The first Finney Centennial lecture was presented by the world-leading Bayesian statistician Derek Dunson of Duke University during May 2018.
Derek described the complexity of the billions of neurons that are hidden inside millions of tracts that together comprise a human’s ‘connectome’. Any Bayesian statistical analysis that refers to the neurons or just to the tracts will consequently be highly complex. Dunson nevertheless found a tentatively statistical relationship between brain connectivity and multiple factors, with high levels of substance use decreasing connectivity and education increasing connectivity.
It is also possible (e.g. Ha et al, 2016) to relate the size and connectivity of certain brain regions with different neurodivergent conditions. This assists neuro-psychiatrists when they diagnose such conditions.
Sungji Ha, In-jung Sohn, Namwook Kim, Hyeon Sim and Keun Cheon (2016) Characteristics of Brains in Autism Spectrum Disorder: Structure, Function and Connectivity across the Lifespan Experimental neurobiology Experimental Biology 24 (4), pp 273-284 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4688328/
SYNOPSIS
There have been parallel revolutions in recent years in technology for imaging of the human brain and in methods for analyzing high-dimensional and complex data. We are interested in exploiting and building on this technology motivated by interest in studying how people vary in their brain connection structure. White matter tracks in the human brain consist of tightly bundled sets of neurons that are geometrically aligned and act as highways for neural activity and communication across the brain. There are on the order of a million such tracts in a normal human brain, and their locations can be estimated using different types of magnetic resonance imaging (MRI) combined with state-of-the-art image processing. We refer to the set of tracts as the human brain “connectome.” The Human Connectome Project (HCP) collects data on connectomes, along with multiple behaviours and traits of each individual under study. We develop state of the art data science tools to study variation in connectomes, and the relationship with factors, such as substance use (alcohol, marijuana) and education. We find a significant relationship between brain connectivity and multiple factors, with high levels of substance use decreasing connectivity and education increasing connectivity. This talk is designed to be accessible to the general public, focusing on describing these amazing new data resources, analysis tools, and results, with a discussion on ongoing directions""""
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