Deep brain stimulation for movement disorder treatment: Exploring frequency-dependent efficacy in a computational network model

A large scale computational model of the basal ganglia (BG) network is proposed to describes movement disorder including deep brain stimulation (DBS). The model of this complex network considers four areas of the basal ganglia network: the subthalamic nucleus (STN) as target area of DBS, globus pallidus, both pars externa and pars interna (GPe-GPi), and the thalamus (THA). Parkinsonian conditions are simulated by assuming reduced dopaminergic input and corresponding pronounced inhibitory or disinhibited projections to GPe and GPi. Macroscopic quantities can be derived which correlate closely to thalamic responses and hence motor programme fidelity. It can be demonstrated that depending on different levels of striatal projections to the GPe and GPi, the dynamics of these macroscopic quantities switch from normal conditions to parkinsonian. Simulating DBS on the STN affects the dynamics of the entire network, increasing the thalamic activity to levels close to normal, while differing from both normal and parkinsonian dynamics. Using the mentioned macroscopic quantities, the model proposes optimal DBS frequency ranges above 130 Hz.Comment: 40 pages, 16 figure

Coarse-Grained Descriptions of Dynamics for Networks with Both Intrinsic and Structural Heterogeneities

TesisLa presente tesis lleva por título “Determinación y evaluación de las patologías en las estructuras de las viviendas de la cuadra 06 de la calle comercio en el distrito de la arena provincia de Piura departamento de Piura febrero 2019”. La preocupación de ver un gran número de viviendas con importantes problemas patológicos fue el factor de motivación para llevar a cabo esta investigación. Para entender mejor la problemática se planteó la siguiente pregunta ¿En qué medida la determinación y evaluación de las patologías de las estructuras de las viviendas de la cuadra 6, nos permite tener el grado de deterioro de sus elementos y su condición de servicio actual? Teniendo como objetivo general: determinar y evaluar las patologías de las viviendas de la cuadra 6. Por ello en la presente tesis para determinar y evaluar las patologías de las estructuras de las viviendas de la cuadra 6 del distrito de la arena, se ha tomado como longitud total de estudio 976.00 metros cuadrados de cuadra 6, las cuales se constituyen de un sistema conformado por columnas, vigas y muros de albañilería confinada, así como elementos de concreto armado con fines estructurales. Después de haber realizado la inspección visual de todas las unidades de muestra con la ayuda de la ficha técnica de evaluación, se concluye que el total de las unidades de muestra analizadas en el cerco perimétrico de las viviendas fue un área de 271.57 m2, de los cuales resulta un área con lesiones de 98.90m2 correspondiente al 36.4 % y un área sin lesiones de 172.68 m2 correspondiente al 63.6 %

Coarse-Grained Descriptions of Dynamics for Networks with Both Intrinsic and Structural Heterogeneities

Finding accurate reduced descriptions for large, complex, dynamically evolving networks is a crucial enabler to their simulation, analysis, and ultimately design. Here, we propose and illustrate a systematic and powerful approach to obtaining good collective coarse-grained observables—variables successfully summarizing the detailed state of such networks. Finding such variables can naturally lead to successful reduced dynamic models for the networks. The main premise enabling our approach is the assumption that the behavior of a node in the network depends (after a short initial transient) on the node identity: a set of descriptors that quantify the node properties, whether intrinsic (e.g., parameters in the node evolution equations) or structural (imparted to the node by its connectivity in the particular network structure). The approach creates a natural link with modeling and “computational enabling technology” developed in the context of Uncertainty Quantification. In our case, however, we will not focus on ensembles of different realizations of a problem, each with parameters randomly selected from a distribution. We will instead study many coupled heterogeneous units, each characterized by randomly assigned (heterogeneous) parameter value(s). One could then coin the term Heterogeneity Quantification for this approach, which we illustrate through a model dynamic network consisting of coupled oscillators with one intrinsic heterogeneity (oscillator individual frequency) and one structural heterogeneity (oscillator degree in the undirected network). The computational implementation of the approach, its shortcomings and possible extensions are also discussed