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

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    <subfield code="a">Jessica Samogin</subfield>
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    <subfield code="a">Gaia Bonassi</subfield>
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    <subfield code="a">Laura Avanzino</subfield>
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    <subfield code="a">Dante Mantini</subfield>
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    <subfield code="a">Michela Chiappalone</subfield>
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    <subfield code="a">10.1109/ojemb.2020.2965323</subfield>
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    <subfield code="a">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</subfield>
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    <subfield code="a">Neuroinformatics</subfield>
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    <subfield code="a">Riccardo Iandolo</subfield>
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    <subfield code="a">Small-World Propensity Reveals the Frequency Specificity of Resting State Networks</subfield>
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