Quantitative approaches for assessment of white matter hyperintensities in elderly populations

Adam M Brickman1, Joel R Sneed, Frank A Provenzano

  • 1Taub Institute for Research on Alzheimer's Disease and the Aging Brain, College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA. amb2139@columbia.edu

Psychiatry Research
|June 18, 2011
PubMed

Insights

Two methods for quantifying white matter hyperintensities (WMH) show good reliability and validity in older adults. Both operator-driven and automated approaches accurately measure WMH volume, correlating with age and depression severity.

Area of Science:

  • Neuroimaging
  • Geriatric Medicine
  • Quantitative Analysis

Background:

  • White matter hyperintensities (WMH) are MRI-detectable lesions linked to cognitive, neurological, and psychiatric issues in older adults.
  • Accurate quantification of WMH burden is crucial for understanding these conditions.

Purpose of the Study:

  • To present and evaluate two distinct methods for quantifying periventricular, deep, and total WMH.
  • To assess the reliability and criterion validity of these WMH quantification approaches in elderly patients.

Main Methods:

  • An operator-driven quantitative approach involving manual MRI scan labeling.
  • A fully automated quantitative approach utilizing image segmentation and seed growing algorithms.
  • Comparison of WMH quantification between the two methods in 28 elderly participants.

Main Results:

  • Excellent agreement between the operator-driven and automated WMH quantification methods (Cronbach's alpha: 0.835–0.968).
  • WMH severity significantly correlated with increased age and depression severity.
  • No significant differences in associations between WMH and age/depression were observed between the two quantification approaches.

Conclusions:

  • Both presented methods demonstrate good reliability and criterion validity for WMH volume determination.
  • The operator-driven method is suitable for smaller, specialized studies.
  • The automated method is advantageous for large-scale, high-throughput research.

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