Exploring connectivity with large-scale Granger causality on resting-state functional MRI

- Resting-state connectivity with large-scale Granger causality (lsGC) is studied.
- LsGC, a data-driven multivariate approach, estimates interactions in large systems.
- Effectiveness of lsGC tested with realistic simulated fMRI and empirical fMRI data.
- Network structure recovered in both datasets.
- Modular functional subnetworks corresponding to motor and visual cortices recovered.

BackgroundLarge-scale Granger causality (lsGC) is a recently developed, resting-state functional MRI (fMRI) connectivity analysis approach that estimates multivariate voxel-resolution connectivity. Unlike most commonly used multivariate approaches, which establish coarse-resolution connectivity by aggregating voxel time-series avoiding an underdetermined problem, lsGC estimates voxel-resolution, fine-grained connectivity by incorporating an embedded dimension reduction.New methodWe investigate application of lsGC on realistic fMRI simulations, modeling smoothing of neuronal activity by the hemodynamic response function and repetition time (TR), and empirical resting-state fMRI data. Subsequently, functional subnetworks are extracted from lsGC connectivity measures for both datasets and validated quantitatively. We also provide guidelines to select lsGC free parameters.ResultsResults indicate that lsGC reliably recovers underlying network structure with area under receiver operator characteristic curve (AUC) of 0.93 at TR = 1.5 s for a 10-min session of fMRI simulations. Furthermore, subnetworks of closely interacting modules are recovered from the aforementioned lsGC networks. Results on empirical resting-state fMRI data demonstrate recovery of visual and motor cortex in close agreement with spatial maps obtained from (i) visuo-motor fMRI stimulation task-sequence (Accuracy = 0.76) and (ii) independent component analysis (ICA) of resting-state fMRI (Accuracy = 0.86).Comparison with existing method(s)Compared with conventional Granger causality approach (AUC = 0.75), lsGC produces better network recovery on fMRI simulations. Furthermore, it cannot recover functional subnetworks from empirical fMRI data, since quantifying voxel-resolution connectivity is not possible as consequence of encountering an underdetermined problem.ConclusionsFunctional network recovery from fMRI data suggests that lsGC gives useful insight into connectivity patterns from resting-state fMRI at a multivariate voxel-resolution.