UCLA

Brain connectivity methods have tremendous potential to expand our understanding of the brain, especially when added to existing knowledge gained through more traditional structural and functional brain imaging methods. Instead of examining isolated brain regions performing a function or individual regions affected by a disease, a network-level approach appreciates the complex organization and interactions of the brain. Through diffusion weighted imaging, we can visualize the white matter pathways of the brain in vivo, thus modeling the structural highways and roads that support efficient brain function. Understanding how these measures of structural connectivity change with development is key for a fuller understanding of healthy brain development, as structure, function, and connectivity all interact. Additionally, it is necessary for determining how and when dysfunction occurs in neurodevelopmental disorders. As many of these disorders are genetically influenced, examining how risk genes affect brain connectivity might shed light on the mechanisms by which these genes have their effect. Lastly, one of the hallmarks of development is sharpening cognitive skills, which are often significantly impaired in neurodevelopmental disorders. In order to understand both typical and atypical development, determining how connectivity supports cognition is key. Especially with new brain metrics, such as those in graph theory, the association between brain connectivity and cognition is not well known, and presents exciting research potential. Altogether, determining the development, genetic, and cognitive correlates of structural brain connectivity will integrate with existing knowledge about brain structure and function to give us a fuller understanding of the interrelated processes that occur throughout development. This will form a foundation from which we can base future investigations into how connectivity is affected in neurodevelopmental disorders.