title: Improved 7 Tesla resting-state fMRI connectivity measurements by cluster-based modeling of respiratory volume and heart rate effects.
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.
authors.affiliation: Institute for Systems and Robotics - Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.
authors.forename: Joana
authors.initials: J
authors.lastname: Pinto
authors.affiliation: Institute for Systems and Robotics - Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.
authors.forename: Sandro
authors.initials: S
authors.lastname: Nunes
authors.affiliation: Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, MGH and Harvard Medical School, Boston, MA, USA.
authors.forename: Marta
authors.initials: M
authors.lastname: Bianciardi
authors.affiliation: Institute for Systems and Robotics - Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.
authors.forename: Afonso
authors.initials: A
authors.lastname: Dias
authors.affiliation: INESC-ID and Department of Electrical and Computer Engineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.
authors.forename: L Miguel
authors.initials: LM
authors.lastname: Silveira
authors.affiliation: Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, MGH and Harvard Medical School, Boston, MA, USA.
authors.forename: Lawrence L
authors.initials: LL
authors.lastname: Wald
authors.affiliation: Institute for Systems and Robotics - Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal. Electronic address: patricia.figueiredo@tecnico.ulisboa.pt.
authors.forename: Patrícia
authors.initials: P
authors.lastname: Figueiredo
mesh_terms: D000328:Adult; D016477:Artifacts; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D016000:Cluster Analysis; D005260:Female; D006339:Heart Rate; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D012119:Respiration; D012815:Signal Processing, Computer-Assisted; D055815:Young Adult
keywords: functional brain connectivity; functional magnetic resonance imaging (fMRI); physiological noise modeling; resting-state networks