Riccardo Iandolo1, Marianna Semprini1, Stefano Buccelli1,1, Federico Barban1,2, Mingqi Zhao3, Jessica Samogin3, Gaia Bonassi4, Laura Avanzino4,5, Dante Mantini3,6, Michela Chiappalone1. 1. Rehab TechnologiesIstituto Italiano di Tecnologia 16163 Genova Italy. 2. Department of Informatics, Bioengineering, Robotics and systems Engineering (DIBRIS)University of Genova Genova Italy. 3. Research Center for Motor Control and NeuroplasticityKatholieke Universiteit Leuven 3001 Leuven Belgium. 4. Department of Experimental Medicine, Section of Human PhysiologyUniversity of Genova 16132 Genova Italy. 5. IRCCS San Martino Hospital 16132 Genova Italy. 6. IRCSS San Camillo Hospital 30126 Venice Lido Italy.
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 demonstrated 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.
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 demonstrated 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.
Entities:
Keywords:
EEG; frequency specificity; functional connectivity; resting state; small-worldness
Authors: Gennady G Knyazev; Alexander N Savostyanov; Andrey V Bocharov; Ivan V Brak; Evgeny A Osipov; Elena A Filimonova; Alexander E Saprigyn; Lyubomir I Aftanas Journal: J Affect Disord Date: 2018-04-07 Impact factor: 4.839
Authors: Francesca Melozzi; Eyal Bergmann; Julie A Harris; Itamar Kahn; Viktor Jirsa; Christophe Bernard Journal: Proc Natl Acad Sci U S A Date: 2019-12-11 Impact factor: 11.205