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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">An investigation of fMRI time series stationarity during motor sequence learning foot tapping tasks</title><author><name sortKey="Muhei Aldin, Othman" sort="Muhei Aldin, Othman" uniqKey="Muhei Aldin O" first="Othman" last="Muhei-Aldin">Othman Muhei-Aldin</name><affiliation><nlm:aff id="A1">Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA</nlm:aff></affiliation></author><author><name sortKey="Vanswearingen, Jessie" sort="Vanswearingen, Jessie" uniqKey="Vanswearingen J" first="Jessie" last="Vanswearingen">Jessie Vanswearingen</name><affiliation><nlm:aff id="A2">Department of Physical Therapy, University of Pittsburgh, Pittsburgh, PA, 15260, USA</nlm:aff></affiliation></author><author><name sortKey="Karim, Helmet" sort="Karim, Helmet" uniqKey="Karim H" first="Helmet" last="Karim">Helmet Karim</name><affiliation><nlm:aff id="A3">Department of Radiology, University of Pittsburgh, Pittsburgh, PA, 15261, USA</nlm:aff></affiliation></author><author><name sortKey="Huppert, Theodore" sort="Huppert, Theodore" uniqKey="Huppert T" first="Theodore" last="Huppert">Theodore Huppert</name><affiliation><nlm:aff id="A3">Department of Radiology, University of Pittsburgh, Pittsburgh, PA, 15261, USA</nlm:aff></affiliation></author><author><name sortKey="Sparto, Patrick J" sort="Sparto, Patrick J" uniqKey="Sparto P" first="Patrick J." last="Sparto">Patrick J. Sparto</name><affiliation><nlm:aff id="A2">Department of Physical Therapy, University of Pittsburgh, Pittsburgh, PA, 15260, USA</nlm:aff></affiliation></author><author><name sortKey="Erickson, Kirk I" sort="Erickson, Kirk I" uniqKey="Erickson K" first="Kirk I." last="Erickson">Kirk I. Erickson</name><affiliation><nlm:aff id="A4">Department of Psychology, University of Pittsburgh, Pittsburgh, PA 15260, USA</nlm:aff></affiliation></author><author><name sortKey="Sejdi, Ervin" sort="Sejdi, Ervin" uniqKey="Sejdi E" first="Ervin" last="Sejdi">Ervin Sejdi</name><affiliation><nlm:aff id="A1">Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">24530436</idno><idno type="pmc">3987746</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3987746</idno><idno type="RBID">PMC:3987746</idno><idno type="doi">10.1016/j.jneumeth.2014.02.003</idno><date when="2014">2014</date><idno type="wicri:Area/Pmc/Corpus">001411</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">001411</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">An investigation of fMRI time series stationarity during motor sequence learning foot tapping tasks</title><author><name sortKey="Muhei Aldin, Othman" sort="Muhei Aldin, Othman" uniqKey="Muhei Aldin O" first="Othman" last="Muhei-Aldin">Othman Muhei-Aldin</name><affiliation><nlm:aff id="A1">Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA</nlm:aff></affiliation></author><author><name sortKey="Vanswearingen, Jessie" sort="Vanswearingen, Jessie" uniqKey="Vanswearingen J" first="Jessie" last="Vanswearingen">Jessie Vanswearingen</name><affiliation><nlm:aff id="A2">Department of Physical Therapy, University of Pittsburgh, Pittsburgh, PA, 15260, USA</nlm:aff></affiliation></author><author><name sortKey="Karim, Helmet" sort="Karim, Helmet" uniqKey="Karim H" first="Helmet" last="Karim">Helmet Karim</name><affiliation><nlm:aff id="A3">Department of Radiology, University of Pittsburgh, Pittsburgh, PA, 15261, USA</nlm:aff></affiliation></author><author><name sortKey="Huppert, Theodore" sort="Huppert, Theodore" uniqKey="Huppert T" first="Theodore" last="Huppert">Theodore Huppert</name><affiliation><nlm:aff id="A3">Department of Radiology, University of Pittsburgh, Pittsburgh, PA, 15261, USA</nlm:aff></affiliation></author><author><name sortKey="Sparto, Patrick J" sort="Sparto, Patrick J" uniqKey="Sparto P" first="Patrick J." last="Sparto">Patrick J. Sparto</name><affiliation><nlm:aff id="A2">Department of Physical Therapy, University of Pittsburgh, Pittsburgh, PA, 15260, USA</nlm:aff></affiliation></author><author><name sortKey="Erickson, Kirk I" sort="Erickson, Kirk I" uniqKey="Erickson K" first="Kirk I." last="Erickson">Kirk I. Erickson</name><affiliation><nlm:aff