Matches in SemOpenAlex for { <https://semopenalex.org/work/W2912398723> ?p ?o ?g. }
- W2912398723 abstract "Abstract Background ‘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) Methods This 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. Results We 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. Conclusions The 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. Highlights Non-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. Abbreviations dFC Directed functional connectivity EEG Electroencephalogram LFP Local field potential MEG Magnetoencephalogram MVAR Multivariate autoregressive (model) NPD Non-parametric directionality NPG Non-parametric Granger (causality) SMA Supplementary motor area SNR Signal-to-noise ratio STN Subthalamic Nucleus" @default.
- W2912398723 created "2019-02-21" @default.
- W2912398723 creator A5026981278 @default.
- W2912398723 creator A5030160648 @default.
- W2912398723 creator A5038948118 @default.
- W2912398723 creator A5041205424 @default.
- W2912398723 creator A5071343779 @default.
- W2912398723 date "2019-01-24" @default.
- W2912398723 modified "2023-10-16" @default.
- W2912398723 title "Measuring Directed Functional Connectivity Using Non-Parametric Directionality Analysis: Validation and Comparison with Non-Parametric Granger Causality" @default.
- W2912398723 cites W1636081627 @default.
- W2912398723 cites W1964769652 @default.
- W2912398723 cites W1966641148 @default.
- W2912398723 cites W1974666382 @default.
- W2912398723 cites W1983523765 @default.
- W2912398723 cites W1989700973 @default.
- W2912398723 cites W2003209004 @default.
- W2912398723 cites W2009914532 @default.
- W2912398723 cites W2018305040 @default.
- W2912398723 cites W2019232098 @default.
- W2912398723 cites W2043301888 @default.
- W2912398723 cites W2050995101 @default.
- W2912398723 cites W2061564920 @default.
- W2912398723 cites W206280282 @default.
- W2912398723 cites W2071941563 @default.
- W2912398723 cites W2077491345 @default.
- W2912398723 cites W2079634169 @default.
- W2912398723 cites W2079733958 @default.
- W2912398723 cites W2091066310 @default.
- W2912398723 cites W2096875305 @default.
- W2912398723 cites W2099610690 @default.
- W2912398723 cites W2109582949 @default.
- W2912398723 cites W2113762408 @default.
- W2912398723 cites W2123346926 @default.
- W2912398723 cites W2129846302 @default.
- W2912398723 cites W2130028041 @default.
- W2912398723 cites W2133280087 @default.
- W2912398723 cites W2136562407 @default.
- W2912398723 cites W2142373488 @default.
- W2912398723 cites W2147899888 @default.
- W2912398723 cites W2154674952 @default.
- W2912398723 cites W2155722796 @default.
- W2912398723 cites W2166073443 @default.
- W2912398723 cites W2168396492 @default.
- W2912398723 cites W2171352244 @default.
- W2912398723 cites W2178225550 @default.
- W2912398723 cites W2219596751 @default.
- W2912398723 cites W2227520796 @default.
- W2912398723 cites W2346454556 @default.
- W2912398723 cites W2612705125 @default.
- W2912398723 cites W2949381541 @default.
- W2912398723 cites W2952526539 @default.
- W2912398723 cites W2962954129 @default.
- W2912398723 cites W4231726431 @default.
- W2912398723 cites W4236287093 @default.
- W2912398723 doi "https://doi.org/10.1101/526566" @default.
- W2912398723 hasPublicationYear "2019" @default.
- W2912398723 type Work @default.
- W2912398723 sameAs 2912398723 @default.
- W2912398723 citedByCount "1" @default.
- W2912398723 countsByYear W29123987232022 @default.
- W2912398723 crossrefType "posted-content" @default.
- W2912398723 hasAuthorship W2912398723A5026981278 @default.
- W2912398723 hasAuthorship W2912398723A5030160648 @default.
- W2912398723 hasAuthorship W2912398723A5038948118 @default.
- W2912398723 hasAuthorship W2912398723A5041205424 @default.
- W2912398723 hasAuthorship W2912398723A5071343779 @default.
- W2912398723 hasBestOaLocation W29123987231 @default.
- W2912398723 hasConcept C102366305 @default.
- W2912398723 hasConcept C105795698 @default.
- W2912398723 hasConcept C11413529 @default.
- W2912398723 hasConcept C115961682 @default.
- W2912398723 hasConcept C117251300 @default.
- W2912398723 hasConcept C119857082 @default.
- W2912398723 hasConcept C129824826 @default.
- W2912398723 hasConcept C153180895 @default.
- W2912398723 hasConcept C154945302 @default.
- W2912398723 hasConcept C159877910 @default.
- W2912398723 hasConcept C162324750 @default.
- W2912398723 hasConcept C169760540 @default.
- W2912398723 hasConcept C176217482 @default.
- W2912398723 hasConcept C199360897 @default.
- W2912398723 hasConcept C21547014 @default.
- W2912398723 hasConcept C24574437 @default.
- W2912398723 hasConcept C2779843651 @default.
- W2912398723 hasConcept C28490314 @default.
- W2912398723 hasConcept C29648211 @default.
- W2912398723 hasConcept C3018011982 @default.
- W2912398723 hasConcept C33923547 @default.
- W2912398723 hasConcept C41008148 @default.
- W2912398723 hasConcept C50644808 @default.
- W2912398723 hasConcept C54355233 @default.
- W2912398723 hasConcept C86803240 @default.
- W2912398723 hasConcept C99498987 @default.
- W2912398723 hasConceptScore W2912398723C102366305 @default.
- W2912398723 hasConceptScore W2912398723C105795698 @default.
- W2912398723 hasConceptScore W2912398723C11413529 @default.
- W2912398723 hasConceptScore W2912398723C115961682 @default.
- W2912398723 hasConceptScore W2912398723C117251300 @default.
- W2912398723 hasConceptScore W2912398723C119857082 @default.