scholarly journals Ising model for neural data: Model quality and approximate methods for extracting functional connectivity

2009 ◽  
Vol 79 (5) ◽  
Author(s):  
Yasser Roudi ◽  
Joanna Tyrcha ◽  
John Hertz
Keyword(s):  
Ising Model ◽  
Functional Connectivity ◽  
Data Model ◽  
Model Quality ◽  
Approximate Methods ◽  
Neural Data

scholarly journals Measuring Directed Functional Connectivity Using Non-Parametric Directionality Analysis: Validation and Comparison with Non-Parametric Granger Causality

2019 ◽  
Author(s):  
Timothy O. West ◽  
David M. Halliday ◽  
Steven L. Bressler ◽  
Simon F. Farmer ◽  
Vladimir Litvak
Keyword(s):  
Functional Connectivity ◽  
Granger Causality ◽  
Local Field ◽  
Autoregressive Model ◽  
Field Potential ◽  
List Type ◽  
Signal To Noise ◽  
Neural Data ◽  
Signal Routing ◽  
Non Parametric

AbstractBackground‘Non-parametric directionality’ (NPD) is a novel method for estimation of directed functional connectivity (dFC) in neural data. The method has previously been verified in its ability to recover causal interactions in simulated spiking networks in Halliday et al. (2015)MethodsThis work presents a validation of NPD in continuous neural recordings (e.g. local field potentials). Specifically, we use autoregressive model to simulate time delayed correlations between neural signals. We then test for the accurate recovery of networks in the face of several confounds typically encountered in empirical data. We examine the effects of NPD under varying: a) signal-to-noise ratios, b) asymmetries in signal strength, c) instantaneous mixing, d) common drive, e) and parallel/convergent signal routing. We also apply NPD to data from a patient who underwent simultaneous magnetoencephalography and deep brain recording.ResultsWe demonstrate that NPD can accurately recover directed functional connectivity from simulations with known patterns of connectivity. The performance of the NPD metric is compared with non-parametric Granger causality (NPG), a well-established methodology for model free estimation of dFC. A series of simulations investigating synthetically imposed confounds demonstrate that NPD provides estimates of connectivity that are equivalent to NPG. However, we provide evidence that: i) NPD is less sensitive than NPG to degradation by noise; ii) NPD is more robust to the generation of false positive identification of connectivity resulting from SNR asymmetries; iii) NPD is more robust to corruption via moderate degrees of instantaneous signal mixing.ConclusionsThe results in this paper highlight that to be practically applied to neural data, connectivity metrics should not only be accurate in their recovery of causal networks but also resistant to the confounding effects often encountered in experimental recordings of multimodal data. Taken together, these findings position NPD at the state-of-the-art with respect to the estimation of directed functional connectivity in neuroimaging.HighlightsNon-parametric directionality (NPD) is a novel directed connectivity metric.NPD estimates are equivalent to Granger causality but more robust to signal confounds.Multivariate extensions of NPD can correctly identify signal routing.AbbreviationsdFCDirected functional connectivityEEGElectroencephalogramLFPLocal field potentialMEGMagnetoencephalogramMVARMultivariate autoregressive (model)NPDNon-parametric directionalityNPGNon-parametric Granger (causality)SMASupplementary motor areaSNRSignal-to-noise ratioSTNSubthalamic Nucleus

scholarly journals Combination of structural and functional connectivity explains unique variation in specific domains of cognitive function.

2021 ◽  
Author(s):  
Marta Czime Litwińczuk ◽  
Nelson Trujillo-Barreto ◽  
Nils Muhlert ◽  
Lauren Cloutman ◽  
Anna Woollams
Keyword(s):  
Functional Connectivity ◽  
Cognitive Performance ◽  
Processing Speed ◽  
Mutual Influence ◽  
Self Regulation ◽  
Cognitive Domain ◽  
Model Quality ◽  
Human Connectome Project ◽  
Effective Models ◽  
Functional Components

The relationship between structural and functional brain networks has been characterised as complex: the two networks mirror each other and show mutual influence but they also diverge in their organisation. This work explored whether a combination of structural and functional connectivity can improve models of cognitive performance, and whether this differs by cognitive domain. Principal Component Analysis (PCA) was applied to cognitive data from the Human Connectome Project. Four components were obtained, reflecting Retention and Retrieval, Processing Speed, Self-regulation, and Encoding. The PCA-Regression approach was applied to predict cognitive performance using structural, functional and joint structural-functional components. Model quality was evaluated using model evidence, model fit and generalisability. Functional connectivity components produced the most effective models of Retention and Retrieval and Encoding, whereas joint structural-functional components produced most effective models of Processing Speed, and Self-regulation. The present study demonstrates that multimodal data fusion using structural and functional connectivity can help predict cognitive performance, but that the additional explanatory value (relative to overfitting) may depend on the specific selection of cognitive domain. We discuss the implications of these results for studies of the brain basis of cognition in health and disease.

scholarly journals Disambiguating brain functional connectivity

2017 ◽  
Author(s):  
Eugene P. Duff ◽  
Tamar Makin ◽  
Stephen M. Smith ◽  
Mark W. Woolrich
Keyword(s):  
Functional Connectivity ◽  
Noise Levels ◽  
Network Modelling ◽  
Flexible Tool ◽  
Neural Data ◽  
Rich Diversity ◽  
Signal Change ◽  
Neural Interactions ◽  
System Properties ◽  
Brain Functional Connectivity

Functional connectivity (FC) analyses of correlations of neural activity are used extensively in neuroimaging and electrophysiology to gain insights into neural interactions. However, correlation fails to distinguish sources as different as changes in neural signal amplitudes or noise levels. This ambiguity substantially diminishes the value of FC for inferring system properties and clinical states. Network modelling approaches may avoid ambiguities, but require specific assumptions. We present an enhancement to FC analysis with improved specificity of inferences, minimal assumptions and no reduction in flexibility. The Additive Signal Change (ASC) approach characterises FC changes into certain prevalent classes involving additions of signal. With FMRI data, the approach reveals a rich diversity of signal changes underlying measured changes in FC, bringing into question standard interpretations. The ASC method can also be used to disambiguate other measures of dependency, such as regression and coherence, providing a flexible tool for the analysis of neural data.

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