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Zheng J, Wang J, Zhang Z, Li K, Zhao H, Liang P. Brain age prediction based on brain region volume modeling under broad network field of view. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2025; 265:108739. [PMID: 40179718 DOI: 10.1016/j.cmpb.2025.108739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2024] [Revised: 03/12/2025] [Accepted: 03/22/2025] [Indexed: 04/05/2025]
Abstract
BACKGROUND AND OBJECTIVE Brain region volume from Structural Magnetic Resonance Imaging (sMRI) can directly reflect abnormal states in brain aging. While promising for clinical brain health assessment, existing volume-based brain age prediction methods fail to explore both linear and nonlinear relationships, resulting in weak representation and suboptimal estimates. METHODS This paper proposes a brain age prediction method, RFBLSO, based on Random Forest (RF), Broad Learning System (BLS), and Leave-One-Out Cross Validation (LOO). Firstly, RF is used to eliminate redundant brain regions with low correlation to the target value. The objective function is constructed by integrating feature nodes, enhancement nodes, and optimal regularization parameters. Subsequently, the pseudo-inverse method is employed to solve for the output coefficients, which facilitates a more accurate representation of the linear and nonlinear relationships between volume features and brain age. RESULTS Across various datasets, RFBLSO demonstrates the capability to formulate brain age prediction models, achieving a Mean Absolute Error (MAE) of 4.60 years within the Healthy Group and 4.98 years within the Chinese2020 dataset. In the Clinical Group, RFBLSO achieves measurement and effective differentiation among Healthy Controls (HC), Mild Cognitive Impairment (MCI), and Alzheimer's disease (AD) (MAE for HC, MCI, and AD: 4.46 years, 8.77 years, 13.67 years; the effect size η2 of the analysis of variance for AD/MCI vs. HC is 0.23; the effect sizes of post-hoc tests are Cohen's d = 0.74 (AD vs. MCI), 1.50 (AD vs. HC), 0.77 (MCI vs. HC)). Compared to other linear or nonlinear brain age prediction methods, RFBLSO offers more accurate measurements and effectively distinguishes between Clinical Groups. This is because RFBLSO can simultaneously explore both linear and nonlinear relationships between brain region volume and brain age. CONCLUSION The proposed RFBLSO effectively represents both linear and nonlinear relationships between brain region volume and brain age, allowing for more accurate individual brain age estimation. This provides a feasible method for predicting the risk of neurodegenerative diseases.
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Affiliation(s)
- Jianjie Zheng
- School of Psychology, Capital Normal University, Beijing, 100048, China.
| | - Junkai Wang
- Department of Imaging, Aerospace Center Hospital, Beijing, 100049, China
| | - Zeyin Zhang
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
| | - Kuncheng Li
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
| | - Huimin Zhao
- College of Electronic Information and Automation, Civil Aviation University of China, Tianjin, 300300, China.
| | - Peipeng Liang
- School of Psychology, Capital Normal University, Beijing, 100048, China.
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Papouli A, Cole JH. Brain age prediction from MRI scans in neurodegenerative diseases. Curr Opin Neurol 2025:00019052-990000000-00247. [PMID: 40396549 DOI: 10.1097/wco.0000000000001383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/22/2025]
Abstract
PURPOSE OF REVIEW This review explores the use of brain age estimation from MRI scans as a biomarker of brain health. With disorders like Alzheimer's and Parkinson's increasing globally, there is an urgent need for early detection tools that can identify at-risk individuals before cognitive symptoms emerge. Brain age offers a noninvasive, quantitative measure of neurobiological ageing, with applications in early diagnosis, disease monitoring, and personalized medicine. RECENT FINDINGS Studies show that individuals with Alzheimer's, mild cognitive impairment (MCI), and Parkinson's have older brain ages than their chronological age. Longitudinal research indicates that brain-predicted age difference (brain-PAD) rises with disease progression and often precedes cognitive decline. Advances in deep learning and multimodal imaging have improved the accuracy and interpretability of brain age predictions. Moreover, socioeconomic disparities and environmental factors significantly affect brain aging, highlighting the need for inclusive models. SUMMARY Brain age estimation is a promising biomarker for identify future risk of neurodegenerative disease, monitoring progression, and helping prognosis. Challenges like implementation of standardization, demographic biases, and interpretability remain. Future research should integrate brain age with biomarkers and multimodal imaging to enhance early diagnosis and intervention strategies.
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Affiliation(s)
| | - James H Cole
- Hawkes Institute, Department of Computer Science
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, UK
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Azzam M, Xu Z, Liu R, Li L, Meng Soh K, Challagundla KB, Wan S, Wang J. A review of artificial intelligence-based brain age estimation and its applications for related diseases. Brief Funct Genomics 2025; 24:elae042. [PMID: 39436320 PMCID: PMC11735757 DOI: 10.1093/bfgp/elae042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2024] [Revised: 10/02/2024] [Accepted: 10/12/2024] [Indexed: 10/23/2024] Open
Abstract
The study of brain age has emerged over the past decade, aiming to estimate a person's age based on brain imaging scans. Ideally, predicted brain age should match chronological age in healthy individuals. However, brain structure and function change in the presence of brain-related diseases. Consequently, brain age also changes in affected individuals, making the brain age gap (BAG)-the difference between brain age and chronological age-a potential biomarker for brain health, early screening, and identifying age-related cognitive decline and disorders. With the recent successes of artificial intelligence in healthcare, it is essential to track the latest advancements and highlight promising directions. This review paper presents recent machine learning techniques used in brain age estimation (BAE) studies. Typically, BAE models involve developing a machine learning regression model to capture age-related variations in brain structure from imaging scans of healthy individuals and automatically predict brain age for new subjects. The process also involves estimating BAG as a measure of brain health. While we discuss recent clinical applications of BAE methods, we also review studies of biological age that can be integrated into BAE research. Finally, we point out the current limitations of BAE's studies.
