Brain oscillations in a random neural network

Emergence of complex oscillatory dynamics in the neuronal networks with long activity time of inhibitory synapses

The brain displays complex dynamics, including collective oscillations, and extensive research has been conducted to understand their generation. However, our understanding of how biological constraints influence these oscillations is incomplete. This study investigates the essential properties of neuronal networks needed to generate oscillations resembling those in the brain. A simple discrete-time model of interconnected excitable elements is developed, capable of closely resembling the complex oscillations observed in biological neural networks. In the model, synaptic connections remain active for a duration exceeding individual neuron activity. We show that the inhibitory synapses must exhibit longer activity than excitatory synapses to produce a diverse range of the dynamical states, including biologically plausible oscillations. Upon meeting this condition, the transition between different dynamical states can be controlled by external stochastic input to the neurons. The study provides a comprehensive explanation for the emergence of distinct dynamical states in neural networks based on specific parameters.

A novel training-free externally-regulated neurofeedback (ER-NF) system using phase-guided visual stimulation for alpha modulation

The efficacy of neurofeedback is a point of great controversy, because a certain proportion of users cannot properly regulate their brain activities and thereby fail to benefit from neurofeedback. To address the neurofeedback inefficacy problem, the present study is aimed to design and implement a new neurofeedback system that can more effectively and consistently regulate users' brain activities than the conventional way of training users to voluntarily regulate brain activities. The new neurofeedback system delivers external visual stimuli continuously at a specific alpha phase, which is real-time decoded from ongoing alpha wave, to regulate the alpha wave. Experimental results show that the proposed training-free externally-regulated neurofeedback (ER-NF) system can achieve consistent (effective in almost all sessions for almost all users), flexible (either increasing or decreasing peak alpha frequency and alpha power), and immediate (taking or losing effect immediately after stimulation is on or off) modulation effects on alpha wave. Therefore, the ER-NF system holds great potential to be able to more reliably and flexibly modulate cognition and behavior.

Random Network Models to Predict the Long-Term Impact of HPV Vaccination on Genital Warts

The Human papillomaviruses (HPV) vaccine induces a herd immunity effect in genital warts when a large number of the population is vaccinated. This aspect should be taken into account when devising new vaccine strategies, like vaccination at older ages or male vaccination. Therefore, it is important to develop mathematical models with good predictive capacities. We devised a sexual contact network that was calibrated to simulate the Spanish epidemiology of different HPV genotypes. Through this model, we simulated the scenario that occurred in Australia in 2007, where 12-13 year-old girls were vaccinated with a three-dose schedule of a vaccine containing genotypes 6 and 11, which protect against genital warts, and also a catch-up program in women up to 26 years of age. Vaccine coverage were 73 % in girls with three doses and with coverage rates decreasing with age until 52 % for 20-26 year-olds. A fast 59 % reduction in the genital warts diagnoses occurred in the model in the first years after the start of the program, similar to what was described in the literature.

Stochastic coupled oscillator model of EEG for Alzheimer's disease

Coupled nonlinear oscillator models of EEG signals during resting eyes-closed and eyes-open conditions are presented based on Duffing-van der Pol oscillator dynamics. The frequency and information entropy contents of the output of the nonlinear model and the actual EEG signal is matched through an optimization algorithm. The framework is used to model and compare EEG signals recorded from Alzheimer's disease (AD) patients and age-matched healthy controls (CTL) subjects. The results show that 1) the generated model signal can capture the frequency and information entropy contents of the EEG signal with very similar power spectral distribution and non-periodic time history; 2) the EEG and the generated signal from the eyes-closed model are α band dominant for CTL subjects and θ band dominant for AD patients; and 3) statistically distinct models represent the EEG signals from AD patients and CTL subject during resting eyes-closed condition.