Journal of Neuroscience
March 7, 2012
Gustavo Deco, Viktor Jirsa
778 citations
The brain's ongoing activity at rest, in the absence of tasks or stimulation, is highly structured into spatiotemporal patterns known as resting state networks, which exhibit low-frequency fluctuations below 0.1 Hz. Using a global spiking attractor network model that incorporates realistic neuroanatomical connectivity from diffusion tensor imaging, the authors demonstrate that resting state functional connectivity quantitatively matches human experimental data when the brain network operates at the edge of instability. Under these conditions, slow fluctuations emerge as structured noise around a stable low firing equilibrium, shaped by latent multistable attractors inherent in the neuroanatomical connectivity.
bioRxiv Preprint Server
April 18, 2023
Martin Breyton, Jan Fousek, Giovanni Rabuffo et al.
preprint
Consciousness depends on the brain's ability to produce complex, variable patterns of activity after a perturbation, but measuring this directly is difficult. Using a whole-brain model, researchers found that such complexity only arises when spontaneous brain activity is highly fluid—meaning functional networks reorganize extensively. This fluid regime can be captured by a small set of dynamical systems metrics, which predict the effects of consciousness-altering drugs like Xenon, Propofol, and Ketamine. These predictions were validated in 15 subjects at different consciousness levels, showing agreement with established perturbational complexity measures but using a simpler, more accessible paradigm. The findings point to complexity properties underlying consciousness.
bioRxiv Preprint Server
September 28, 2022
Yonatan Sanz Perl, Carla Pallavicini, Juan Piccinini et al.
preprint
Brain states are often described on a single scale from full consciousness to unconsciousness, but this ignores the complex, high-dimensional nature of brain activity. By combining whole-brain modeling, data augmentation, and deep learning, researchers mapped states of consciousness into a low-dimensional space where distances reflect similarities between states. They found an orderly trajectory from wakefulness to brain-injured patients, with coordinates related to functional modularity and structure-function coupling, both increasing as consciousness is lost. Model perturbations provided a geometric interpretation of state stability and reversibility. The work suggests conscious awareness depends on functional patterns encoded as a low-dimensional trajectory within the vast space of brain configurations.