Accurate modeling of temporal correlations in rapidly sampled fMRI time series

Abstract

Rapid imaging techniques are increasingly used in functional MRI studies because they allow a greater number of samples to be acquired per unit time, thereby increasing statistical power. However, temporal correlations limit the increase in functional sensitivity and must be accurately accounted for to control the false‐positive rate. A common approach to accounting for temporal correlations is to whiten the data prior to estimating fMRI model parameters. Models of white noise plus a first‐order autoregressive process have proven sufficient for conventional imaging studies, but more elaborate models are required for rapidly sampled data. Here we show that when the “FAST” model implemented in SPM is used with a well‐controlled number of parameters, it can successfully prewhiten 80% of grey matter voxels even with volume repetition times as short as 0.35 s. We further show that the temporal signal‐to‐noise ratio (tSNR), which has conventionally been used to assess the relative functional sensitivity of competing imaging approaches, can be augmented to account for the temporal correlations in the time series. This amounts to computing the t‐score testing for the mean signal. We show in a visual perception task that unlike the tSNR weighted by the number of samples, the t‐score measure is directly related to the t‐score testing for activation when the temporal correlations are correctly modeled. This score affords a more accurate means of evaluating the functional sensitivity of different data acquisition options.

Accurate autocorrelation modeling substantially improves fMRI reliability

“…By default, SPM estimates temporal autocorrelation globally as an autoregressive AR(1) plus white noise process 9 . SPM has an alternative approach: FAST, but we know of only three studies which have used it [10][11][12] . FAST uses a dictionary of covariance components based on exponential covariance functions 12 .…”

A circular echo planar sequence for fast volumetric fMRI

“…This will also likely increase the t‐score values obtained by standard fMRI statistical tests. Although methods for modeling temporal correlations have been proposed, the actual effect of increased sampling rates on the BOLD statistics is still an open question for investigation. In this work, we applied a less conservative approach by only decimating the data as discussed in the work of Todd et al We are currently performing research into the temporal correlations of the BOLD signal as well as the noise structure of accelerated fMRI data.…”

“…Improved spatial resolution is useful for better identification of activation as well as reduced signal dropout in regions with large magnetic susceptibility induced distortion and signal loss . Increased temporal resolution has received particular interest in fMRI for enhanced statistical power, better resolution of the hemodynamic response function (HRF), and better identification of nuisance physiological components . A common method for increasing speed in MRI is to acquire reduced datasets with receiver coil arrays using parallel imaging .…”

“…3,4 Increased temporal resolution has received particular interest in fMRI for enhanced statistical power, better resolution of the hemodynamic response function (HRF), and better identification of nuisance physiological components. [5][6][7][8][9] A common method for increasing speed in MRI is to acquire reduced datasets with receiver coil arrays using parallel imaging. [10][11][12] A drawback of parallel imaging is a reduction in image SNR equal to √ R, where R is the k-space reduction factor, as well as residual aliasing and "g-factor" noise enhancement.…”

Fine-scale computations for adaptive processing in the human brain

“…This method allows us to accurately model and remove the cross-block mean response for each condition (adaptation, non-adaptation) to account for potential task-timing confounds that have been shown to inflate the strength of the computed task-based functional connectivity. Within the GLM, the data were high-pass filtered at 0.01Hz and treated for serial autocorrelations using the FAST autoregressive model (Corbin, Todd, Friston, & Callaghan, 2018;Olszowy, Aston, Rua, & Williams, 2019). For each ROI and layer, we then computed the first eigenvariate across all voxels within the region to derive a single representative time course per layer and ROI for connectivity analysis.…”