In a study published this January by a team of scientists including Gustavo Deco, ICREA researcher at the Department of Information and Communication Technologies (DTIC) and director of the Cognition and Brain Centre  at the Pompeu Fabra University (UPF), have shown that functional connectivity dynamics (FCD) of the neural networks of the cerebral cortex, are a good biomarker of neural activity in a state of rest of the brain, so that the study of disorders of the FCD can guide the diagnosis, prognosis and individual treatment of some of the more prevalent mental diseases, such as Alzheimer's disease or schizophrenia.

The study of cerebral  functional connectivity shows the interactions that take place between the different regions of the brain. Functional connectivity has been seen to be especially relevant clinically in several mental illnesses, such as those mentioned above, as well as being a relevant piece of information in surgical planning and in cases of epilepsy and traumatic brain damage.

The whole simulation of the brain, through computational models, in parallel with real functional connectivity studies obtained by tractography, have enabled accurately reproducing functional connectivity in brain resting state.

Tractography is a functional test that uses special magnetic resonance imaging and computer-assisted analysis techniques that results in bi- and three-dimensional images.

Until now, many computational studies had not taken into account the functional connectivity of the brain in a state of rest, which may present a great variability, both in a single and among different individuals.  The authors of this study have focused on the non-stationarity phases of brain connectivity in a state of rest and have revealed a rich functional structure, characterized by rapid transitions of state between stable periods of discrete functional connectivity.

The authors of the study have also shown that optimized computational models by adapting to the average time of functional connectivity have not been able to capture these spontaneous transitions of functionality. On the other hand, the authors have shown that the non-linear modelling of the behaviour of the neural connection nodes of the network adapt much better to the real behaviour of the brain than linear models, and that this non-linearity is enough to generate a wide range of behaviours characteristic of the neural network of the prefrontal cortex and far from seeking equilibrium. 

Reference work:

Enrique C.A. Hansen, Demian Battaglia, Andreas Spiegler, Gustavo Deco, Viktor K. Jirsa (2015), "Functional connectivity dynamics: Modelling the switching behavior of the resting state", NeuroImage, 105, 525-535.