Scale-invariant rearrangement of resting state networks in the human brain under sustained stimulation

Silvia Tommasin, Daniele Mascali, Marta Moraschi, Tommaso Gili, Ibrahim Eid Hassan, Michela Fratini, Mauro DiNuzzo, Richard G. Wise, Silvia Mangia, Emiliano Macaluso, Federico Giove

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Brain activity at rest is characterized by widely distributed and spatially specific patterns of synchronized low-frequency blood-oxygenation level-dependent (BOLD) fluctuations, which correspond to physiologically relevant brain networks. This network behaviour is known to persist also during task execution, yet the details underlying task-associated modulations of within- and between-network connectivity are largely unknown. In this study we exploited a multi-parametric and multi-scale approach to investigate how low-frequency fluctuations adapt to a sustained n-back working memory task. We found that the transition from the resting state to the task state involves a behaviourally relevant and scale-invariant modulation of synchronization patterns within both task-positive and default mode networks. Specifically, decreases of connectivity within networks are accompanied by increases of connectivity between networks. In spite of large and widespread changes of connectivity strength, the overall topology of brain networks is remarkably preserved. We show that these findings are strongly influenced by connectivity at rest, suggesting that the absolute change of connectivity (i.e., disregarding the baseline) may not be the most suitable metric to study dynamic modulations of functional connectivity. Our results indicate that a task can evoke scale-invariant, distributed changes of BOLD fluctuations, further confirming that low frequency BOLD oscillations show a specialized response and are tightly bound to task-evoked activation.

Original languageEnglish (US)
Pages (from-to)570-581
Number of pages12
JournalNeuroImage
Volume179
DOIs
StatePublished - Oct 1 2018

Bibliographical note

Funding Information:
The present work was supported by the Italian Ministry for Education, University and Research (Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR) under the grant “Progetto premiale NETFUN: NETwork FUNzionali cerebrali studiati con NMR” (Functional brain networks studied by NMR). Research reported in this publication was also supported by Regione Lazio, grant PAMINA (to F.G.) and by the National Institutes of Health , award number R01DK099137 (to S.M.). This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 691110 (MICROBRADAM). M.D.N. is supported by the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 701635 . The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding bodies.

Keywords

  • Connectivity dynamics
  • Functional connectivity
  • Steady-state networks
  • Working memory

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