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BOLD temporal SNR bias and variance across the HCP population as a function of cortical B0-orientation and orientation variability
Olivia Viessmann1, Jingyuan Chen1, Kawin Setsompop1,2, Lawrence L Wald1,2, and Jonathan R Polimeni1,2

1Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, United States, 2Massachusetts Institute of Technology, Division of Health Sciences and Technology, Cambridge, MA, United States

Synopsis

BOLD fMRI signals vary with the orientation of the cortex to the B0-field as extravascular susceptibility effects vary with the orientation of the cortical pial vasculature. This creates regional tSNR biases with cortical folding. Certain cortical folds are more homogenous across the population than others and orientation variability across subjects should introduce tSNR variability at the group-level. Here, we use HCP 3T rs-fMRI data to show that B0-orientation contributes to within-subject tSNR bias and orientation variability contributes to tSNR variance across subjects. We found that functional connectivity networks with more perpendicular orientation exhibit higher tSNR and networks with high orientation consistency have lower tSNR variability.

Introduction

The BOLD fMRI signal has been shown to be strongly dependent on the angle$$$\,\theta\,$$$between the cerebral cortex surface normal and the main magnetic field B0-axis1,2, as extravascular susceptibility effects scale with the orientation angle of the blood vessels relative to the B0-axis by approximately cosine-squared. This results in regional differences in BOLD amplitudes that vary with cortical orientation (Fig.1a). Recently, in a pilot 7T resting-state fMRI (rs-fMRI) study, a 70% difference in tSNR was reported between parallelly and perpendicularly B0-orientated cortical locations3. Here we characterise this effect in data from the Human Connectome Project (HCP)4 that represents a modern 3T fMRI acquisition across a large subject cohort. We test whether specific cortical regions exhibit consistent B0-orientations and therefore tSNR biases across the population. We confirm a detectable tSNR bias with orientation in a single-subject rs-fMRI data (Fig.1b), and the bias becomes more evident when averaged over 118 HCP subjects (Fig.1c). This effect therefore raises the following questions to be addressed below:

  1. Within-subject tSNR bias: We created surface-based average orientation (<θ>) and tSNR (<tSNR>) atlases over 118 subjects and show how <tSNR> increases with more perpendicular B0-orientation, with a bias of 20% between parallel and perpendicular orientations.
  2. Across-subject tSNR variability: Certain cortical folds vary more than others in geometry across the population5, thus orientation consistency is expected to vary spatially. We found that tSNR variability σ(tSNR) increases with orientation inconsistency across subjects by up to 30%.
  3. Functional connectivity (FC) networks: We evaluated the effects in common FC-networks. Networks with more perpendicular orientation exhibited higher tSNR across subjects, and networks with inconsistent orientation exhibited higher across-subject tSNR variability.

Methods

118 subjects from the HCP 3T dataset (S1200, release 03/2017) were randomly chosen. The unprocessed T1w-MPRAGE and four rs-fMRI scans (2mm isotropic resolution,TR=720 ms,TE=33ms,SMS=8, no in-plane acceleration) were downloaded. The HCP minimal processing pipeline6 (including motion and distortion correction, no spatial or temporal smoothing), was followed except for spatial normalisation to MNI space, to leave the rs-fMRI data in native subject space. We calculated tSNR maps from these preprocessed rs-fMRI time-series and normalised by the global mean and divided out the provided B1−estimate (to account for bias from the receive coil sensitivities that are proportional to tSNR for thermal-noise-dominated data). Cortical surfaces were reconstructed using FreeSurfer and registered to the subject's head position during the rs-fMRI run. For every run vertex-wise orientations were calculated3,7. The tSNR volume maps were then projected onto the surfaces. Each subject's orientation and tSNR surface-maps were then projected into the “fsaverage” surface-based atlas space to generate group-level <tSNR> and σ(tSNR) maps. Orientation mean <θ> and orientation dispersion D were calculated using directional statistics8:$$$\langle\theta\rangle=arctan2[\sum_i sin(\theta_i),\sum_i cos(\theta_i)]\,\text{and}\,D=1-\sqrt{\sum_i sin(\theta_i)^2+\sum_i cos(\theta_i)^2}$$$. We used the Yeo2011 Atlas seven-network FC-based parcellation9 to calculate network averages $$$\overline{<tSNR>},\overline{<\theta>},\overline{\sigma(tSNR)}\,$$$and$$$\,\overline{D}$$$. Here$$$\,$$$we$$$\,$$$use$$$\,\langle\cdot\rangle\,$$$denote$$$\,$$$pooling$$$\,$$$across$$$\,$$$subjects$$$\,$$$and$$$\,^\overline{\,\cdot\,}$$$across$$$\,$$$space/network-ROI.

