Analyzing information flow in brain networks with nonparametric Granger causality

By M. Dhamala, Govindan Rangarajan, Mingzhou Ding. NeuroImage, 2008

398 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/b0acd495acda03cf8c9e73d02b4ec3042b779770

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Citations in the corpus

Recurrence is required to capture the representational dynamics of the human visual system (2019, neutral): “56).”
Attentional Stimulus Selection through Selective Synchronization between Monkey Visual Areas (2012, neutral): “The GC influence of time series A onto time series B quantifies the variance in B that is not explained by the past of B but by the past of A (Kami nski et al., 2001; Dhamala et al., 2008).”
Phase transfer entropy: A novel phase-based measure for directed connectivity in networks coupled by oscillatory interactions (2014, background): “Existing frequency space measures of GC directed connectivity (Baccalá and Sameshima, 2001; Dhamala et al., 2008; Kaminski and Blinowska, 1991) assess frequency-band specific interactions between oscillation amplitudes and therefore do not reflect phase-specific information flow.”
Analysing connectivity with Granger causality and dynamic causal modelling (2013, methodology): “However, GC is not limited to this implementation; it can use nonlinear, time-varying, and non-parametric models [17,18].”
The MVGC multivariate Granger causality toolbox: a new approach to Granger-causal inference. (2014, methodology): “…routine: core / var to tsdata A 4 Spectral estimation Although we do not recommend spectral estimation as a starting point for MVGC estimation (Sections 3.1, 3.1.1 - although see Dhamala et al., 2008b) examination of cross-power spectra may be a useful preliminary step in time series data analysis.”
Granger Causality Analysis in Neuroscience and Neuroimaging (2015, neutral): “Nonparametric implementations of G-causality are also possible (Dhamala et al., 2008).”
Real-time Neuroimaging and Cognitive Monitoring Using Wearable Dry EEG (2015, neutral): “We note that a number of alternatives for time-varying VAR estimation exist, including Kalman or RLS filtering [37] and minimumphase factorization of spectral matrices [38].”
Effective connectivity: Influence, causality and biophysical modeling (2011, neutral): “The other, apparently unrelated, approach rests on some variant of Granger Causality for which we prefer the terms WAGS in ﬂ uence 8 for reasons stated below.”
Wiener-Granger Causality: A well established methodology (2011, neutral): “More complex but potentially more widely applicable alternatives include spectral factorization of wavelet transformations (Dhamala et al., 2008), and adaptive recursive least-squares modelling (Hesse et al.”
Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review (2019, methodology): “Non-linear and non-parametric models are used to solve this problem (Dhamala et al., 2008; Roebroeck et al., 2011).”
Multiplexed Levels of Cognitive Control through Delta and Theta Neural Oscillations (2024, methodology): “…measures of Granger – Geweke causality (Geweke, 1982) were derived from the spectral estimates of that interval, by using a multivariate method based on nonparametric spectral factorization (Pagnotta, Dhamala, & Plomp, 2018; Wen, Rangarajan, & Ding, 2013; Dhamala, Rangarajan, & Ding, 2008a, 2008b).”