Title: Serial Correlations in Single-Subject fMRI with Sub-Second TR,
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.