Frequency and amplitude modulation of resting-state fMRI signals and their functional relevance in normal aging

The intrinsic composition and functional relevance of resting-state blood oxygen level–dependent signals are fundamental in research using functional magnetic resonance imaging (fMRI). Using the Hilbert–Huang Transform to estimate high-resolution time-frequency spectra, we investigated the instantaneous frequency and amplitude modulation of resting-state fMRI signals, as well as their functional relevance in a large normal-aging cohort (n = 420, age = 21–89 years). We evaluated the cognitive function of each participant and recorded respiratory signals during fMRI scans. The results showed that the Hilbert–Huang Transform effectively categorized resting-state fMRI power spectra into high (0.087–0.2 Hz), low (0.045–0.087 Hz), and very-low (≤0.045 Hz) frequency bands. The high-frequency power was associated with respiratory activity, and the low-frequency power was associated with cognitive function. Furthermore, within the cognition-related low-frequency band (0.045–0.087 Hz), we discovered that aging was associated with the increased frequency modulation and reduced amplitude modulation of the resting-state fMRI signal. These aging-related changes in frequency and amplitude modulation of resting-state fMRI signals were unaccounted for by the loss of gray matter volume and were consistently identified in the default mode and salience network. These findings indicate that resting-state fMRI signal modulations are dynamic during the normal aging process. In summary, our results refined the functionally related blood oxygen level–dependent frequency band in a considerably narrow band at a low-frequency range (0.045–0.087 Hz) and challenged the current method of resting-fMRI preprocessing by using low-frequency filters with a relatively wide range below 0.1 Hz.