Network modelling methods for FMRI

By Stephen M. Smith, Karla L. Miller, Gholamreza Salimi Khorshidi, Matthew A. Webster, Christian F. Beckmann, Thomas E. Nichols, J. Ramsey, Mark William Woolrich. 2011

2,073 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks (2014, background): “Despite the large number of methods for bivariate causal discovery that has been proposed over the last few years, their practical performance has not been studied very systematically (although domain-specific studies have been performed, see (Smith et al., 2011; Statnikov et al., 2012)).”
Decreased segregation of brain systems across the healthy adult lifespan (2014, background): “Older Adults (65-89) Older adults with better memory performance and higher association system segregation”
Network centrality in the human functional connectome. (2012, background): “Although establishing a ‘‘true’’ network model for the brain connectome is difficult (Smith et al. 2011), recent work suggests”
Contributions and challenges for network models in cognitive neuroscience (2014, background): “Alternative measures such as partial correlations or Bayes nets have been proposed 21 .”
How reliable are MEG resting-state connectivity metrics? (2016, background): “(Although in high-quality fMRI recordings, partial methods are found to be more discriminative than marginal approaches (Smith et al., 2011, 2013).)”
Functional and Effective Connectivity: A Review (2011, neutral): “Indeed, a recent study (Smith et al., 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.”
Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review (2019, methodology): “Due to the time mismatch between sampling interval and neural events, the causality method cannot be applied directly to the fMRI signals because it leads to the prediction of causal relationships in BOLD signals rather than neuronal responses (Smith et al., 2011, 2012).”
Fast transient networks in spontaneous human brain activity (2014, methodology): “It is worth noting that a similar limitation exists for more established data-driven decompositions such as ICA, in which the choice of model order is driven by the application; for example, lower model orders are used to obtain the classic RSNs, and higher model orders are used to obtain finer grai”
Global connectivity of prefrontal cortex predicts cognitive control and intelligence (2012, background): “It has been argued that using functionally defined regions is superior to using anatomically or arbitrarily defined regions for connectivity analysis since inadvertently including 2 regions in a single node can be detrimental to characterizing a graph (Smith et al., 2010; Wig et al., 2011).”
Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples (2012, methodology): “There reader is õreferred to Smith et al. (2011) for a full description of the data simulation process or Homogenous data: Smith et al. (2011) simulation 2 above for a brief overview.”
Estimation of functional connectivity in fMRI data using stability selection-based sparse partial correlation with elastic net penalty (2011, neutral): “In previous studies, the critical regularization parameters which influence the selection of connections between brain regions were set arbitrarily (Huang et al., 2010; Smith et al., 2011).”
Functional network organization of the human brain (2011, background): “…treating a graph as a model of a real system, if the nodes of the graph do not accurately represent real items in the system, the graph itself is a distorted model and graph theoretic properties will diverge from the true properties of the system (Butts, 2009; Smith et al., 2011; Wig et al., 2011).”
Causal Discovery with Attention-Based Convolutional Neural Networks (2019, background): “The second benchmark, called FMRI, contains realistic, simulated BOLD (Blood-oxygen-level dependent) datasets for 28 different underlying brain networks [58].”
Causal Inference on Time Series using Restricted Structural Equation Models (2013, background): “It has been reported that these data often suffer from different time delays [e.g. Buxton et al., 1998, Smith et al., 2011].”