Brain age prediction method based on deep convolutional generative adversarial network / 医疗卫生装备

Objective To propose a brain age prediction method based on deep convolutional generative adversarial networks(DCGAN)for objective assessment of brain health status.Methods The DCGAN model was extended from 2D to 3D and improved by integrating the concept of residual block to enhance the ability for feature extraction.The classifiers were pre-trained with unsupervised adversarial learning and fine-tuned with migration learning to eliminate the overfitting of 3D convolutional neural network(CNN)due to small sample size.To verify the effectiveness of the improved model,comparison analyses based on UK Biobank(UKB)database were carried out between the improved model and least absolute shrinkage and selection operator(LASSO)model,machine learning model,3D CNN model and graph convolutional network model by using mean absolute error(MAE)as the evaluation metric.Results The model proposed gained advantages over LASSO model,machine learning model,3D CNN model and graph convolutional network model in predicting brain age with a MAE error of 2.896 years.Conclusion The method proposed behaves well for large-scale datasets,which can predict brain age accurately and assess brain health status objectively.

Difference in Brain Age Between Alzheimer's Disease and Mild Cognitive Impairment / 中国医学科学院学报

Objective To investigate the brain age differences between Alzheimer's disease(AD)and mild cognitive impairment(MCI)patients,and further explore the correlations between brain age gap(BAG)and clinical features.Methods The clinical data and radiologic findings of 132 probable AD and AD-derived MCI patients diagnosed at Beijing Tiantan Hospital,Capital Medical University from December 2018 to July 2021 were retrospectively analyzed.According to the diagnostic criteria for AD and MCI,the patients were assigned into AD and MCI groups.In addition,156 volunteers without neurological diseases and other severe diseases were recruited as the control group.The general data,Montreal cognitive assessment(MoCA)score,and mini-mental state examination(MMSE)score were compared among the three groups.The deep learning-based brain age prediction model was employed to calculate the BAGs of the three groups.Spearman correlation analysis was conducted to explore the correlations between BAG and clinical features.Results The 132 patients included 106 patients in the AD group and 26 patients in the MCI group.The MoCA and MMSE scores followed an ascending trend of AD group<MCI group<control group(all P<0.001).The predicted brain age and BAG in the AD group were higher than those in the MCI group(P=0.040,P=0.003)and control group(P=0.001,P<0.001).There was no significant difference in predicted brain age or BAG between MCI and control groups(P=0.352,P=0.224).BAG was negatively correlated with MoCA score(r=-0.341,P<0.001)and MMSE score(r=-0.324,P=0.001)in the AD group.Conclusion BAG can be used as an imaging biomarker to evaluate the degree of brain structural variation and the severity of brain injury in the patients with cognitive impairment.

Study of the relationship between smoking and brain aging using machine learning model based on MRI / 中华放射学杂志

Objective:To explore the value of machine learning models based on MRI predict the brain age of smokers and healthy controls, and further to explore the relationship between smoking and brain aging.Methods:This was a retrospective study. Dataset 1 consisted of 95 male smokers [20-50 (34±7) years old] and 49 healthy controls [20-50 (33±7) years old] recruited from August 2014 to October 2017 in First Affiliated Hospital of Zhengzhou University. Dataset 2 contained 114 healthy male volunteers [20-50 (34±11) years old] from the Southwestern University Adult Imaging Database from 2010 to 2015. All subjects underwent high-resolution 3D T 1WI scan. Gaussian process regression (GPR) model and support vector machine model were constructed to predict brain age based on structural MR images of healthy controls in dataset 1 and dataset 2. After the performance of the model was verified by the cross-validation method, the mean absolute error (MAE) between the predicted brain age and the actual age and the correlation ( r-value) between the actual age and the predicted brain age were calculated, and the best model was finally selected. The best models were applied to smokers and healthy controls to predict brain age. Finally, a general linear model was used to compare the differences in brain-predicted age difference (PAD) between smokers and healthy controls with age, taking years of education and total intracranial volume as covariates. Result:The performance of GPR model (MAE=5.334, r=0.747) in predicting brain age was better than support vector machine model (MAE=6.040, r=0.679). The GPR model predicted that PAD value of smokers in dataset 1 (2.19±6.64) was higher than that of healthy controls in dataset 1 (-0.80±8.94), and the difference was statistically significant ( F=8.52, P=0.004). Conclusion:GPR model based MRI has better performance in predicting brain age in smokers and healthy controls, and smokers show increased PAD values, further indicating that smoking accelerates brain aging.

A review on brain age prediction in brain ageing / 生物医学工程学杂志

The human brain deteriorates as we age, and the rate and the trajectories of these changes significantly vary among brain regions and among individuals. Because neuroimaging data are potentially important indicators of individual's brain health, they are commonly used in brain age prediction. In this review, we summarize brain age prediction model from neuroimaging-based studies in the last ten years. The studies are categorized based on their image modalities and feature types. The results indicate that the prediction frameworks based on neuroimaging holds promise toward individualized brain age prediction. Finally, we addressed the challenges in brain age prediction and suggested some future research directions.

Quantitative study on effect of enriched environmental stimulation on mouse brain cognitive reserve / 医疗卫生装备

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.

Quantitative study on effect of enriched environmental stimulation on mouse brain cognitive reserve / 医疗卫生装备

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.