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

Abstract：

BACKGROUND:Large-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 METHOD:We 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. RESULTS:Results indicate that lsGC reliably recovers underlying network structure with area under receiver operator characteristic curve (AUC) of 0.93 at TR=1.5s 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. CONCLUSIONS:Functional network recovery from fMRI data suggests that lsGC gives useful insight into connectivity patterns from resting-state fMRI at a multivariate voxel-resolution.

journal_name

J Neurosci Methods

journal_title

Journal of neuroscience methods

authors

DSouza AM,Abidin AZ,Leistritz L,Wismüller A

doi

10.1016/j.jneumeth.2017.06.007

subject

Has Abstract

pub_date

2017-08-01 00:00:00

pages

68-79

eissn

0165-0270

issn

1872-678X

pii

S0165-0270(17)30171-1

journal_volume

287

pub_type

杂志文章

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