Beat-to-beat blood pressure fluctuations are present in time-frequency dynamics of resting-state fMRI

Joseph R Whittaker^{1}, Molly G Bright^{1,2}, Ian D Driver^{1}, and Kevin Murphy^{1}

Two twenty-minute resting-state fMRI runs were
acquired for 4 subjects (1 female) on a GE 3T HDx scanner with a gradient-echo
EPI readout (TR/TE = 2000/35 ms;
33 slices; resolution = 3.5 × 3.5 × 4.0 mm3).
Concurrent beat-to-beat fluctuations in mean arterial pressure (MAP)
were obtained via a non-invasive MR compatible device (Caretaker, BIOPAC). Data
were motion corrected, and physiological noise fluctuations corresponding to P_{ET}CO_{2},
cardiac and respiratory phase (RETROICOR), heart rate, and respiration volume
(RVT) were regressed out along with 6 motion parameters.

De-trended resampled MAP traces were decomposed into 6 frequency scales using the maximal overlap discrete wavelet transform (MODWT). We expect scale 2, which corresponds with the frequency range 0.0625-0.125Hz to be of most interest. Average grey matter (GM) time-series were also decomposed into the same frequency scales, and correlated with frequency matched MAP wavelet coefficients to identify which scales explains the most variance in fMRI data. For a voxel-wise analysis, de-trended, de-spiked, smoothed (5mm) time-series were decomposed, and voxel-wise correlations between fMRI and MAP wavelet coefficients of matching scales was performed. Spatial correlations between correlation maps across sessions were calculated for each scale to assess the reproducibility.

For a basic FC analysis, unsmoothed fMRI time-series from 90 anatomically derived ROIs in the AAL template [5] were decomposed, and the correlation matrix for each scale computed. For each scale, node-strength (mean connectivity per node) was correlated with a vector of correlations between MAP and each node. For comparison, correlation matrices, node strength and node-MAP correlation vectors were also calculated from un-decomponsed fMRI time-series and MAP traces.

Fig. 1A,B shows a subject MAP trace and the corresponding wavelet coefficients for the 6 frequency scales. Fig. 1C shows the group mean correlation between mean GM fMRI signals and MAP. Scale 2 shows significant (p<0.01, Bonferroni corrected) negative correlations. Fig. 1D shows the group mean spatial correlation between scans 1 & 2 MAP correlation maps. Scale 2 shows significant spatial correlation between scans 1 and 2 (p<0.05, Bonferroni corrected). Fig. 2 shows voxel-wise z-scores for the group mean correlation coefficients between fMRI and MAP wavelet coefficients for scale 2.

Fig. 3Ai shows the group mean correlation matrix between 90 ROIs for un-decomposed data, alongside a vector of correlation coefficients between each ROI and MAP trace. Fig. 3Aii shows the session mean node-MAP correlation vectors vs node-strengths for each subject. Fig. 3Bi,ii are the same as Fig. 3A, but for scale 2 wavelet coefficients. It is obvious from Figs 3A and 3B that far more variance in node strength is explained by MAP when data are wavelet decomposed compared with when they are not (group means ~48% and ~9% respectively).

Here we show that fluctuations in wavelet coefficients of a decomposed MAP time-series are highly correlated in a spatially structured manner with fMRI wavelet coefficients of matching frequency centred on 0.1Hz. We used node strength as a simple network metric, and have demonstrated that in scale 2 wavelet coefficients, node-strength is significantly and highly inversely correlated with node-MAP correlations. Although wavelet and dynamic FC techniques provide more information than simple single correlation FC measures, these data illustrate the potential confounds that can arise due to increased sensitivity to dynamic physiological processes.

Further research on the relationship between MAP fluctuations and fMRI time-series is required, i.e. temporal lag, cross frequency effects etc. Furthermore, validation of the Caretaker signal as an accurate representation of blood pressure fluctuations is needed. However, these preliminary results are cause for concern regarding the interpretability of dynamic FC studies and the effect of latent physiological variables on fMRI time-frequency dynamics.

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2. Hutchison, R.M., et al., Dynamic functional connectivity: promise, issues, and interpretations. Neuroimage, 2013. 80: p. 360-78.

3. Murphy, K., R.M. Birn, and P.A. Bandettini, Resting-state fMRI confounds and cleanup. Neuroimage, 2013. 80: p. 349-59.

4. Julien, C., The enigma of Mayer waves: Facts and models. Cardiovasc Res, 2006. 70(1): p. 12-21.

5. Tzourio-Mazoyer, N., et al., Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage, 2002. 15(1): p. 273-89.

Figure 1: A) example MAP trace and B) corresponding MODWT
wavelet coefficients. C) Group mean correlations between average GM wavelet
coefficients and matched frequency scale MAP wavelet coefficients (**
significant p<0.01, Bonferroni corrected). D) Group mean spatial correlation
between scan1 and scan2 MAP correlation maps (* significant p<0.05,
Bonferroni corrected).

Figure 2: Group level voxel-wise z-scores for
correlation between scale 2 wavelet coefficients for fMRI time-series and MAP
time-series.

Figure 3: A:
Raw time-series connectivity. i) Mean session correlation matrices for each
subject and vector of node MAP correlations. ii) Node strength vs node-MAP
correlation for each subject and R2 values (* p<0.05, *** p<10-6,
Bonferroni corrected).
B:
Scale 2 wavelet coefficients connectivity. i) Mean session correlation matrices
for each subject and vector of node MAP correlations. ii) Node strength vs
node-MAP correlation for each subject and R2 values (**** p<10-8,
corrected)

Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)

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