Dysfunctional and compensatory duality in mild cognitive impairment during a continuous recognition memory task

One of the current issues of debate in the study of mild cognitive impairment (MCI) is deviations of oscillatory brain responses from normal brain states and its dynamics. This work aims to characterize the differences of power in brain oscillations during the execution of a recognition memory task in MCI subjects in comparison with elderly controls. Magnetoencephalographic (MEG) signals were recorded during a continuous recognition memory task performance. Oscillatory brain activity during the recognition phase of the task was analyzed by wavelet transform in the source space by means of minimum norm algorithm. Both groups obtained a 77% hit ratio. In comparison with healthy controls, MCI subjects showed increased theta (p < 0.001), lower beta reduction (p < 0.001) and decreased alpha and gamma power (p < 0.002 and p < 0.001 respectively) in frontal, temporal and parietal areas during early and late latencies. Our results point towards a dual pattern of activity (increase and decrease) which is indicative of MCI and specific to certain time windows, frequency bands and brain regions. These results could represent two neurophysiological sides of MCI. Characterizing these opposing processes may contribute to the understanding of the disorder.

Age-Related Effects in Working Memory Recognition Modulated by Retroactive Interference

One of the main causes for age-related declines in working memory is a higher vulnerability to retroactive interference due to a reduced ability to suppress irrelevant information. However, the underlying neural correlates remain to be established. Magnetoencephalography was used to investigate differential neural patterns in young and older adults performing an interference-based memory task with two experimental conditions, interrupting and distracting, during successful recognition. Behaviorally, both types of retroactive interference significantly impaired accuracy at recognition more in older adults than in young adults with the latter exhibiting greater disruptions by interrupters. Magnetoencephalography revealed the presence of differential age-related neural patterns. Specifically, time-modulated activations in temporo-occipital and superior parietal regions were higher in young adults compared with older adults for the interrupting condition. These results suggest that age-related deficits in inhibitory mechanisms that increase vulnerability to retroactive interference may be associated with neural under-recruitments in a high-interference task.

Towards a fuzzy-based multi-classifier selection module for activity recognition applications

Performing activity recognition using the information provided by the different sensors embedded in a smartphone face limitations due to the capabilities of those devices when the computations are carried out in the terminal. In this work a fuzzy inference module is implemented in order to decide which classifier is the most appropriate to be used at a specific moment regarding the application requirements and the device context characterized by its battery level, available memory and CPU load. The set of classifiers that is considered is composed of Decision Tables and Trees that have been trained using different number of sensors and features. In addition, some classifiers perform activity recognition regardless of the on-body device position and others rely on the previous recognition of that position to use a classifier that is trained with measurements gathered with the mobile placed on that specific position. The modules implemented show that an evaluation of the classifiers allows sorting them so the fuzzy inference module can choose periodically the one that best suits the device context and application requirements.