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Default mode network neurodegeneration reveals the remote effects of ischaemic stroke
  1. Michele Veldsman1,2,
  2. Evan Curwood2,
  3. Sarah Pathak2,
  4. Emilio Werden2,
  5. Amy Brodtmann2,3,4
  1. 1Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
  2. 2Behavioural Neuroscience, The Florey Institute for Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia
  3. 3Austin Health, University of Melbourne, Melbourne, Australia
  4. 4Department of Medicine, Eastern Cognitive Disorders Clinic, Monash University, Melbourne, Australia
  1. Correspondence to Michele Veldsman, Department of Clinical Neurosciences, University of Oxford, Nuffield, Oxfordshire, UK; michele.veldsman{at}ndcn.ox.ac.uk

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Introduction

Dementia is estimated to occur in 15%–30% patients after ischaemic stroke.1 Stroke may initiate or accelerate neurodegeneration associated with cognitive impairment.1 Brain atrophy is an important marker of neurodegeneration, preceding the emergence of cognitive symptoms in Alzheimer's disease (AD).2 Atrophy occurs in distributed regions that collectively mirror known brain networks, including the default mode network (DMN). Atrophy and dysfunction within the DMN is evident in healthy ageing, accelerated in pathological ageing2 and evident in acute and subacute stroke.3 Lesion location rarely predicts long-term outcome in stroke. Network-wide changes may better explain neurodegeneration and conversion to dementia after stroke. Atrophy after stroke has not been well investigated and has been limited to cross-sectional studies and regional volume changes.

Structural covariance is an increasingly popular method of examining network-wide correlations in morphometric estimates of brain structure, such as cortical thickness or grey matter volume. There is a close relationship between estimates of network-based structural covariance and intrinsic functional network architecture.4 Structural covariance can be tracked over time to reveal changes in brain organisation, either developmental or degenerative, via cross-sectional comparisons within and between groups.4 Cross-sectional differences can be difficult to detect when there is normal variability across individuals.5 Longitudinal imaging has the benefit of overcoming interindividual differences in cortical morphology by using each individual as their own control.5 Longitudinal imaging also provides an opportunity for more direct examination of atrophy within networks by looking at correlations in the rate of cortical atrophy (see figure 1 in the online Supplementary file 1) across the brain, rather than just correlations in the morphometric measure itself. Atrophy across the brain can also be examined by …

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