Normalized Cut Group Clustering of Resting-State fMRI Data

By Martijn P. van den Heuvel, René C. W. Mandl, H. H. Hulshoff Pol. PLoS ONE, 2008

424 citations recorded by the source corpus.

Background Functional brain imaging studies have indicated that distinct anatomical brain regions can show coherent spontaneous neuronal activity during rest. Regions that show such correlated behavior are said to form resting-state networks (RSNs). RSNs have been investigated using seed-dependent functional connectivity maps and by using a number of model-free methods. However, examining RSNs across a group of subjects is still a complex task and often involves human input in selecting meaningful networks. Methodology/Principal Findings We report on a voxel based model-free normalized cut graph clustering approach with whole brain coverage for group analysis of resting-state data, in which the number of RSNs is computed as an optimal clustering fit of the data. Inter-voxel correlations of time-series are grouped at the individual level and the consistency of the resulting networks across subjects is clustered at the group level, defining the group RSNs. We scanned a group of 26 subjects at rest with a fast BOLD sensitive fMRI scanning protocol on a 3 Tesla MR scanner. Conclusions/Significance An optimal group clustering fit revealed 7 RSNs. The 7 RSNs included motor/visual, auditory and attention networks and the frequently reported default mode network. The found RSNs showed large overlap with recently reported resting-state results and support the idea of the formation of spatially distinct RSNs during rest in the human brain.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/446b4f9eb0532c1fb44341166cfb0921f88b93bf

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In Cognitive Neuroscience

Citations in the corpus

Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples (2012, background): “All doi:10.1016/j.neuroimage.2012.06.026 a b s t r a c t a r t i c l e i n f o”
On constrained spectral clustering and its applications (2012, background): “…work has shown that by applying spectral clustering to the restingstate fMRI we can find the substructures in the brain that are periodically and simultaneously activated over time in the resting state, which may indicate a network associated with certain functions (van den Heuvel et al (2008)).”
Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review (2019, methodology): “…such as node degree, clustering coeﬃcient, average path length, hubs, centrality, modularity, robustness, and assortativity can be utilized to detect the topological patterns of brain networks and reﬂect cognitive and behavioral performances (Sporns et al., 2004; van den Heuvel et al., 2008b).”
Resting state fMRI: A review of methods and clinical applications (2012, neutral): “fMRI and was confirmed in many studies by using a variety of analysis methods.(2-6,10,11) Studies have hypothesized that there are”
Functionally linked resting‐state networks reflect the underlying structural connectivity architecture of the human brain (2009, methodology): “Functional connections between multiple cortical brain regions were examined by computing the correlation between fMRI time-series measured during rest [Achard et al., 2006; Salvador et al., 2005b; Van den Heuvel et al., 2008b].”
Greater than the sum of its parts: a review of studies combining structural connectivity and resting-state functional connectivity (2009, methodology): “Van den Heuvel et al. (2008b) extended this qualitative approach by taking two of the same DMN regions (PCC and MPFC) and quantifying the relationship between functional and structural connectivity.”
Effects of aging on functional and structural brain connectivity☆ (2017, background): “…organization in line with the functional segregation principle, i.e. multiple distinct functional connectivity networks, including default mode, executive control, attention, salience, sensorimotor, and visual networks (Damoiseaux et al., 2006; van den Heuvel et al., 2008; Zuo et al., 2010).”
Three Systems of Insular Functional Connectivity Identified with Cluster Analysis (2010, background): “A recent line of work has used patterns of correlations between resting-state functional magnetic resonance imaging (fMRI) signals to subdivide functional regions in humans (Cohen et al. 2008; van den Heuvel et al. 2008; Shen et al. 2010).”
Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex (2017, methodology): “Finally, the graph is subdivided into different number of regions, typically by a graph partitioning algorithm, such as spectral clustering with normalised cuts (van den Heuvel et al., 2008; Craddock235 et al., 2012).”
Spatio-Temporal Data Mining (2017, methodology): “…[Mezer et al. 2009], hierarchical clustering [Goutte et al. 1999], shared nearest neighbor (SNN) clustering [Steinbach et al. 2003], and normalized-cut spectral clustering [Van Den Heuvel et al. 2008] to cluster time series (using the time series similarity measures discussed in Section 3.4.3).”
A Convergent Functional Architecture of the Insula Emerges Across Imaging Modalities (2012, positive): “For the iFC data, we were able to apply a consensus clustering approach (Bellec et al., 2010; Kelly et al., 2010; van den Heuvel et al., 2008) to identify the most stable clustering solutions across subjects within each data collection site (sitelevel solutions) and then across the 20 data…”
Modular and Hierarchically Modular Organization of Brain Networks (2010, background): “…that functional neuroimaging data, recorded “at rest” or during performance of an experimentally controlled task, can generally be decomposed into sub-systems of functionally connected brain regions (Salvador et al., 2005; Calhoun et al., 2008; Van den Heuvel et al., 2008; Smith et al., 2009).”