Preliminary Analysis of Volume-Based Resting-State Functional MRI Characteristics of Successful Aging in China.

BACKGROUND: Resting-state function MRI (rs-fMRI) research on successful aging can provide insight into the mechanism of aging with a different perspective from aging-related disease. OBJECTIVE: rs-fMRI research was used to analyze the brain function characteristics of successful aging. METHODS: A total of 47 usual aging individuals and 26 successful aging (SA) individuals underwent rs-fMRI scans and neuropsychological tests. Volume-based rs-fMRI data analysis was performed with DPASF to obtain ALFF, ReHo, DC, and VMHC. RESULTS: The SA group showed increased ALFF in right opercular part of inferior frontal gyrus (Frontal_Inf_Oper_R) and right supramarginal gyrus; increased ReHo in right middle temporal pole gyrus and decreased ReHo in left superior frontal gyrus and middle occipital gyrus; increased DC in right medial orbitofrontal gyrus and pulvinar part of thalamus; decreased DC in left fusiform gyrus and right medial frontal gyrus; increased VMHC in right medial orbitofrontal gyrus; and decreased VMHC in the right superior temporal gyrus, right and left middle temporal gyrus, right and left triangular part of inferior frontal gyrus. ALFF in Frontal_Inf_Oper_R were found to be significantly correlated with MMSE scores (r = 0.301, p = 0.014) and ages (r = -0.264, p = 0.032) in all subjects, which could be used to distinguish the SA (AUC = 0.733, 95% CI: 0.604-0.863) by ROC analysis. CONCLUSION: The brain regions with altered fMRI characteristics in SA group were concentrated in frontal (6 brain regions) and temporal (4 brain regions) lobes. ALFF in Frontal_Inf_Oper_R was significantly correlated to cognitive function and ages, which might be used to distinguish the SA.

A Study of Dementia Prediction Models Based on Machine Learning with Survey Data of Community-Dwelling Elderly People in China.

BACKGROUND: For community-dwelling elderly individuals without enough clinical data, it is important to develop a method to predict their dementia risk and identify risk factors for the formulation of reasonable public health policies to prevent dementia. OBJECTIVE: A community elderly survey data was used to establish machine learning prediction models for dementia and analyze the risk factors. METHODS: In a cluster-sample community survey of 9,387 elderly people in 5 subdistricts of Wuxi City, data on sociodemographics and neuropsychological self-rating scales for depression, anxiety, and cognition evaluation were collected. Machine learning models were developed to predict their dementia risk and identify risk factors. RESULTS: The random forest model (AUC = 0.686) had slightly better dementia prediction performance than logistic regression model (AUC = 0.677) and neural network model (AUC = 0.664). The sociodemographic data and psychological evaluation revealed that depression (OR = 3.933, 95% CI = 2.995-5.166); anxiety (OR = 2.352, 95% CI = 1.577-3.509); multiple physical diseases (OR = 2.486, 95% CI = 1.882-3.284 for three or above); "disability, poverty or no family member" (OR = 1.859, 95% CI = 1.337-2.585) and "empty nester" (OR = 1.339, 95% CI = 1.125-1.595) in special family status; "no spouse now" (OR = 1.567, 95% CI = 1.118-2.197); age older than 80 years (OR = 1.645, 95% CI = 1.335-2.026); and female (OR = 1.214, 95% CI = 1.048-1.405) were risk factors for suspected dementia, while a higher education level (OR = 0.365, 95% CI = 0.245-0.546 for college or above) was a protective factor. CONCLUSION: The machine learning models using sociodemographic and psychological evaluation data from community surveys can be used as references for the prevention and control of dementia in large-scale community populations and the formulation of public health policies.