Hierarchical clustering to measure connectivity in fMRI resting-state data.

By D. Cordes, V. Haughton, J. Carew, K. Arfanakis, K. Maravilla. Magnetic Resonance Imaging, 2002

391 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/c164bbecaf885daebd431f357c5e3168ee33a418

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In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Normalized Cut Group Clustering of Resting-State fMRI Data (2008, methodology): “Several model-free methods have been applied to individual (resting) PET and fMRI data, including principal component analysis (PCA) [12], independent component analysis (ICA) ) [2,13,14], hierarchical [15] and Laplacian based clustering [16].”
Resting state fMRI: A review of methods and clinical applications (2012, neutral): “One example of a clustering algorithm is hierarchical clustering, 42,46 which builds a dendrogram (a treelike structure) of all members.”
Neurophysiological architecture of functional magnetic resonance images of human brain. (2005, methodology): “…to acquire images at high frequencies (TR < 1 s) and low-pass filter the resulting time series to isolate lowfrequency components (subtending functional connectivity) from the higher-frequency peaks at cardiac and respiratory cycle frequencies (Cordes et al., 2001, 2002; Rombouts et al., 2003).”
Age-related changes in modular organization of human brain functional networks (2009, positive): “Various forms of hierarchical cluster analysis were subsequently applied to human functional MRI data acquired in a no-task or resting state (Cordes et al., 2002; Salvador et al., 2005).”
Spatiotemporal dynamics of low frequency BOLD fluctuations in rats and humans (2010, neutral): “…in the BOLD signal may contain a wealth of information not captured by most current analysis techniques, which focus on detecting correlations among different brain regions that are assumed to persist over several minutes (Biswal et al., 1995; Cordes et al., 2002; van de Ven et al., 2004).”
Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review (2019, methodology): “Although this method exhibits good efficacy in the presence of respiratory or cardiac noise, its high computational complexity is a serious limitation when examining the whole brain connectivity (Cordes et al., 2002).”
The resting brain: unconstrained yet reliable. (2009, methodology): “For each scan and each participant, we 1) applied hierarchical clustering in a manner similar to previous fcMRI studies (Cordes et al. 2002; Salvador et al. 2005; Dosenbach et al. 2007) using average linkage to each 28 3 28 matrix of distances (1 - r) representing all pairwise correlations for seed…”
Exploring the brain network: a review on resting-state fMRI functional connectivity. (2010, methodology): “…(PCA) (Friston et al., 1993), singular value decomposition (Worsley et al., 2005), independent component analysis (Beckmann et al., 2005; Calhoun et al., 2001; van de Ven et al., 2004) and clustering (Cordes et al., 2002; Salvador et al., 2005a; Thirion et al., 2006; Van den Heuvel et al., 2008a).”
Disrupted Modularity and Local Connectivity of Brain Functional Networks in Childhood-Onset Schizophrenia (2010, positive): “…as objects in n-dimensional functional space to be classified into their “natural” groups, hierarchical cluster analysis has been used to decompose the brain into a small number of functional modules that resemble known patterns of neural connectivity (Cordes et al., 2002; Salvador et al., 2005).”
A supervised clustering approach for fMRI-based inference of brain states (2011, neutral): “p(w|Xp,y, α, λ) ∝ N (w|μ,Σ) , (7) where: { μ = αΣXp y Σ = (λIp + αXp Xp) −1 (8)”
EEG microstates as a tool for studying the temporal dynamics of whole‐brain neuronal networks: A review (2017, neutral): “Similar arguments are used to justify spatial clustering procedures in other brain imaging modalities such as fMRI (Cordes et al., 2002), in intracranial animal studies (Stopfer et al.”
Functional connectivity density mapping (2010, methodology): “Cluster analyses are also used to evaluate the degree of functional connectivity among multiple seed regions (12).”
Defining functional areas in individual human brains using resting functional connectivity MRI (2008, methodology): “3, hierarchical clustering analysis was performed on the eta(2) profiles to find any strong divisions among the set (Cordes et al., 2002; Dosenbach et al., 2007; Salvador et al., 2005).”
Multi-level bootstrap analysis of stable clusters in resting-state fMRI (2009, methodology): “…component analysis (ICA) (McKeown et al., 1998) and various clustering algorithms such as k-means (Baumgartner et al., 1998), hierarchical clustering (Cordes et al., 2002), normalized cut-graph (van den Heuvel et al., 2008), self-organizing maps and neural gas (Meyer-Baese et al., 2004).”