Partial correlation for functional brain interactivity investigation in functional MRI

By Guillaume Marrelec, Alexandre Krainik, Hugues Duffau, Mélanie Pélégrini-Issac, Stéphane Lehéricy, Julien Doyon, Habib Benali. NeuroImage, 2006

512 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/9de8dcd4a9f1a882dbb19d1fe440c79b44b17068

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Citations in the corpus

Network modelling methods for FMRI (2011, unknown): “This is not surprising—for example, partial correlation will be expected to remove (some fraction of) the global confound from any pair of timeseries before they are correlated, because it is present in all the other timeseries (Marrelec et al., 2006).”
Estimation of functional connectivity in fMRI data using stability selection-based sparse partial correlation with elastic net penalty (2011, unknown): “…e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / y n i m g correlations for estimating functional connectivity (Hampson et al., 2002; Huang et al., 2010; Lee et al., 2011; Marrelec et al., 2006, 2007; Smith et al., 2011), but they work well only when the number of brain regions is small.”
Electrophysiological Imaging of Brain Activity and Connectivity – Challenges and Opportunities (2011, unknown): “tified from the fMRI paradigms [101]–[103].”
Clinical applications of the functional connectome (2013, unknown): “However, full correlation can include artifactual connections, such as those dependent on a third source, which can be addressed with partial correlation (Marrelec et al., 2006; Smith et al., 2011).”
ICA-based artefact and accelerated fMRI acquisition for improved Resting State Network imaging (2014, methodology): “Full correlation evaluates the similarity between two time series directly, reflecting both direct and indirect functional connections, while partial correlation evaluates the similarity between two time series after regressing out all other time series, and should emphasize direct functional connec”
Functional and Effective Connectivity: A Review (2011, unknown): “, 2010) used simulated fMRI time series to compare Granger causality against a series of procedures based on functional connectivity (partial correlations, mutual information, coherence, generalized synchrony, and Bayesian networks; e.g., Baccalá and Sameshima, 2001; Marrelec et al., 2006; Patel et ”
Learning brain connectivity of Alzheimer’s disease by sparse inverse covariance estimation (2010, methodology): “To overcome this limitation, partial correlation analysis has been adopted (Salvador et al., 2005a,b; Marrelec et al., 2006, 2007; Hampson et al., 2002).”
Resting-state fMRI in the Human Connectome Project (2013, methodology): “…such simple measures are not quantitative.1 More complex (and, importantly, multivariate) analysis methods such as independent component analysis (ICA — McKeown et al., 1998; Kiviniemi et al., 2003) allow, from a single data-driven analysis, the simultaneous estimation of multiple distinct…”
Intrinsic architecture underlying the relations among the default, dorsal attention, and frontoparietal control networks of the human brain (2012, methodology): “As partial correlation methods have demonstrated enhanced sensitivity for edge detection in rsfcMRI data and allow for estimation of direct connections between nodes (Marrelec et al., 2006; Smith et al., 2011), we also constructed a partial correlation matrix in which all correlations were orthogona”
Graph analysis of functional brain networks: practical issues in translational neuroscience (2014, background): “Finally, brain nodes are typically assigned to sensors or sources, but they can also be assigned to ICA components [51].”
How reliable are MEG resting-state connectivity metrics? (2016, background): “Published by Elsevier Inc a b s t r a c t a r t i c l e i n f o”
The structural-functional connectome and the default mode network of the human brain (2014, unknown): “In theory, partial correlations only describe direct connectivity between signals of a network, i.e. suppress indirect connectivity (Marrelec et al., 2006; Varoquaux and Craddock, 2013).”