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D'Souza, Adora M.; Abidin, Anas Z.; Leistritz, Lutz and Wismüller, Axel (2017): Exploring connectivity with large-scale Granger causality on resting-state functional MRI. In: Journal of Neuroscience Methods, Vol. 287: pp. 68-79

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Background: Large-scale Granger causality (IsGC) 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, IsGC estimates voxel-resolution, fine-grained connectivity by incorporating an embedded dimension reduction. New method: We investigate application of IsGC 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 IsGC connectivity measures for both datasets and validated quantitatively. We also provide guidelines to select IsGC free parameters. Results: Results indicate that IsGC 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 IsGC 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), IsGC 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. Conclusions: Functional network recovery from fMRI data suggests that IsGC gives useful insight into connectivity patterns from resting-state fMRI at a multivariate voxel-resolution.

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