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Tuning Minimum-Norm regularization parameters for optimal MEG connectivity estimation

bioRxiv (Cold Spring Harbor Laboratory)(2023)10.1101/2023.04.15.537017Source: DataRank Database

Tuning Minimum-Norm regularization parameters for optimal MEG connectivity estimation is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2023). On theSindex it has a DataRank of 0.

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Enrichment:Funding (2 grants)OA: greenTopics (28)IDs (PubMed, OpenAlex)

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Authors (7)

Ana Sofia Hincapié,Karim Jerbi ORCID,Richard M. Leahy ORCID,Annalisa Pascarella ORCID,Alberto Sorrentino ORCID

Live enrichment

PubMed:19162232 OpenAlex:https://openalex.org/W4366224453

Natural Sciences and Engineering Research Council of Canada

Grant: unidentified

unidentified

National Institutes of Health

Grant: 5R01EB026299-03

BrainStorm: Highly Extensible Software for Advanced Electrophysiology and MEG/EEG Imaging

greenLicense: cc-by-nc-nd

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2023/04/16/2023.04.15.537017.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2023.04.15.537017

repositoryhttp://dx.doi.org/10.1101/2023.04.15.537017

https://doi.org/10.1016/j.neuroimage.2023.120356

Fields of Study

Functional Brain Connectivity StudiesNeural dynamics and brain functionAdvanced MRI Techniques and Applications

Keywords

Regularization (linguistics)Computer scienceFalse positives and false negativesAlgorithmSource codeMathematical optimizationMathematicsFalse positive paradoxArtificial intelligenceCerebral CortexFunctional connectivity; MEG; Minimum norm estimate; Regularization parameter; Surrogate dataMEGMagnetoencephalographyBrainNeurosciences. Biological psychiatry. NeuropsychiatryFunctional connectivityMinimum norm estimateConnectomeHumansComputer SimulationRegularization parameterSurrogate dataNerve NetAlgorithmsRC321-571