Synchronization likelihood: an unbiased measure of generalized synchronization in multivariate data sets

By Cornelis J. Stam, B. Dijk. 2002

644 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Networks and Communications, Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/74ebdb50d3b486dda586444daee600c8ccb1f4ed

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Citations in the corpus

HERMES: Towards an Integrated Toolbox to Characterize Functional and Effective Brain Connectivity (2013, methodology): “We can finally get a value of the SL for the whole time interval considered ( SL m ) by averaging SL m,n for all n. REMARKS More detailed information about SL and its use in filtered signals can be found in (Montez et al. 2006; Stam and van Dijk 2002).”
Activity Dependent Degeneration Explains Hub Vulnerability in Alzheimer's Disease (2012, methodology): “To quantify large-scale synchronization as a measure of interaction between different cortical areas, we used the Synchronization likelihood (SL), which is sensitive to both linear and nonlinear coupling [83,84].”
Graph theoretical analysis of complex networks in the brain (2007, methodology): “Correlations between the time series of the 126 artefact-free channels studied were analysed with the synchronization likelihood (SL), a non-linear measure of statistical interdependencies [111,112].”
Diagnosis of Alzheimer's disease from EEG signals: where are we standing? (2010, background): “An interesting alternative family of synchrony measures, referred to as state space based synchrony or generalized synchrony, stems from physics [123, 133].”
Loss of ‘Small-World’ Networks in Alzheimer's Disease: Graph Analysis of fMRI Resting-State Functional Connectivity (2010, methodology): “Linear and non-linear dynamic correlations between all pair wise combinations of time-series were measured with the SL [22,44] (for mathematical details on the SL computation, see File S1).”
Complex brain networks: graph theoretical analysis of structural and functional systems (2009, methodology): “…cortex and the superior frontal cortex, were found to have high ‘betweenness centrality’ and thus to constitute putative hubs. another study that mapped connections between 78 cortical regions using DTI also identified several hub regions, including the precuneus and the superior frontal gyrus 56 .”
Measuring functional connectivity using MEG: Methodology and comparison with fcMRI (2011, positive): “…(field spread and cross talk would cause a symmetric distribution and is therefore eliminated)) and synchronization likelihood (Stam and van Dijk, 2002) (which takes two separate electrical signals and looks for isochronous recurrence to a certain part of their (individually different)…”
Functional neural network analysis in frontotemporal dementia and Alzheimer's disease using EEG and graph theory (2009, background): “A more detailed technical description is provided in [42,43].”