January 9, 2020 Journal article Closed Access

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

JSON Export

{
  "conceptrecid": "46655", 
  "created": "2020-09-27T05:31:19.416142+00:00", 
  "doi": "10.1109/ojemb.2020.2965323", 
  "id": 46656, 
  "links": {
    "badge": "https://www.openaccessrepository.it/badge/doi/10.1109/ojemb.2020.2965323.svg", 
    "doi": "https://doi.org/10.1109/ojemb.2020.2965323", 
    "html": "https://www.openaccessrepository.it/record/46656", 
    "latest": "https://www.openaccessrepository.it/api/records/46656", 
    "latest_html": "https://www.openaccessrepository.it/record/46656"
  }, 
  "metadata": {
    "access_right": "closed", 
    "access_right_category": "danger", 
    "communities": [
      {
        "id": "itmirror"
      }
    ], 
    "creators": [
      {
        "name": "Riccardo Iandolo"
      }, 
      {
        "name": "Marianna Semprini"
      }, 
      {
        "name": "Stefano Buccelli"
      }, 
      {
        "name": "Federico Barban"
      }, 
      {
        "name": "Mingqi Zhao"
      }, 
      {
        "name": "Jessica Samogin"
      }, 
      {
        "name": "Gaia Bonassi"
      }, 
      {
        "name": "Laura Avanzino"
      }, 
      {
        "name": "Dante Mantini"
      }, 
      {
        "name": "Michela Chiappalone"
      }
    ], 
    "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", 
    "doi": "10.1109/ojemb.2020.2965323", 
    "keywords": [
      "Neuroinformatics"
    ], 
    "language": "eng", 
    "license": {
      "id": "other-open"
    }, 
    "notes": "", 
    "publication_date": "2020-01-09", 
    "relations": {
      "version": [
        {
          "count": 1, 
          "index": 0, 
          "is_last": true, 
          "last_child": {
            "pid_type": "recid", 
            "pid_value": "46656"
          }, 
          "parent": {
            "pid_type": "recid", 
            "pid_value": "46655"
          }
        }
      ]
    }, 
    "resource_type": {
      "subtype": "article", 
      "title": "Journal article", 
      "type": "publication"
    }, 
    "title": "Small-World Propensity Reveals the Frequency Specificity of Resting State Networks"
  }, 
  "owners": [
    14
  ], 
  "revision": 1, 
  "stats": {
    "downloads": 38.0, 
    "unique_downloads": 37.0, 
    "unique_views": 20.0, 
    "version_downloads": 38.0, 
    "version_unique_downloads": 37.0, 
    "version_unique_views": 20.0, 
    "version_views": 22.0, 
    "version_volume": 139552264.0, 
    "views": 22.0, 
    "volume": 139552264.0
  }, 
  "updated": "2020-09-27T05:31:19.510515+00:00"
}