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Magnetic resonance imaging (MRI) is a non-destructive technique that is capable of localizing pathologies and assessing other anatomical features (e.g., tissue volume, microstructure, and white matter connectivity) in postmortem, ex vivo human brains. However, when brains are removed from the skull and cerebrospinal fluid (i.e., their normal in vivo magnetic environment), air bubbles and air–tissue interfaces typically cause magnetic susceptibility artifacts that severely degrade the quality of ex vivo MRI data. In this report, we describe a relatively simple and cost-effective experimental setup for acquiring artifact-free ex vivo brain images using a clinical MRI system with standard hardware. In particular, we outline the necessary steps, from collecting an ex vivo human brain to the MRI scanner setup, and have also described changing the formalin (as might be necessary in longitudinal postmortem studies). Finally, we share some representative ex vivo MRI images that have been acquired using the proposed setup in order to demonstrate the efficacy of this approach. We hope that this protocol will provide both clinicians and researchers with a straight-forward and cost-effective solution for acquiring ex vivo MRI data from whole postmortem human brains.

Recently proposed tractography and connectomics approaches often require a very large number of streamlines, in the order of millions. Generating, storing and interacting with these datasets is currently quite difficult, since they require a lot of space in memory and processing time. Compression is a common approach to reduce data size. Recently such an approach has been proposed consisting in removing collinear points in the streamlines. Removing points from streamlines results in files that cannot be robustly post-processed and interacted with existing tools, which are for the most part point-based. The aim of this work is to improve visualization, interaction and tractometry algorithms to robustly handle compressed tractography datasets. Our proposed improvements are threefold: (i) An efficient loading procedure to improve visualization (reduce memory usage up to 95% for a 0.2 mm step size); (ii) interaction techniques robust to compressed tractograms; (iii) tractometry techniques robust to compressed tractograms to eliminate biased in tract-based statistics. The present work demonstrates the need of correctly handling compressed streamlines to avoid biases in future tractometry and connectomics studies.

Across species, increases in white matter volume outpace increases in gray-matter volume, but increases in gray- matter volume outpace increases in the size of the corpus callosum. This dissertation explores the hypothesis that this hyposcaling of the callosum stems from the impact of the conduction delays and cellular costs of the long- distance connections on normal developmental mechanisms. Neuroanatomy research to date has only indirectly examined this relation, using measures such as brain volume. The research in this dissertation uses diffusion tensor imaging to more directly measure the relation between the length of the interhemispheric connections and the degree of connectivity -- the ratio of between-area connections to total projection neurons in the areas connected. Using tractography to detail the patterns of interhemispheric connectivity and to determine the length of the connections, and formulae based on histological results to estimate degree of connectivity, we show that, across normal young adult males, connection length is significantly negatively correlated with degree of connectivity in the anterior, posterior, and body of the callosum. Using the same methodology, in typically developing boys a significant relation between connection length and degree of connectivity was found only in the posterior of the callosum. The combined results indicate that the relation between connection length and degree of connectivity develops during childhood and adolescence. Children with autism are known to have enlarged brains during the first years of life. This is predicted to lead to decreased long-distance connectivity. To explore this prediction, neural networks which modeled inter- hemispheric interaction were grown at the rate of either typically developing children or children with autism. By 2 years of simulated age, the networks that modeled autistic growth showed a reduced reliance on long-distance connections, performance reductions, and reductions in structural connectivity. Using the same methodology as with the adults and children, the relation between connection length and degree of connectivity in adults with autism was examined. Connection length and degree of connectivity showed the typical negative relation, but with a reduced degree of connectivity in anterior regions -- the locus of development during the period of maximal brain overgrowth, and where axon diameters are smallest