Statistical Frequency-Dependent Analysis of Trial-to-Trial Variability in Single Time Series by Recurrence Plots
Identifieur interne : 000138 ( Main/Exploration ); précédent : 000137; suivant : 000139Statistical Frequency-Dependent Analysis of Trial-to-Trial Variability in Single Time Series by Recurrence Plots
Auteurs : Tamara Toši [France] ; Kristin K. Sellers [États-Unis] ; Flavio Fröhlich [États-Unis] ; Mariia Fedotenkova [France] ; Peter Beim Graben [Allemagne] ; Axel Hutt [France]Source :
- Frontiers in Systems Neuroscience [ 1662-5137 ] ; 2016.
English descriptors
- mix :
Abstract
For decades, research in neuroscience has supported the hypothesis that brain dynamics exhibits recurrent metastable states connected by transients, which together encode fundamental neural information processing. To understand the system's dynamics it is important to detect such recurrence domains, but it is challenging to extract them from experimental neuroscience datasets due to the large trial-to-trial variability. The proposed methodology extracts recurrent metastable states in univariate time series by transforming datasets into their time-frequency representations and computing recurrence plots based on instantaneous spectral power values in various frequency bands. Additionally, a new statistical inference analysis compares different trial recurrence plots with corresponding surrogates to obtain statistically significant recurrent structures. This combination of methods is validated by applying it to two artificial datasets. In a final study of visually-evoked Local Field Potentials in partially anesthetized ferrets, the methodology is able to reveal recurrence structures of neural responses with trial-to-trial variability. Focusing on different frequency bands, the δ-band activity is much less recurrent than α-band activity. Moreover, α-activity is susceptible to pre-stimuli, while δ-activity is much less sensitive to pre-stimuli. This difference in recurrence structures in different frequency bands indicates diverse underlying information processing steps in the brain.
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DOI: 10.3389/fnsys.2015.00184
PubMed: 26834580
PubMed Central: 4712310
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last="Hutt">Axel Hutt</name><affiliation wicri:level="1"><nlm:aff id="aff1"><institution>Team Neurosys, Inria</institution><country>Villers-lès-Nancy, France</country></nlm:aff><country xml:lang="fr">France</country><wicri:regionArea></wicri:regionArea></affiliation><affiliation wicri:level="1"><nlm:aff id="aff2"><institution>Loria, Centre National de la Recherche Scientifique, UMR no 7503</institution><country>Villers-lès-Nancy, France</country></nlm:aff><country xml:lang="fr">France</country><wicri:regionArea></wicri:regionArea></affiliation><affiliation wicri:level="1"><nlm:aff id="aff3"><institution>Université de Lorraine, Loria, UMR no 7503</institution><country>Villers-lès-Nancy, France</country></nlm:aff><country xml:lang="fr">France</country><wicri:regionArea></wicri:regionArea></affiliation></author></analytic><series><title level="j">Frontiers in Systems Neuroscience</title><idno type="eISSN">1662-5137</idno><imprint><date when="2016">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>Local Field Potentials</term><term>Recurrence plot analysis</term><term>Statistical inference</term><term>Surrogate data analysis</term><term>Time-frequency analysis</term><term>Trial-to-trial variability</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>For decades, research in neuroscience has supported the hypothesis that brain dynamics exhibits recurrent metastable states connected by transients, which together encode fundamental neural information processing. To understand the system's dynamics it is important to detect such recurrence domains, but it is challenging to extract them from experimental neuroscience datasets due to the large trial-to-trial variability. The proposed methodology extracts recurrent metastable states in univariate time series by transforming datasets into their time-frequency representations and computing recurrence plots based on instantaneous spectral