Rafael B. Stern, Matheus Silva d'Alencar, Yanina L. Uscapi, Marco D. Gubitoso, Antonio C. Roque, André F. Helene and Maria Elisa Pimentel Piemonte
Background: People with Parkinson's disease (PD) display poorer gait performance when walking under complex conditions than under simple conditions. Screening tests that evaluate gait performance changes under complex walking conditions may be valuable tools for early intervention, especially if allowing for massive data collection.
Mauricio Girardi-Schappo, Ludmila Brochini, Ariadne A. Costa, Tawan T. A. Carvalho and Osame Kinouchi
Recent experiments suggested that a homeostatic regulation of synaptic balance leads the visual system to recover and maintain a regime of power-law avalanches. Here we study an excitatory/inhibitory (E/I) meanfield neuronal network that has a critical point with power-law avalanches and synaptic balance. When shortterm depression in inhibitory synapses and firing threshold adaptation are added, the system hovers around the critical point. This homeostatically self-organized quasicritical (SOqC) dynamics generates E/I synaptic current cancellation in fast timescales, causing fluctuation-driven asynchronous-irregular (AI) firing. We present the full phase diagram of the model without adaptation varying external input versus synaptic coupling. This system has a rich dynamical repertoire of spiking patterns: synchronous regular (SR), asynchronous regular (AR), synchronous irregular (SI), slow oscillations (SO), and AI. It also presents dynamic balance of synaptic currents, since inhibitory currents try and compensate excitatory currents over time, resulting in both of them scaling linearly with external input. Our model thus unifies two different perspectives on cortical spontaneous activity: both critical avalanches and fluctuation-driven AI firing arise from SOqC homeostatic adaptation and are indeed two sides of the same coin.
The Research, Innovation and Dissemination Center on Neuromathematics (NeuroMat) is offering a FAPESP scholarship for information technology professionals interested in being part of a breakthrough and innovative scientific project. The recipient will interact with researchers from USP and other NeuroMat collaborating institutions in activities of development, customization, maintenance and deployment of open software related to the scientific goals of the center.
Arthur Valencio
Electroencephalography (EEG) is a primary tool for diagnostics and follow-up of neurological conditions. From EEG data, it is possible to obtain functional connectivity matrices, enabling the identification of associated brain areas during a task, hence supporting hypothesis for specific brain links. However, usually these matrices are built using correlation tools, which are not appropriate when characterizing nonlinear signals such as EEG. Hence, here we apply the same procedure using information theoretical measures (mutual information, transfer entropy and causal mutual information) and compare the results. For the test case applied, we show that both ways lead to similar links identified, however the information-theoretical measures provide the extra indicator of the direction to which the information flows.
Noslen Hernández, Raymundo Machado de Azevedo Neto, Aline Duarte, Guilherme Ost, Ricardo Fraiman, Antonio Galves, Claudia D. Vargas
Using a new probabilistic approach we model the relationship between sequences of auditory stimuli generated by stochastic chains and the electroencephalographic (EEG) data acquired while participants are exposed to those stimuli. Herein, the structure of the chain generating the stimuli is characterized by a rooted and labeled tree whose branches, henceforth called contexts, represent the sequences of past stimuli governing the choice of the next stimulus. A classical conjecture claims that the brain assigns probabilistic models to samples of stimuli. If this is true, then the context tree generating the sequence of stimuli should be encoded in the brain activity. Using an innovative statistical procedure we show that this context tree can effectively be extracted from the EEG data, thus giving support to the classical conjecture.
