Written by Wang Shuojia

Editor in charge ︱ Wang Sizhen

Editor︱Yang Binwei

Cognitive decline may progress to cognitive impairment

Machine learning techniques help reveal potential associations between research factors and outcomes [4]

In August 2022, the team of Associate Professor Zeng Yanbing of Capital Medical University and the team of Tencent Tianyan Laboratory published an article entitled "Using machine learning algorithms for predicting cognitive impairment and identifying modifiable factors among Chinese elderly people" on Frontiers in Aging Neuroscience, proposing developed a predictive model of cognitive impairment risk and explored the impact of behavioral changes on cognitive impairment

First, to provide an early predictor of the risk of developing cognitive impairment, the authors used CLHLS data from 2002 to 2014, incorporating sociodemographic information, psychological status, lifestyle, social/recreational activities, and activities of daily living.

Figure 1 Flowchart of the prediction model

(Image source: Wang SJ et al.

Table 1 Prediction model results

(Table source: Wang SJ et al.

In addition to unchangeable factors, researchers also focused on the relationship between longitudinal behavioral changes and changes in the types of activities involved and cognitive impairment in order to better serve policy management and intervention

Table 2 Association between behavioral changes and cognitive impairment

(Table source: Wang SJ et al.
, Front Aging Neurosci, 2022)

Original link:thanks to Mr.
Zheng Yefeng from Tianyan Laboratory for his help in the revision and writing of the article
.

The research was supported by the National Natural Science Foundation of China, the Open Project of the Capital Health Management and Policy Research Base and other projects
.

The first author Wang Shuojia (left), the co-author Wang Weiren (middle), and the corresponding author Zeng Yanbing (right)
.

(Photo provided by: Zeng Yanbing's Research Group of Capital Medical University/Tencent Tianyan Laboratory)

【1】Mol Neurobiol︱Cao Zigang's research group revealed the molecular mechanism of paamiparib-induced neurodevelopmental defects and cerebral hemorrhage in zebrafish embryos

【2】Cereb Cortex︱Wang Qian/Wang Xiongfei/Luan Guoming team work together to reveal the functional connection between cerebral gray matter heterotopia and neocortex

【3】Sci Adv︱ Zhao Cunyou/Chen Rongqing's team reveals the mechanism of microRNA-induced social and memory abnormalities in mice: miR-501-3p expression deficiency enhances glutamatergic transmission

【4】Sci Adv︱Zhang Yi's research group discovered important neurons that regulate drug addiction behavior

【5】J Infect︱Wang Yifei's team revealed that the highly expressed gene MAMDC2 in Alzheimer's disease microglia positively regulates the innate antiviral response to neurotropic virus infection

【6】Sci Adv︱Xia Kun/Shen Yiping/Guo Hui Reveal the relationship between key regulatory genes of stress granules and neurodevelopmental disorders

【7】Cell Prolif︱Lai Liangxue/Zhang Kun/Zou Qingjian team successfully constructed a technical system for safe and efficient directional induction of motor neurons in vivo

【8】Nat Commun︱Peng Yueqing's team discovered a new brain area that controls non-REM sleep

【9】eClinicalMedicine︱Wang Qing’s team reports Parkinson’s disease dementia related index determination: quantitative EEG, serum metabolism and inflammation

【10】Cell Biosci | Wang Yongjun's research group revealed the molecular mechanism of D-type dopachrome isomerase-mediated inflammatory response in injured spinal cord

【1】Training course︱R language clinical prediction biomedical statistics special training

[1] 2022 World Artificial Intelligence Conference | Brain and Machine Intelligence Fusion - Connecting the Brain to the Future (September 2, Shanghai World Expo Center)

References (swipe up and down to read) 

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