Pandemics often arise from the diffusion of infectious diseases over large regions, spanning multiple continents. Effective non-pharmaceutical interventions (NPIs) at the onset of an outbreak can significantly curb the spread. However, the complexity of human interactions can hinder the strict and timely imposition of such measures. Given the extensive damage caused by COVID-19, it is imperative to develop computational strategies that control and prevent the global spread of infectious diseases. This study proposes a model of COVID-19 spread networks, formulated based on daily confirmed cases per country, with mutual information as a measure of interdependence between countries. Utilizing graph theory, this approach identifies key nodes (countries) that play influential roles in the COVID-19 spread network. A control framework based on network theory is then introduced to mitigate and potentially eradicate further infection spread. Results demonstrate that the proposed framework holds substantial promise in preventing the transmission of COVID-19 and similar outbreaks when implemented promptly.

The landscape of cognitive states and their underlying neurobiological mechanisms has been significantly illuminated through advancements in neuroimaging and computational modeling. This study introduces an integrated approach that harnesses network analysis and machine learning techniques to characterize and differentiate cognitive groups—Normal Control (NC), Mild Cognitive Impairment (MCI), and Alzheimer’s Disease (AD). Structural networks are formulated and analyzed based on diffusion tensor data through a fusion of graph theory and mass-spring model methodologies. Notably, features extracted from both graph theoretic and mass-spring model computations drive a two-step framework. This process commences with a random forest-based feature extraction, followed by a support vector-based classification approach, culminating in an impressive accuracy of 82.7% for classifying individuals across cognitive groups, with an AUC of 0.893. This study significance is underscored by the pressing need for enhanced cognitive impairment detection and differentiation strategies. The identified features offer nuanced insights into the intricate interplay among brain structure, dynamics, and cognitive function, thereby bridging gaps in our understanding of cognitive decline and neurodegeneration. By fortifying our diagnostic repertoire and facilitating personalized interventions, this research paves the way for refined clinical practices.

Dementia is one of the leading causes of mortality across the globe, yet, its treatment remains practically elusive. Preventing the later onsets of dementia by treating the early onset stands a better chance to forestall further surge in the cases of dementia around the world. Unfortunately, a lot of issues are associated with the detection of early onset as their clinical symptoms overlap with those of normal aging. Therefore, in this framework, the gray matter tissue probability map (TPM) is extracted from the magnetic resonance imaging (MRI) data of dementia related subjects. Generalized improved multiscale permutation entropy (GIMPE) based networks are formulated on the extracted gray matter TPM of normal control (NC), stable mild cognitive impairment (sMCI), progressive mild cognitive impairment (sMCI) and Alzheimer’s disease (AD) subjects. The network disruption of dementia onsets are assessed taking the networks of NC subjects as reference. A technique is developed and validated for the formulation of brain network from connectivity matrix and the formulated network topologies are quantified using graph theory metrics at nodal levels. The topological metrics at nodal levels are statistically analyzed using a non-parametric statistical (Kruskal-Wallis) test to extract the network signatures corresponding to NC, sMCI, pMCI and AD groups. Results show that the proposed framework is potentially viable for the detection and identification of the normal aging as well as the various stages of dementia onset at network level.

A lot of efforts have been made by researchers for easy detection of the prodromal phase of Alzheimer’s disease (AD) and other dementia to enable curative measures. Among the leading approaches that show promising results is the use of complex network theory on neuroimaging data such as functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), magnetoencephalogram (MEG), electroencephalogram (EEG) etc. However, exploring the network theory using the tissue probability Map (TPM) of magnetic resonance imaging (MRI) data has been quite unexplored. Therefore, in this paper, we developed the generalized improved multiscale permutation entropy (GIMPE) for the computation of complexity of grey matter (GM) TPM for all the considered region of interests (ROIs). In order to formulate a well defined network, the vectors, GIMPEs of all ROIs are taken as nodes and the edges between the nodes are defined by the Euclidean distance between the corresponding vectors (GIMPEs). The validation of our approach on MRI data accentuates the importance of the proposed approach as well as the significance of TPM based brain networks for the discrimination and differential diagnosis of normal aging, prodromal phase and the later phase of AD.

Neurocognitive impairments such as mild cognitive impairment (MCI), alzheimer's disease (AD) and vascular dementia (VD) effect the functional connectivity across the brain networks that aid proper neurocognitive functioning. Identification of dementia related disorders has remained a challenge due to their overlapping underlying complex structures. In this paper, we analyze the loss of functional connections of dementia networks in comparison with the network of average healthy control (HC) subjects. We then perform the topological quantification of the minimum spanning tree (MST) networks using graph theory metrics to identify the brain functional networks of MCI, AD and VD in comparison with healthy control (HC) subjects. A common reactive band is identified and MST is formed for all the subjects. The MST topological quantifications are used to identify the dementia related disorders based on the data recorded from 10 HC subjects and 30 dementia subjects. Our results show that the proposed approach has the potential to identify the dementia stages and can enhance the diagnosis of dementia related disorders.