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Characterizing brain function with the help of computational neuroscience

Described in a review published on 8 September in the journal Cell Reports, co-authored by Gustavo Deco, director of the Center for Brain and Cognition and ICREA research professor of the DTIC, and Morten L. Kringelbach, a researcher at the University of Oxford (UK).

22.09.2020

Imatge inicial

In the field of computational neuroscience there are great expectations of finding new ways to rebalance the complex dynamic system of the human brain by inducing controlled pharmacological or electromagnetic perturbation. However, there is still much uncertainty as to the ability to accurately predict how and where best to induce perturbation to study, in a controlled setting, the transition from one brain state to another (states of wakefulness, sleep, anaesthesia, etc.) .

In a paper published on 8 September in the journal Cell Reports, Gustavo Deco, director of the Center for Brain and Cognition (CBC) and ICREA research professor at the UPF (DTIC), together with Morten L. Kringelbach, a researcher at the University of Oxford (UK), present a review of the latest breakthroughs in this area of knowledge and propose a new theoretical framework for determining the functional hierarchical organization that describes any in silico brain state to predict and design novel pharmacological and electromagnetic interventions in order to rebalance the brain in the event of disease.

Recent advances in computational neuroscience have enabled robustly defining brain states and their transitions

One of the main challenges is to have a commonly agreed definition of what a brain state means and how the brain changes from one state to another. Recent advances in computational neuroscience have enabled robustly defining brain states and possible transitions between brain states. As Deco states, “the baseline argument of our work is that computational neuroscience provides a mechanistic framework for characterizing brain states in terms of the underlying causal mechanisms and dynamical complexity”.

Brain states consist of the continuously evolving dynamics of widespread networks that are characterized by condition-dependent self-organization, going through stable, “quasi-stable,” high or low activities, and transient arrangements. The key question is how best to identify states from functional neuroimaging data with a focus on capturing their whole-brain dynamics over time and space. Until now, existing definitions of brain states have been restricted to resting-state networks and to describing functional brain activity.

Modelling brain activity associated with behaviour

Brain activity associated with a particular behaviour can be measured on many different temporal and spatial scales. As the authors point out, “there has been significant progress in characterizing the spatiotemporal dynamics of neuroimaging data”.

The properties of each of the many analytical tools vary substantially. The empirical measurements of behaviour can capture information from a time scale of tens of milliseconds. Also, empirical measurements of the brain can capture information on a much faster timescale of submilliseconds. fMRI is still one of the most widespread methods to measure activity throughout the brain with excellent submillimetric spatial resolution, but it is limited in the time domain by haemodynamics. Another popular method of whole-brain imaging is PET (positron emission tomography), which is far less precise both as regards temporal and spatial information, but can measure important information about the density of neurotransmitters.

“We have shown that we could soon be in a position to accurately characterize and control brain states in health and disease”

The empirical measurements obtained are combined in complete computational models of the brain, which helps in understanding the causal mechanics of the data obtained from various analytical tools available to study the behaviour of the human brain. Using whole-brain modelling to bring about an enhanced description of brain states is not just useful for understanding the healthy brain but holds great promise for helping support diagnosis and therapeutic interventions in disease (epilepsy, brain tumours, stroke, etc.).

In this review, “we have shown that we could soon be in a position to accurately characterize and control brain states in health and disease. The novel ideas put forward in this review are highly relevant for basic and clinical neuroscientists across many disciplines, potentially unifying and explaining a number of hitherto complex problems”, the authors conclude.

Reference work:

Morten L.Kringelbach, Gustavo Deco (2020),"Brain States and Transitions: Insights from Computational Neuroscience", 8 de setembre, vol. 32, 10, Cell Reports. DOI: https://doi.org/10.1016/j.celrep.2020.108128

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