Discovering dynamic brain networks from big data in rest and task

By Diego Vidaurre, Romesh G. Abeysuriya, Robert Becker, Andrew J. Quinn, Fidel Alfaro-Almagro, Stephen M. Smith, Mark William Woolrich. NeuroImage, 2017

355 citations recorded by the source corpus.

ABSTRACT Brain activity is a dynamic combination of the responses to sensory inputs and its own spontaneous processing. Consequently, such brain activity is continuously changing whether or not one is focusing on an externally imposed task. Previously, we have introduced an analysis method that allows us, using Hidden Markov Models (HMM), to model task or rest brain activity as a dynamic sequence of distinct brain networks, overcoming many of the limitations posed by sliding window approaches. Here, we present an advance that enables the HMM to handle very large amounts of data, making possible the inference of very reproducible and interpretable dynamic brain networks in a range of different datasets, including task, rest, MEG and fMRI, with potentially thousands of subjects. We anticipate that the generation of large and publicly available datasets from initiatives such as the Human Connectome Project and UK Biobank, in combination with computational methods that can work at this scale, will bring a breakthrough in our understanding of brain function in both health and disease.

Subfields: Cognitive Neuroscience, Health, Toxicology and Mutagenesis

View Paper: https://www.semanticscholar.org/paper/4b0043a4d8dd8c35703b82b28ddafa619ebfc8d6

How researchers see this paper

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Chiara Rossi
D. Vidaurre
M. D’hooghe
F. Akbarian
M. D’haeseleer
G. Nagels
M. Woolrich
L. Costers

Citations in the corpus

Questions and controversies in the study of time-varying functional connectivity in resting fMRI (2020, neutral): “Highlyarticulated“data-driven” models (e.g., autoregressive models, Rogers, Katwal, Morgan, Asplund, & Gore, 2010; or HMMs, Vidaurre, Abeysuriya, et al., 2018) may explain the data very well without recourse to biological assumptions, but do not provide information about the underlying neuronal…”
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”