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      Dissecting the pathobiology of altered MRI signal in amyotrophic lateral sclerosis: A post mortem whole brain sampling strategy for the integration of ultra-high-field MRI and quantitative neuropathology

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          Abstract

          Background

          Amyotrophic lateral sclerosis (ALS) is a clinically and histopathologically heterogeneous neurodegenerative disorder, in which therapy is hindered by the rapid progression of disease and lack of biomarkers. Magnetic resonance imaging (MRI) has demonstrated its potential for detecting the pathological signature and tracking disease progression in ALS. However, the microstructural and molecular pathological substrate is poorly understood and generally defined histologically. One route to understanding and validating the pathophysiological correlates of MRI signal changes in ALS is to directly compare MRI to histology in post mortem human brains.

          Results

          The article delineates a universal whole brain sampling strategy of pathologically relevant grey matter (cortical and subcortical) and white matter tracts of interest suitable for histological evaluation and direct correlation with MRI. A standardised systematic sampling strategy that was compatible with co-registration of images across modalities was established for regions representing phosphorylated 43-kDa TAR DNA-binding protein (pTDP-43) patterns that were topographically recognisable with defined neuroanatomical landmarks. Moreover, tractography-guided sampling facilitated accurate delineation of white matter tracts of interest. A digital photography pipeline at various stages of sampling and histological processing was established to account for structural deformations that might impact alignment and registration of histological images to MRI volumes. Combined with quantitative digital histology image analysis, the proposed sampling strategy is suitable for routine implementation in a high-throughput manner for acquisition of large-scale histology datasets. Proof of concept was determined in the spinal cord of an ALS patient where multiple MRI modalities (T1, T2, FA and MD) demonstrated sensitivity to axonal degeneration and associated heightened inflammatory changes in the lateral corticospinal tract. Furthermore, qualitative comparison of R2* and susceptibility maps in the motor cortex of 2 ALS patients demonstrated varying degrees of hyperintense signal changes compared to a control. Upon histological evaluation of the same region, intensity of signal changes in both modalities appeared to correspond primarily to the degree of microglial activation.

          Conclusion

          The proposed post mortem whole brain sampling methodology enables the accurate intraindividual study of pathological propagation and comparison with quantitative MRI data, to more fully understand the relationship of imaging signal changes with underlying pathophysiology in ALS.

          Electronic supplementary material

          The online version of this article (10.1186/s12868-018-0416-1) contains supplementary material, which is available to authorized users.

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          The NumPy array: a structure for efficient numerical computation

          In the Python world, NumPy arrays are the standard representation for numerical data. Here, we show how these arrays enable efficient implementation of numerical computations in a high-level language. Overall, three techniques are applied to improve performance: vectorizing calculations, avoiding copying data in memory, and minimizing operation counts. We first present the NumPy array structure, then show how to use it for efficient computation, and finally how to share array data with other libraries.
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            Myelination of the nervous system: mechanisms and functions.

            Myelination of axons in the nervous system of vertebrates enables fast, saltatory impulse propagation, one of the best-understood concepts in neurophysiology. However, it took a long while to recognize the mechanistic complexity both of myelination by oligodendrocytes and Schwann cells and of their cellular interactions. In this review, we highlight recent advances in our understanding of myelin biogenesis, its lifelong plasticity, and the reciprocal interactions of myelinating glia with the axons they ensheath. In the central nervous system, myelination is also stimulated by axonal activity and astrocytes, whereas myelin clearance involves microglia/macrophages. Once myelinated, the long-term integrity of axons depends on glial supply of metabolites and neurotrophic factors. The relevance of this axoglial symbiosis is illustrated in normal brain aging and human myelin diseases, which can be studied in corresponding mouse models. Thus, myelinating cells serve a key role in preserving the connectivity and functions of a healthy nervous system.
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              BigBrain: an ultrahigh-resolution 3D human brain model.

              Reference brains are indispensable tools in human brain mapping, enabling integration of multimodal data into an anatomically realistic standard space. Available reference brains, however, are restricted to the macroscopic scale and do not provide information on the functionally important microscopic dimension. We created an ultrahigh-resolution three-dimensional (3D) model of a human brain at nearly cellular resolution of 20 micrometers, based on the reconstruction of 7404 histological sections. "BigBrain" is a free, publicly available tool that provides considerable neuroanatomical insight into the human brain, thereby allowing the extraction of microscopic data for modeling and simulation. BigBrain enables testing of hypotheses on optimal path lengths between interconnected cortical regions or on spatial organization of genetic patterning, redefining the traditional neuroanatomy maps such as those of Brodmann and von Economo.
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                Author and article information

                Contributors
                menuka.pallebagegamarallage@ndcn.ox.ac.uk
                foxley@uchicago.edu
                ricarda.menke@ndcn.ox.ac.uk
                istvan.huszar@dtc.ox.ac.uk
                mark.jenkinson@ndcn.ox.ac.uk
                benjamin.tendler@ndcn.ox.ac.uk
                chaoyue.wang@ndcn.ox.ac.uk
                saad.jbabdi@ndcn.ox.ac.uk
                martin.turner@ndcn.ox.ac.uk
                karla.miller@ndcn.ox.ac.uk
                olaf.ansorge@ndcn.ox.ac.uk
                Journal
                BMC Neurosci
                BMC Neurosci
                BMC Neuroscience
                BioMed Central (London )
                1471-2202
                13 March 2018
                13 March 2018
                2018
                : 19
                : 11
                Affiliations
                [1 ]ISNI 0000 0004 1936 8948, GRID grid.4991.5, Nuffield Department of Clinical Neurosciences, , University of Oxford, ; Oxford, UK
                [2 ]ISNI 0000 0004 1936 8948, GRID grid.4991.5, Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, , University of Oxford, ; Oxford, UK
                [3 ]ISNI 0000 0004 1936 7822, GRID grid.170205.1, Department of Radiology, , University of Chicago, ; Chicago, IL USA
                Author information
                http://orcid.org/0000-0001-5117-0120
                Article
                416
                10.1186/s12868-018-0416-1
                5848544
                29529995
                b2c9ff4f-7956-4f71-8d11-64fe00b09bc8
                © The Author(s) 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 9 October 2017
                : 2 March 2018
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: MR/K02213X/1
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100004440, Wellcome Trust;
                Categories
                Methodology Article
                Custom metadata
                © The Author(s) 2018

                Neurosciences
                amyotrophic lateral sclerosis,magnetic resonance imaging,post mortem brain,systematic sampling,histology,mri-histology correlation

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