Graph Theoretical Analysis of Functional Brain Networks: Test-Retest Evaluation on Short- and Long-Term Resting-State Functional MRI Data

Jin-Hui Wang,Michael P Milham,Xi-Nian Zuo,Suril Gohel,Yong He,Bharat B Biswal,Marcus Kaiser

doi:10.1371/journal.pone.0021976

Abstract

Graph-based computational network analysis has proven a powerful tool to quantitatively characterize functional architectures of the brain. However, the test-retest (TRT) reliability of graph metrics of functional networks has not been systematically examined. Here, we investigated TRT reliability of topological metrics of functional brain networks derived from resting-state functional magnetic resonance imaging data. Specifically, we evaluated both short-term (<1 hour apart) and long-term (>5 months apart) TRT reliability for 12 global and 6 local nodal network metrics. We found that reliability of global network metrics was overall low, threshold-sensitive and dependent on several factors of scanning time interval (TI, long-term>short-term), network membership (NM, networks excluding negative correlations>networks including negative correlations) and network type (NT, binarized networks>weighted networks). The dependence was modulated by another factor of node definition (ND) strategy. The local nodal reliability exhibited large variability across nodal metrics and a spatially heterogeneous distribution. Nodal degree was the most reliable metric and varied the least across the factors above. Hub regions in association and limbic/paralimbic cortices showed moderate TRT reliability. Importantly, nodal reliability was robust to above-mentioned four factors. Simulation analysis revealed that global network metrics were extremely sensitive (but varying degrees) to noise in functional connectivity and weighted networks generated numerically more reliable results in compared with binarized networks. For nodal network metrics, they showed high resistance to noise in functional connectivity and no NT related differences were found in the resistance. These findings provide important implications on how to choose reliable analytical schemes and network metrics of interest.

Highlights

The human brain is a highly complex system represented as a structurally interconnected network by a dense of cortico-cortical axonal pathways and a functionally synchronized network by external or intrinsic coherent neural activity
We examined the test-retest reliability of topological metrics of intrinsic connectivity networks derived from human brain R-fMRI data
Further reliability analyses of network metrics highlighted several main findings: 1) that global network metrics showed overall poor to low but threshold-sensitive reliability; 2) that local nodal metrics were fairly reliable for association and limbic/paralimbic cortex regions; 3) that reliability of network metrics differed significantly among the measures examined; 4) that reliability of global network metrics depended on multiple experiment and analytical factors while nodal reliability was robust to these factors; and 5) that weighted networks and nodal network metrics were numerically more reliable in the face of noise in functional connectivity

Summary

Introduction

The human brain is a highly complex system represented as a structurally interconnected network by a dense of cortico-cortical axonal pathways (i.e., structural connectome, [1]) and a functionally synchronized network by external or intrinsic coherent neural activity (i.e., functional connectome, [2]). Recent studies have manifested that human brain connectome networks can be constructed using neuroimaging (e.g., functional MRI (fMRI) and diffusion tensor imaging (DTI)) or electrophysiological (e.g., electroencephalography (EEG) and magnetoencephalography (MEG)) data and further investigated by graph theoretical approaches. These brain networks have consistently demonstrated many non-trivial topological properties, such as small-worldness, modularity and highly connected hubs (for reviews, see [3,4,5,6,7]), and exhibited distinct alterations associated with different neurocognitive disorders (for reviews, see [8,9]). Telesford et al [13] constructed functional brain networks using baseline

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