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Keywords:

  • Science & Technology
  • Life Sciences & Biomedicine
  • Neurosciences
  • Neurosciences & Neurology
  • Networks to dynamics
  • Brain network models
  • Neural mass models
  • Diffusion tensor imaging
  • Resting-state fMRI
  • Recurrent neural networks
  • Generative models
  • FUNCTIONAL CONNECTIVITY
  • CEREBRAL-CORTEX
  • MOTOR CORTEX
  • DYNAMICS
  • REGIONS

Brain network constraints and recurrent neural networks reproduce unique trajectories and state transitions seen over the span of minutes in resting-state fMRI

Tools:

Journal Title:

NETWORK NEUROSCIENCE

Volume:

Volume 4, Number 2

Publisher:

, Pages 448-466

Type of Work:

Article

Abstract:

Large-scale patterns of spontaneous whole-brain activity seen in resting-state functional magnetic resonance imaging (rs-fMRI) are in part believed to arise from neural populations interacting through the structural network (Honey, Kötter, Breakspear, & Sporns, 2007). Generative models that simulate this network activity, called brain network models (BNM), are able to reproduce global averaged properties of empirical rs-fMRI activity such as functional connectivity (FC) but perform poorly in reproducing unique trajectories and state transitions that are observed over the span of minutes in whole-brain data (Cabral, Kringelbach, & Deco 2017; Kashyap & Keilholz, 2019). The manuscript demonstrates that by using recurrent neural networks, it can fit the BNM in a novel way to the rs-fMRI data and predict large amounts of variance between subsequent measures of rs-fMRI data. Simulated data also contain unique repeating trajectories observed in rs-fMRI, called quasiperiodic patterns (QPP), that span 20 s and complex state transitions observed using k-means analysis on windowed FC matrices (Allen et al., 2012; Majeed et al., 2011). Our approach is able to estimate the manifold of rs-fMRI dynamics by training on generating subsequent time points, and it can simulate complex resting-state trajectories better than the traditional generative approaches.
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