The main symptom of patients with Alzheimer’s disease is cognitive dysfunction. Alzheimer’s disease is mainly diagnosed based on changes in brain structure. Functional connectivity reflects the synchrony of function...The main symptom of patients with Alzheimer’s disease is cognitive dysfunction. Alzheimer’s disease is mainly diagnosed based on changes in brain structure. Functional connectivity reflects the synchrony of functional activities between non-adjacent brain regions, and changes in functional connectivity appear earlier than those in brain structure. In this study, we detected resting-state functional connectivity changes in patients with Alzheimer’s disease to provide reference evidence for disease prediction. Functional magnetic resonance imaging data from patients with Alzheimer’s disease were used to show whether particular white and gray matter areas had certain functional connectivity patterns and if these patterns changed with disease severity. In nine white and corresponding gray matter regions, correlations of normal cognition, early mild cognitive impairment, and late mild cognitive impairment with blood oxygen level-dependent signal time series were detected. Average correlation coefficient analysis indicated functional connectivity patterns between white and gray matter in the resting state of patients with Alzheimer’s disease. Functional connectivity pattern variation correlated with disease severity, with some regions having relatively strong or weak correlations. We found that the correlation coefficients of five regions were 0.3–0.5 in patients with normal cognition and 0–0.2 in those developing Alzheimer’s disease. Moreover, in the other four regions, the range increased to 0.45–0.7 with increasing cognitive impairment. In some white and gray matter areas, there were specific connectivity patterns. Changes in regional white and gray matter connectivity patterns may be used to predict Alzheimer’s disease;however, detailed information on specific connectivity patterns is needed. All study data were obtained from the Alzheimer’s Disease Neuroimaging Initiative Library of the Image and Data Archive Database.展开更多
The pandemic of COVID-19 initiated in 2019 and spread all over the world in 2020 has caused significant damages to the human society,making troubles to all aspects of our daily life.Facing the serious outbreak of the ...The pandemic of COVID-19 initiated in 2019 and spread all over the world in 2020 has caused significant damages to the human society,making troubles to all aspects of our daily life.Facing the serious outbreak of the virus,we consider possible solutions from the perspectives of both governments and enterprises.Particularly,this paper discusses several applications of supply chain management,public resource allocation,and pandemic prevention using optimization and machine learning methods.Some useful insights in mitigating the pandemic and economy reopening are provided at the end of this paper.These insights might help governments to reduce the severity of the current pandemic and prevent the next round of outbreak.They may also improve companies'reactions to the increasing uncertainties appearing in the business operations.Although the coronavirus imposes challenges to the entire society at the moment,we are confident to develop new techniques to prevent and eradicate the disease.展开更多
基金financially supported by the National Natural Science Foundation of China(No.52071276)the Natural Science Foundation of Chongqing City,China(No.cstc2020jcyj-msxm X0933)。
基金supported by the National Natural Science Foundation of China,No.61401308,61572063(both to XHW)the Natural Science Foundation of Beijing of China,No.L172055(to XHW)+3 种基金the Beijing Municipal Science&Technology Commission Research Fund of China,No.Z171100000417004(to XHW)the China Postdoctoral Fund,No.2018M631755(to XHW)the Special Fund for Improving Comprehensive Strength of Hebei University in the Midwest of China,No.801260201011(to XHW)the High-Level Talent Funding Project—Selective Post-doctoral Research Project Fund of Hebei Province of China,No.B2018003002(to XHW)
文摘The main symptom of patients with Alzheimer’s disease is cognitive dysfunction. Alzheimer’s disease is mainly diagnosed based on changes in brain structure. Functional connectivity reflects the synchrony of functional activities between non-adjacent brain regions, and changes in functional connectivity appear earlier than those in brain structure. In this study, we detected resting-state functional connectivity changes in patients with Alzheimer’s disease to provide reference evidence for disease prediction. Functional magnetic resonance imaging data from patients with Alzheimer’s disease were used to show whether particular white and gray matter areas had certain functional connectivity patterns and if these patterns changed with disease severity. In nine white and corresponding gray matter regions, correlations of normal cognition, early mild cognitive impairment, and late mild cognitive impairment with blood oxygen level-dependent signal time series were detected. Average correlation coefficient analysis indicated functional connectivity patterns between white and gray matter in the resting state of patients with Alzheimer’s disease. Functional connectivity pattern variation correlated with disease severity, with some regions having relatively strong or weak correlations. We found that the correlation coefficients of five regions were 0.3–0.5 in patients with normal cognition and 0–0.2 in those developing Alzheimer’s disease. Moreover, in the other four regions, the range increased to 0.45–0.7 with increasing cognitive impairment. In some white and gray matter areas, there were specific connectivity patterns. Changes in regional white and gray matter connectivity patterns may be used to predict Alzheimer’s disease;however, detailed information on specific connectivity patterns is needed. All study data were obtained from the Alzheimer’s Disease Neuroimaging Initiative Library of the Image and Data Archive Database.
基金The first author was supported by the Natural Science Foundation of Jiangsu Province(No.BK20181259)the National Natural Science Foundation of China(No.11871269)The third author was supported by the National Natural Science Foundation of China(Nos.11831002 and 11471205).
文摘The pandemic of COVID-19 initiated in 2019 and spread all over the world in 2020 has caused significant damages to the human society,making troubles to all aspects of our daily life.Facing the serious outbreak of the virus,we consider possible solutions from the perspectives of both governments and enterprises.Particularly,this paper discusses several applications of supply chain management,public resource allocation,and pandemic prevention using optimization and machine learning methods.Some useful insights in mitigating the pandemic and economy reopening are provided at the end of this paper.These insights might help governments to reduce the severity of the current pandemic and prevent the next round of outbreak.They may also improve companies'reactions to the increasing uncertainties appearing in the business operations.Although the coronavirus imposes challenges to the entire society at the moment,we are confident to develop new techniques to prevent and eradicate the disease.
