The dynamic functional connectome: State-of-the-art and perspectives

Resting-state functional magnetic resonance imaging (fMRI) has highlighted the rich structure of brain activity in absence of a task or stimulus. A great effort has been dedicated in the last two decades to investigate functional connectivity (FC), i.e. the functional interplay between different reg...

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Published in	NeuroImage (Orlando, Fla.) Vol. 160; pp. 41 - 54
Main Authors	Preti, Maria Giulia, Bolton, Thomas AW, Van De Ville, Dimitri
Format	Journal Article
Language	English
Published	United States Elsevier Inc 15.10.2017 Elsevier Limited
Subjects	Brain Brain - anatomy & histology Brain - physiology Brain mapping Connectome - methods Dynamic functional connectivity Dynamic graph analysis EEG Frame-wise description Functional magnetic resonance imaging Gene expression Image Processing, Computer-Assisted - methods Magnetic Resonance Imaging - methods Neural networks Neural Pathways - anatomy & histology Neural Pathways - physiology Neuroimaging Sliding window analysis State characterization Studies Temporal modeling Therapeutic applications Time/frequency analysis Frame-wise description Sliding window analysis Temporal modeling Time/frequency analysis State characterization Dynamic functional connectivity Dynamic graph analysis
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Abstract	Resting-state functional magnetic resonance imaging (fMRI) has highlighted the rich structure of brain activity in absence of a task or stimulus. A great effort has been dedicated in the last two decades to investigate functional connectivity (FC), i.e. the functional interplay between different regions of the brain, which was for a long time assumed to have stationary nature. Only recently was the dynamic behaviour of FC revealed, showing that on top of correlational patterns of spontaneous fMRI signal fluctuations, connectivity between different brain regions exhibits meaningful variations within a typical resting-state fMRI experiment. As a consequence, a considerable amount of work has been directed to assessing and characterising dynamic FC (dFC), and several different approaches were explored to identify relevant FC fluctuations. At the same time, several questions were raised about the nature of dFC, which would be of interest only if brought back to a neural origin. In support of this, correlations with electroencephalography (EEG) recordings, demographic and behavioural data were established, and various clinical applications were explored, where the potential of dFC could be preliminarily demonstrated. In this review, we aim to provide a comprehensive description of the dFC approaches proposed so far, and point at the directions that we see as most promising for the future developments of the field. Advantages and pitfalls of dFC analyses are addressed, helping the readers to orient themselves through the complex web of available methodologies and tools. [Display omitted] •A great effort has been spent on dynamic functional connectivity characterization.•We exhaustively describe existing approaches, their advantages and pitfalls.•We discuss future analytical directions: frame-wise analysis and temporal modeling.•Frame-wise analysis extracts the meaningful functional networks from events.•Temporal modeling parameterizes brain dynamics in flexible and realistic manners.
AbstractList	Resting-state functional magnetic resonance imaging (fMRI) has highlighted the rich structure of brain activity in absence of a task or stimulus. A great effort has been dedicated in the last two decades to investigate functional connectivity (FC), i.e. the functional interplay between different regions of the brain, which was for a long time assumed to have stationary nature. Only recently was the dynamic behaviour of FC revealed, showing that on top of correlational patterns of spontaneous fMRI signal fluctuations, connectivity between different brain regions exhibits meaningful variations within a typical resting-state fMRI experiment. As a consequence, a considerable amount of work has been directed to assessing and characterising dynamic FC (dFC), and several different approaches were explored to identify relevant FC fluctuations. At the same time, several questions were raised about the nature of dFC, which would be of interest only if brought back to a neural origin. In support of this, correlations with electroencephalography (EEG) recordings, demographic and behavioural data were established, and various clinical applications were explored, where the potential of dFC could be preliminarily demonstrated. In this review, we aim to provide a comprehensive description of the dFC approaches proposed so far, and point at the directions that we see as most promising for the future developments of the field. Advantages and pitfalls of dFC analyses are addressed, helping the readers to orient themselves through the complex web of available methodologies and tools. [Display omitted] •A great effort has been spent on dynamic functional connectivity characterization.•We exhaustively describe existing approaches, their advantages and pitfalls.•We discuss future analytical directions: frame-wise analysis and temporal modeling.•Frame-wise analysis extracts the meaningful functional networks from events.•Temporal modeling parameterizes brain dynamics in flexible and realistic manners. Resting-state functional magnetic resonance imaging (fMRI) has highlighted the rich structure of brain activity in absence of a task or stimulus. A great effort has been dedicated in the last two decades to investigate functional connectivity (FC), i.e. the functional interplay between different regions of the brain, which was for a long time assumed to have stationary nature. Only recently was the dynamic behaviour of FC revealed, showing that on top of correlational patterns of spontaneous fMRI signal fluctuations, connectivity between different brain regions exhibits meaningful variations within a typical resting-state fMRI experiment. As a consequence, a considerable amount of work has been directed to assessing and characterising dynamic FC (dFC), and several different approaches were explored to identify relevant FC fluctuations. At the same time, several questions were raised about the nature of dFC, which would be of interest only if brought back to a neural origin. In support of this, correlations with electroencephalography (EEG) recordings, demographic and behavioural data were established, and various clinical applications were explored, where the potential of dFC could be preliminarily demonstrated. In this review, we aim to provide a comprehensive description of the dFC approaches proposed so far, and point at the directions that we see as most promising for the future developments of the field. Advantages and pitfalls of dFC analyses are addressed, helping the readers to orient themselves through the complex web of available methodologies and tools.
Author	Bolton, Thomas AW Van De Ville, Dimitri Preti, Maria Giulia
Author_xml	– sequence: 1 givenname: Maria Giulia surname: Preti fullname: Preti, Maria Giulia email: maria.preti@epfl.ch organization: Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland – sequence: 2 givenname: Thomas AW surname: Bolton fullname: Bolton, Thomas AW organization: Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland – sequence: 3 givenname: Dimitri surname: Van De Ville fullname: Van De Ville, Dimitri organization: Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/28034766$$D View this record in MEDLINE/PubMed
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SubjectTerms	Brain Brain - anatomy & histology Brain - physiology Brain mapping Connectome - methods Dynamic functional connectivity Dynamic graph analysis EEG Frame-wise description Functional magnetic resonance imaging Gene expression Image Processing, Computer-Assisted - methods Magnetic Resonance Imaging - methods Neural networks Neural Pathways - anatomy & histology Neural Pathways - physiology Neuroimaging Sliding window analysis State characterization Studies Temporal modeling Therapeutic applications Time/frequency analysis
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Title	The dynamic functional connectome: State-of-the-art and perspectives
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