Phase locking value revisited: teaching new tricks to an old dog

Simple item page
dc.contributor.authorBruña Fernández, Ricardo
dc.contributor.authorMaestu Unturbe, Fernando
dc.contributor.authorPereda, Ernesto
dc.date.accessioned2024-02-08T08:19:38Z
dc.date.available2024-02-08T08:19:38Z
dc.date.issued2018-07-28
dc.description.abstractObjective. Despite the increase in calculation power over the last few decades, the estimation of brain connectivity is still a tedious task. The high computational cost of the algorithms escalates with the square of the number of signals evaluated, usually in the range of thousands. In this work we propose a re-formulation of a widely used algorithm that allows the estimation of whole brain connectivity in much smaller times. Approach. We start from the original implementation of phase locking value (PLV) and re-formulated it in a computationally very efficient way. What is more, this formulation stresses its strong similarity with coherence, which we used to introduce two new metrics insensitive to zero lag synchronization: the imaginary part of PLV (iPLV) and its corrected counterpart (ciPLV). Main results. The new implementation of PLV avoids some highly CPU-expensive operations and achieves a 100-fold speedup over the original algorithm. The new derived metrics were highly robust in the presence of volume conduction. Moreover, ciPLV proved capable of ignoring zero-lag connectivity, while correctly estimating nonzero-lag connectivity. Significance. Our implementation of PLV makes it possible to calculate whole-brain connectivity in much shorter times. The results of the simulations using ciPLV suggest that this metric is ideal to measure synchronization in the presence of volume conduction or source leakage effects.
dc.description.departmentDepto. de Psicología Experimental, Procesos Cognitivos y Logopedia
dc.description.departmentDepto. de Radiología, Rehabilitación y Fisioterapia
dc.description.facultyFac. de Psicología
dc.description.facultyFac. de Medicina
dc.description.refereedTRUE
dc.description.sponsorshipMinisterio de Economía y Competitivida
dc.description.statuspub
dc.identifier.citationRicardo Bruña et al 2018 J. Neural Eng. 15 056011 DOI 10.1088/1741-2552/aacfe4
dc.identifier.doi10.1088/1741-2552/aacfe4
dc.identifier.issn1741-2552
dc.identifier.officialurlhttps://iopscience.iop.org/article/10.1088/1741-2552/aacfe4
dc.identifier.pmid29952757
dc.identifier.relatedurlhttps://pubmed.ncbi.nlm.nih.gov/29952757/
dc.identifier.urihttps://hdl.handle.net/20.500.14352/100167
dc.journal.titleJournal of Neural Engineering
dc.language.isoeng
dc.page.initial056011
dc.publisherIOP Publishing
dc.rightsAttribution 4.0 Internationalen
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject.cdu612.8
dc.subject.keywordsynchronization
dc.subject.keywordneuroscience
dc.subject.keywordcomputational efficiency
dc.subject.keywordbrain connectivity
dc.subject.ucmNeurociencias (Medicina)
dc.subject.unesco2490 Neurociencias
dc.titlePhase locking value revisited: teaching new tricks to an old dog
dc.typejournal article
dc.type.hasVersionVoR
dc.volume.number15
dspace.entity.typePublication
relation.isAuthorOfPublicationef335315-bb52-49b1-8703-63c7caae45f8
relation.isAuthorOfPublicationafa98131-b2fe-40fd-8f89-f3994d80ab72
relation.isAuthorOfPublication.latestForDiscoveryef335315-bb52-49b1-8703-63c7caae45f8
Download
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Bruña et al. 2018 J. Neural Eng. 15.056011.pdf
Size:
1.85 MB
Format:
Adobe Portable Document Format
downoload Download
Collections
Artículos