Related Experiment Video

Updated: May 13, 2026

Evaluation of the Cognitive Performance of Hypertensive Patients with Silent Cerebrovascular Lesions
07:30

Evaluation of the Cognitive Performance of Hypertensive Patients with Silent Cerebrovascular Lesions

Published on: April 23, 2021

Spatial distribution of cerebral white matter lesions predicts progression to mild cognitive impairment and dementia

Marion Mortamais1, Christelle Reynes, Adam M Brickman

  • 1Inserm, U1061, La Colombière Hospital, Montpellier, France.

Plos One
|March 5, 2013
PubMed
Abstract

Insights

White matter lesions (WML) in the temporal lobe significantly increase the risk of mild cognitive impairment (MCI) or dementia. Identifying this WML pattern early can help detect at-risk individuals before symptoms appear.

Area of Science:

  • Neurology
  • Neuroimaging
  • Geriatrics

Background:

  • White matter lesions (WML) are linked to dementia risk.
  • The specific impact of WML location on cognitive decline is not well understood.

Purpose of the Study:

  • To investigate if a specific pattern of white matter lesion (WML) density and location predicts the risk of mild cognitive impairment (MCI) or dementia.
  • To analyze WML profiles in relation to cognitive decline over a 7-year period.

Main Methods:

  • Utilized data from 426 community-dwelling individuals aged 65+ (ESPRIT Project).
  • Employed a data mining approach (decision trees) to analyze regional and total WML volumes at baseline.
  • Applied Cox proportional hazard models to assess the association between WML patterns and MCI/dementia risk.

Main Results:

  • Total WML volume and the percentage of WML in the temporal region were the strongest predictors of MCI and dementia.
  • A high WML load, particularly with significant temporal lobe involvement, was significantly associated with increased risk.

Conclusions:

  • A WML pattern characterized by temporal lobe clustering, above a certain damage threshold, identifies individuals at higher risk for MCI or dementia.
  • This WML pattern can be detected before clinical symptoms, aiding in early identification of at-risk individuals.

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