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      Fibre-specific white matter reductions in Alzheimer’s disease and mild cognitive impairment

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          Abstract

          Alzheimer's disease is increasingly considered a large-scale network disconnection syndrome, associated with progressive aggregation of pathological proteins, cortical atrophy, and functional disconnections between brain regions. These pathological changes are posited to arise in a stereotypical spatiotemporal manner, targeting intrinsic networks in the brain, most notably the default mode network. While this network-specific disruption has been thoroughly studied with functional neuroimaging, changes to specific white matter fibre pathways within the brain's structural networks have not been closely investigated, largely due to the challenges of modelling complex white matter structure. Here, we applied a novel technique known as 'fixel-based analysis' to comprehensively investigate fibre tract-specific differences at a within-voxel level (called 'fixels') to assess potential axonal loss in subjects with Alzheimer's disease and mild cognitive impairment. We hypothesized that patients with Alzheimer's disease would exhibit extensive degeneration across key fibre pathways connecting default network nodes, while patients with mild cognitive impairment would exhibit selective degeneration within fibre pathways connecting regions previously identified as functionally implicated early in Alzheimer's disease. Diffusion MRI data from Alzheimer's disease (n = 49), mild cognitive impairment (n = 33), and healthy elderly control subjects (n = 95) were obtained from the Australian Imaging, Biomarkers and Lifestyle study of ageing. We assessed microstructural differences in fibre density, and macrostructural differences in fibre bundle morphology using fixel-based analysis. Whole-brain analysis was performed to compare groups across all white matter fixels. Subsequently, we performed a tract of interest analysis comparing fibre density and cross-section across 11 selected white matter tracts, to investigate potentially subtle degeneration within fibre pathways in mild cognitive impairment, initially by clinical diagnosis alone, and then by including amyloid status (i.e. a positive or negative amyloid PET scan). Our whole-brain analysis revealed significant white matter loss manifesting both microstructurally and macrostructurally in Alzheimer's disease patients, evident in specific fibre pathways associated with default mode network nodes. Reductions in fibre density and cross-section in mild cognitive impairment patients were only exhibited within the posterior cingulum when statistical analyses were limited to tracts of interest. Interestingly, these degenerative changes did not appear to be associated with high amyloid accumulation, given that amyloid-negative, but not positive, mild cognitive impairment subjects exhibited subtle focal left posterior cingulum deficits. The findings of this study demonstrated a stereotypical distribution of white matter degeneration in patients with Alzheimer's disease, which was in line with canonical findings from other imaging modalities, and with a network-based conceptualization of the disease.awx355media15726254535001.

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          Author and article information

          Journal
          Brain
          Oxford University Press (OUP)
          0006-8950
          1460-2156
          March 2018
          March 01 2018
          January 04 2018
          March 2018
          March 01 2018
          January 04 2018
          : 141
          : 3
          : 888-902
          Affiliations
          [1 ]Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, 3084, Australia
          [2 ]Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, 3084, Australia
          [3 ]Department of Neurology, Austin Health, Heidelberg, Victoria, 3084, Australia
          [4 ]Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, WC2R 2LS, UK
          [5 ]Centre for the Developing Brain, King’s College London, London, WC2R 2LS, UK
          [6 ]CSIRO, Health and Biosecurity, The Australian eHealth Research Centre, Brisbane, Queensland, 4029, Australia
          [7 ]Eastern Clinical Research Unit, Monash University, Box Hill Hospital, Melbourne, Victoria, 3128, Australia
          [8 ]Department of Medicine, Austin Health, University of Melbourne, Heidelberg, Victoria, 3084, Australia
          [9 ]Department of Molecular Imaging and Therapy, Austin Health, Heidelberg, Victoria, 3084, Australia
          Article
          10.1093/brain/awx355
          29309541
          b2b14a54-9797-45d1-a5cd-5a0b6a1435d1
          © 2018

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