Estimating feedforward and feedback effective connections from fMRI time series: Assessments of statistical methods

Autor: Ruben Sanchez-Romero, Joseph D. Ramsey, Kun Zhang, Madelyn R. K. Glymour, Biwei Huang, Clark Glymour

Publikováno v: Network Neuroscience, Vol 3, Iss 2, Pp 274-306 (2019)

We test the adequacies of several proposed and two new statistical methods for recovering the causal structure of systems with feedback from synthetic BOLD time series. We compare an adaptation of the first correct method for recovering cyclic linear

Externí odkaz: https://doaj.org/article/5f701e87d17b49db9e340fbd020c66f4

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Beyond Objects in Space-Time: Towards a Movement Analysis Framework with ‘How’ and ‘Why’ Elements

Autor: Saeed Rahimi, Antoni B. Moore, Peter A. Whigham

Publikováno v: ISPRS International Journal of Geo-Information, Vol 10, Iss 3, p 190 (2021)

Current spatiotemporal data has facilitated movement studies to shift objectives from descriptive models to explanations of the underlying causes of movement. From both a practical and theoretical standpoint, progress in developing approaches for the

Externí odkaz: https://doaj.org/article/8a4e76a5d79e4b26878f526e15066c9b

Graphical Causal Models and Imputing Missing Data: A Preliminary Study

Autor: Almeida, R.J., Adriaans, G., Shapovalova, Y., Lesot, M.-J.

Publikováno v: Information Processing and Management of Uncertainty in Knowledge-Based Systems
Lesot, M.-J. (ed.), Information Processing and Management of Uncertainty in Knowledge-Based Systems: 18th International Conference, IPMU 2020, Lisbon, Portugal, June 15–19, 2020, Proceedings, Part I, 485-496. Cham : Springer International Publishing
STARTPAGE=485;ENDPAGE=496;TITLE=Lesot, M.-J. (ed.), Information Processing and Management of Uncertainty in Knowledge-Based Systems: 18th International Conference, IPMU 2020, Lisbon, Portugal, June 15–19, 2020, Proceedings, Part I
Lesot, M.-J. (ed.), Information Processing and Management of Uncertainty in Knowledge-Based Systems: 18th International Conference, IPMU 2020, Lisbon, Portugal, June 15–19, 2020, Proceedings, Part I, pp. 485-496

Real-world datasets often contain many missing values due to several reasons. This is usually an issue since many learning algorithms require complete datasets. In certain cases, there are constraints in the real world problem that create difficultie

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pmc_dedup___::570451b21c050191be9aea8b540e659c
http://europepmc.org/articles/PMC7274349

Estimating feedforward and feedback effective connections from fMRI time series: Assessments of statistical methods

Autor: Clark Glymour, Ruben Sanchez-Romero, Joseph D. Ramsey, Biwei Huang, Madelyn R. K. Glymour, Kun Zhang

Publikováno v: Network Neuroscience, Vol 3, Iss 2, Pp 274-306 (2019)
Network Neuroscience

We test the adequacies of several proposed and two new statistical methods for recovering the causal structure of systems with feedback from synthetic BOLD time series. We compare an adaptation of the first correct method for recovering cyclic linear

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1879e68d1351d8c4a434356fd9258e58
https://doi.org/10.1162/netn_a_00061