id="A4">Department of Psychology, University of Pittsburgh, Pittsburgh, PA 15260, USA</nlm:aff></affiliation></author><author><name sortKey="Sejdi, Ervin" sort="Sejdi, Ervin" uniqKey="Sejdi E" first="Ervin" last="Sejdi">Ervin Sejdi</name><affiliation><nlm:aff id="A1">Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA</nlm:aff></affiliation></author></analytic><series><title level="j">Journal of neuroscience methods</title><idno type="ISSN">0165-0270</idno><idno type="eISSN">1872-678X</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec id="S1"><title>Background:</title><p id="P1">Understanding complex brain networks using functional magnetic resonance imaging (fMRI) is of great interest to clinical and scientific communities. To utilize advanced analysis methods such as graph theory for these investigations, the stationarity of fMRI time series needs to be understood as it has important implications on the choice of appropriate approaches for the analysis of complex brain networks.</p></sec><sec id="S2"><title>New Method:</title><p id="P2">In this paper, we investigated the stationarity of fMRI time series acquired from twelve healthy participants while they performed a motor (foot tapping sequence) learning task. Since prior studies have documented that learning is associated with systematic changes in brain activation, a sequence learning task is an optimal paradigm to assess the degree of non-stationarity in fMRI time-series in clinically relevant brain areas. We predicted that brain regions involved in a “learning network” would demonstrate non-stationarity and may violate assumptions associated with some advanced analysis approaches. Six blocks of learning, and six control blocks of a foot tapping sequence were performed in a fixed order. The reverse arrangement test was utilized to investigate the time series stationarity.</p></sec><sec id="S3"><title>Results:</title><p id="P3">Our analysis showed some non-stationary signals with a time varying first moment as a major source of non-stationarity. We also demonstrated a decreased number of non-stationarities in the third block as a result of priming and repetition.</p></sec><sec id="S4"><title>Comparison with Existing Methods:</title><p id="P4">Most of the current literature does not examine stationarity prior to processing.</p></sec><sec id="S5"><title>Conclusions:</title><p id="P5">The implication of our findings is that future investigations analyzing complex brain networks should utilize approaches robust to non-stationarities, as graph-theoretical approaches can be sensitive to non-stationarities present in data.</p></sec></div></front></TEI><pmc article-type="research-article"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
<pmc-dir>properties manuscript</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-journal-id">7905558</journal-id><journal-id journal-id-type="pubmed-jr-id">5306</journal-id><journal-id journal-id-type="nlm-ta">J Neurosci Methods</journal-id><journal-id journal-id-type="iso-abbrev">J. Neurosci. Methods</journal-id><journal-title-group><journal-title>Journal of neuroscience methods</journal-title></journal-title-group><issn pub-type="ppub">0165-0270</issn><issn pub-type="epub">1872-678X</issn></journal-meta><article-meta><article-id pub-id-type="pmid">24530436</article-id><article-id pub-id-type="pmc">3987746</article-id><article-id pub-id-type="doi">10.1016/j.jneumeth.2014.02.003</article-id><article-id pub-id-type="manuscript">NIHMS567744</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>An investigation of fMRI time series stationarity during motor sequence learning foot tapping tasks</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Muhei-aldin</surname><given-names>Othman</given-names></name><xref ref-type="aff" rid="A1">a</xref></contrib><contrib contrib-type="author"><name><surname>VanSwearingen</surname><given-names>Jessie</given-names></name><xref ref-type="aff" rid="A2">b</xref></contrib><contrib contrib-type="author"><name><surname>Karim</surname><given-names>Helmet</given-names></name><xref ref-type="aff" rid="A3">c</xref></contrib><contrib contrib-type="author"><name><surname>Huppert</surname><given-names>Theodore</given-names></name><xref ref-type="aff" rid="A3">c</xref></contrib><contrib contrib-type="author"><name><surname>Sparto</surname><given-names>Patrick J.