Physics is a field of science that has traditionally used the scientific method to answer
questions about why natural phenomena occur and to make testable models that explain the …

Network science provides theoretical, computational, and empirical tools that can be used to
understand the structure and function of the human brain in novel ways using simple …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

The construction of a meaningful graph topology plays a crucial role in the effective
representation, processing, analysis, and visualization of structured data. When a natural …

As an early stage of Alzheimer's disease (AD), mild cognitive impairment (MCI) is able to be
detected by analyzing the brain connectivity networks. For this reason, we devise a new …

Functional brain networks reconfigure spontaneously during rest. Such network dynamics
can be studied by dynamic functional connectivity (dynFC); ie, sliding-window correlations …

Modern neuroimaging techniques provide us with unique views on brain structure and
function; ie, how the brain is wired, and where and when activity takes place. Data acquired …

In recent years, artificial intelligence (AI) techniques have been developed rapidly. With the
help of big data, massive parallel computing, and optimization algorithms, machine learning …

The increasing power of computer technology does not dispense with the need to extract
meaningful information out of data sets of ever growing size, and indeed typically …

This paper presents methods to analyze functional brain networks and signals from graph
spectral perspectives. The notion of frequency and filters traditionally defined for signals …