Background: Resting-state fMRI (rs-fMRI) is employed to assess “functional connections” of signal between brain regions. However, multiple rs-fMRI paradigms and data-filtering strategies have been used, highlighting the need to explore BOLD signal across the spectrum. Rs-fMRI data is typically filtered at frequencies ranging between 0.008∼0.2 Hz to mitigate nuisance signal (e.g. cardiac, respiratory) and maximize neuronal BOLD signal. However, some argue neuronal BOLD signal may be parsed at higher frequencies. New method: To assess the contributions of rs-fMRI along the BOLD spectra on functional network connectivity (FNC) matrices, a spatially constrained independent component analysis (ICA) was performed at seven different frequency “bins”, after which FNC values and FNC measures of matrix-randomness were assessed using linear mixed models. Results: Results show FNCs at higher-frequency bins display similar randomness to those from the typical frequency bins (0.01−0.15), while the largest values are in the 0.31−0.46 Hz bin. Eyes open (EO) vs eyes closed (EC) comparison found EC was less random than EO across most frequency bins. Further, FNC was greater in EC across auditory and cognitive control pairings while EO values were greater in somatomotor, visual, and default mode FNC. Comparison with existing methods: Effect sizes of frequency and resting-state paradigm vary from small to large, but the most notable results are specific to frequency ranges and resting-state paradigm with artifacts like motion displaying negligible effect sizes. Conclusions: These suggest unique information may be derived from FNC matrices across frequencies and paradigms, but additional data is necessary prior to any definitive conclusions.
- Frequency spectrum analysis
- Independent component analysis
- Resting-state functional connectivity
ASJC Scopus subject areas