Small-world networks and functional connectivity in Alzheimer's disease.

By Cornelis J. Stam, B. Jones, Guido Nolte, Michael Breakspear, Philip Scheltens. Cerebral Cortex, 2006

1,293 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5dfb49cd8cc0568e2cc8204de7fa4aca30ff0ca7

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In Cognitive Neuroscience

Citations in the corpus

Analyzing and modeling real-world phenomena with complex networks: a survey of applications (2007, result): “[459] compared a group of 15 Alzheimer patients to a healthy control group of 13 patients and showed that the networks obtained for the first group have less pronounced small-world features than for the healthy patients.”
Mapping the Structural Core of Human Cerebral Cortex (2008, background): “In human cortex, the topology of functional connectivity patterns has recently been investigated [8–11], and key attributes of these patterns have been characterized across different conditions of rest or cognitive load.”
The Ubiquity of Small-World Networks (2011, background): “ween the number of nodes N and the average node degree k was the same across all subjects. Specifically, the threshold was defined so that S=log(N)/log(k) with S=2.5 across all subjects for the study(Stam et al. 2007; Supekar et al. 2008). From the adjacency matrix, the following graph metrics were ”
Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease. (2009, positive): “However, Ĉw was very close to one in both groups, and much lower than reported in other studies, where Ĉw was usually around two (Stam, 2004; Salvador et al., 2005; Achard et al., 2006; Bassett et al., 2006; Stam et al., 2007a).”
Structural Insights into Aberrant Topological Patterns of Large-Scale Cortical Networks in Alzheimer's Disease (2008, background): “Recently, using electroencephalogram (EEG) recording, Stam et al. (2007) have reported the loss of small-world characteristics in functional brain networks in AD, implicating disruptive system integrity associated with specific cognitive states caused by the disease.”
Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex (2017, methodology): “…to the parcellation method renders them a convenient means for the1230 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 Sporns, 2009; Stam et al., 2009, 2007).”
Graph-Based Network Analysis of Resting-State Functional MRI (2010, neutral): “…et al., 2009), aging (Achard and Bullmore, 2007; Gong et al., 2009b; Meunier et al., 2009a; Micheloyannis et al., 2009; Wang et al., 2010), and various brain disorders (Stam et al., 2007; He et al., 2008, 2009c; Liu et al., 2008; Supekar et al., 2009; Wang et al., 2009b; Buckner et al., 2009).”
Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer's Disease (2008, background): “analyzing betaband EEG in controls and AD subjects are also largely consistent with our results [25].”
Parcellation‐dependent small‐world brain functional networks: A resting‐state fMRI study (2009, methodology): “…in development (Micheloyannis et al., in press), normal aging (Achard and Bullmore, 2007) and brain disorders (Bartolomei et al., 2006; He et al., 2008; Liu et al., 2008; Micheloyannis et al., 2006b; Ponten et al., 2007; Rubinov et al., in press; Stam et al., 2007; Wang et al., in press).”
Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. (2009, background): “Previous studies have demonstrated the alteration of topological properties of functional or morphological brain networks in normal aging (Achard and Bullmore 2007) and brain disorders such as Alzheimer’s disease ( Stam et al. 2007; He et al. 2008).”
Activity Dependent Degeneration Explains Hub Vulnerability in Alzheimer's Disease (2012, result): “Finally, the ADD induced changes in functional network topology, such as the weakening of small-world structure and modularity (see figure 5), are in line with recent findings in restingstate EEG and MEG studies in AD [14,39,53–55].”
Graph theoretical analysis of structural and functional connectivity MRI in normal and pathological brain networks (2010, neutral): “clustering coefficient [84].”