Tensor Based Temporal and Multilayer Community Detection for Studying Brain Dynamics During Resting State fMRI.

OBJECTIVE: In recent years, resting state fMRI has been widely utilized to understand the functional organization of the brain for healthy and disease populations. Recent studies show that functional connectivity during resting state is a dynamic process. Studying this temporal dynamics provides a better understanding of the human brain compared to static network analysis. METHODS: In this paper, a new tensor based temporal and multi-layer community detection algorithm is introduced to identify and track the brain network community structure across time and subjects. The framework studies the temporal evolution of communities in fMRI connectivity networks constructed across different regions of interests. The proposed approach relies on determining the subspace that best describes the community structure using Tucker decomposition of the tensor. RESULTS: The brain dynamics are summarized into a set of functional connectivity states that are repeated over time and subjects. The dynamic behavior of the brain is evaluated in terms of consistency of different subnetworks during resting state. The results illustrate that some of the networks, such as the default mode, cognitive control and bilateral limbic networks, have low consistency over time indicating their dynamic behavior. CONCLUSION: The results indicate that the functional connectivity of the brain is dynamic and the detected community structure experiences smooth temporal evolution. SIGNIFICANCE: The work in this paper provides evidence for temporal brain dynamics during resting state through dynamic multi-layer community detection which enables us to better understand the behavior of different subnetworks.

A signal processing approach for the diagnosis of asthma from cough sounds.

Asthma is not very easy to be correctly diagnosed by physicians where asthma is considered a common chronic inflammatory disease. Distinguishing of cough sound can be used to diagnose asthma. The use of signal processing techniques of cough sound for detecting asthma will be addressed in this paper to help the diagnosis of asthma by physicians. Since cough sounds are non-stationary and are stochastic signals inherently, time-frequency transform techniques are used to deal with such signals. Time-frequency analyses are performed to show in a comprehensive approach the characteristics of the cough sound signal. Time-frequency analysis techniques, specifically Wigner distribution in addition to wavelet transform to analyse cough signals, are used in this paper. The features extracted from the time-frequency domain of the cough sound are used as inputs to the asthma and non-asthma classifier. The results of the proposed algorithm are competitive to the best existing algorithms in the literature.