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Abstract:
Introduction:
The temporal resolution of fMRI is ultimately limited by the slow dynamics of the hemodynamic response function (HRF) [1]. Canonical HRF models predict that neural activity above ~0.3 Hz should not be detectable with fMRI [2]. However, recent work has demonstrated that neural oscillations can be measured at up to 0.75 Hz at 7T [3] due to nonlinear responses to rapid neural activity [4-7]. High-frequency oscillations are challenging to detect, as signal amplitude is small and cardiac noise appears around ~1 Hz. To probe the upper limit of fast fMRI, we tested whether neurally driven BOLD signals at 1 Hz could be detected by taking advantage of improved signal strength at 9.4 T.
Methods:
Two healthy volunteers provided informed consent and were scanned on a 9.4T scanner using a custom-built 31-channel receive coil array. Functional runs were single-shot gradient-echo blipped-CAIPI SMS-EPI with 15 oblique slices, 2 mm isotropic, targeting the calcarine sulcus (R=2 acceleration, MB=3, TR=227 ms, TE=24 ms, FA=30°). Stimuli consisted of a 12 Hz flickering radial checkerboard displayed for 4 minutes. The luminance contrast of the stimulus oscillated at either 0.2 Hz (localizer run) or 1 Hz (test runs). Data were slice-timing corrected, motion corrected, and high-pass filtered. Mean responses were computed by upsampling the BOLD timecourse and averaging every cycle of the oscillation. ICA was performed with FSL MELODIC. Statistical comparison used three control analyses: 1) a manually defined non-visual contiguous gray matter ROI; 2) a random draw of non-visual voxels with similar tSNR, resampled 1000 times; and 3) using jittered cycle times within the visual ROI, resampled 1000 times. Reported p-values are from resampling tests and reported amplitude is of the best fit sine wave.
Results:
The localizer identified 0.2 Hz oscillatory responses in visual cortex (Fig 1a). Response phase varied by hundreds of milliseconds across voxels (Fig. 1b), suggesting that selecting a subset of voxels with similar phases could reduce cancellation across the ROI. Voxels also exhibited ~1-1.3 Hz cardiac noise contamination (Fig. 1c). Given the ~3 magnitude of the 0.2 Hz oscillation, canonical models would predict a ~0.0003 response magnitude at 1 Hz (undetectable at current SNR levels) whereas extrapolating from previous results at 0.75 Hz would suggest a ~0.01 response. Analyzing the mean ROI signal during 1 Hz stimulation yielded a significant oscillation in one of the two subjects (S1 p=0.19; S2 p=0.03; Fig. 2a) when compared to a jittered control within the same ROI, but not significantly larger than other ROIs (p>0.05), suggesting that noise levels were too high to detect signals with the predicted magnitude. To reduce noise, we performed ICA with 0.2 Hz highpassing to constrain the components to be high-frequency, and observed that the first 15 components reflected cardiac contamination within slice groups (Fig. 2b). After excluding these components and selecting only voxels with a narrow range of phase responses in the localizer run, we observed significant 1 Hz oscillations in both subjects (Fig. 2c). These oscillations were significantly larger than in resampled control ROIs (S1 p=0.028; S2 p=0.015; Fig. 2d) and than jittered control analyses within the visual ROI (S1 p=0.010; S2 p=0.025; Fig. 2e) in each subject.
Conclusions:
These results suggest it may be possible to measure 1 Hz neural oscillations within single subjects using fMRI at ultra-high fields. However, due to the extremely small signal magnitudes, additional subjects will need to be studied to confirm this finding. Our results highlight the need for analyses that can separate noise and signal at high frequencies, particularly when approaching the 1 Hz range due to strong cardiac contamination. We conclude that combining physiological noise correction, fast acquisition, single-voxel phase estimation, and ultra-high field strengths may enable detection of surprisingly rapid neural oscillations using fMRI.
