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Pestilli, F.

Publications and source records attributed to Pestilli, F..

2 recordsLinked to original sources

Age-related macular degeneration affects the optic radiation white matter projecting to locations of retinal damage

PurposeWe investigated the impact of age-related macular degeneration (AMD) on visual acuity and the visual white matter.\n\nMethodsWe combined an adaptive cortical atlas and diffusion-weighted magnetic resonance imaging (dMRI) and tractography to separate optic radiation (OR) projections to different retinal eccentricities in human primary visual cortex. We exploited the known anatomical organization of the OR and clinically relevant data to segment the OR into three primary components projecting to fovea, mid- and far-periphery. We measured white matter tissue properties - (fractional anisotropy, linearity, planarity, sphericity) along the aforementioned three components of the optic radiation to compare AMD patients and controls.\n\nResultsWe found differences in white matter properties specific to OR white matter fascicles projecting to primary visual cortex locations corresponding to the location of retinal damage (fovea). Additionally, we show that the magnitude of white matter properties in AMD patients correlates with visual acuity. In sum, we demonstrate a specific relation between visual loss, anatomical location of retinal damage and white matter damage in AMD patients. Importantly, we demonstrate that these changes are so profound that can be detected using magnetic resonance imaging data with clinical resolution. The conserved mapping between retinal and white matter damage suggests that retinal neurodegeneration might be a primary cause of white matter degeneration in AMD patients.\n\nConclusionsThe results highlight the impact of eye disease on brain tissue, a process that may become an important target to monitor during the course of treatment.

neuroscience

Multidimensional encoding of brain connectomes

The ability to map brain networks at the macroscale in living individuals is fundamental in efforts to chart the relation between human behavior, health and disease. We present a framework to encode structural brain connectomes and diffusion-weighted magnetic resonance data into multidimensional arrays (tensors). The framework overcomes current limitations in building connectomes; it prevents information loss by integrating the relation between connectome nodes, edges, fascicles and diffusion data. We demonstrate the utility of the framework for in vivo white matter mapping and anatomical computing. The framework reduces dramatically storage requirements for connectome evaluation methods, with up to 40x compression factors. We apply the framework to evaluate 1,980 connectomes, thirteen tractography methods, and three data sets. We describe a general equation to predicts connectome resolution (number of fascicles) given data quality and tractography model parameters. Finally, we provide open-source software implementing the method and data to reproduce the results.

neuroscience