Large Scale Functional Connectivity Networks of Resting State Magnetoencephalography

Understanding relationships between cortical neural activity is an important area of research. Investigations of the neural dynamics associated with the healthy and disordered brains could lead to new insights about disease models. Functional connectivity is a promising method for investigating these neural dynamics by observing intrinsic neural activity arising during spontaneous cortical activations recorded via magnetoencephalography (MEG). MEG is a non-invasive measure of the magnetic fields produced during neural activity and provides information regarding neural synchrony within the brain. Phase locking is a time frequency analysis method that provides frequency band specific results of neural communication. Leveraging multiple computers operating in a cluster extends the scale of these investigations to whole brain functional connectivity. Quantification of these large-scale networks would allow for the quantitative characterization of healthy connectivity in a mathematically rigorous manner. However, the volume of data required to characterize these networks creates a multiple comparison problem (MCP) in which upward of 33 million simultaneous hypothesis are tested. Conservative approaches such as Bonferroni can eliminate most of the results while more liberal methods may under-correct therefore leading to an increase in the true type I error rate. Here we used a combination of functionally defined cortical surface clustering methods followed by non-parametric permutation testing paradigm to control the family wise error rate and provide robust statistical networks. These methods were validated with simulation studies to characterize limitations in inferences from the resultant whole brain networks. We then examined healthy subject’s MEG during resting state recordings to characterize intrinsic network activity across four physiological frequency bands: theta – 4-8 Hz, alpha – 8-13 Hz, beta-low – 13-20 Hz, beta-high – 20-30 Hz. Quantifying large-scale functional connectivity networks allowed for the investigation of healthy electrophysiological networks within specific frequency bands. Understanding the intrinsic network connections would allow for better understanding of the electrophysiological processes underlying brain function. Quantification of these networks would also allow future studies to explore the ability of network aberrations to predict disordered brain states

Big Data Security (Volume 3)

After a short description of the key concepts of big data the book explores on the secrecy and security threats posed especially by cloud based data storage. It delivers conceptual frameworks and models along with case studies of recent technology

Deep Learning and parallelization of Meta-heuristic Methods for IoT Cloud

Healthcare 4.0 is one of the Fourth Industrial Revolution’s outcomes that make a big revolution in the medical field. Healthcare 4.0 came with more facilities advantages that improved the average life expectancy and reduced population mortality. This paradigm depends on intelligent medical devices (wearable devices, sensors), which are supposed to generate a massive amount of data that need to be analyzed and treated with appropriate data-driven algorithms powered by Artificial Intelligence such as machine learning and deep learning (DL). However, one of the most significant limits of DL techniques is the long time required for the training process. Meanwhile, the realtime application of DL techniques, especially in sensitive domains such as healthcare, is still an open question that needs to be treated. On the other hand, meta-heuristic achieved good results in optimizing machine learning models. The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. IoT technologies are crucial in enhancing several real-life smart applications that can improve life quality. Cloud Computing has emerged as a key enabler for IoT applications because it provides scalable and on-demand, anytime, anywhere access to the computing resources. In this thesis, we are interested in improving the efficacity and performance of Computer-aided diagnosis systems in the medical field by decreasing the complexity of the model and increasing the quality of data. To accomplish this, three contributions have been proposed. First, we proposed a computer aid diagnosis system for neonatal seizures detection using metaheuristics and convolutional neural network (CNN) model to enhance the system’s performance by optimizing the CNN model. Secondly, we focused our interest on the covid-19 pandemic and proposed a computer-aided diagnosis system for its detection. In this contribution, we investigate Marine Predator Algorithm to optimize the configuration of the CNN model that will improve the system’s performance. In the third contribution, we aimed to improve the performance of the computer aid diagnosis system for covid-19. This contribution aims to discover the power of optimizing the data using different AI methods such as Principal Component Analysis (PCA), Discrete wavelet transform (DWT), and Teager Kaiser Energy Operator (TKEO). The proposed methods and the obtained results were validated with comparative studies using benchmark and public medical data