Fast transient networks in spontaneous human brain activity

By Adam P. Baker, Matthew J. Brookes, Iead Rezek, Stephen M. Smith, Timothy Edward John Behrens, P. Probert Smith, Mark William Woolrich. eLife, 2014

549 citations recorded by the source corpus.

To provide an effective substrate for cognitive processes, functional brain networks should be able to reorganize and coordinate on a sub-second temporal scale. We used magnetoencephalography recordings of spontaneous activity to characterize whole-brain functional connectivity dynamics at high temporal resolution. Using a novel approach that identifies the points in time at which unique patterns of activity recur, we reveal transient (100–200 ms) brain states with spatial topographies similar to those of well-known resting state networks. By assessing temporal changes in the occurrence of these states, we demonstrate that within-network functional connectivity is underpinned by coordinated neuronal dynamics that fluctuate much more rapidly than has previously been shown. We further evaluate cross-network interactions, and show that anticorrelation between the default mode network and parietal regions of the dorsal attention network is consistent with an inability of the system to transition directly between two transient brain states. DOI: http://dx.doi.org/10.7554/eLife.01867.001

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/eaed0dff6efd6b7a972f05a177abb4560cc0218d

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In Cognitive Neuroscience

Citations in the corpus

Brain network dynamics are hierarchically organized in time (2017, background): “HMM states represent unique brain networks of distinct activity and functional connectivity (where functional connectivity is defined as Pearson correlation over time between brain areas) that repeat at different points in time (19, 20).”
Questions and controversies in the study of time-varying functional connectivity in resting fMRI (2020, unknown): “…using varied imaging modalities (e.g., fMRI, Sakoglu et al., 2010; EEG, Tagliazucchi, von Wegner, Morzelewski, Brodbeck, & Laufs, 2012; and MEG, Baker et al., 2014) to investigate ﬂuctuations in FC during a wide range of cognitive and behavioral states ranging from explicitly cued task…”
The graphical brain: Belief propagation and active inference (2017, background): “Casting neuronal dynamics as deterministic belief propagation may seem to preclude characterizations that appeal to dynamical systems theory (Baker et al., 2014; Breakspear, 2004); Network Neuroscience in particular, notions like metastability, itinerancy, and self-organized criticality (Bak, Tang,…”
How reliable are MEG resting-state connectivity metrics? (2016, methodology): “, 2013), and as a feasible tool for assessing the short-timescale fluctuations of connectivity at rest (Baker et al., 2014; de Pasquale et al., 2010, 2015).”
Dynamic models of large-scale brain activity (2017, background): “These mechanisms differ, depending on the nature of the data analyzed (EEG, MEG, electrocorticography or fMRI) and the details of the model employed.”
Great Expectations: Using Whole-Brain Computational Connectomics for Understanding Neuropsychiatric Disorders (2014, background): “It is also an important research area to develop models that take into account the non-stationarity of signals which has been shown in rs-MRI (Allen et al., 2014; Hutchison et al., 2013) and rs-MEG (Baker et al., 2014).”
Microstates in Resting-State EEG: Current Status and Future Directions (2014, background): “…individual RSN activity, the authors found that these states each remained stable for approximately 100-200 ms before abruptly transitioning to a new state, and exhibited nonrandom transition patterns among states – both features also characteristic of EEG microstates (Baker et al., 2014).”
Discovering dynamic brain networks from big data in rest and task (2017, methodology): “In resting, the dynamics of whole-brain resting state networks have been characterised in MEG (Baker et al., 2014) and fMRI (Vidaurre et al.”
Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: Models and mechanisms (2017, methodology): “Applying a k-means clustering (Allen et al., 2014, Hansen et al., 2015) or HMM (Baker et al., 2014) to both empirical and simulated FC(t) to compare the properties of re-occurring FC patterns would provide an additional validation of the FC dynamics generated by resting-state models. iii.”
The dynamics of resting fluctuations in the brain: metastability and its dynamical cortical core (2016, unknown): “This problem arises most likely from the lack of taking time into account, i.e. the non-stationary nature of the resting state dynamics 44, 45 .”
Spectrally resolved fast transient brain states in electrophysiological data (2016, unknown): “We refer to HMM–Gaussian as the model with Gaussian observations on the power (Hilbert envelope) of the signals (Baker et al., 2014), HMM–AR as themodel for which only the selfautoregression coefficients (the diagonal elements ofWl(k)) are allowed to vary across states, and HMM–crossMAR as the…”
Insular intracranial activity identifies multiple facial expressions via diverse, intermixed temporal patterns at the single-contact level (2026, unknown): “…each of the frequency bands (theta:2 to 7 Hz, alpha: 8 to 12 Hz, beta: 13 to 30 Hz, low-gamma: 31 to 60 Hz, and high-gamma: 61 to 150 Hz), epochs were band-pass filtered according to the specified frequency range, followed by calculation of magnitude of the Hilbert transform for each trial 33,40 .”