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

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.

A significant risk factor for poststroke depression: the depression-related subnetwork

“…We chose nodal degree because the number of connections or edges a node has to other nodes has been demonstrated to be a reliable index of network integrity. 43 We identified 17 brain regions whose nodal degree was negatively correlated with HAMD-24 scores. They included the prefrontal cortex, parietal cortex (right precentral gyrus and left precuneus gyrus), temporal cortex (bilateral fusiform and left superior temporal gyrus), the limbic system (bilateral posterior cingulum) and related brain areas (bilateral insula) and the basal ganglia (bilateral caudate and right putamen).…”

Spatiotemporal Reconfiguration of Large-Scale Brain Functional Networks during Propofol-Induced Loss of Consciousness

“…Second, one has to note that present graph theoretical analysis provides only a macroscopic view on brain functional organization and previous research has shown that how brain graphs are constructed, compared, and normalized to reference models may change results considerably (Zalesky et al, 2010;Wang et al, 2011). To control for a methodological bias, we compared metrics across different brain parcellations and performed analyses on degree-preserved graph partitions.…”

Tutorial on use of intraclass correlation coefficients for assessing intertest reliability and its application in functional near-infrared spectroscopy–based brain imaging

“…In the present study, we follow the same criteria since most of the previous publications in the neuroscience field have utilized the same or very similar criteria. 6,[8][9][10]15,18,19 Note that different applications may vary the ICC range to a large extent based on specific needs and definitions given by individual clinical applications. 20,21 In general, using ICC ¼ 0.40 as the floor of an acceptable range for the reliability of measurements is still reasonable as most fMRI results have ICC values of 0.33 to 0.66, 22 which are commonly considered reliable.…”