MEG connectivity and power detections with minimum norm estimates require different regularization parameters

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Hincapié, Ana Sofía
dc.contributor.author Kujala, Jan
dc.contributor.author Mattout, Jérémie
dc.contributor.author Daligault, Sebastien
dc.contributor.author Delpuech, Claude
dc.contributor.author Mery, Domingo
dc.contributor.author Cosmelli, Diego
dc.contributor.author Jerbi, Karim
dc.date.accessioned 2017-04-20T10:11:23Z
dc.date.available 2017-04-20T10:11:23Z
dc.date.issued 2016
dc.identifier.citation Hincapié , A S , Kujala , J , Mattout , J , Daligault , S , Delpuech , C , Mery , D , Cosmelli , D & Jerbi , K 2016 , ' MEG connectivity and power detections with minimum norm estimates require different regularization parameters ' COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE , vol 2016 , 3979547 , pp. 1-11 . DOI: 10.1155/2016/3979547 en
dc.identifier.issn 1687-5265
dc.identifier.issn 1687-5273
dc.identifier.other PURE UUID: 07794d7e-722e-4d28-b8e9-008c382f772b
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/meg-connectivity-and-power-detections-with-minimum-norm-estimates-require-different-regularization-parameters(07794d7e-722e-4d28-b8e9-008c382f772b).html
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=84964811351&partnerID=8YFLogxK
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/11443867/3979547.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/25186
dc.description.abstract Minimum Norm Estimation (MNE) is an inverse solution method widely used to reconstruct the source time series that underlie magnetoencephalography (MEG) data. MNE addresses the ill-posed nature of MEG source estimation through regularization (e.g., Tikhonov regularization). Selecting the best regularization parameter is a critical step. Generally, once set, it is common practice to keep the same coefficient throughout a study. However, it is yet to be known whether the optimal lambda for spectral power analysis of MEG source data coincides with the optimal regularization for source-level oscillatory coupling analysis. We addressed this question via extensive Monte-Carlo simulations of MEG data, where we generated 21,600 configurations of pairs of coupled sources with varying sizes, signal-to-noise ratio (SNR), and coupling strengths. Then, we searched for the Tikhonov regularization coefficients (lambda) that maximize detection performance for (a) power and (b) coherence. For coherence, the optimal lambda wastwo orders of magnitude smaller than the best lambda for power. Moreover, we found that the spatial extent of the interacting sources and SNR, but not the extent of coupling, were the main parameters affecting the best choice for lambda. Our findings suggest using less regularization when measuring oscillatory coupling compared to power estimation. en
dc.format.extent 1-11
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE en
dc.relation.ispartofseries Volume 2016 en
dc.rights openAccess en
dc.subject.other Computer Science(all) en
dc.subject.other Mathematics(all) en
dc.subject.other Neuroscience(all) en
dc.subject.other 113 Computer and information sciences en
dc.subject.other 3112 Neurosciences en
dc.title MEG connectivity and power detections with minimum norm estimates require different regularization parameters en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Institut National de la Santé et de la Recherche Médicale
dc.contributor.department Department of Neuroscience and Biomedical Engineering
dc.contributor.department CERMEP Centre d'Exploration et de Recherche Medicales par Emission de Positons
dc.contributor.department Pontificia Universidad Catolica de Chile
dc.subject.keyword Computer Science(all)
dc.subject.keyword Mathematics(all)
dc.subject.keyword Neuroscience(all)
dc.subject.keyword 113 Computer and information sciences
dc.subject.keyword 3112 Neurosciences
dc.identifier.urn URN:NBN:fi:aalto-201704203616
dc.identifier.doi 10.1155/2016/3979547
dc.type.version publishedVersion


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