Spectrally resolved fast transient brain states in electrophysiological data

By Diego Vidaurre, Andrew J. Quinn, Adam P. Baker, David Dupret, Álvaro Tejero-Cantero, Mark William Woolrich. NeuroImage, 2016

381 citations recorded by the source corpus.

The brain is capable of producing coordinated fast changing neural dynamics across multiple brain regions in order to adapt to rapidly changing environments. However, it is non-trivial to identify multiregion dynamics at fast sub-second time-scales in electrophysiological data. We propose a method that, with no knowledge of any task timings, can simultaneously identify and describe fast transient multiregion dynamics in terms of their temporal, spectral and spatial properties. The approach models brain activity using a discrete set of sequential states, with each state distinguished by its own multiregion spectral properties. This can identify potentially very short-lived visits to a brain state, at the same time as inferring the state's properties, by pooling over many repeated visits to that state. We show how this can be used to compute state-specific measures such as power spectra and coherence. We demonstrate that this can be used to identify short-lived transient brain states with distinct power and functional connectivity (e.g., coherence) properties in an MEG data set collected during a volitional motor task.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/727a8ca6138b60afcecefeb8628a35b2e5d83573

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In Cognitive Neuroscience

Citations in the corpus

Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: Models and mechanisms (2017, background): “Notably, the FC states were found to last very shortly (100-200ms), suggesting that the resting brain is constantly changing between different patterns of activity much more rapidly than previously thought (Vidaurre et al., 2016).”
Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank (2017, methodology): “Next, tissue-type segmentation is applied using FAST (FMRIB’s Automated Segmentation Tool Zhang et al. (2001)).”
Discovering dynamic brain networks from big data in rest and task (2017, methodology): “This strategy can reveal task-related processes that are too fast to be seen by sliding window analyses (Vidaurre et al., 2016).”
Brain network dynamics are hierarchically organized in time (2017, methodology): “The HMM is inferred using the (publicly available) HMM-MAR (multivariate autoregressive) toolbox, which provides estimates of the parameters of the state distributions, the (group-level) transition probability matrix, and the probabilities of each state to be active at each time point (20, 42).”
Disrupted working memory event-related network dynamics in multiple sclerosis (2024, unknown): “The estimation of the model parameters is done using stochastic Bayesian variational inference 61,63 .”
Impaired activation of the prefrontal executive network during working memory processing in multiple sclerosis (2023, methodology): “32,34 We ran the TDE-HMM TDE-HMM at the group level , i. MEG recording of the activation of the hidden state 1 103”