Programa para el entrenamiento de la actividad cognitiva en el adulto mayor

According to demographic studies it is estimated that in 2020 the proportion of the number of elderly will be 20-25%. This aging of the population causes a notable increase in the diseases linked to aging. It has been suggested that the Cognitive Reserve is a mechanism that links high levels of learning, physical exercise and cognitive activity with a lower risk of suffering from a neurodegenerative process. The research aims to implement a program of study and training of cognitive activity in the elderly. It shows as a result the application of the program for the study and training of cognitive activity in the elderly, causing changes in aspects such as operating memory, attention, and speed of processing and executive function of the participants in the program.Según los estudios demográficos se calcula que en el año 2020 la proporción del número de ancianos será del 20 al 25%. Este envejecimiento de la población provoca un incremento notable de las enfermedades ligadas a la ancianidad. Se ha sugerido que la reserva cognitiva es un mecanismo que vincula los niveles altos de aprendizaje, ejercicio físico y actividad cognitiva con un menor riesgo de padecer un proceso neurodegenerativo. La investigación tiene como objetivo instrumentar un programa de estudio y entrenamiento de la actividad cognitiva en el adulto mayor. Se muestra como resultado la aplicación del programa en el estudio y entrenamiento de la actividad cognitiva en el adulto mayor, provocando cambios en aspectos como la memoria operativa, la atención, la velocidad de procesamiento y la función ejecutiva de los participantes en el programa

Advancing functional connectivity research from association to causation

Cognition and behavior emerge from brain network interactions, such that investigating causal interactions should be central to the study of brain function. Approaches that characterize statistical associations among neural time series-functional connectivity (FC) methods-are likely a good starting point for estimating brain network interactions. Yet only a subset of FC methods ('effective connectivity') is explicitly designed to infer causal interactions from statistical associations. Here we incorporate best practices from diverse areas of FC research to illustrate how FC methods can be refined to improve inferences about neural mechanisms, with properties of causal neural interactions as a common ontology to facilitate cumulative progress across FC approaches. We further demonstrate how the most common FC measures (correlation and coherence) reduce the set of likely causal models, facilitating causal inferences despite major limitations. Alternative FC measures are suggested to immediately start improving causal inferences beyond these common FC measures

Plataforma neurotecnológica aplicada al estudio y entrenamiento de la actividad cognitiva del ajedrecista

RESUMEN El ajedrez es un juego que demanda una continua actividad atencional en la solución de problemas. El entrenamiento adecuado de la atención en el ajedrecista mediante el empleo de técnicas psicológicas permite una mayor regulación de la precisión perceptiva, capacidad para integrar y procesar estímulos simultáneamente, fiabilidad de la respuesta, tiempo de reacción y concentración. En correspondencia con estos particulares la investigación tiene como objetivo diseñar una plataforma neurotecnológica que permita el estudio y entrenamiento de la actividad cognitiva de los ajedrecistas. El sistema neurotecnológico presentado es pertinente para el entrenamiento de los ajedrecistas en los marcos de los sistemas de entrenamientos contemporáneos. Su instrumentación permite la regulación del componente cognitivo, incrementa la tolerancia a la interferencia atencional, la concentración, el control espacial del tablero de ajedrez, la resistencia atencional y la velocidad de procesamiento de la información en la solución de problemas ajedrecísticos. ABSTRACT Chess is a game that demands a continuous attention activity in the solution of problems. The adequate training of the attention in the chess player through the use of psychological techniques allows a greater regulation of the perceptive precision, ability to integrate and process stimuli simultaneously, reliability of the response, reaction time and concentration. In correspondence with these individuals, the research aims to design a neurotechnological platform that allows the study and training of the cognitive activity of chess players. The neurotechnological system presented is relevant for the training of chess players in the frames of contemporary training systems. Its instrumentation allows the regulation of the cognitive component, it increases the tolerance to the attentional interference, the concentration, the spatial control of the chessboard, the attentional resistance and the speed of information processing in the solution of chess problems

Finding electrophysiological sources of aging-related processes using penalized least squares with Modified Newton-Raphson algorithm

In this work, we evaluate the flexibility of a modified Newton-Raphson (MNR) algorithm for finding electrophysiological sources in both simulated and real data, and then apply it to different penalized models in order to compare the sources of the EEG theta rhythm in two groups of elderly subjects with different levels of declined physical performance. As a first goal, we propose the MNR algorithm for estimating general multiple penalized least squares (MPLS) models and show that it is capable to find solutions that are simultaneously sparse and smooth. This algorithm allowed to address known and novel models such as the Smooth Non-negative Garrote and the Non-negative Smooth LASSO. We test its ability to solve the EEG inverse problem with multiple penalties -using simulated data- in terms of localization error, blurring and visibility, as compared with traditional algorithms. As a second goal, we explore the electrophysiological sources of the theta activity extracted from resting-state EEG recorded in two groups of older adults, which belong to a longitudinal study to assess the relationship between measures of physical performance (gait speed) decline and normal cognition. The groups contained subjects with good and bad physical performance in the two evaluations (6 years apart). In accordance to clinical studies, we found differences in EEG theta sources for the two groups, specifically, subjects with declined physical performance presented decreased temporal sources while increased prefrontal sources that seem to reflect compensating mechanisms to ensure a stable walking

Regularized logistic regression and multi-objective variable selection for classifying MEG data

This paper addresses the question of maximizing classifier accuracy for classifying task-related mental activity from Magnetoencelophalography (MEG) data. We propose the use of different sources of information and introduce an automatic channel selection procedure. To determine an informative set of channels, our approach combines a variety of machine learning algorithms: feature subset selection methods, classifiers based on regularized logistic regression, information fusion, and multiobjective optimization based on probabilistic modeling of the search space. The experimental results show that our proposal is able to improve classification accuracy compared to approaches whose classifiers use only one type of MEG information or for which the set of channels is fixed a priori