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BIRS Workshop Lecture Videos

A multi-resolution scheme for analysis of brain connectivity networks Singh, Vikas


There is significant interest in understanding how structural/functional connectivity changes in the brain explain behavioral symptoms in neurodegenerative diseases such as Alzheimer’s disease (AD). Clear variations in connectivity at the dementia stage of the disease have been identified in the literature. Despite such findings, AD-related connectivity changes in the preclinical stage of the disease still remain poorly characterized. Such preclinical datasets are typically smaller in size and group differences are subtle, making analysis challenging. This talk will describe some of our recent efforts to overcome these difficulties in an effort to elucidate how brain connectivity varies as a function of genotype and various other risk factors, even in asymptomatic individuals. The engine driving these analyses is a new multi-resolution scheme for performing statistical analysis of connectivity networks derived from neuroimaging data. Our algorithm derives a wavelet representation at each connection edge which captures the graph context at multiple resolutions. Extensive empirical evidence shows how this framework offers improved statistical power in analyzing structural connectivity in diffusion tensor images (DTI) obtained via so-called tractography methods. We will present results showing connectivity differences between AD patients and controls that were not evident using standard approaches. Later, we will show results on individuals that are not yet diagnosed with AD but have a positive family history risk of AD where our algorithm helps in identifying potentially subtle differences between patient groups. An open source toolbox implementing this framework has been made available. Joint work with Nagesh Adluru, Emily Balczewski, Barb Bendlin, Moo Chung, SeongJae Hwang, Sterling Johnson, WonHwa Kim and Ozioma Okonkwo.

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