Multi-level bootstrap analysis of stable clusters in resting-state fMRI

By Lune P Bellec, Pedro Rosa-Neto, Oliver C. Lyttelton, Habib Benali, Alan C. Evans. NeuroImage, 2009

337 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/f68d086f9154338dae831bca074aee2bbb302e4e

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Weight-conserving characterization of complex functional brain networks (2011, background): “Degenerate partitions have not been previously reported in functional brain networks, although similar concepts have been considered in the context of cluster stability (Bellec et al., 2010).”
Clinical applications of the functional connectome (2013, neutral): “Alternatively , non-parametric methods, such as wavestrapping (Breakspear et al., 2004) or circular block bootstrap (Bellec et al., 2010), can be employed.”
Predicting brain-age from multimodal imaging data captures cognitive impairment (2016, neutral): “(2010) called brainAGE - brain age gap estimate, or Cole 584 et al. (2015) called PAD - predicted age difference).”
Modular Brain Networks (2015, methodology): “…“best” partition but as a meaningful average across multiple near-optimal partitions, and by treating nodes’ community affiliations as fuzzy variables (Bellec et al. 2010, Rubinov & Sporns 2011) or characterizing them by probabilistic clustering (Hinne et al. 2014), rather than as hard assignments.”
Linking Structure and Function in Macroscale Brain Networks. (2020, positive): “While intrinsic networks can be reproducibly defined using independent component analysis [19], community detection [39], or data-driven clustering [20,21], both in resting state recordings and in meta-analytic coactivation [24], application of comparable methods to diffusion-weighted SC or anatomic”
Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex (2017, methodology): “These methods are based on hier-50 archical clustering (Mumford et al., 2010; Bellec et al., 2010; Arslan and Rueckert, 2015; Moreno-Dominguez et al., 2014), k -means (and its fuzzy counterpart) (Tomassini et al., 2007; Mezer et al., 2009; Golland et al., 2008), Gaus- sian mixture models (Yeo et…”
Trouble at Rest: How Correlation Patterns and Group Differences Become Distorted After Global Signal Regression (2012, neutral): “…magnetic resonance imaging (RS-FMRI) data are spectacularly easy to collect (Biswal et al., 1995) and tantalizingly rich in detail (Anderson et al., 2011a; Bellec et al., 2010; Biswal et al., 2010; Gee et al., 2011; Honey et al., 2009; Jo et al., 2010; Kelly et al., 2010; Smith et al., 2009).”
On the Definition of Signal-To-Noise Ratio and Contrast-To-Noise Ratio for fMRI Data (2013, neutral): “Again, the ratio of the standard deviations is also found in dB scale [39–44],”
A Convergent Functional Architecture of the Insula Emerges Across Imaging Modalities (2012, neutral): “…has been observed in previous studies using both clustering and independent component analysis (ICA)-based approaches, at the level of the entire brain (Bellec et al., 2010; Smith et al., 2009; Yeo et al., 2011), as well as specific regions such as the cerebellum (Buckner et al., 2011).”
Contributions and challenges for network models in cognitive neuroscience (2014, background): “…estimate the strength with which individual regions associate with their own network community—for example, through measures of participation 40 , cluster stability 40,41 and consensus clustering 42 —thus providing information on regional roles in neural processing within and across communities.”
Multi-scale brain networks (2016, positive): “A potentially interesting avenue for future work in this area is to apply multi-scale community detection to voxellevel networks to generate parcellations of the brain at different resolutions (Bellec et al., 2010).”