Modern network science of neurological disorders

By Cornelis J. Stam. Nature Reviews Neuroscience, 2014

1,272 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Molecular Biology, Physiology

View Paper: https://www.semanticscholar.org/paper/99f711c4133f5f15006d44cf527203cee997d6b9

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Citations in the corpus

GRETNA: a graph theoretical network analysis toolbox for imaging connectomics (2015, result): “Given the fact that: (i) the R-fMRI data were mainly used to illustrate the usage of GRETNA; (ii) the analyzed network properties have been frequently studied under both healthy and pathological conditions (For relevant reviews, see Bullmore and Sporns, 2009; He and Evans, 2010; Stam, 2014); and (ii”
Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data (2018, background): “However, previous studies (Bullmore & Sporns, 2012; Fornito, Zalesky, & Breakspear, 2015; Stam, 2014; Yu et al., 2016, 2017) have shown that functional brain network organizations are highly correlated with other demographic (e.”
Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review (2019, background): “…Liang et al., 2016), as well as in neurological and mental disorders (Xia and He, 2011; Fornito et al., 2012; Filippi et al., 2013; Dai and He, 2014; Stam, 2014; Fornito and Bullmore, 2015; Gong and He, 2015; Abós et al., 2017; Fleischer et al., 2017; Hojjati et al., 2017; Jalili, 2017; Miri…”
How reliable are MEG resting-state connectivity metrics? (2016, background): “, 2015), as well as of the fundamental differences in connectivity between healthy and diseased populations (Stam and van Straaten, 2012; Stam, 2014; van Straaten and Stam, 2013).”
The minimum spanning tree: An unbiased method for brain network analysis (2015, neutral): “(Stam and van Straaten, 2012; Stam, 2014).”
Two’s company, three (or more) is a simplex (2016, background): “Over the last decade, network models have demonstrated broad utility in uncovering fundamental architectural principles (Bassett and Bullmore, 2006; Bullmore and Bassett, 2011) and their implications for cognition (Medaglia et al, 2015) and disease (Stam, 2014).”
Functional brain networks are dominated by stable group and individual factors, not cognitive or daily variation (2018, background): “Thus, successful past studies measuring differences in brain networkswithdisease (seeBaldassarre et al., 2016;Bassett and Bullmore, 2009; Greene et al., 2016; Menon, 2011; Sheffield and Barch, 2016; Stam, 2014) aremost likely underpinned by differences in a stable network architecture, although comp”
State-space model with deep learning for functional dynamics estimation in resting-state fMRI (2016, positive): “Hidden Markov Models (HMMs) Recent studies that reported network changes in neurological and psychiatric diseases (Handwerker et al., 2012; Leonardi et al., 2013; Stam, 2014) inspired us to hypothesize that MCIs may exhibit different dynamic functional characteristics from NCs (Greicius, 2008), depe”
The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives (2024, background): “…of reviews [see, e.g. ( Richardson, 2010; Kramer and Cash, 2012; Laufs, 2012; Engel et al., 2013; van Diessen et al., 2013; Chiang and Haneef, 2014; Stam, 2014; van Mierlo et al., 2014; Bernhardt et al., 2015; Bartolomei et al., 2017; Zijlmans et al., 2019; Gil et al., 2020; Royer et al.,…”