Magnetic Resonance Imaging to Detect Early Molecular and Cellular Changes in Alzheimer’s Disease

Michael J. Knight, Bryony McCann, Risto A. Kauppinen, Elizabeth J. Coulthard*

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

31 Citations (Scopus)
518 Downloads (Pure)


Recent pharmaceutical trials have demonstrated that slowing or reversing pathology in Alzheimer’s disease is likely to be possible only in the earliest stages of disease, perhaps even before significant symptoms develop. Pathology in Alzheimer’s disease accumulates for well over a decade before symptoms are detected giving a large potential window of opportunity for intervention. It is therefore important that imaging techniques detect subtle changes in brain tissue before significant macroscopic brain atrophy. Current diagnostic techniques often do not permit early diagnosis or are too expensive for routine clinical use. Magnetic Resonance Imaging (MRI) is the most versatile, affordable and powerful imaging modality currently available, being able to deliver detailed analyses of anatomy, tissue volumes and tissue state. In this mini-review, we consider how MRI might detect patients at risk of future dementia in the early stages of pathological change when symptoms are mild. We consider the contributions made by the various modalities of MRI (structural, diffusion, perfusion, relaxometry) in identifying not just atrophy (a late-stage AD symptom) but more subtle changes reflective of early dementia pathology. The sensitivity of MRI not just to gross anatomy but to the underlying “health” at the cellular (and even molecular) scales, makes it very well suited to this task.
Original languageEnglish
Article number139
Number of pages9
JournalFrontiers in Aging Neuroscience
Publication statusPublished - 16 Jun 2016

Structured keywords

  • CRICBristol
  • Brain and Behaviour
  • Cognitive Science


  • Alzheimer's
  • Diffusion tensor imaging
  • Magnetic resonance imaging
  • Relaxometry
  • Shape analysis


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