Topic: graph theory

Functional connectivity has been the focus of many research groups aiming to study the interaction between cells and brain regions. A standard method for analyzing connectivity is to statistically compare pairwise interactions between cells or brain regions across behavioral states or conditions. This methodology ignores the intrinsic properties of functional connectivity as a multivariate and dynamic signal, expressing the correlational configuration of the network. In this talk, I will present a geometric approach, combining Graph Theory and Riemannian Geometry to build "a graph of graphs" a

Richard Montgomery explores graph theory as the mathematics underlying networks, from infrastructure to biological connections. He discusses his contribution to the proof of Ringel’s conjecture and why extremal questions about graphs are mathematically productive. The interview follows recognition of his work with an EMS Prize.

Understanding communication and information processing in nervous systems is a central goal of neuroscience. Over the past two decades, advances in connectomics and network neuroscience have opened new avenues for investigating polysynaptic communication in complex brain networks. Recent work has brought into question the mainstay assumption that connectome signalling occurs exclusively via shortest paths, resulting in a sprawling constellation of alternative network communication models. This Review surveys the latest developments in models of brain network communication. We begin by drawing

One of the important subfields of computational neuroscience is network neuroscience, which widely uses machine learning techniques, these include artificial neural networks. The latter ones owe a lot to neuroscience in the history of their development. Artificial neural networks (ANN) are often represented in the form of a graph – a visual representation of connections between the elements of the system, which form a network. However, this does not mean that an ANN was designed based on graph theory or with consideration of the special properties of complex networks. These representations ar

INTRODUCTION Graph theory is a state of the art approach to assess functional connectivity (FC) disruption in neurological disorders. However, this methodology does not allow distinction between positive and negative FC correlations; negative correlations, or anticorrelations, are usually either discardes or taken as positive values, and therefore its influence is rarely studied. RESEARCH QUESTION In the present study, we used magnetoencephalography (MEG) to construct cross-frequency networks, and studied the positive and negative correlations independently and jointly, to evaluate the contri

The human brain is a complex organ characterized by heterogeneous patterns of interconnections. Non-invasive imaging techniques now allow for these patterns to be carefully and comprehensively mapped in individual humans, paving the way for a better understanding of how wiring supports cognitive processes. While a large body of work now focuses on descriptive statistics to characterize these wiring patterns, a critical open question lies in how the organization of these networks constrains the potential repertoire of brain dynamics. In this talk, I will describe an approach for understanding