Background / Introduction: There is considerable interest in using real-time fMRI for monitoring functional connectivity dynamics. To date, the majority of real-time resting-state fMRI studies have examined limited number of brain regions. This is in part due to the computational demands of traditional seed- and ICA-based methods, in particular when using increasingly available high-speed fMRI methods.
METHODS
This study describes a computationally efficient real-time seed-based resting-state fMRI analysis pipeline using moving averaged sliding-windows with partial correlations and regression of motion parameters and signals from white matter and cerebrospinal fluid.
RESULTS
Analytical and numerical analyses of averaged sliding-window correlation and sliding-window regression as a function of window width show selectable bandpass filter characteristics and effective suppression of artifactual correlations resulting from signal drifts and transients. The analysis pipeline is compatible with multi-slab echo-volumar imaging and simultaneous multi-slice echo-planar imaging with repetition times as short as 136 ms. High-speed resting-state fMRI data in healthy controls demonstrate the effectiveness of this approach for minimizing artifactual correlations in white and gray matter, which was comparable to conventional regression across the entire scan. Integrating sliding-window averaging (width: W1) within a 2nd level sliding-window (width: W2) enabled monitoring of intra- and inter-network correlation dynamics of up to 12 resting-state networks with bandpass filter characteristics determined by the 1st level sliding-window and temporal resolution W1+W2.
CONCLUSIONS
The computational performance and confound tolerance make this seed-based resting-state fMRI approach suitable for real-time monitoring of data quality and resting-state connectivity dynamics in neuroscience and clinical research studies.