Test–Retest Reliability of Graph Metrics in High‐resolution Functional Connectomics: A Resting‐State Functional <scp>MRI</scp> Study

Du, Haixiao; Liao, Xuhong; Lin, Qixiang; Li, Gushu; Chi, Yuze; Liu, Xiang; Yang, Huazhong; Wang, Yu; Xia, Mingrui

doi:10.1111/cns.12431

Cited by 44 publications

(67 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…5,6). These observations converge with previous findings based on resting‐state fMRI [Andellini et al, ; Braun et al, ; Du et al, ; Guo et al, ].…”

Section: Discussionsupporting

confidence: 93%

“…In general, functional neuroimaging measures during resting‐state condition show moderate test–retest reliability. Consistently with previous reports, ICCs range between fair to good for functional connectivity measures, and good to moderate for graph metrics like degree centrality [Braun et al, ; Cao et al, ; Du et al, ; Guo et al, ; Patriat et al, ; Shehzad et al, ]. To improve the reliability of resting‐state functional measures, previous studies have tested a variety of experimental and analytical strategies.…”

Section: Discussionsupporting

confidence: 81%

See 1 more Smart Citation

Test–retest reliability of functional connectivity networks during naturalistic fMRI paradigms

Wang

Ren

et al. 2017

Human Brain Mapping

133

View full text Add to dashboard Cite

Functional connectivity analysis has become a powerful tool for probing the human brain function and its breakdown in neuropsychiatry disorders. So far, most studies adopted resting-state paradigm to examine functional connectivity networks in the brain, thanks to its low demand and high tolerance that are essential for clinical studies. However, the test-retest reliability of resting-state connectivity measures is moderate, potentially due to its low behavioral constraint. On the other hand, naturalistic neuroimaging paradigms, an emerging approach for cognitive neuroscience with high ecological validity, could potentially improve the reliability of functional connectivity measures. To test this hypothesis, we characterized the test-retest reliability of functional connectivity measures during a natural viewing condition, and benchmarked it against resting-state connectivity measures acquired within the same functional magnetic resonance imaging (fMRI) session. We found that the reliability of connectivity and graph theoretical measures of brain networks is significantly improved during natural viewing conditions over resting-state conditions, with an average increase of almost 50% across various connectivity measures. Not only sensory networks for audio-visual processing become more reliable, higher order brain networks, such as default mode and attention networks, but also appear to show higher reliability during natural viewing. Our results support the use of natural viewing paradigms in estimating functional connectivity of brain networks, and have important implications for clinical application of fMRI. Hum Brain Mapp 38:2226-2241, 2017. © 2017 Wiley Periodicals, Inc.

show abstract

“…5,6). These observations converge with previous findings based on resting‐state fMRI [Andellini et al, ; Braun et al, ; Du et al, ; Guo et al, ].…”

Section: Discussionsupporting

confidence: 93%

Section: Discussionsupporting

confidence: 81%

Test–retest reliability of functional connectivity networks during naturalistic fMRI paradigms

Wang

Ren

et al. 2017

Human Brain Mapping

133

View full text Add to dashboard Cite

show abstract

“…Using a publicly available dataset from the HCP, we identified a stable small‐world property in voxel‐based functional networks and highly connected hubs located in the DMN, the salience network, the FPN, and the primary visual cortex. These results are consistent with those of previous voxel‐based functional network studies (Du et al, ; Liao et al, ; van den Heuvel & Sporns, ). Significantly higher modularity and BC were observed in the female group mainly in the medial/lateral fronto‐parietal and occipital cortices.…”

Section: Discussionsupporting

confidence: 93%

“…Finally, we observed a significant effect of global signal regression on the calculation of network metrics, although both kinds of networks retained predominant small-world and modular architectures. fMRI studies have revealed that functional metrics could be affected by global signal regression for reduced BOLD spectral power and improvement in the detection of system-level correlations in resting-state brain networks (Du et al, 2015;Fox, Zhang, Snyder, & Raichle, 2009;Liang et al, 2012;Liu, Nalci, & Falahpour, 2017). However, the biological mechanism for the global signal remains largely unknown (Murphy & Fox, 2017;Qing, Dong, Li, Zang, & Liu, 2015;Saad et al, 2012;Schwarz & McGonigle, 2011…”

Section: Future Studiesmentioning

confidence: 99%

PAGANI Toolkit: Parallel graph‐theoretical analysis package for brain network big data

Xia

Zhao

et al. 2018

Human Brain Mapping

Self Cite

View full text Add to dashboard Cite

The recent collection of unprecedented quantities of neuroimaging data with high spatial resolution has led to brain network big data. However, a toolkit for fast and scalable computational solutions is still lacking. Here, we developed the PArallel Graph-theoretical ANalysIs (PAGANI) Toolkit based on a hybrid central processing unit-graphics processing unit (CPU-GPU) framework with a graphical user interface to facilitate the mapping and characterization of high-resolution brain networks. Specifically, the toolkit provides flexible parameters for users to customize computations of graph metrics in brain network analyses. As an empirical example, the PAGANI Toolkit was applied to individual voxel-based brain networks with ∼200,000 nodes that were derived from a resting-state fMRI dataset of 624 healthy young adults from the Human Connectome Project. Using a personal computer, this toolbox completed all computations in ∼27 h for one subject, which is markedly less than the 118 h required with a single-thread implementation. The voxel-based functional brain networks exhibited prominent small-world characteristics and densely connected hubs, which were mainly located in the medial and lateral fronto-parietal cortices. Moreover, the female group had significantly higher modularity and nodal betweenness centrality mainly in the medial/lateral fronto-parietal and occipital cortices than the male group. Significant correlations between the intelligence quotient and nodal metrics were also observed in several frontal regions. Collectively, the PAGANI Toolkit shows high computational performance and good scalability for analyzing connectome big data and provides a friendly interface without the complicated configuration of computing environments, thereby facilitating high-resolution connectomics research in health and disease.

show abstract

“…In this issue, Du et al. performed an important resting‐state functional MRI study by systematically evaluating the test–retest reliability of various graph metrics in the high‐resolution (voxel level) functional networks in 53 healthy participants. Specifically, using different nodal metrics, they identified functional hubs of the brain networks mainly located at the default‐mode, salience, and executive control systems.…”

mentioning

confidence: 99%

Imaging Brain Networks in Neurodegenerative Diseases

2015

CNS Neurosci Ther

View full text Add to dashboard Cite

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

Abstract: 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.

Cited by 44 publications

References 73 publications

Test–retest reliability of functional connectivity networks during naturalistic fMRI paradigms

Test–retest reliability of functional connectivity networks during naturalistic fMRI paradigms

PAGANI Toolkit: Parallel graph‐theoretical analysis package for brain network big data

Imaging Brain Networks in Neurodegenerative Diseases

Contact Info

Product

Resources

About