A systematic framework for functional connectivity measures

Wang, Huifang; Bénar, Christian; Quilichini, Pascale; Friston, Karl J.; Jirsa, Viktor K.; Bernard, Christophe

doi:10.3389/fnins.2014.00405

Cited by 286 publications

(190 citation statements)

References 43 publications

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“…However, approaches to network analysis remain largely confined to the identification of “undirected” connections, despite the fact that alternative “directed” methods are capable of richer insight into causal mechanisms. This neglect has arisen from uncertainty over the base validity and best practices in applying directional algorithms to human imaging data, which remains despite prior efforts to validate directed connectivity in simulated fMRI and MEG/EEG (Ramsey et al, 2011; Roebroeck et al, 2005; Smith et al, 2011; Wang et al, 2014). The present report aimed to clarify some of these uncertainties by developing a principled approach to validating different directional algorithms in empirical data collected from the same subjects across different imaging modalities.…”

Section: Discussionmentioning

confidence: 99%

“…This might have contributed to the poor performance of lag-based Granger causality reported in the Smith simulations. Other fMRI simulations that included a realistic lag in neural signaling yielded far higher Granger detection accuracy (Roebroeck et al, 2005; Deshpande, Sathian & Hu, 2010; Wang et al, 2014). The findings of these fMRI simulations should highlight general limitations of an overreliance on synthetic approaches to directed connectivity validation.…”

Section: Introductionmentioning

confidence: 99%

“…To this end, Wang and colleagues (2014) embedded directional ground truths in simulated MEG/EEG (via a neural mass model, Moran et al, 2013) and fMRI data (via the Buxton-Friston balloon model used in the Smith simulations), and compared the performance of a variety of directed connectivity algorithms across both modalities. The results support the efficacy of MEG/EEG directed connectivity analysis across a number of algorithms, as well as demonstrating comparably high detection performance in the fMRI simulations (again questioning the negative findings of the Smith simulations).…”

Section: Introductionmentioning

confidence: 99%

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Empirical validation of directed functional connectivity

et al. 2017

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Mapping directions of influence in the human brain connectome represents the next phase in understanding its functional architecture. However, a host of methodological uncertainties have impeded the application of directed connectivity methods, which have primarily been validated via “ground truth” connectivity patterns embedded in simulated functional MRI (fMRI) and magneto-/electro-encephalography (MEG/EEG) datasets. Such simulations rely on many generative assumptions, and we hence utilized a different strategy involving empirical data in which a ground truth directed connectivity pattern could be anticipated with confidence. Specifically, we exploited the established “sensory reactivation” effect in episodic memory, in which retrieval of sensory information reactivates regions involved in perceiving that sensory modality. Subjects performed a paired associate task in separate fMRI and MEG sessions, in which a ground truth reversal in directed connectivity between auditory and visual sensory regions was instantiated across task conditions. This directed connectivity reversal was successfully recovered across different algorithms, including Granger causality and Bayes network (IMAGES) approaches, and across fMRI (“raw” and deconvolved) and source-modeled MEG. These results extend simulation studies of directed connectivity, and offer practical guidelines for the use of such methods in clarifying causal mechanisms of neural processing.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Empirical validation of directed functional connectivity

et al. 2017

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“…The connectivity between two STEPs was defined as the temporal synchronization between them. With the developing field of imaging connectomics (Fornito and Bullmore, 2015) and its relation to cognitive processes (Sporns, 2014), other modeling methods of functional interactions between brain regions should be examined (Wang et al, 2014) in order to improve the spatiotemporal network model currently embedded in BNA analysis. Modeling techniques to be tested will include structural equation modeling (SEM, Büchel and Friston, 1997; Tsubomi et al, 2009), dynamic causal modeling (Friston et al, 2003; Moran et al, 2013), Granger causality (Ding et al, 2006; Deshpande et al, 2010) and multivariate regression (Friston, 1994; for a review, see Craddock et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

Brain Network Activation Analysis Utilizing Spatiotemporal Features for Event Related Potentials Classification

Stern

Reches

Geva

2016

Front. Comput. Neurosci.

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The purpose of this study was to introduce an improved tool for automated classification of event-related potentials (ERPs) using spatiotemporally parcellated events incorporated into a functional brain network activation (BNA) analysis. The auditory oddball ERP paradigm was selected to demonstrate and evaluate the improved tool.Methods: The ERPs of each subject were decomposed into major dynamic spatiotemporal events. Then, a set of spatiotemporal events representing the group was generated by aligning and clustering the spatiotemporal events of all individual subjects. The temporal relationship between the common group events generated a network, which is the spatiotemporal reference BNA model. Scores were derived by comparing each subject's spatiotemporal events to the reference BNA model and were then entered into a support vector machine classifier to classify subjects into relevant subgroups. The reliability of the BNA scores (test-retest repeatability using intraclass correlation) and their utility as a classification tool were examined in the context of Target-Novel classification.Results: BNA intraclass correlation values of repeatability ranged between 0.51 and 0.82 for the known ERP components N100, P200, and P300. Classification accuracy was high when the trained data were validated on the same subjects for different visits (AUCs 0.93 and 0.95). The classification accuracy remained high for a test group recorded at a different clinical center with a different recording system (AUCs 0.81, 0.85 for 2 visits).Conclusion: The improved spatiotemporal BNA analysis demonstrates high classification accuracy. The BNA analysis method holds promise as a tool for diagnosis, follow-up and drug development associated with different neurological conditions.

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“…2,3 The computation of functional connectivity requires sophisticated analysis tools and modeling that use measured brain activity as input data. 4,5 Multivariate brain activity data with an appropriate time-resolution is a prerequisite for a time-variant interaction analysis.…”

Section: Introductionmentioning

confidence: 99%

Tracking the Reorganization of Module Structure in Time-Varying Weighted Brain Functional Connectivity Networks

Schmidt

Piper

Pester

et al. 2018

Int. J. Neur. Syst.

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Identification of module structure in brain functional networks is a promising way to obtain novel insights into neural information processing, as modules correspond to delineated brain regions in which interactions are strongly increased. Tracking of network modules in time-varying brain functional networks is not yet commonly considered in neuroscience despite its potential for gaining an understanding of the time evolution of functional interaction patterns and associated changing degrees of functional segregation and integration. We introduce a general computational framework for extracting consensus partitions from defined time windows in sequences of weighted directed edge-complete networks and show how the temporal reorganization of the module structure can be tracked and visualized. Part of the framework is a new approach for computing edge weight thresholds for individual networks based on multiobjective optimization of module structure quality criteria as well as an approach for matching modules across time steps. By testing our framework using synthetic network sequences and applying it to brain functional networks computed from electroencephalographic recordings of healthy subjects that were exposed to a major balance perturbation, we demonstrate the framework's potential for gaining meaningful insights into dynamic brain function in the form of evolving network modules. The precise chronology of the neural processing inferred with our framework and its interpretation helps to improve the currently incomplete understanding of the cortical contribution for the compensation of such balance perturbations.

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A systematic framework for functional connectivity measures

Cited by 286 publications

References 43 publications

Empirical validation of directed functional connectivity

Empirical validation of directed functional connectivity

Brain Network Activation Analysis Utilizing Spatiotemporal Features for Event Related Potentials Classification

Tracking the Reorganization of Module Structure in Time-Varying Weighted Brain Functional Connectivity Networks

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