Estudio de las funciones ejecutivas en la detección del envejecimiento patológico

Este trabajo Estudio de las funciones ejecutivas en la detección del envejecimiento patológico se centra en uno de los fenómenos actuales más importantes: el evidente envejecimiento de la población y el consecuente aumento de la prevalencia de enfermedades asociadas a la edad, entre las que destacan enfermedades neurodegenerativas como la enfermedad de Alzheimer, el caso más común de demencia. Actualmente no existe tratamiento farmacológico que pueda parar el proceso neuropatológico de la enfermedad de Alzheimer, aunque sí se ha visto que, con la aplicación muy temprana de tratamientos, especialmente los no farmacológicos, se pueden disminuir o atenuar los síntomas cognitivos. Esto ha planteado la necesidad de encontrar biomarcadores y marcadores cognitivos tempranos de la misma. Tradicionalmente, las investigaciones se han centrado en la afectación de la memoria como síntoma inicial de la enfermedad de Alzheimer. Sin embargo, las evidencias actuales indican que hay otras capacidades, como las funciones ejecutivas que se ven afectadas en las etapas más tempranas de esta enfermedad, en muchas ocasiones, incluso en etapas asintomáticas, donde los problemas de memoria aún no son evidentes..

Patterns of brain atrophy in dysexecutive amnestic mild cognitive impairment raise confidence about prodromal AD dementia

Background: Prediction models aimed at detecting risk of progression from Mild Cognitive Impairment (MCI) to Alzheimer’s disease (AD) dementia increase their accuracy when impaired executive functions enter the analysis. This suggests that impaired executive functions in MCI are likely linked to the prodromal stages of AD dementia. Neuroimaging assessment of such patients would allow exploring if they show AD related patterns of brain atrophy. We hypothesized that AD sensitive brain regions would show discrimination between dysexecutive amnestic MCI (maMIC) and healthy controls. Method: We analysed 32 healthy controls and 23 MCI patients. Patients were divided in single domain amnestic MCI, multidomain amnestic MCI (i.e., with the dysexecutive component), and non-amnestic MCI. Brain volume data entered regression models to analyse which brain regions predict group membership (control vs maMCI). Stepwise lineal regression model was then conducted to identify the brain regions with better prediction power. Results: Four variables were able to predict group membership in simple lineal regression models: entorhinal cortex, lingual gyrus and parahippocampal gyrus in the left hemisphere and fusiform gyrus in the right hemisphere. The entorhinal cortex provided the most accurate model (F(1, 42) = 14.19, p=0.001, R2=0.24). Linear regression models were run with performance on executive function tasks including tests of switching, planning, verbal fluency and working memory. The most accurate model returned Letters and Numbers and categories fluency (F(2, 44) = 21.35, p=0.000, R2=0.48) suggesting that working memory and category generation are the functions contributing to the dysexecutive profiles observed in maMCI patients. Conclusion: Dysexecutive profiles in multidomain amnestic MCI together with neuroimaging volumetric analysis increase the probability of identifying the prodromal stages of AD dementia

Role of executive functions in the conversion from mild cognitive impairment to dementia

BACKGROUND: Recent research pointed to executive dysfunction as a potential early predictor of the progression of mild cognitive impairment (MCI) to dementia in Alzheimer's clinical syndrome (ACS). Such cognitive impairments account for functional impairments in instrumental activities of daily living (IADL). OBJECTIVE: The present study analyzes the contributions of executive functions to predict MCI-dementia progression in ACS. METHODS: We assessed 145 participants, 51 cognitively unimpaired and 94 MCI. The latter were divided using the traditional, memory-based MCI classification (single domain amnestic, multidomain amnestic, and non-amnestic). Eight tests assessing executive functions were administered at baseline and at 1-year follow-up, together with cognitive screening tools and IADL measures. MCI patients were reclassified based on the outcomes from a K-mean cluster analysis which identified three groups. A simple lineal regression model was used to examine whether the classification based on executive functioning could more accurately predict progression to dementia a year later. RESULTS: Clusters based on executive function deficits explained a significant proportion of the variance linked to MCI-dementia conversion, even after controlling for the severity of MCI at baseline (F(1, 68) = 116.25, p = 0.000, R2 = 0.63). Classical memory-based MCI classification failed to predict such a conversion (F(1, 68) = 5.09, p = 0.955, R2 = 0.07). Switching, categories generation, and planning were the executive functions that best distinguished between MCI converters and stable. CONCLUSION: MCI with a dysexecutive phenotype significantly predicts conversion to dementia in ACS a year later. Switching abilities and verbal fluency (categories) must be evaluated in MCI patients to assess risk of future dementia

Clustering executive functions yields MCI profiles that significantly predict conversion to AD dementia

Background: It has been acknowledged that executive dysfunctions hold potential as early predictors of progression from Mild Cognitive Impairment (MCI) to Alzheimer’s disease (AD) dementia. Executive deficits have a significant impact on the ability to perform activities of daily living (ADL), and as such, can lead to the transition from MCI to AD dementia. However, the extent to which executive impairments can yield identifiable cognitive profiles which can increase the risk of MCI to AD dementia progression has not been well investigated to date. Method: We analysed 152 patients (52 healthy controls and 100 MCI) who were subsequently divided based on the traditional classification of MCI (single domain amnestic MCI, multidomain amnestic MCI and non-amnestic MCI). Eight tests, assessing executive functions were administered at baseline and at 1-year-follow-up. Screening tests of cognitive and functional abilities were also used. A new dysexecutive MCI classification was developed relying on k-means cluster analysis through which three clusters were identified. Baseline data entered simple lineal regression models to examine whether such a classification based on executive profiles could significantly predict progression to AD dementia a year later. Results: The dysexecutive classification accounted for 63% of the variance linked to MCI to AD conversion even when controlling for the severity of disease at baseline (F(1, 68) = 116.25, p=0.000, R2=0.63). Such a prediction power was not observed when the classical MCI classification based on memory profiles alone entered the model as a predictor (F(1, 68) = 5.09, p=0.955, R2=0.07). Conclusion: Considering dysexecutive profiles of MCI patients may increase the accuracy of prediction models aimed at detecting risk of progressing to AD dementia. MCI patients with worse performance on executive tests seem to hold a higher risk of conversion and such a risk seems to be accounted for neither by memory impairments nor by the severity of the disease at baseline