An Evolutionary Game Theory Model of Spontaneous Brain Functioning

Exploration-imitation competition in well-mixed and structured populations

Social physics is the mathematical study of how the flow of ideas can change the behavior of individuals. Models in social physics include processes for searching after new ideas and dispersing them inside the population. The present paper makes use of exploration and imitation processes for the mentioned purpose. A new type of imitation is employed here, in which the imitant strategy is not simply replaced by role model strategy, but rather moves toward it. Our exploration process is additive Gaussian noise. To study these processes, we both analyze and simulate the strategy distribution function. By combining exploration and soft imitation, the width of the strategy distribution function becomes stationary. In this model, regular imitation is a particular limit of soft imitation, where an imitant copies the role model, through which the stationary width of the distribution function disappears. As the network causes complex behavior, soft imitation and exploration have been studied in some complex networks as well. Simulation results show that for a small world, scale-free, random regular, and random network, and by selected parameters in this research, distribution function width reaches stationary states.

Emulative, coherent, and causal dynamics between large-scale brain networks are neurobiomarkers of Accelerated Cognitive Ageing in epilepsy

Accelerated cognitive ageing (ACA) is an ageing co-morbidity in epilepsy that is diagnosed through the observation of an evident IQ decline of more than 1 standard deviation (15 points) around the age of 50 years old. To understand the mechanism of action of this pathology, we assessed brain dynamics with the use of resting-state fMRI data. In this paper, we present novel and promising methods to extract brain dynamics between large-scale resting-state networks: the emulative power, wavelet coherence, and granger causality between the networks were extracted in two resting-state sessions of 24 participants (10 ACA, 14 controls). We also calculated the widely used static functional connectivity to compare the methods. To find the best biomarkers of ACA, and have a better understanding of this epilepsy co-morbidity we compared the aforementioned between-network neurodynamics using classifiers and known machine learning algorithms; and assessed their performance. Results show that features based on the evolutionary game theory on networks approach, the emulative powers, are the best descriptors of the co-morbidity, using dynamics associated with the default mode and dorsal attention networks. With these dynamic markers, linear discriminant analysis could identify ACA patients at 82.9% accuracy. Using wavelet coherence features with decision-tree algorithm, and static functional connectivity features with support vector machine, ACA could be identified at 77.1% and 77.9% accuracy respectively. Granger causality fell short of being a relevant biomarker with best classifiers having an average accuracy of 67.9%. Combining the features based on the game theory, wavelet coherence, Granger-causality, and static functional connectivity- approaches increased the classification performance up to 90.0% average accuracy using support vector machine with a peak accuracy of 95.8%. The dynamics of the networks that lead to the best classifier performances are known to be challenged in elderly. Since our groups were age-matched, the results are in line with the idea of ACA patients having an accelerated cognitive decline. This classification pipeline is promising and could help to diagnose other neuropsychiatric disorders, and contribute to the field of psychoradiology.

Seizure onset detection based on detection of changes in brain activity quantified by evolutionary game theory model

BACKGROUND AND OBJECTIVE

Epilepsy is one of the most common diseases of the nervous system, affecting about 1% of the world's population. The unpredictable nature of the epilepsy seizures deprives the patients and those around them of living a normal life. Therefore, the development of new methods that can help these patients will increase the life quality of these people and can bring a lot of economic savings in the health sector.

METHODS

In this study, we introduced a new framework for seizure onset detection. Our framework provides a new modelling for brain activity using evolutionary game theory and Kalman filter. If the patterns in the electroencephalogram (EEG) signal violate the predicted patterns by the proposed model, using a novel detection algorithm that has been also introduced in this paper, it can be determined whether the observed violation is the result of the onset of an epileptic seizure or not.

RESULTS

The proposed approach was able to detect the onset of all the seizures in CHB-MIT dataset with an average delay of -0.8 s and a false alarm of 0.39 per hour. Also, our proposed approach is about 20 times faster compared to recent studies.

CONCLUSIONS

The experimental results of applying the proposed framework on the CHB-MIT dataset show that our framework not only performed well with respect to the sensitivity, delay, and false alarm metrics but also performed much better in terms of run time compared to recent studies. This appropriate run time, along with other suitable metrics, makes it possible to use this framework in many cases where processing power or energy is limited and to think about creating new and inexpensive solutions for the treatment and care of people diagnosed with epilepsy.

Potential for Resting-State fMRI of the Amygdala in Elucidating Neural Mechanisms of Adaptive Self-Regulatory Strategies: A Systematic Review

Evolutionary-developmental theories consider the evolved mechanisms underlying adaptive behavioral strategies shaped in response to early environmental cues. Identifying neural mechanisms mediating processes of conditional adaptation in humans is an active area of research. Resting-state functional magnetic resonance imaging (RS-fMRI) captures functional connectivity theorized to represent the underlying functional architecture of the brain. This allows for investigating how underlying functional brain connections are related to early experiences during development, as well as current traits and behaviors. This review explores the potential of RS-fMRI of the amygdala (AMY) for advancing research on the neural mechanisms underlying adaptive strategies developed in early adverse environments. RS-fMRI studies of early life stress (ELS) and AMY functional connectivity within the frame of evolutionary theories are reviewed, specifically regarding the development of self-regulatory strategies. The potential of RS-fMRI for investigating the effects of ELS on developmental trajectories of self-regulation is discussed.

Modulation of network-to-network connectivity via spike-timing-dependent noninvasive brain stimulation

Human cognitive abilities and behavior are linked to functional coupling of many brain regions organized in distinct networks. Gaining insights on the role those networks' dynamics play in cognition and pathology requires their selective, reliable, and reversible manipulation. Here we document the possibility to manipulate the interplay between two brain networks in a controlled manner, by means of a Transcranial Magnetic Stimulation (TMS) protocol inducing spike timing dependent plasticity (STDP). Pairs of TMS pulses at specific inter-stimulus intervals, repeatedly delivered over two negatively correlated nodes of the default mode network (DMN) and the task-positive network (TPN) defined on the basis of individual functional magnetic resonance imaging (fMRI) data, induced a modulation of network-to-network connectivity, even reversing correlation from negative to slightly positive in 30% of cases. Results also suggest a baseline-dependent effect, with a greater connectivity modulation observed in participants with weaker between-networks connectivity strength right before TMS. Finally, modulation of task-evoked fMRI activity patterns during a sustained attention task was also observed after stimulation, with a faster or slower switch between rest and task blocks according to the timing of TMS pulses. The present findings promote paired associative TMS as a promising technique for controlled manipulation of fMRI connectivity dynamics in humans, as well as the causal investigation of brain-behavior relations.