A simple measure of cognitive reserve is relevant for cognitive performance in MS patients.
Marida Della CorteGabriella SantangeloAlvino BiseccoRosaria SaccoMattia SicilianoAlessandro d'AmbrosioRenato DocimoTeresa CuomoLuigi LavorgnaSimona BonavitaGioacchino TedeschiAntonio GalloPublished in: Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology (2018)
Cognitive reserve (CR) contributes to preserve cognition despite brain damage. This theory has been applied to multiple sclerosis (MS) to explain the partial relationship between cognition and MRI markers of brain pathology. Our aim was to determine the relationship between two measures of CR and cognition in MS. One hundred and forty-seven MS patients were enrolled. Cognition was assessed using the Rao's Brief Repeatable Battery and the Stroop Test. CR was measured as the vocabulary subtest of the WAIS-R score (VOC) and the number of years of formal education (EDU). Regression analysis included raw score data on each neuropsychological (NP) test as dependent variables and demographic/clinical parameters, VOC, and EDU as independent predictors. A binary logistic regression analysis including clinical/CR parameters as covariates and absence/presence of cognitive deficits as dependent variables was performed too. VOC, but not EDU, was strongly correlated with performances at all ten NP tests. EDU was correlated with executive performances. The binary logistic regression showed that only the Expanded Disability Status Scale (EDSS) and VOC were independently correlated with the presence/absence of CD. The lower the VOC and/or the higher the EDSS, the higher the frequency of CD. In conclusion, our study supports the relevance of CR in subtending cognitive performances and the presence of CD in MS patients.
Keyphrases
- multiple sclerosis
- end stage renal disease
- white matter
- mass spectrometry
- ejection fraction
- newly diagnosed
- ms ms
- chronic kidney disease
- mild cognitive impairment
- prognostic factors
- peritoneal dialysis
- magnetic resonance imaging
- healthcare
- patient reported outcomes
- machine learning
- patient reported
- artificial intelligence
- deep learning
- diffusion weighted imaging