Evaluation of different measures of functional connectivity using a neural mass model

By Olivier David, Diego Cosmelli, Karl J. Friston. NeuroImage, 2004

371 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/76e8250ab515208f914238c80932f8e28337a172

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Citations in the corpus

Cognitive fitness of cost-efficient brain functional networks (2009, methodology): “Frequency band-specific functional networks were derived from the MEG data by thresholding the mutual information—a statistical measure of linear and nonlinear association—between wavelet coefficients for each pair of sensors to construct undirected graphs or whole-brain functional networks at each ”
Estimation of the cortical functional connectivity with the multimodal integration of high-resolution EEG and fMRI data by directed transfer function (2005, background): “The concept of brain connectivity now plays a central role in neuroscience as a way to understand one possible dcode T of the functioning brain, as well as the organized behavior of cortical regions beyond the simple mapping of their activity (David et al., 2004; Horwitz, 2003; Lee et al., 2003).”
Task-Based Core-Periphery Organization of Human Brain Dynamics (2012, background): “Future studies could employ multiple estimates of statistical associations in the form of diagnostics [67–69] and/or models [70,71] that might uncover other sets of interactions that Core-Periphery Organization of Brain Dynamics”
Modelling event-related responses in the brain (2005, methodology): “The ensuing model is general, and can be used to model various cognitive paradigms (David et al., 2004b).”
Nonlinear dynamical analysis of EEG and MEG: review of an emerging field. (2005, background): “They also concluded that most measures performed quite well with the exception of mutual information (David et al., 2004).”
Brain graphs: graphical models of the human brain connectome. (2011, background): “We cannot offer definitive guidance about which of these or other possible association statistics is “best” for the purposes of graph analysis [though see David et al. (2004) and Smith et al. (2010) for evaluation of multiple possible connectivity metrics in the context of simulated data].”
Graph analysis of functional brain networks: practical issues in translational neuroscience (2014, background): “ivity). A few studies that have attempted to compare a subset of FC methods with respect to their ability to correctly detect the presence of simulated connectivity schemes in a multivariate data set [58, 59, 60]. Unfortunately the results are not unequivocal and the performance of the measures seem”
Nonlinear multivariate analysis of neurophysiological signals. (2005, methodology): “…they also suffer from systematic errors, that can be corrected, albeit only partially, by using ad hoc asymptotic series (Grassberger, 1988; Roulston, 1999), a procedure often used in practice in neurophysiology (e.g. (David et al., 2004; Netoff and Schiff, 2002; Quian Quiroga et al., 2002a)).”
Structural foundations of resting-state and task-based functional connectivity in the human brain (2013, background): “Alternative choices of functional connectivity measures, including nonlinear measures such as the mutual information or phase synchronization between two regional time series, may have different sensitivities to narrow and broad-band frequency information [55].”
Dynamic causal modelling of evoked responses in EEG/MEG with lead field parameterization (2006, neutral): “(2) and (3)) in terms of a set of differential equations as described in David et al. (2004).”
Functional and Effective Connectivity: A Review (2011, neutral): “The construct validity of these models calls on established electrophysiological phenomena and metrics of coupling (e.g., David and Friston, 2003; David et al., 2004).”