Consistency of Network Modules in Resting-State fMRI Connectome Data

Yu-Feng Zang,Malaak N. Moussa,Paul J. Laurienti,Matthew R. Steen,Satoru Hayasaka

doi:10.1371/journal.pone.0044428

Yu-Feng Zang, Malaak N. Moussa + Show 3 more

Open Access

https://doi.org/10.1371/journal.pone.0044428

Copy DOI

Journal: PLoS ONE	Publication Date: Aug 31, 2012
Citations: 149	License type: CC BY 4.0

Affiliation: Wake Forest University

Abstract

At rest, spontaneous brain activity measured by fMRI is summarized by a number of distinct resting state networks (RSNs) following similar temporal time courses. Such networks have been consistently identified across subjects using spatial ICA (independent component analysis). Moreover, graph theory-based network analyses have also been applied to resting-state fMRI data, identifying similar RSNs, although typically at a coarser spatial resolution. In this work, we examined resting-state fMRI networks from 194 subjects at a voxel-level resolution, and examined the consistency of RSNs across subjects using a metric called scaled inclusivity (SI), which summarizes consistency of modular partitions across networks. Our SI analyses indicated that some RSNs are robust across subjects, comparable to the corresponding RSNs identified by ICA. We also found that some commonly reported RSNs are less consistent across subjects. This is the first direct comparison of RSNs between ICAs and graph-based network analyses at a comparable resolution.

Highlights

In a typical fMRI data set acquired during resting-state, BOLD signals often exhibit strong correlations between distant brain areas despite a lack of external stimuli or a cognitive engagement [1,2,3]
We examined network modules in these voxelbased networks for consistency across subjects, and whether consistent modules are comparable to the resting-state networks (RSNs) found by ICA studies
Similar to the results reported by Damoiseaux et al [5], the consistency of this module was lower than that observed for both default mode network (DMN) and visual modules (Figure 2)

Summary

Introduction

In a typical fMRI data set acquired during resting-state, BOLD (blood-oxygen level-dependent) signals often exhibit strong correlations between distant brain areas despite a lack of external stimuli or a cognitive engagement [1,2,3] Such elevated correlation, known as functional connectivity, has been identified in the motor cortex [1], the dorsal and ventral pathways [3], and the default mode network (DMN) [4], to name a few. Doucet et al [7] examined the hierarchical structure of 23 components found by ICA and identified 5 major clusters among those Throughout the text, such a network following a similar temporal pattern discovered by ICA is referred as a ‘‘component.’’

Methods

Results

Conclusion