http://www.nitrc.org/forum/forum.php?forum_id=7547 <table border="0" width="100%"><tr><td align="left"/><td align="right"><a href="https://www.ncbi.nlm.nih.gov/sites/entrez?db=pubmed&amp;cmd=Link&amp;LinkName=pubmed_pubmed&amp;from_uid=28629720">Related Articles</a></td></tr></table> <p><b>Exploring Connectivity with Large-Scale Granger Causality on Resting-State Functional MRI.</b></p> <p>J Neurosci Methods. 2017 Jun 16;:</p> <p>Authors: DSouza AM, Abidin AZ, Leistritz L, Wismüller A</p> <p>Abstract<br/> 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.<br/> 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.<br/> 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-minute 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).<br/> 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.<br/> 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.<br/> </p><p>PMID: 28629720 [PubMed - as supplied by publisher]</p> en-us Copyright 2000-2026 NITRC OSI Sun, 03 May 2026 14:10:01 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator