analytically derived to be a generalized Student's-t distribution, providing support for the Gaussianity assumption typically imposed by fMRI connectivity methods.
Highlights
Functional magnetic resonance imaging (MRI) connectivity analysis utilizes blood oxygen level dependent (BOLD) data to determine how spatially remote brain regions cooperate when a subject is completing a task or is at rest
We propose a model of non-stationarity for resting state BOLD data in which each two-dimensional slice variance is characterized by a distinct time-varying signal power
Connectivity analyses of resting state Functional MRI (fMRI) data typically require stationarity of timeseries. This assumption is violated by non-stationarity derived from noise sources such as head movement, or by variable signal power originating from sources such as inhomogeneous RF amplification (Tanase et al, 2011) and signal attenuation (Stejskal, 1965)
Summary
Functional MRI (fMRI) connectivity analysis utilizes blood oxygen level dependent (BOLD) data to determine how spatially remote brain regions cooperate when a subject is completing a task or is at rest. BOLD data is noisy, being impacted by both equipment-related noise, such as coil interference (Bandettini et al, 1998) and scanner drift (Bianciardi et al, 2009), and subject noise, such as physiological interference (Biswal et al, 1995) and subject movement (Barry et al, 2010) Both equipment and subject noise can be irregular and, non-stationary. Delay between slice acquisitions can render the introduced non-stationarity slice dependent, which is significant for connectivity in Correcting for Non-stationarity which comparison between voxels is key, in contrast to an activation analysis in which voxels are typically analyzed independently (Kimberg, 2008)
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