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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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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Riccardo Iandolo</dc:creator>
  <dc:creator>Marianna Semprini</dc:creator>
  <dc:creator>Stefano Buccelli</dc:creator>
  <dc:creator>Federico Barban</dc:creator>
  <dc:creator>Mingqi Zhao</dc:creator>
  <dc:creator>Jessica Samogin</dc:creator>
  <dc:creator>Gaia Bonassi</dc:creator>
  <dc:creator>Laura Avanzino</dc:creator>
  <dc:creator>Dante Mantini</dc:creator>
  <dc:creator>Michela Chiappalone</dc:creator>
  <dc:description>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</dc:description>
  <dc:title>Small-World Propensity Reveals the Frequency Specificity of Resting State Networks</dc:title>
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