Journal article Open Access

Small-World Propensity Reveals the Frequency Specificity of Resting State Networks

Riccardo Iandolo; Marianna Semprini; Stefano Buccelli; Federico Barban; Mingqi Zhao; Jessica Samogin; Gaia Bonassi; Laura Avanzino; Dante Mantini; Michela Chiappalone

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      <creatorName>Riccardo Iandolo</creatorName>
      <creatorName>Marianna Semprini</creatorName>
      <creatorName>Stefano Buccelli</creatorName>
      <creatorName>Federico Barban</creatorName>
      <creatorName>Mingqi Zhao</creatorName>
      <creatorName>Jessica Samogin</creatorName>
      <creatorName>Gaia Bonassi</creatorName>
      <creatorName>Laura Avanzino</creatorName>
      <creatorName>Dante Mantini</creatorName>
      <creatorName>Michela Chiappalone</creatorName>
    <title>Small-World Propensity Reveals the Frequency Specificity of Resting State Networks</title>
  <publisher>INFN Open Access Repository</publisher>
    <date dateType="Issued">2020-01-09</date>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
    <alternateIdentifier alternateIdentifierType="url"></alternateIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/ojemb.2020.2965323</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf"></relatedIdentifier>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">Goal: Functional connectivity (FC) is an important indicator of the brain's state in different conditions, such as rest/task or health/pathology. Here we used high-density electroencephalography coupled to source reconstruction to assess frequency-specific changes of FC during resting state. Specifically, we computed the Small-World Propensity (SWP) index to characterize network small-world architecture across frequencies. Methods: We collected resting state data from healthy participants and built connectivity matrices maintaining the heterogeneity of connection strengths. For a subsample of participants, we also investigated whether the SWP captured FC changes after the execution of a working memory (WM) task. Results: We found that SWP demonstrates a selective increase in the alpha and low beta bands. Moreover, SWP was modulated by a cognitive task and showed increased values in the bands entrained by the WM task. Conclusions: SWP is a valid metric to characterize the frequency-specific behavior of resting state networks. ispartof: IEEE Open Journal of Engineering in Medicine and Biology status: accepted</description>
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