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

By Maria Giulia Preti, Thomas William Arthur Bolton, D. Van de Ville. 2017

1,362 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Geometric Deep Learning: Going beyond Euclidean data (2016, background): “A recent trend in neuroscience is to associate functional MRI traces with a pre-computed connectivity rather than inferring it from the traces themselves [116].”
Questions and controversies in the study of time-varying functional connectivity in resting fMRI (2020, neutral): “The dynamic functional connectome: State-of-the-art and perspectives Preti et al., 2017; https://doi.org/10.1016/j.neuroimage.2016.12.061 Detailed review of a wide range of methods for studying BOLD TVFC. Temporal metastates are associated with differential patterns of time-resolved connectivity,…”
Electroencephalographic Resting-State Networks: Source Localization of Microstates (2017, neutral): “New fMRI studies looking at spontaneous dynamic BOLD functional connectivity [see Preti et al. (2016) for a recent review] promote a redeﬁnition of metabolic RSNs closer to the EEG (Hutchison et al., 2013; Zalesky et al., 2014).”
Gradients of structure–function tethering across neocortex (2019, positive): “For example, the conventional static and dyadic representation of functional connectivity employed here may not adequately capture the dynamic character of functional interactions (77) or the possibility of higher-order interactions (78).”
Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: Models and mechanisms (2017, methodology): “While different methodological approaches have been proposed to analyze the FC in the temporal domain (see Preti et al. (2016) for a comprehensive review) and to test for its statistical significance (Hutchison et al., 2013b, Hindriks et al., 2015a, Leonardi and Van De Ville, 2015, Zalesky and…”
Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging (2018, methodology): “While dynamic functional connectivity (DFC) has emerged as a promising topic in the recent fMRI literature, there are also some critical comments on the theory of dynamic connectivity.”
Complexity in speech and music listening via neural manifold flows (2024, unknown): “…of neuronal populations at the millimeter scale and the temporal dynamics of their engagement at the millisecond scale, which are necessary for depicting accurately the neurophysiological underpinning of specific cognitive processes (Mercier et al., 2017; Preti, Bolton, & Van De Ville, 2017).”