Estimating brain functional connectivity with sparse multivariate autoregression

By Pedro A. Valdes-Sosa, J. Sanchez-Bornot, Agustin Lage-Castellanos, Mayrim Vega-Hernández, Jorge Bosch-Bayard, Lester Melie-García, Erick Jorge Canales-Rodríguez. Philosophical Transactions of the Royal Society B: Biological Sciences, 2005

344 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/1f5649d77ac70a9a15af9dcf3b82618d9639fdb4

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Citations in the corpus

LASSO-TYPE RECOVERY OF SPARSE REPRESENTATIONS FOR HIGH-DIMENSIONAL DATA (2008, background): “This effect has already been observed empirically in [33].”
Effective connectivity: Influence, causality and biophysical modeling (2011, methodology): “autoregressive model (Valdés-Sosa et al., 2005) thus forcing the columns of the connectivity matrix to be sparse.”
Sparse Vector Autoregressive Modeling (2012, methodology): “Then, Lasso can be applied to select the AR coefficients and fit sVAR models, for example, see Valdés-Sosa (2005), Arnold, Liu, and Abe (2008), Lozano et al. (2009), Shojaie and Michailidis (2010), Song and Bickel (2011).”
Temporal causal modeling with graphical granger methods (2007, background): “Overall, however, the difference in performance between the various penalization schemes is relatively small [21, 30].”
Assessing functional connectivity in the human brain by fMRI. (2007, methodology): “This approach can reduce but not eliminate the problem of unmodeled common inputs; there have been efforts to apply MAR modeling to all voxels at once, reducing the common input problem even further and using more of the acquired data [41;42].”
Functional linear regression that's interpretable (2009, methodology): “For example, Tibshirani et al. (2005) uses an L1 lasso-type penalty on both the zeroth and first derivatives of a set of coefficients to produce an estimate which is both exactly zero over some regions and exactly constant over other regions.”
Analysing connectivity with Granger causality and dynamic causal modelling (2013, methodology): “The ability to handle large numbers of sources for regions is facilitated by multivariate (ensemble) GC [22] and sparse regression techniques [51], as well as recent developments in post hoc model optimisation for network discovery with DCM [52].”
Learning brain connectivity of Alzheimer’s disease by sparse inverse covariance estimation (2010, background): “Using sparse models of other kinds for brain connectivity modeling, such as multivariate or vector autoregressions, have been explored in the past, with a focus on time series data such as fMRI and EEG (Chiang et al., 2009; Thompson et al., 2009; Valdes-Sosa et al., 2005).”
Wiener-Granger Causality: A well established methodology (2011, positive): “The first issue can be addressed in several ways, for example by using dimensionality reduction techniques such as principal components analysis prior toWGC (Zhou et al., 2009) or by using sparse regression techniques in combination with pruning of ‘unlikely’ connections (Valdes-Sosa et al., 2005).”
Real-time Neuroimaging and Cognitive Monitoring Using Wearable Dry EEG (2015, neutral): “In this paper, we follow previous work [41], [42] and”