Dataset Information

Semiparametric Estimation of Task-Based Dynamic Functional Connectivity on the Population Level.

ABSTRACT: Dynamic functional connectivity (dFC) estimates time-dependent associations between pairs of brain region time series as typically acquired during functional MRI. dFC changes are most commonly quantified by pairwise correlation coefficients between the time series within a sliding window. Here, we applied a recently developed bootstrap-based technique (Kudela et al., 2017) to robustly estimate subject-level dFC and its confidence intervals in a task-based fMRI study (24 subjects who tasted their most frequently consumed beer and Gatorade as an appetitive control). We then combined information across subjects and scans utilizing semiparametric mixed models to obtain a group-level dFC estimate for each pair of brain regions, flavor, and the difference between flavors. The proposed approach relies on the estimated group-level dFC accounting for complex correlation structures of the fMRI data, multiple repeated observations per subject, experimental design, and subject-specific variability. It also provides condition-specific dFC and confidence intervals for the whole brain at the group level. As a summary dFC metric, we used the proportion of time when the estimated associations were either significantly positive or negative. For both flavors, our fully-data driven approach yielded regional associations that reflected known, biologically meaningful brain organization as shown in prior work, as well as closely resembled resting state networks (RSNs). Specifically, beer flavor-potentiated associations were detected between several reward-related regions, including the right ventral striatum (VST), lateral orbitofrontal cortex, and ventral anterior insular cortex (vAIC). The enhancement of right VST-vAIC association by a taste of beer independently validated the main activation-based finding (Oberlin et al., 2016). Most notably, our novel dFC methodology uncovered numerous associations undetected by the traditional static FC analysis. The data-driven, novel dFC methodology presented here can be used for a wide range of task-based fMRI designs to estimate the dFC at multiple levels-group-, individual-, and task-specific, utilizing a combination of well-established statistical methods.

SUBMITTER: Kudela MA

PROVIDER: S-EPMC6598619 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Semiparametric Estimation of Task-Based Dynamic Functional Connectivity on the Population Level.

Kudela Maria A MA Dzemidzic Mario M Oberlin Brandon G BG Lin Zikai Z Goñi Joaquín J Kareken David A DA Harezlak Jaroslaw J

Frontiers in neuroscience 20190621

Dynamic functional connectivity (dFC) estimates time-dependent associations between pairs of brain region time series as typically acquired during functional MRI. dFC changes are most commonly quantified by pairwise correlation coefficients between the time series within a sliding window. Here, we applied a recently developed bootstrap-based technique (Kudela et al., 2017) to robustly estimate subject-level dFC and its confidence intervals in a task-based fMRI study (24 subjects who tasted their ...[more]

PMID: 31293367

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Project description:The goal of this work was to examine task-dependent functional connectivity of the brain in people with stroke. The work was motivated by prior observations indicating that, during pedaling, cortical activation volume is lower in people with stroke than controls. During paretic foot tapping, activation volume tends to be higher in people with stroke than controls. This study asked whether these differences could be explained by altered network function of the brain. Functional magnetic resonance imaging was used to examine local and global network function of the brain during tapping and pedaling in 15 stroke and 8 control participants. Independent component analysis was used to identify six task regions of interest (ROIs) in the primary sensorimotor cortex (M1S1), anterior lobe of cerebellum (AlCb), and secondary sensory cortex (S2) on the lesioned and non-lesioned sides of the brain (left, right for controls). Global connectivity was calculated as the correlation between mean time series for each ROI. Local connectivity was calculated as the mean correlation between voxels within each ROI. Local efficiency, weighted sum, and clustering coefficient were also calculated. Results suggested that local and global networks of the brain were altered in stroke, but not in the same direction. Detection of both global and local network changes was task-dependent. We found that global network function of the brain was reduced in stroke participants as compared with controls. This effect was detected during pedaling and nonparetic tapping, but not during paretic tapping. Local network function of the brain was elevated in stroke participants during paretic tapping and reduced during pedaling. No between-group differences in local connectivity were seen during nonparetic tapping. Connections involving S2, M1S1, and AlCb were significantly affected. Reduced global connectivity of the brain might contribute to reduced brain activation volume during pedaling poststroke.

Dataset Information

Semiparametric Estimation of Task-Based Dynamic Functional Connectivity on the Population Level.

Publications

Semiparametric Estimation of Task-Based Dynamic Functional Connectivity on the Population Level.

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