RESUMEN
The current proxy indicators and quantitative strategy were summarized for cognitive reserve,the findings of cognitive reserve was discussed then in brain aging and Alzheimer's disease,and some future research directions were presented about cognitive reserve.The reserve of brain explains the disjunction between clinical symptoms and the degree of brain damage,whereby some people can tolerate more of age-related or Alzheimer's disease pathology than others and maintain brain function.The cognitive reserve hypothesis has been widely used in epidemiological and neuroimaging studies,but it lacks a unified quantitative indicator.The future research of cognitive reserve should be focused on the development of quantitative indicators that cover a variety of potential factors dynamically.
RESUMEN
Objective To explore the effect of enriched environmental stimulation on mouse brain cognitive reserve to enhance the sensitivity of brain age gap estimation (BrainAGE).Methods Twenty-one healthy adult C57BL / 6J male mice,15 months old,were divided into a group with a standard environment and two groups with enriched environments.All the groups underwent magnetic resonance microcopy.Scaled subprofile model was used to analyze the features reflecting the changes of brain cognitive reserve.Results There were significant differences between the mean BrainAGE of the two groups with enriched environments and that of the remained standard environment group,then it's proved that some assumption might be reasonable that brain cognitive reserve could be estimated based on BrainAGE.Optim ized BrainAGE model made explanations for 58.9％ differences during stimulus phase in enriched environment.Conclusion Improved BrainAGE model gains high sensitivity when used to measure the redundancy of brain cognitive reserve.
RESUMEN
Mild cognitive impairment (MCI) is a prodromal stage of dementia.Predicting MCI's conversion to Alzheimer's disease (AD) plays critical roles in preventing the progression of AD.Alzheimer's disease neuroimaging initiative (ADNI) was introduced briefly,which was a widely used neuroimaging database for the study on AD related diseases,and the application of machine learning algorithm was reviewed in MCI classification.Deep learning network,which transforms the original data into a higher level and more abstract expression,has shown great promise in MCI conversion and classification.Two main kinds of deep learning approaches were described,including supervised learning and unsupervised learning,and their new application was discussed in MCI conversion and classification based on structural magnetic resonance imaging (sMRI).Finally,the current limitations and future trends of deep learning in this area were explored.
RESUMEN
The current proxy indicators and quantitative strategy were summarized for cognitive reserve,the findings of cognitive reserve was discussed then in brain aging and Alzheimer's disease,and some future research directions were presented about cognitive reserve.The reserve of brain explains the disjunction between clinical symptoms and the degree of brain damage,whereby some people can tolerate more of age-related or Alzheimer's disease pathology than others and maintain brain function.The cognitive reserve hypothesis has been widely used in epidemiological and neuroimaging studies,but it lacks a unified quantitative indicator.The future research of cognitive reserve should be focused on the development of quantitative indicators that cover a variety of potential factors dynamically.
RESUMEN
Objective To explore the effect of enriched environmental stimulation on mouse brain cognitive reserve to enhance the sensitivity of brain age gap estimation (BrainAGE).Methods Twenty-one healthy adult C57BL / 6J male mice,15 months old,were divided into a group with a standard environment and two groups with enriched environments.All the groups underwent magnetic resonance microcopy.Scaled subprofile model was used to analyze the features reflecting the changes of brain cognitive reserve.Results There were significant differences between the mean BrainAGE of the two groups with enriched environments and that of the remained standard environment group,then it's proved that some assumption might be reasonable that brain cognitive reserve could be estimated based on BrainAGE.Optim ized BrainAGE model made explanations for 58.9％ differences during stimulus phase in enriched environment.Conclusion Improved BrainAGE model gains high sensitivity when used to measure the redundancy of brain cognitive reserve.
RESUMEN
Mild cognitive impairment (MCI) is a prodromal stage of dementia.Predicting MCI's conversion to Alzheimer's disease (AD) plays critical roles in preventing the progression of AD.Alzheimer's disease neuroimaging initiative (ADNI) was introduced briefly,which was a widely used neuroimaging database for the study on AD related diseases,and the application of machine learning algorithm was reviewed in MCI classification.Deep learning network,which transforms the original data into a higher level and more abstract expression,has shown great promise in MCI conversion and classification.Two main kinds of deep learning approaches were described,including supervised learning and unsupervised learning,and their new application was discussed in MCI conversion and classification based on structural magnetic resonance imaging (sMRI).Finally,the current limitations and future trends of deep learning in this area were explored.
