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Related Concept Videos

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Personalizing progressive changes to brain structure in Alzheimer's disease using normative modeling.

Serena Verdi1,2, Saige Rutherford3,4, Charlotte Fraza3,4

  • 1Centre for Medical Image Computing, University College London, London, UK.

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Summary

Neuroanatomical normative modeling tracks Alzheimer's disease (AD) progression using brain imaging. Increased outliers in mild cognitive impairment (MCI) predict conversion to AD, with the hippocampus showing the fastest change.

Keywords:
Alzheimer's diseasedisease progressionlongitudinal serial magnetic resonance imagingmild cognitive impairmentneuroimagingnormative modeling

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Area of Science:

  • Neuroimaging
  • Neuroscience
  • Biostatistics

Background:

  • Alzheimer's disease (AD) is characterized by neurodegeneration.
  • Tracking individual disease progression is crucial for understanding AD.
  • Neuroanatomical normative modeling offers a method to capture individual variability.

Purpose of the Study:

  • To apply neuroanatomical normative modeling to serial Alzheimer's disease (AD) MRI data.
  • To track disease progression in individuals with mild cognitive impairment (MCI) and AD.
  • To identify brain regions and quantify changes associated with disease progression.

Main Methods:

  • Generated normative models from a large dataset of healthy controls (n ≈ 58k).
  • Calculated regional z-scores from 3233 T1-weighted MRI scans of 1181 participants.
  • Defined outliers as regions with z-scores < -1.96 and summarized by total outlier count (tOC).

Main Results:

  • tOC increased in AD patients and in MCI individuals who converted to AD.
  • Higher annual tOC change correlated with increased risk of MCI to AD progression.
  • Hippocampus exhibited the highest rate of change in brain outliers.

Conclusions:

  • Neuroanatomical normative modeling can track individual atrophy rates in AD.
  • Regional outlier maps and tOC provide valuable insights into disease progression.
  • This approach enhances the understanding of individual variability in AD progression.