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      Abnormal age-related cortical folding and neurite morphology in children with developmental dyslexia

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          Abstract

          There is increasing recognition of a relationship between regional variability in cerebral gyrification and neurodevelopment. Recent work in morphometric MRI has shown that the local gyrification index (lGI), a measure of regional brain folding, may be altered in certain neurodevelopmental disorders. Other studies report that the lGI generally decreases with age in adolescence and young adulthood; however, little is known about how these age-dependent differences in brain maturation occur in atypical neurodevelopment and mechanisms underlying gyrification, such as synaptic pruning. Organization and optimization of dendrites and axons connections across the brain might be driving gyrification and folding processes. In this study, we first assessed lGI differences in the left hemisphere in a cohort of 39 children with developmental dyslexia (DD) between the ages of 7 and 15 years in comparison to 56 typically developing controls (TDC). To better understand the microstructural basis of these changes, we next explored the relationship between lGI differences and cortical thickness and neurite morphology by applying neurite orientation dispersion and density imaging (NODDI). We identified significant differences in lGI between children with DD and TDC in left lateral temporal and middle frontal regions. Further, DD failed to show the expected age-related decreases in lGI in the same regions. Age-related differences in lGI in DD were not explained by differences in cortical thickness, but did correlate with NODDI neurite density and orientation dispersion index. Our findings suggest that gyrification changes in DD are related to abnormal neurite morphology, and are possibly an expression of differences in synaptic pruning.

          Highlights

          • Dyslexic children showed left-hemisphere differences in cortical gyrification.

          • In dyslexia, cortical folding does not show age-related decreases seen in controls.

          • Gyrification differences correlated with NODDI-derived neurite organization.

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          Most cited references30

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          A tension-based theory of morphogenesis and compact wiring in the central nervous system.

          Many structural features of the mammalian central nervous system can be explained by a morphogenetic mechanism that involves mechanical tension along axons, dendrites and glial processes. In the cerebral cortex, for example, tension along axons in the white matter can explain how and why the cortex folds in a characteristic species-specific pattern. In the cerebellum, tension along parallel fibres can explain why the cortex is highly elongated but folded like an accordion. By keeping the aggregate length of axonal and dendritic wiring low, tension should contribute to the compactness of neural circuitry throughout the adult brain.
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            High-resolution intersubject averaging and a coordinate system for the cortical surface.

            The neurons of the human cerebral cortex are arranged in a highly folded sheet, with the majority of the cortical surface area buried in folds. Cortical maps are typically arranged with a topography oriented parallel to the cortical surface. Despite this unambiguous sheetlike geometry, the most commonly used coordinate systems for localizing cortical features are based on 3-D stereotaxic coordinates rather than on position relative to the 2-D cortical sheet. In order to address the need for a more natural surface-based coordinate system for the cortex, we have developed a means for generating an average folding pattern across a large number of individual subjects as a function on the unit sphere and of nonrigidly aligning each individual with the average. This establishes a spherical surface-based coordinate system that is adapted to the folding pattern of each individual subject, allowing for much higher localization accuracy of structural and functional features of the human brain.
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              A surface-based approach to quantify local cortical gyrification.

              The high complexity of cortical convolutions in humans is very challenging both for engineers to measure and compare it, and for biologists and physicians to understand it. In this paper, we propose a surface-based method for the quantification of cortical gyrification. Our method uses accurate 3-D cortical reconstruction and computes local measurements of gyrification at thousands of points over the whole cortical surface. The potential of our method to identify and localize precisely gyral abnormalities is illustrated by a clinical study on a group of children affected by 22q11 Deletion Syndrome, compared to control individuals.
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                Author and article information

                Contributors
                Journal
                Neuroimage Clin
                Neuroimage Clin
                NeuroImage : Clinical
                Elsevier
                2213-1582
                14 March 2018
                2018
                14 March 2018
                : 18
                : 814-821
                Affiliations
                [a ]Dyslexia Center, University of California, San Francisco, San Francisco, CA, USA
                [b ]Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
                [c ]Biomedical Sciences PhD, Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
                [d ]Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
                [e ]Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
                [f ]Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
                [g ]Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Russel Square House, London, United Kingdom
                [h ]Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
                [i ]Brain MRI 3T Mondino Research Center, C. Mondino National Neurological Institute, Pavia, Italy
                [j ]Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA
                [k ]Neuroradiology Section, Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA
                Author notes
                [* ]Corresponding author at: Department of Neurology, University of California, San Francisco, 675 Nelson Rising Lane, Box: 3206, San Francisco, CA 94158, USA. eduardo.caverzasi@ 123456ucsf.edu
                Article
                S2213-1582(18)30080-9
                10.1016/j.nicl.2018.03.012
                5988019
                29876267
                0738ae65-5857-4212-95e8-a84667663b86
                © 2018 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 31 August 2017
                : 16 February 2018
                : 13 March 2018
                Categories
                Regular Article

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