Chen Wang, Kai Li, Shouqiang Huang, Jiakang Liu, Shuwu Li, Yuting Tu, Bo Wang, Pengpeng Zhang, Yuntian Luo, Tong Chen
{"title":"AD-MCI和PD-MCI人群在数字时钟绘制测试中的差异认知功能。","authors":"Chen Wang, Kai Li, Shouqiang Huang, Jiakang Liu, Shuwu Li, Yuting Tu, Bo Wang, Pengpeng Zhang, Yuntian Luo, Tong Chen","doi":"10.3389/fnins.2025.1558448","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Mild cognitive impairment (MCI) is common in Alzheimer's disease (AD) and Parkinson's disease (PD), but there are differences in pathogenesis and cognitive performance between Mild cognitive impairment due to Alzheimer's disease (AD-MCI) and Parkinson's disease with Mild cognitive impairment (PD-MCI) populations. Studies have shown that assessments based on the digital clock drawing test (dCDT) can effectively reflect cognitive deficits. Based on this, we proposed the following research hypothesis: there is a difference in cognitive functioning between AD-MCI and PD-MCI populations in the CDT, and the two populations can be effectively distinguished based on this feature.</p><p><strong>Methods: </strong>To test this hypothesis, we designed the dCDT to extract digital biomarkers that can characterize and quantify cognitive function differences between AD-MCI and PD-MCI populations. We enrolled a total of 40 AD-MCI patients, 40 PD-MCI patients, 41 PD with normal cognition (PD-NC) patients and 40 normal cognition (NC) controls.</p><p><strong>Results: </strong>Through a cross-sectional study, we revealed a difference in cognitive function between AD-MCI and PD-MCI populations in the dCDT, which distinguished AD-MCI from PD-MCI patients, the area under the roc curve (AUC) = 0.923, 95% confidence interval (CI) = 0.866-0.983. The AUC for effective differentiation between AD-MCI and PD-MCI patients with high education (≥12 years of education) was 0.968, CI = 0.927-1.000. By correlation analysis, we found that the overall plotting of task performance score (<i>VFDB</i> <sub>1</sub>) correlated with the [visuospatial/executive] subtest score on the Montreal Cognitive Assessment (MoCA) scale (Spearman rank correlation coefficient [R] = 0.472, <i>p</i> < 0.001).</p><p><strong>Conclusion: </strong>The dCDT is a tool that can rapidly and accurately characterize and quantify differences in cognitive functioning in AD-MCI and PD-MCI populations.</p>","PeriodicalId":12639,"journal":{"name":"Frontiers in Neuroscience","volume":"19 ","pages":"1558448"},"PeriodicalIF":3.2000,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11965901/pdf/","citationCount":"0","resultStr":"{\"title\":\"Differential cognitive functioning in the digital clock drawing test in AD-MCI and PD-MCI populations.\",\"authors\":\"Chen Wang, Kai Li, Shouqiang Huang, Jiakang Liu, Shuwu Li, Yuting Tu, Bo Wang, Pengpeng Zhang, Yuntian Luo, Tong Chen\",\"doi\":\"10.3389/fnins.2025.1558448\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Mild cognitive impairment (MCI) is common in Alzheimer's disease (AD) and Parkinson's disease (PD), but there are differences in pathogenesis and cognitive performance between Mild cognitive impairment due to Alzheimer's disease (AD-MCI) and Parkinson's disease with Mild cognitive impairment (PD-MCI) populations. Studies have shown that assessments based on the digital clock drawing test (dCDT) can effectively reflect cognitive deficits. Based on this, we proposed the following research hypothesis: there is a difference in cognitive functioning between AD-MCI and PD-MCI populations in the CDT, and the two populations can be effectively distinguished based on this feature.</p><p><strong>Methods: </strong>To test this hypothesis, we designed the dCDT to extract digital biomarkers that can characterize and quantify cognitive function differences between AD-MCI and PD-MCI populations. We enrolled a total of 40 AD-MCI patients, 40 PD-MCI patients, 41 PD with normal cognition (PD-NC) patients and 40 normal cognition (NC) controls.</p><p><strong>Results: </strong>Through a cross-sectional study, we revealed a difference in cognitive function between AD-MCI and PD-MCI populations in the dCDT, which distinguished AD-MCI from PD-MCI patients, the area under the roc curve (AUC) = 0.923, 95% confidence interval (CI) = 0.866-0.983. The AUC for effective differentiation between AD-MCI and PD-MCI patients with high education (≥12 years of education) was 0.968, CI = 0.927-1.000. By correlation analysis, we found that the overall plotting of task performance score (<i>VFDB</i> <sub>1</sub>) correlated with the [visuospatial/executive] subtest score on the Montreal Cognitive Assessment (MoCA) scale (Spearman rank correlation coefficient [R] = 0.472, <i>p</i> < 0.001).</p><p><strong>Conclusion: </strong>The dCDT is a tool that can rapidly and accurately characterize and quantify differences in cognitive functioning in AD-MCI and PD-MCI populations.</p>\",\"PeriodicalId\":12639,\"journal\":{\"name\":\"Frontiers in Neuroscience\",\"volume\":\"19 \",\"pages\":\"1558448\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2025-03-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11965901/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Neuroscience\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3389/fnins.2025.1558448\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Neuroscience","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3389/fnins.2025.1558448","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
Differential cognitive functioning in the digital clock drawing test in AD-MCI and PD-MCI populations.
Background: Mild cognitive impairment (MCI) is common in Alzheimer's disease (AD) and Parkinson's disease (PD), but there are differences in pathogenesis and cognitive performance between Mild cognitive impairment due to Alzheimer's disease (AD-MCI) and Parkinson's disease with Mild cognitive impairment (PD-MCI) populations. Studies have shown that assessments based on the digital clock drawing test (dCDT) can effectively reflect cognitive deficits. Based on this, we proposed the following research hypothesis: there is a difference in cognitive functioning between AD-MCI and PD-MCI populations in the CDT, and the two populations can be effectively distinguished based on this feature.
Methods: To test this hypothesis, we designed the dCDT to extract digital biomarkers that can characterize and quantify cognitive function differences between AD-MCI and PD-MCI populations. We enrolled a total of 40 AD-MCI patients, 40 PD-MCI patients, 41 PD with normal cognition (PD-NC) patients and 40 normal cognition (NC) controls.
Results: Through a cross-sectional study, we revealed a difference in cognitive function between AD-MCI and PD-MCI populations in the dCDT, which distinguished AD-MCI from PD-MCI patients, the area under the roc curve (AUC) = 0.923, 95% confidence interval (CI) = 0.866-0.983. The AUC for effective differentiation between AD-MCI and PD-MCI patients with high education (≥12 years of education) was 0.968, CI = 0.927-1.000. By correlation analysis, we found that the overall plotting of task performance score (VFDB1) correlated with the [visuospatial/executive] subtest score on the Montreal Cognitive Assessment (MoCA) scale (Spearman rank correlation coefficient [R] = 0.472, p < 0.001).
Conclusion: The dCDT is a tool that can rapidly and accurately characterize and quantify differences in cognitive functioning in AD-MCI and PD-MCI populations.
期刊介绍:
Neural Technology is devoted to the convergence between neurobiology and quantum-, nano- and micro-sciences. In our vision, this interdisciplinary approach should go beyond the technological development of sophisticated methods and should contribute in generating a genuine change in our discipline.