UCLA

The human brain, one of the most complex structures known, is composed of more than 100 billion neurons that process, disseminate, transform and attract information through more than a 100 trillion synapses. Interactions among brain cells give us the freedom to think, feel, move and maintain homeostasis all at the same time. To understand the systematic communication among brain cells, we require not only knowledge at elementary levels, but also at macroscopic level - aimed at the discovery of the emerging patterns and properties of neuronal interactions. Here, we used diffusion imaging to reveal the organization of neural pathways by capturing subtle changes in white matter make-up through measures sensitive to fiber integrity and microstructure - otherwise not detectable with standard MRI techniques. In addition, tractography was performed to infer neural pathways and connectivity patterns, yielding additional, more complex mathematical metrics describing the connectomics of brain networks. To assess the brain's network, graph theory was used - a branch of mathematics employed to model the topological organization of the white matter structure. Similarly, algebraic connectivity was also applied, not previously seen in the context of brain networks, which uses linear algebra and matrix theory to study the properties of graphs. These methods have all contributed to the discovery of potential biomarkers that can aid the understanding of white matter deterioration in the brain; special focus was directed towards neurodegenerative diseases such as Alzheimer's disease and all of its clinical stages, as well as frontotemporal dementia.