dc.contributor.author | Thompson, William Hedley | |
dc.contributor.author | Richter, Craig Geoffrey | |
dc.contributor.author | Plavén-Sigray, Pontus | |
dc.contributor.author | Fransson, Peter | |
dc.date.accessioned | 2018-06-12T09:26:20Z | |
dc.date.available | 2018-06-12T09:26:20Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | Thompson WH, Richter CG, PlaveÂn- Sigray P, Fransson P (2018) Simulations to benchmark time-varying connectivity methods for fMRI. PLoS Comput Biol 14(5): e1006196. https:// doi.org/10.1371/journal.pcbi.1006196 | es_ES |
dc.identifier.issn | 1553-734X | |
dc.identifier.uri | http://hdl.handle.net/10810/27493 | |
dc.description | Published: May 29, 2018 | es_ES |
dc.description.abstract | There is a current interest in quantifying time-varying connectivity (TVC) based on neuroimaging data such as fMRI. Many methods have been proposed, and are being applied, revealing new insight into the brain’s dynamics. However, given that the ground truth for TVC in the brain is unknown, many concerns remain regarding the accuracy of proposed estimates. Since there exist many TVC methods it is difficult to assess differences in time-varying connectivity between studies. In this paper, we present tvc_benchmarker, which is a Python package containing four simulations to test TVC methods. Here, we evaluate five different methods that together represent a wide spectrum of current approaches to estimating TVC (sliding window, tapered sliding window, multiplication of temporal derivatives, spatial distance and jackknife correlation). These simulations were designed to test each method’s ability to track changes in covariance over time, which is a key property in TVC analysis. We found that all tested methods correlated positively with each other, but there were large differences in the strength of the correlations between methods. To facilitate comparisons with future TVC methods, we propose that the described simulations can act as benchmark tests for evaluation of methods. Using tvc_benchmarker researchers can easily add, compare and submit their own TVC methods to evaluate its performance. | es_ES |
dc.description.sponsorship | WHT acknowledges support from the
Knut och Alice Wallenbergs Stiftelse (SE) (grant no.
2016.0473, http://kaw.wallenberg.org). PR
acknowledges support from the Swedish Research
Council (Vetenskapsrådet) (grants no. 2016-03352
and 773 013-61X-08276-26-4) (http://vr.se) and
the Swedish e-Science Research Center (http://e-
science.se/). CGR acknowledges financial support
from the Spanish Ministry of Economy and
Competitiveness, through the ªSevero Ochoaº Programme for Centres/Units of Excellence in
R&Dº (SEV-2015-490, http://csic.es/). | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | PLOS Computational Biology | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/SEV-2015-0490 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.title | Simulations to benchmark time-varying connectivity methods for fMRI | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © 2018 Thompson et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited. | es_ES |
dc.relation.publisherversion | http://journals.plos.org/ploscompbiol/ | es_ES |
dc.identifier.doi | 10.1371/journal.pcbi.1006196 | |