Design and implement of a multi-channel electrophysiological stimulation software / 国际生物医学工程杂志

Objective To design and implement a universal multi-channel software for neural electrophysiological stimulation experimental platforms. Method The layered design of software and hardware was adopted for the logical architecture to avoid excessive reliance on specific hardware. On the premise of ensuring compatibility with existing devices, an extensible control algorithm based on the .NET Frameworks platform was developed to realize multi-channel, feedback-controlled program-controlled stimulus output. The proposed software was designed with a user-friendly interface and stimulating/recording switch function, and could dynamically change stimulation programs and switch electrodes during the experiment process. Results The results showed that the software could control the stimulators steadily and generate random stimulation protocols and synchronization control signals according to the user-supplied dynamical parameters, including electrodes, amplitudes, and intervals. In the stimulation sequence, the switching delay between two electrodes was around 600 ms level. Conclusion The software has good compatibility with existing equipment systems. It can achieve multi-channel, real-time, feedback-controlled program-controlled stimulation according to the characteristics and needs of multi-lead neural electrophysiological stimulation researches. It has the functions of dynamically changing the stimulation program and switching electrodes during operation. This software provides tools for the study of the mechanism of network-level neural network feedback loops.

Algoritmo para predecir tensiones con técnicas de inteligencia artificial en una tibia humana / Algorithm for prediction of strains in human tibia by means of artificial intelligence techniques

INTRODUCCIÓN: el desarrollo de la informática y sus herramientas influyen de forma significativa en los avances científicos tecnológicos, en la esfera de la salud. La simulación de problemas reales mediante redes neuronales, relaciona intrínseco, la medicina y la informática, por utilizar estas redes modelos basados en el funcionamiento de neuronas humanas. Si a esta potente herramienta unimos un método numérico de cálculo, que permita servir de fuente de datos a la red neuronal, se podrán modelar tejidos y partes del cuerpo humano. Una de las ramas de mayor implementación, podría ser la ortopedia, debido en lo fundamental, a la similitud que tiene el cuerpo humano y su estructura ósea, con las propiedades de los materiales de ingeniería, la cual es un área clave en la aplicación del Método de los Elementos Finitos. OBJETIVO: crear un algoritmo que permita dar solución al problema de remodelación ósea de una tibia humana bajo diferentes valores de cargas mecánicas. MÉTODOS: se empleó el Método de los Elementos Finitos. Se usó el software profesional ABAQUS/CAE para el cálculo de tensiones y deformaciones y una red neuronal para el procesamiento de los valores obtenidos. La red neuronal fue establecida; se aplicó el software MATLAB R2013a. RESULTADOS: se logró un modelo de red neuronal que posibilita predecir las cargas que una determinada zona de la tibia puede soportar. CONCLUSIONES: mediante el uso de las técnicas de inteligencia artificial y con el empleo del método de los elementos finitos, fue posible obtener un modelo que pronosticò las magnitudes de tensiones, que una región de la tibia humana podría soportar, en dependencia de los valores de densidades óseas presente en dicha región.

INTRODUCTION: the development of information sciences and their influence in a significant way the scientific and technological advances in the field of health care. The simulation of real-life problems through neuronal networks intrinsically relates medicine and informatics since these networks use models based on human neuron functioning. If we add to this potent tool a numerical calculation method that allows the neuronal network to serve as a data source, then tissues and parts of the body could be modeled. One of the branches with more implementation in this regard could be orthopedics due to the similarities of the human body and its osseous structures with the properties of the engineering materials and this is a key area in the application of finite element method. OBJECTIVE: to create an algorithm that may solve the problems of osseous remodeling of a human tibia under different mechanical load values. METHODS: the Finite Element Method was used together with the professional software ABAQUS/CAE for estimation of strains and deformations and a neuronal network to process the obtained values. The neuronal network was set and then the software MATLAB R2013a was applied. RESULTS: a neuronal network model that makes it possible to predict the loads that certain area of the tibia may stand. CONCLUSIONS: through the artificial intelligence techniques and the use of the finite element the strain magnitude that may be supported by a human tibia area depending on the osseous density values present in this area.method, it was possible to obtain a model that predicts the strain magnitude that may be supported by a human tibia area depending on the osseous density values present in this area.

The Homogeneity of Phenomenology of Gerstmann Syndrome: The Study in Patients with Alzheimer's Disease

BACKGROUND: It remains unclear whether the four signs of Gerstmann syndrome are a cluster because the neuronal nets responsible for these symptoms are closer together, or because they shares a common networks. If the latter is correct, then with degenerative disorders such as Alzheimer's disease, each sign associated with Gerstmann syndrome should correlate with the other three signs more closely than they correlate with other cognitive dysfunctions. METHODS: Cluster and correlation analyses for various cognitive deficits including signs of Gerstmann syndrome were done among sixty-nine patients with probable Alzheimer's disease. RESULTS: The four signs of Gerstmann syndrome did not cluster together. With the exception of calculation and writing, other signs including right-left orientation and finger naming placed in other groups and did not significantly correlate each other. CONCLUSIONS: A detailed statistical analysis of the tetrad showed that Gerstmann syndrome was not attributable to a common neuronal network, and the phenomenological association of the four signs may be related to the anatomical proximity of the different networks mediating these functions.