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

• Published in: PloS one 2013-02, Vol.8 (2), p.e56972-e56972
• Main Authors: Mortamais, Marion, Reynes, Christelle, Brickman, Adam M, Provenzano, Frank A, Muraskin, Jordan, Portet, Florence, Berr, Claudine, Touchon, Jacques, Bonafé, Alain, le Bars, Emmanuelle, Maller, Jerome J, Meslin, Chantal, Sabatier, Robert, Ritchie, Karen, Artero, Sylvaine
• Format: Article
• Language: English
• Subjects: Aged; Aging; Alzheimer's disease; Alzheimers disease; Attention deficit hyperactivity disorder; Biology; Brain; Brain - pathology; Brain research; Clustering; Cognitive ability; Cognitive Dysfunction - pathology; Damage detection; Damage patterns; Data Mining; Data processing; Decision Trees; Dementia; Dementia - pathology; Dementia disorders; Development and progression; Discriminant Analysis; Disease Progression; Female; Follow-Up Studies; Hospitals; Humans; Impairment; Lesions; Life Sciences; Male; Medicine; Mild Cognitive Impairment; Neurons and Cognition; NMR; Nuclear magnetic resonance; Older people; Population; Psychiatry; Risk; Santé publique et épidémiologie; Spatial Analysis; Spatial distribution; Statistics as Topic; Studies; Substantia alba; Surgeons; Survival analysis
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0056972

White matter lesions (WML) increase the risk of dementia. The relevance of WML location is less clear. We sought to determine whether a particular WML profile, based on the density and location of lesions, could be associated with an increased risk of mild cognitive impairment (MCI) or dementia over the following 7 years. In 426 healthy subjects from a cohort of community-dwelling people aged 65 years and over (ESPRIT Project), standardized cognitive and neurological evaluations were repeated after 2, 4 and 7 years. Patterns of WML were computed with a supervised data mining approach (decision trees) using the regional WML volumes (frontal, parietal, temporal, and occipital regions) and the total WML volume estimated at baseline. Cox proportional hazard models were then constructed to study the association between WML patterns and risk of MCI/dementia. Total WML volume and percentage of WML in the temporal region proved to be the best predictors of progression to MCI and dementia. Specifically, severe total WML load with a high proportion of lesions in the temporal region was significantly associated with the risk of developing MCI or dementia. Above a certain threshold of damage, a pattern of WML clustering in the temporal region identifies individuals at increased risk of MCI or dementia. As this WML pattern is observed before the onset of clinical symptoms, it may facilitate the detection of patients at risk of MCI/dementia.