Sci Bull: Hippophonology is an early identification marker for Alzheimer's disease

Alzheimer's disease (AD), commonly known as Alzheimer's disease, is an irreversible neurodegenerative disease and is the most common cognitive impairment disorder in older peopleTraditionally, the shrinkage of the hippocampus in the brain was considered an imaging marker associated with AD, but previous imaging studies of AD were mostly based on the results of single-center, small samples, and the generalization and controversy of markersto break through the huge bottleneck of early diagnosis and efficacy evaluation of AD, led by the Brain Network Group Research Center of the Chinese Academy of Sciences Automation, jointly Chinese the People's Liberation Army General Hospital, The Capital Medical University Xuanwu Hospital, Tianjin Ring Lake Hospital and Shandong University Qilu Hospital and other domestic and foreign units jointly developed a set of a set of a set of adearly based on hippocampus: Hippoal radiomics ADBased on more than 1900 brain imaging samples, the team found that hippocampus imaging groupology can be used as an imaging marker for AD, and carried out systematic research on the generalization of markers, the feasibility of personalized precision diagnosis and the analysis of biological mechanismsThe HR4AD Research Frameworkthe critical problem of inadequate generalization of imaging markers for existing clinical direct applications, the Brain Network Group Research Center focused on the goal of assisting early identification of AD with magnetic resonance imaging, collected data on brain imaging, clinical cognitive and cognitive assessment of patients with mild cognitive impairment (mild impairment, AD high risk population) in six centers, and used the American ADNI database (including genomics, brain imaging, brain imaging, and clinical cognitive assessment)On the basis of multi-center and large-sample data, the hippocampus's imaging groupage is confirmed as a stable biomarker in multi-center and large-sample data for the first time, and the potential relationship between imaging markers and clinical information such as genetic risk, cognitive ability, protein precipitation and so on is explored, and imaging markers can be used to track the development of disease in high-risk populations in longitudinal tracking dataThe main innovations in HR4AD research are as follows: (1) Multicenter fusion analysis shows that there are significantly repeatable abnormal hippocampus imaging group expressionin in AD; (2) The cross-verification of the independent center shows that HR4AD can be used for the individual identification of AD; (3) Genes, biomarkers, and longitudinal changes depict the biological significance behind HR4AD, the study shows that hippocampus imaging group is a kind of personalized diagnostic biomarker of AD stable, effective, generalized, and promising lynx for clinical-assisted diagnosis, which may have important clinical significance for the accurate auxiliary identification of aD clinical and the longitudinal tracking of high-risk populationinthe study, entitled "
Independent and reproducible hippocampal radiomicics for multisite Alzheimer's disease: diagnosis, longitudinal al-progress and biological basis", was recently published online inScience BulletinLiu Yong, a researcher at the Brain Network Research Center, and Professor Zhang Xi of the Chinese People's Liberation Army General Hospital, are the authors of the newsletter, and the first author is Zhao Kun, a master's student in the Liu Yong Research Groupthe corresponding research methods can be extended to other neuropsychiatric studiesThe team has shared the updated application for the study (
https://github.com/YongLiulab ) This is another important step forward in the team's cerebral imaging abnormality in AD, following the multicenter-based functional magnetic resonance Alzheimer's brain activity change study (
Science Bulletin , 2019, 64: 998-1010 ) Team follow-up will focus on more independent centers, vertical tracking of high-risk population validation work, hope to build further cooperation with more teams this research is supported by projects such as the National Key Research and Development Program, the National Natural Science Foundation of China, the Pilot Project of the Chinese Academy of Sciences and the Open Project of the National Key Laboratory for Model Identification author: Zi Dongjun Source: , Institute of Automation, Chinese Academy of Sciences