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A neuroimaging biomarker for Individual Brain-Related Abnormalities In Neurodegeneration (IBRAIN): a cross-sectional study.
Zhao, Kun; Chen, Pindong; Alexander-Bloch, Aaron; Wei, Yongbin; Dyrba, Martin; Yang, Fan; Kang, Xiaopeng; Wang, Dawei; Fan, Dongsheng; Ye, Shan; Tang, Yi; Yao, Hongxiang; Zhou, Bo; Lu, Jie; Yu, Chunshui; Wang, Pan; Liao, Zhengluan; Chen, Yan; Huang, Longjian; Zhang, Xi; Han, Ying; Li, Shuyu; Liu, Yong.
Affiliation
  • Zhao K; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
  • Chen P; School of Artificial Intelligence, University of Chinese Academy of Sciences & Brainnetome Centre, Chinese Academy of Sciences, Beijing, China.
  • Alexander-Bloch A; Department of Psychiatry, University of Pennsylvania, Philadelphia, USA.
  • Wei Y; Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, USA.
  • Dyrba M; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
  • Yang F; German Centre for Neurodegenerative Diseases (DZNE), Rostock, Germany.
  • Kang X; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, China.
  • Wang D; School of Artificial Intelligence, University of Chinese Academy of Sciences & Brainnetome Centre, Chinese Academy of Sciences, Beijing, China.
  • Fan D; Department of Radiology, Qilu Hospital of Shandong University, Ji'nan, China.
  • Ye S; Department of Neurology, Peking University Third Hospital, Beijing, China.
  • Tang Y; Key Laboratory for Neuroscience, National Health Commission/Ministry of Education, Peking University, Beijing, China.
  • Yao H; Beijing Key Laboratory of Biomarker and Translational Research in Neurodegenerative Diseases, Beijing, China.
  • Zhou B; Department of Neurology, Peking University Third Hospital, Beijing, China.
  • Lu J; Key Laboratory for Neuroscience, National Health Commission/Ministry of Education, Peking University, Beijing, China.
  • Yu C; Beijing Key Laboratory of Biomarker and Translational Research in Neurodegenerative Diseases, Beijing, China.
  • Wang P; Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China.
  • Liao Z; Department of Radiology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China.
  • Chen Y; Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China.
  • Huang L; Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China.
  • Zhang X; Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China.
  • Han Y; Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China.
  • Li S; Department of Psychiatry, People's Hospital of Hangzhou Medical College, Zhejiang Provincial People's Hospital, Hangzhou, China.
  • Liu Y; Department of Psychiatry, People's Hospital of Hangzhou Medical College, Zhejiang Provincial People's Hospital, Hangzhou, China.
EClinicalMedicine ; 65: 102276, 2023 Nov.
Article in En | MEDLINE | ID: mdl-37954904
ABSTRACT

Background:

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder that poses a worldwide public health challenge. A neuroimaging biomarker would significantly improve early diagnosis and intervention, ultimately enhancing the quality of life for affected individuals and reducing the burden on healthcare systems.

Methods:

Cross-sectional and longitudinal data (10,099 participants with 13,380 scans) from 12 independent datasets were used in the present study (this study was performed between September 1, 2021 and February 15, 2023). The Individual Brain-Related Abnormalities In Neurodegeneration (IBRAIN) score was developed via integrated regional- and network-based measures under an ensemble machine learning model based on structural MRI data. We systematically assessed whether IBRAIN could be a neuroimaging biomarker for AD.

Findings:

IBRAIN accurately differentiated individuals with AD from NCs (AUC = 0.92) and other neurodegenerative diseases, including Frontotemporal dementia (FTD), Parkinson's disease (PD), Vascular dementia (VaD) and Amyotrophic Lateral Sclerosis (ALS) (AUC = 0.92). IBRAIN was significantly correlated to clinical measures and gene expression, enriched in immune process and protein metabolism. The IBRAIN score exhibited a significant ability to reveal the distinct progression of prodromal AD (i.e., Mild cognitive impairment, MCI) (Hazard Ratio (HR) = 6.52 [95% CI 4.42∼9.62], p < 1 × 10-16), which offers similar powerful performance with Cerebrospinal Fluid (CSF) Aß (HR = 3.78 [95% CI 2.63∼5.43], p = 2.13 × 10-14) and CSF Tau (HR = 3.77 [95% CI 2.64∼5.39], p = 9.53 × 10-15) based on the COX and Log-rank test. Notably, the IBRAIN shows comparable sensitivity (beta = -0.70, p < 1 × 10-16) in capturing longitudinal changes in individuals with conversion to AD than CSF Aß (beta = -0.26, p = 4.40 × 10-9) and CSF Tau (beta = 0.12, p = 1.02 × 10-5).

Interpretation:

Our findings suggested that IBRAIN is a biologically relevant, specific, and sensitive neuroimaging biomarker that can serve as a clinical measure to uncover prodromal AD progression. It has strong potential for application in future clinical practice and treatment trials.

Funding:

Science and Technology Innovation 2030 Major Projects, the National Natural Science Foundation of China, Beijing Natural Science Funds, the Fundamental Research Funds for the CentralUniversity, and the Startup Funds for Talents at Beijing Normal University.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: EClinicalMedicine Year: 2023 Type: Article Affiliation country: China

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: EClinicalMedicine Year: 2023 Type: Article Affiliation country: China