power values in various frequency bands. Additionally, a new statistical inference analysis compares different trial recurrence plots with corresponding surrogates to obtain statistically significant recurrent structures. This combination of methods is validated by applying it to two artificial datasets. In a final study of visually-evoked Local Field Potentials in partially anesthetized ferrets, the methodology is able to reveal recurrence structures of neural responses with trial-to-trial variability. Focusing on different frequency bands, the δ-band activity is much less recurrent than α-band activity. Moreover, α-activity is susceptible to pre-stimuli, while δ-activity is much less sensitive to pre-stimuli. This difference in recurrence structures in different frequency bands indicates diverse underlying information processing steps in the brain.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Alkire, M" uniqKey="Alkire M">M. Alkire</name></author><author><name sortKey="Haier, R J" uniqKey="Haier R">R. J. Haier</name></author><author><name sortKey="Fallon, J H" uniqKey="Fallon J">J. H. Fallon</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Allefeld, C" uniqKey="Allefeld C">C. Allefeld</name></author><author><name sortKey="Atmanspacher, H" uniqKey="Atmanspacher H">H. Atmanspacher</name></author><author><name sortKey="Wackermann, J" uniqKey="Wackermann J">J. 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Kiebel</name></author></analytic></biblStruct></listBibl></div1></back></TEI><affiliations><list><country><li>Allemagne</li><li>France</li><li>États-Unis</li></country></list><tree><country name="France"><noRegion><name sortKey="Tosi, Tamara" sort="Tosi, Tamara" uniqKey="Tosi T" first="Tamara" last="Toši">Tamara Toši</name></noRegion><name sortKey="Fedotenkova, Mariia" sort="Fedotenkova, Mariia" uniqKey="Fedotenkova M" first="Mariia" last="Fedotenkova">Mariia Fedotenkova</name><name sortKey="Fedotenkova, Mariia" sort="Fedotenkova, Mariia" uniqKey="Fedotenkova M" first="Mariia" last="Fedotenkova">Mariia Fedotenkova</name><name sortKey="Fedotenkova, Mariia" sort="Fedotenkova, Mariia" uniqKey="Fedotenkova M" first="Mariia" last="Fedotenkova">Mariia Fedotenkova</name><name sortKey="Hutt, Axel" sort="Hutt, Axel" uniqKey="Hutt A" first="Axel" last="Hutt">Axel Hutt</name><name sortKey="Hutt, Axel" sort="Hutt, Axel" uniqKey="Hutt A" first="Axel" last="Hutt">Axel Hutt</name><name sortKey="Hutt, Axel" sort="Hutt, Axel" uniqKey="Hutt A" first="Axel" last="Hutt">Axel Hutt</name><name sortKey="Tosi, Tamara" sort="Tosi, Tamara" uniqKey="Tosi T" first="Tamara" last="Toši">Tamara Toši</name><name sortKey="Tosi, Tamara" sort="Tosi, Tamara" uniqKey="Tosi T" first="Tamara" last="Toši">Tamara Toši</name></country><country name="États-Unis"><noRegion><name sortKey="Sellers, Kristin K" sort="Sellers, Kristin K" uniqKey="Sellers K" first="Kristin K." last="Sellers">Kristin K. Sellers</name></noRegion><name sortKey="Frohlich, Flavio" sort="Frohlich, Flavio" uniqKey="Frohlich F" first="Flavio" last="Fröhlich">Flavio Fröhlich</name><name sortKey="Frohlich, Flavio" sort="Frohlich, Flavio" uniqKey="Frohlich F" first="Flavio" last="Fröhlich">Flavio Fröhlich</name><name sortKey="Frohlich, Flavio" sort="Frohlich, Flavio" uniqKey="Frohlich F" first="Flavio" last="Fröhlich">Flavio Fröhlich</name><name sortKey="Frohlich, Flavio" sort="Frohlich, Flavio" uniqKey="Frohlich F" first="Flavio" last="Fröhlich">Flavio Fröhlich</name><name sortKey="Frohlich, Flavio" sort="Frohlich, Flavio" uniqKey="Frohlich F" first="Flavio" last="Fröhlich">Flavio Fröhlich</name><name sortKey="Sellers, Kristin K" sort="Sellers, Kristin K" uniqKey="Sellers K" first="Kristin K." last="Sellers">Kristin K. Sellers</name></country><country name="Allemagne"><noRegion><name sortKey="Beim Graben, Peter" sort="Beim Graben, Peter" uniqKey="Beim Graben P" first="Peter" last="Beim Graben">Peter Beim Graben</name></noRegion><name sortKey="Beim Graben, Peter" sort="Beim Graben, Peter" uniqKey="Beim Graben P" first="Peter" last="Beim Graben">Peter Beim Graben</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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