Neural network modeling of memory deterioration in Alzheimer's disease

The clinical course of Alzheimer's disease (AD) is generally characterized by progressive gradual deterioration, although large clinical variability exists. Motivated by the recent quantitative reports of synaptic changes in AD, we use a neural network model to investigate how the interplay between synaptic deletion and compensation determines the pattern of memory deterioration, a clinical hallmark of AD. Within the model we show that the deterioration of memory retrieval due to synaptic deletion can be much delayed by multiplying all the remaining synaptic weights by a common factor, which keeps the average input to each neuron at the same level. This parallels the experimental observation that the total synaptic area per unit volume (TSA) is initially preserved when synaptic deletion occurs. By using different dependencies of the compensatory factor on the amount of synaptic deletion one can define various compensation strategies, which can account for the observed variation in the severity and progression rate of AD

A Research Platform for Artificial Neural Networks with Applications in Pediatric Epilepsy

This dissertation established a state-of-the-art programming tool for designing and training artificial neural networks (ANNs) and showed its applicability to brain research. The developed tool, called NeuralStudio, allows users without programming skills to conduct studies based on ANNs in a powerful and very user friendly interface. A series of unique features has been implemented in NeuralStudio, such as ROC analysis, cross-validation, network averaging, topology optimization, and optimization of the activation function’s slopes. It also included a Support Vector Machines module for comparison purposes. Once the tool was fully developed, it was applied to two studies in brain research. In the first study, the goal was to create and train an ANN to detect epileptic seizures from subdural EEG. This analysis involved extracting features from the spectral power in the gamma frequencies. In the second application, a unique method was devised to link EEG recordings to epileptic and non-epileptic subjects. The contribution of this method consisted of developing a descriptor matrix that can be used to represent any EEG file regarding its duration and the number of electrodes. The first study showed that the inter-electrode mean of the spectral power in the gamma frequencies and its duration above a specific threshold performs better than the other frequencies in seizure detection, exhibiting an accuracy of 95.90%, a sensitivity of 92.59%, and a specificity of 96.84%. The second study yielded that Hjorth’s parameter activity is sufficient to accurately relate EEG to epileptic and non-epileptic subjects. After testing, accuracy, sensitivity and specificity of the classifier were all above 0.9667. Statistical tests measured the superiority of activity at over 99.99 % certainty. It was demonstrated that 1) the spectral power in the gamma frequencies is highly effective in locating seizures from EEG and 2) activity can be used to link EEG recordings to epileptic and non-epileptic subjects. These two studies required high computational load and could be addressed thanks to NeuralStudio. From a medical perspective, both methods proved the merits of NeuralStudio in brain research applications. For its outstanding features, NeuralStudio has been recently awarded a patent (US patent No. 7502763)

Observation et modélisation des processus exécutifs et de leur dégradation lors du vieillissement cognitif dans la réalisation des activités de la vie quotidienne

Résumé : Pour assister efficacement les personnes en perte d'autonomie dans le contexte des habitats intelligents, il est essentiel d'identifier les difficultés auxquelles ces personnes sont confrontées dans leur quotidien. L'objectif de ce travail est d'observer les processus exécutifs durant les activités de la vie quotidienne, ainsi que leur dysfonctionnement lors du vieillissement cognitif (normal ou lié à la maladie d'Alzheimer), puis d'élaborer un modèle théorique et informatique capable de simuler les comportements observés. Une phase d'observation et de qualification des processus de contrôle exécutif (capacités de régulation de l'action, de correction et d'adaptation lors de situations imprévues) a d'abord été réalisée, dornnant lieu à la spécification d'un modèle théorique fondé sur le modèle de contrôle attentionnel de l'action de Norman et Shallice. Le modèle théorique a ensuite été implémenté informatiquement et permet de simuler une activité quotidienne spécifique. // Abstract : In order to assist patients who are loosing their autonomy, smart homes and cognitive assistance systems have to be based on a good knowledge of people's disorders and on the difficulties they are likely to encounter in daily life. The specific objective of this PhD is to observe executive processes involved in the completion of daily activities and their impairment during ageing and dementia of the Alzheimer's type, and then to design both theoretical and computational models which are able to generate the observed behaviours. An observation and a qualification phase, allowing to observe executive control processes (action regulation, correction and adaptation when unexpected situations occur) have been first realized, leading to the specification of a theoretical model based on the Norman and Shallice model. This theoretical model has then been implemented to obtain a computational model, which allows the simulation of a specific activity of daily living