Sydney Bailes1 and Laura D. Lewis1
1Biomedical Engineering, Boston University, Boston, MA, United States
1Biomedical Engineering, Boston University, Boston, MA, United States
There
are significant differences in spectral properties of resting state fMRI signals
between voxels with fast and slow hemodynamics and these properties can be used
to classify voxels as fast or slow.
Figure 1: Simulating HRFs with
different temporal dynamics. A)
Six HRFs with varying TTPs and FWHMs that were compared in our simulation. SPM
HRF is the canonical two-gamma HRF used in SPM software while the five other
HRFs have TTPs and FWHMs based on literature7. B) Spectrum of each HRF C)
Changes in predicted fMRI response amplitude across different frequencies for
different HRF timings.
Figure 3: Differences in resting
state spectra across fast and slow voxels. A) Example spectrum showing how each variable shown in
Fig. 3b-d were calculated. B-D)
Differences in the average of the B) slopes of the resting state spectrum, C) average
power, and D) exponent of fit to b-log10Freqx fast and slow
voxels in a session, 8/8 slope, 7/8 low frequency power, 7/8 aperiodic exponent
pairings have significant difference (p<0.5), error bars report SEM.