</given-names></name><xref ref-type="aff" rid="A2">b</xref></contrib><contrib contrib-type="author"><name><surname>Erickson</surname><given-names>Kirk I.</given-names></name><xref ref-type="aff" rid="A4">d</xref></contrib><contrib contrib-type="author"><name><surname>Sejdić</surname><given-names>Ervin</given-names></name><xref ref-type="aff" rid="A1">a</xref><xref ref-type="corresp" rid="CR1">*</xref></contrib></contrib-group><aff id="A1"><label>a</label>Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA</aff><aff id="A2"><label>b</label>Department of Physical Therapy, University of Pittsburgh, Pittsburgh, PA, 15260, USA</aff><aff id="A3"><label>c</label>Department of Radiology, University of Pittsburgh, Pittsburgh, PA, 15261, USA</aff><aff id="A4"><label>d</label>Department of Psychology, University of Pittsburgh, Pittsburgh, PA 15260, USA</aff><author-notes><corresp id="CR1"><label>*</label>Corresponding author. <email>esejdic@ieee.org</email></corresp></author-notes><pub-date pub-type="nihms-submitted"><day>4</day><month>4</month><year>2014</year></pub-date><pub-date pub-type="epub"><day>11</day><month>2</month><year>2014</year></pub-date><pub-date pub-type="ppub"><day>30</day><month>4</month><year>2014</year></pub-date><pub-date pub-type="pmc-release"><day>30</day><month>4</month><year>2015</year></pub-date><volume>227</volume><fpage>75</fpage><lpage>82</lpage><pmc-comment>elocation-id from pubmed: 10.1016/j.jneumeth.2014.02.003</pmc-comment>
<permissions><copyright-statement>© 2014 Elsevier B.V. All rights reserved</copyright-statement><copyright-year>2014</copyright-year></permissions><abstract><sec id="S1"><title>Background:</title><p id="P1">Understanding complex brain networks using functional magnetic resonance imaging (fMRI) is of great interest to clinical and scientific communities. To utilize advanced analysis methods such as graph theory for these investigations, the stationarity of fMRI time series needs to be understood as it has important implications on the choice of appropriate approaches for the analysis of complex brain networks.</p></sec><sec id="S2"><title>New Method:</title><p id="P2">In this paper, we investigated the stationarity of fMRI time series acquired from twelve healthy participants while they performed a motor (foot tapping sequence) learning task. Since prior studies have documented that learning is associated with systematic changes in brain activation, a sequence learning task is an optimal paradigm to assess the degree of non-stationarity in fMRI time-series in clinically relevant brain areas. We predicted that brain regions involved in a “learning network” would demonstrate non-stationarity and may violate assumptions associated with some advanced analysis approaches. Six blocks of learning, and six control blocks of a foot tapping sequence were performed in a fixed order. The reverse arrangement test was utilized to investigate the time series stationarity.</p></sec><sec id="S3"><title>Results:</title><p id="P3">Our analysis showed some non-stationary signals with a time varying first moment as a major source of non-stationarity. We also demonstrated a decreased number of non-stationarities in the third block as a result of priming and repetition.</p></sec><sec id="S4"><title>Comparison with Existing Methods:</title><p id="P4">Most of the current literature does not examine stationarity prior to processing.</p></sec><sec id="S5"><title>Conclusions:</title><p id="P5">The implication of our findings is that future investigations analyzing complex brain networks should utilize approaches robust to non-stationarities, as graph-theoretical approaches can be sensitive to non-stationarities present in data.</p></sec></abstract><kwd-group><kwd>functional magnetic resonance imaging</kwd><kwd>time series</kwd><kwd>stationarity</kwd><kwd>reverse arrangement test</kwd><kwd>foot tapping</kwd></kwd-group></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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