January 9, 2020 Journal article Open Access

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

JSON-LD (schema.org) Export

{
  "@context": "https://schema.org/", 
  "@id": "https://doi.org/10.1109/ojemb.2020.2965323", 
  "@type": "ScholarlyArticle", 
  "creator": [
    {
      "@type": "Person", 
      "name": "Riccardo Iandolo"
    }, 
    {
      "@type": "Person", 
      "name": "Marianna Semprini"
    }, 
    {
      "@type": "Person", 
      "name": "Stefano Buccelli"
    }, 
    {
      "@type": "Person", 
      "name": "Federico Barban"
    }, 
    {
      "@type": "Person", 
      "name": "Mingqi Zhao"
    }, 
    {
      "@type": "Person", 
      "name": "Jessica Samogin"
    }, 
    {
      "@type": "Person", 
      "name": "Gaia Bonassi"
    }, 
    {
      "@type": "Person", 
      "name": "Laura Avanzino"
    }, 
    {
      "@type": "Person", 
      "name": "Dante Mantini"
    }, 
    {
      "@type": "Person", 
      "name": "Michela Chiappalone"
    }
  ], 
  "datePublished": "2020-01-09", 
  "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", 
  "headline": "Small-World Propensity Reveals the Frequency Specificity of Resting State Networks", 
  "identifier": "https://doi.org/10.1109/ojemb.2020.2965323", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "inLanguage": {
    "@type": "Language", 
    "alternateName": "eng", 
    "name": "English"
  }, 
  "keywords": [
    "Neuroinformatics"
  ], 
  "license": "", 
  "name": "Small-World Propensity Reveals the Frequency Specificity of Resting State Networks", 
  "url": "https://www.openaccessrepository.it/record/46656"
}