Graph-Based Network Analysis of Resting-State Functional MRI

By Jinhui Wang, Xi-Nian Zuo, Yong He. Frontiers in Systems Neuroscience, 2010

620 citations recorded by the source corpus.

In the past decade, resting-state functional MRI (R-fMRI) measures of brain activity have attracted considerable attention. Based on changes in the blood oxygen level-dependent signal, R-fMRI offers a novel way to assess the brain's spontaneous or intrinsic (i.e., task-free) activity with both high spatial and temporal resolutions. The properties of both the intra- and inter-regional connectivity of resting-state brain activity have been well documented, promoting our understanding of the brain as a complex network. Specifically, the topological organization of brain networks has been recently studied with graph theory. In this review, we will summarize the recent advances in graph-based brain network analyses of R-fMRI signals, both in typical and atypical populations. Application of these approaches to R-fMRI data has demonstrated non-trivial topological properties of functional networks in the human brain. Among these is the knowledge that the brain's intrinsic activity is organized as a small-world, highly efficient network, with significant modularity and highly connected hub regions. These network properties have also been found to change throughout normal development, aging, and in various pathological conditions. The literature reviewed here suggests that graph-based network analyses are capable of uncovering system-level changes associated with different processes in the resting brain, which could provide novel insights into the understanding of the underlying physiological mechanisms of brain function. We also highlight several potential research topics in the future.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/77120e32fea8d2220cb84d7f44c7c9a9958d1ff5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Olaf Sporns
2. Yong He
3. Xi-Nian Zuo
4. D. Bassett
5. E. Bullmore
6. Jinhui Wang
7. Alan C. Evans
8. Michael P. Milham

In Radiology, Nuclear Medicine and Imaging

1. Yong He
2. Olaf Sporns
3. Xi-Nian Zuo
4. D. Bassett
5. Jinhui Wang
6. E. Bullmore
7. Alan C. Evans
8. Chaogan Yan

Citations in the corpus

• Network centrality in the human functional connectome. (2012, methodology): “Recently, researchers have begun to apply graph theory-based network analysis to explore brain connectivity (Bullmore and Sporns 2009; Rubinov and Sporns 2010; Wang et al. 2010).”
• Local-Global Parcellation of the Human Cerebral Cortex From Intrinsic Functional Connectivity MRI (2017, background): “2015b), graph theoretic analyses (Salvador et al. 2005; Supekar et al. 2008; Bullmore and Sporns 2009; Meunier et al. 2009; Guye et al. 2010; He and Evans 2010; Wang et al. 2010; Zalesky et al. 2010a; Fornito et al. 2013) or neural mass modeling (Ghosh et al.”
• Construct validation of a DCM for resting state fMRI (2015, background): “In recent years there has been amarked increase in research that combines resting state fMRI with large-scale ‘network analyses’ (Nakagawa et al., 2013; Smith et al., 2013;Wang et al., 2010).”
• Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex (2017, positive): “The robustness of these network measures to the parcellation method renders them a convenient means for the analysis of population differences and explains their popularity in recent neuroscience studies on healthy and diseased subjects (Wang et al., 2010; Rubinov and Sporns, 2010; Bullmore and Spor”
• GRETNA: a graph theoretical network analysis toolbox for imaging connectomics (2015, background): “…brain connectivity patterns and studying the topological architecture of diseased brains (Biswal et al., 1995; Fox and Raichle, 2007; Van Dijk et al., 2010; Wang et al., 2010), GRETNA exclusively extends the capabilities for R-fMRI data preprocessing and subsequent network construction procedures.”
• Modular and Hierarchically Modular Organization of Brain Networks (2010, background): “Several recent reviews (Bassett and Bullmore, 2006; Reijneveld et al., 2007; Bullmore and Sporns, 2009; He and Evans, 2010; Rubinov and Sporns, 2010; Wang et al., 2010; Bullmore and Bassett, 2010) have focused on the data analytic methods that can be used to extract complex networks from structural…”
• Hemisphere- and gender-related differences in small-world brain networks: A resting-state functional MRI study (2011, background): “…in graph theoretical analysis of R-fMRIdata have allowedus to map the topological organization of brain functional networks under normal and pathological conditions (for reviews, see Bullmore and Sporns, 2009; He et al., 2009a; Bassett and Bullmore, 2009; He and Evans, 2010; Wang et al., 2010).”
• Why use a connectivity-based approach to study stroke and recovery of function? (2012, positive): “Preliminary support for this framework comes from proof-of-concept studies in neglect and motor deficits (Deco and Rolls, 2004; Grecucci et al., 2008; Krebs et al., 2009; Park et al., 2011; J. Wang et al., 2010).”
• Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review (2019, background): “Studies in the field of complex brain networks have demonstrated that analyzing the network properties and metrics derived from brain topology using rs-fMRI can help neurologists distinguish patient groups from control subjects in mental disorders (Bassett and Bullmore, 2009; Wang et al., 2010; Stam”
• Toward reliable characterization of functional homogeneity in the human brain: Preprocessing, scan duration, imaging resolution and computational space (2012, background): “, 2012), and has been extensively explored by using resting state functional connectivity (RSFC) approaches (Biswal et al., 1995; Bullmore and Bassett, 2011; Fox and Raichle, 2007; Wang et al., 2010).”
• Brain graphs: graphical models of the human brain connectome. (2011, background): “…et al. 2009), absence seizures (Ponten et al. 2009), medial temporal lobe seizures (Ponten et al. 2007), attention deficit hyperactivity disorder (Wang et al. 2010), stroke (de Vico Fallani et al. 2009, Wang et al. 2009), spinal cord injury (de Vico Fallani et al. 2008b), fronto-temporal lobar…”
• BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis (2020, methodology): “For feature engineering, studies have used graph theoretical metrics to summarize the functional connectivity for each node into statistical measurements ( Wang et al., 2010; Karwowski et al., 2019 ).”
• Graph Theoretical Analysis of Functional Brain Networks: Test-Retest Evaluation on Short- and Long-Term Resting-State Functional MRI Data (2011, methodology): “R–fMRI is a promising tool to non-invasively map intrinsic functional connectivity patterns of the human brain in vivo [2,14,15,16] and has been extensively used to investigate inherent brain network topological organization (for a review, see [17]).”