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P2–170: Structural changes of the corpus callosum predicts severe cognitive decline and dementia: A longitudinal 7‐year study
Author(s) -
Sylvaine Artero,
Mortamais Marion,
Poulain Vanessa,
Maller Jerome,
Meslin Chantal,
Bonafé Alain,
Le Bars Emmanuelle,
Touchon Jacques,
Berr Claudine,
Ritchie Karen
Publication year - 2013
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2013.05.815
Subject(s) - dementia , cognitive decline , atrophy , medicine , corpus callosum , cohort , white matter , neuropsychology , clinical dementia rating , cognition , population , psychology , disease , magnetic resonance imaging , psychiatry , pathology , radiology , environmental health
Pre-processing including measurement of cortical thickness was carried out by the FreeSurfer Software. Generated values for all retrieved vertices were analysed by qdec, a single binary application implemented in the FreeSurfer software. It was used to perform a vertex wise comparison using a general linear model. We compared the cortical thickness between subjects with SMI and healthy controls. A statistic threshold of p<0.05 (uncorrected) was applied. Results: We found cortical thickness reduction in SMI subjects bilaterally in the precuneus, parahippocampal regions and parts of the entorhinal region. Conclusions: The spatial distribution of cortical thickness reduction in parietal (right) and bilateral medial temporal lobe regions is in agreement with known patterns of cortical atrophy in AD. The described findings may thus reflect first structural alterations related to Alzheimer’s pathology. We interpret our data as initial evidence for mild cortical atrophy in SMI, which corresponds in part to brain regions that are affected early in AD. The limitation of our data is the likelihood of false positive results related to the uncorrected statistical threshold, thus, requiring replication in larger and independent datasets in the future.