Test-Retest Reliability of Graph Metrics in High-resolution Functional Connectomics: A Resting-State Functional MRI Study.

CNS Neurosci Ther. 2015 Jul 27;

Authors: Du HX, Liao XH, Lin QX, Li GS, Chi YZ, Liu X, Yang HZ, Wang Y, Xia MR

Abstract
BACKGROUND: The combination of resting-state functional MRI (R-fMRI) technique and graph theoretical approaches has emerged as a promising tool for characterizing the topological organization of brain networks, that is, functional connectomics. In particular, the construction and analysis of high-resolution brain connectomics at a voxel scale are important because they do not require prior regional parcellations and provide finer spatial information about brain connectivity. However, the test-retest reliability of voxel-based functional connectomics remains largely unclear.
AIMS: This study tended to investigate both short-term (∼20 min apart) and long-term (6 weeks apart) test-retest (TRT) reliability of graph metrics of voxel-based brain networks.
METHODS: Based on graph theoretical approaches, we analyzed R-fMRI data from 53 young healthy adults who completed two scanning sessions (session 1 included two scans 20 min apart; session 2 included one scan that was performed after an interval of ∼6 weeks).
RESULTS: The high-resolution networks exhibited prominent small-world and modular properties and included functional hubs mainly located at the default-mode, salience, and executive control systems. Further analysis revealed that test-retest reliabilities of network metrics were sensitive to the scanning orders and intervals, with fair to excellent long-term reliability between Scan 1 and Scan 3 and lower reliability involving Scan 2. In the long-term case (Scan 1 and Scan 3), most network metrics were generally test-retest reliable, with the highest reliability in global metrics in the clustering coefficient and in the nodal metrics in nodal degree and efficiency.
CONCLUSION: We showed high test-retest reliability for graph properties in the high-resolution functional connectomics, which provides important guidance for choosing reliable network metrics and analysis strategies in future studies.

PMID: 26212146 [PubMed - as supplied by publisher]