Results

The orientation atlas (Fig.2a) shows how orientation varies smoothly with cortical folding. Fig.2b displays where orientations are more consistent $$$(D\approx0)$$$ or random $$$(D\approx1)$$$ across subjects. Fig.3 displays the <tSNR> atlas where areas with high orientation consistency are bright, and areas that are more orientationally random across subjects are darkened out. We indeed found an approximately linear relationship (r=0.11,p<0.01) between across-subject tSNR variability σ(tSNR) and orientation variability D, plotted in Fig.4b. The across-subject mean <tSNR> shows a non-linear dependence on mean orientation (Fig.4a), with a tSNR bias of 20% between parallel and perpendicular orientations. Low <θ> values are noisier due to the smaller counts number (see histogram in Fig.2a). The tSNR variability σ(tSNR) varies by approximately 30%, with a lower σ(tSNR) of about 0.21 for vertices that are highly orientation consistent $$$(D\approx0)$$$ and a higher σ(tSNR) of about 0.27 for vertices that are highly inconsistent $$$(D\approx1)$$$ across subjects (Fig.4b). We also find that FC-networks follow this tSNR bias trend. Networks with more perpendicular orientation exhibit higher tSNR (Fig.5b) and networks with greater orientation consistency have lower tSNR variability (except for the limbic network, which may be an outlier due to its distortion- and low sensitivity-prone location).

Discussion

Cortical orientation contributes to tSNR bias and variability within and across subjects. Here, we accounted for coil sensitivities that alter tSNR and also change with head position. Orientation effects are expected to increase with field strength and in physiologically noise dominated data. Some FC-networks exhibit consistent cortical orientations across the population, and more perpendicularly orientated networks will have enhanced detectability. For group-level analysis orientation variability is another nuisance that should be accountable for, e.g. via regression. On a positive outlook orientation biases represent vascular information and could test the sensitivity of fMRI methods (gradient-echo vs. spin-echo). Orientation bias in the signal reflects the signal contribution of the vascular hierarchies which have distinct orientations with respect to the cortex (pials=parallel, divers=perpendicular, capillaries=pseudo-random).

Acknowledgements

We thank F. Isik Karahanoglu for insightful discussion on fc-network parcellations. This work was supported in part by the NIH NIBIB (grants P41-EB015896 and R01-EB019437), by the BRAIN Initiative (NIH NIMH grant R01-MH111419), and by the MGH/HST Athinoula A. Martinos Center for Biomedical Imaging; and was made possible by the resources provided by NIH Shared Instrumentation Grants S10-RR023043 and S10-RR019371.

References

[1] Gagnon et al., Journal of Neuroscience; 35(8):3663-3675, 2015.

[2] Baez-Yanez et al., NeuroImage; 163:13-23,2017.

[3] Viessmann et al., Proceedings of the ISMRM-ESMRMB 2018.

[4] Van Essen et al., NeuroImage; 80:62-97, 2013.

[5] Fischl et al., Cerebral Cortex; 18:1973-1980, 2008.

[6] Glasser et al., NeuroImage; 80:105-124, 2013.

[7] Cohen-Adad et al., NeuroImage; 60:1006-1014, 2012.

[8] Ley and Verdebout, Modern Directional Statistics; New York: Chapman and Hall/CRC, 2017.

[9] Yeo et al. Journal of Neurophysiology; 106:1125-1165, 2011.

Figures

Figure 1: BOLD B0-orientation effects across a large population.a) B0-orientation effects explained: orientation of the pial vasculature changes with cortical folding—tSNR is minimised when vessels are perpendicular to B0, which is the case when the cortical normal is parallel to B0(red colours); tSNR is maximised when vessels are randomly orientated, i.e. when the cortical normal is perpendicular (blue colours).b) tSNR bias from a single subject when plotted over cortical B0-orientations (error bars=standard deviation within each angular bin).c) tSNR from 118 subjects vs. B0-orientations exhibits a clear bias (note change in vertical scales to b), error bars = standard deviation of the tSNR mean across subjects).

Figure 2:A surface-based atlas of mean orientation and orientation variability across subjects.a) Mean orientation (118 subjects) on the fsaverage pial surface with the distribution plotted to the right. More perpendicular orientations are blue, more parallel orientations are yellow/red (the distribution is similar to the sinθ-curve of the uniform distribution of orientations on a sphere).b) Orientation variability quantified as dispersion D, where D=0 corresponds to consistent orientation across subjects (white shade) and D=1 corresponds to random orientation across subjects (black shade). c) Merged map of cortical orientation and variability. Areas with more consistent orientation across subjects are left bright, and more inconsistently orientated areas are dark.

Figure 3: Cortical atlas of tSNR masked by orientation variability. Areas that are bright have higher orientation consistency across subjects (D<0.5) and areas that are dark are less consistent in orientation (D>0.5).

Figure 4: Orientation effect over 118 subjects computed in the fsaverage surface-based atlas space. a) Mean <tSNR> sorted by mean orientation <θ>. The error bars are the standard deviation within each bin. b) tSNR variability (standard deviation over all subjects) sorted by orientation variability (dispersion D over all subjects).

Figure 5: tSNR bias and variability as a function of orientation and orientation consistency in well-known global FC-networks. a) Colour labels of the seven networks. b) Mean $$$\overline{\langle tSNR \rangle}$$$ vs. mean orientation $$$\overline{\langle \theta \rangle}$$$ in seven networks. c) tSNR variability $$$\overline{\sigma(tSNR)}$$$ (standard deviation of normalized tSNR across subjects in units of tSNR) vs. orientation consistency $$$\overline{D}$$$ in each network. The limbic system appears as an outlier, which is likely due to its central position near the sinuses, which is low in sensitivity and prone to geometric distortion in EPI data.

Proc. Intl. Soc. Mag. Reson. Med. 27 (2019)
0369