Serial Correlations in Single-Subject fMRI with Sub-Second TR

TL;DR

This study investigates how sub-second TR in fast fMRI affects serial correlation modeling, showing that advanced noise models are necessary for valid single-subject inference due to complex noise structures.

Contribution

It demonstrates that traditional AR(1) noise models are insufficient for sub-second TR fMRI data and proposes the use of physiological noise modeling with advanced pre-whitening.

Findings

Physiological noise modeling reduces AR model order requirements.

Standard AR(1) models are inadequate for sub-second TR fMRI.

Advanced noise modeling enables valid inference in fast fMRI analysis.

Abstract

When performing statistical analysis of single-subject fMRI data, serial correlations need to be taken into account to allow for valid inference. Otherwise, the variability in the parameter estimates might be under-estimated resulting in increased false-positive rates. Serial correlations in fMRI data are commonly characterized in terms of a first-order autoregressive (AR) process and then removed via pre-whitening. The required noise model for the pre-whitening depends on a number of parameters, particularly the repetition time (TR). Here we investigate how the sub-second temporal resolution provided by simultaneous multislice (SMS) imaging changes the noise structure in fMRI time series. We fit a higher-order AR model and then estimate the optimal AR model order for a sequence with a TR of less than 600 ms providing whole brain coverage. We show that physiological noise modelling successfully reduces the required AR model order, but remaining serial correlations necessitate an advanced noise model. We conclude that commonly used noise models, such as the AR(1) model, are inadequate for modelling serial correlations in fMRI using sub-second TRs. Rather, physiological noise modelling in combination with advanced pre-whitening schemes enable valid inference in single-subject analysis using fast fMRI sequences.