Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2017 Jun;153:262-272.
doi: 10.1016/j.neuroimage.2017.04.009. Epub 2017 Apr 6.

Improved 7 Tesla Resting-State fMRI Connectivity Measurements by Cluster-Based Modeling of Respiratory Volume and Heart Rate Effects

Affiliations
Free PMC article

Improved 7 Tesla Resting-State fMRI Connectivity Measurements by Cluster-Based Modeling of Respiratory Volume and Heart Rate Effects

Joana Pinto et al. Neuroimage. .
Free PMC article

Abstract

Several strategies have been proposed to model and remove physiological noise from resting-state fMRI (rs-fMRI) data, particularly at ultrahigh fields (7 T), including contributions from respiratory volume (RV) and heart rate (HR) signal fluctuations. Recent studies suggest that these contributions are highly variable across subjects and that physiological noise correction may thus benefit from optimization at the subject or even voxel level. Here, we systematically investigated the impact of the degree of spatial specificity (group, subject, newly proposed cluster, and voxel levels) on the optimization of RV and HR models. For each degree of spatial specificity, we measured the fMRI signal variance explained (VE) by each model, as well as the functional connectivity underlying three well-known resting-state networks (RSNs) obtained from the fMRI data after removal of RV+HR contributions. Whole-brain, high-resolution rs-fMRI data were acquired from twelve healthy volunteers at 7 T, while simultaneously recording their cardiac and respiratory signals. Although VE increased with spatial specificity up to the voxel level, the accuracy of functional connectivity measurements improved only up to the cluster level, and subsequently decreased at the voxel level. This suggests that voxelwise modeling over-fits to local fluctuations with no physiological meaning. In conclusion, our results indicate that 7 T rs-fMRI connectivity measurements improve if a cluster-based physiological noise correction approach is employed in order to take into account the individual spatial variability in the HR and RV contributions.

Keywords: functional brain connectivity; functional magnetic resonance imaging (fMRI); physiological noise modeling; resting-state networks.

Figures

Figure 1
Figure 1
Top: Curves of the GM-averaged VE by RV (left) and HR (right) regressors, for each individual subject (color) and on average across subjects (black), as a function of the time-lag that was applied to the RV and HR regressors. Error bars represent the standard error of the mean. Bottom: RRF and CRF curves derived from the GM global signal for each subject (color), overlayed with the standard RRF and CRF curves reported in Birn et al., 2006 and Chang et al., 2009, respectively (black, dashed).
Figure 2
Figure 2
Group average and associated standard error maps of the 1st optimal time-lag value, obtained for both RV and HR physiological noise models in 6 representative axial slices (MNI coordinates Z = 56, 68, 80, 92, 104, 116).
Figure 3
Figure 3
Illustrative example of the newly proposed GM spatial clustering approach based on each voxel’s VE vs lag optimization curve, for both RV and HR physiological noise models: Left) Cluster average VE vs lag curves for the three clusters; and Right) spatial maps of the three clusters in seven representative axial slices.
Figure 4
Figure 4
Group average VE in GM, for RV (top) and HR (bottom) physiological noise models, and for the different model types tested (Single-Lag/Dual-Lag/Standard IRF Convolution/GS-derived IRF Convolution), as a function of the specificity level (Group/Subject/Cluster (k = 2, 3, 4, 5, and 6)/Voxel) used for the model optimization. Statistically significant differences between different specificity levels are indicated.
Figure 5
Figure 5
Group average VE results by the optimal RV+HR physiological noise model at each level of spatial specificity adopted for lag optimization (Dual-Lag for Group, Subject and Cluster, and Single-Lag for Voxel): GM mean values (bars represent group average and error bars the respective standard error) (left) and VE maps (right).
Figure 6
Figure 6
Group average FCS measurements for each seed (PCC, SMA, IPS), as a function of the spatial specificity level of the deemed optimal RV+HR physiological noise: Left) FCS averaged inside the RSN (defined by the suprathresholed group Fischer-Z maps), across the whole GM, and across WM and CSF; and Right) ratio between the average FCS inside the RSNs and the average FCS across the whole GM. Statistically significant differences between specificity levels are indicated.
Figure 7
Figure 7
PCC-based functional connectivity maps (group-level Z-stat maps), obtained for each physiological noise correction condition.

Similar articles

See all similar articles

Cited by 2 articles

  • Resting-state "physiological networks".
    Chen JE, Lewis LD, Chang C, Tian Q, Fultz NE, Ohringer NA, Rosen BR, Polimeni JR. Chen JE, et al. Neuroimage. 2020 Jun;213:116707. doi: 10.1016/j.neuroimage.2020.116707. Epub 2020 Mar 5. Neuroimage. 2020. PMID: 32145437
  • EEG-Informed fMRI: A Review of Data Analysis Methods.
    Abreu R, Leal A, Figueiredo P. Abreu R, et al. Front Hum Neurosci. 2018 Feb 6;12:29. doi: 10.3389/fnhum.2018.00029. eCollection 2018. Front Hum Neurosci. 2018. PMID: 29467634 Free PMC article. Review.

Publication types

Feedback