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Affiliation(s)
- Mohamed Azzam
- Department of Neurological Sciences, College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198, United States
- Department of Computer Science and Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt
| | - Ziyang Xu
- Department of Neurological Sciences, College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198, United States
| | - Ruobing Liu
- Department of Neurological Sciences, College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198, United States
| | - Lie Li
- Department of Neurological Sciences, College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198, United States
| | - Kah Meng Soh
- Department of Biostatistics, College of Public Health, University of Nebraska Medical Center, Omaha, NE 68198, United States
| | - Kishore B Challagundla
- Department of Neurological Sciences, College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198, United States
| | - Shibiao Wan
- Department of Genetics, Cell Biology and Anatomy, College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198, United States
| | - Jieqiong Wang
- Department of Neurological Sciences, College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198, United States
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Moon S, Lee J, Lee WH. Predicting brain age with global-local attention network from multimodal neuroimaging data: Accuracy, generalizability, and behavioral associations. Comput Biol Med 2025; 184:109411. [PMID: 39556917 DOI: 10.1016/j.compbiomed.2024.109411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Revised: 11/08/2024] [Accepted: 11/08/2024] [Indexed: 11/20/2024]
Abstract
Brain age, an emerging biomarker for brain diseases and aging, is typically predicted using single-modality T1-weighted structural MRI data. This study investigates the benefits of integrating structural MRI with diffusion MRI to enhance brain age prediction. We propose an attention-based deep learning model that fuses global-context information from structural MRI with local details from diffusion metrics. The model was evaluated using two large datasets: the Human Connectome Project (HCP, n = 1064, age 22-37) and the Cambridge Center for Aging and Neuroscience (Cam-CAN, n = 639, age 18-88). It was tested for generalizability and robustness on three independent datasets (n = 546, age 20-86), reproducibility on a test-retest dataset (n = 44, age 22-35), and longitudinal consistency (n = 129, age 46-92). We also examined the relationship between predicted brain age and behavioral measures. Results showed that the multimodal model improved prediction accuracy, achieving mean absolute errors (MAEs) of 2.44 years in the HCP dataset (sagittal plane) and 4.36 years in the Cam-CAN dataset (axial plane). The corresponding R2 values were 0.258 and 0.914, respectively, reflecting the model's ability to explain variance in the predictions across both datasets. Compared to single-modality models, the multimodal approach showed better generalization, reducing MAEs by 10-76 % and enhancing robustness by 22-82 %. While the multimodal model exhibited superior reproducibility, the sMRI model showed slightly better longitudinal consistency. Importantly, the multimodal model revealed unique associations between predicted brain age and behavioral measures, such as walking endurance and loneliness in the HCP dataset, which were not detected with chronological age alone. In the Cam-CAN dataset, brain age and chronological age exhibited similar correlations with behavioral measures. By integrating sMRI and dMRI through an attention-based model, our proposed approach enhances predictive accuracy and provides deeper insights into the relationship between brain aging and behavior.
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Affiliation(s)
- SungHwan Moon
- Department of Software Convergence, Kyung Hee University, Yongin, Republic of Korea
| | - Junhyeok Lee
- Department of Software Convergence, Kyung Hee University, Yongin, Republic of Korea
| | - Won Hee Lee
- Department of Software Convergence, Kyung Hee University, Yongin, Republic of Korea.
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Gaser C, Kalc P, Cole JH. A perspective on brain-age estimation and its clinical promise. NATURE COMPUTATIONAL SCIENCE 2024; 4:744-751. [PMID: 39048692 DOI: 10.1038/s43588-024-00659-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 06/12/2024] [Indexed: 07/27/2024]
Abstract
Brain-age estimation has gained increased attention in the neuroscientific community owing to its potential use as a biomarker of brain health. The difference between estimated and chronological age based on neuroimaging data enables a unique perspective on brain development and aging, with multiple open questions still remaining in the brain-age research field. This Perspective presents an overview of current advancements in the field and envisions the future evolution of the brain-age framework before its potential deployment in hospital settings.
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Affiliation(s)
- Christian Gaser
- Structural Brain Mapping Group, Department of Neurology, Jena University Hospital, Jena, Germany.
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany.
- German Centre for Mental Health (DZPG), Jena-Halle-Magdeburg, Jena, Germany.
| | - Polona Kalc
- Structural Brain Mapping Group, Department of Neurology, Jena University Hospital, Jena, Germany
| | - James H Cole
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, UK
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Li X, Hao Z, Li D, Jin Q, Tang Z, Yao X, Wu T. Brain age prediction via cross-stratified ensemble learning. Neuroimage 2024; 299:120825. [PMID: 39214438 DOI: 10.1016/j.neuroimage.2024.120825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 08/06/2024] [Accepted: 08/28/2024] [Indexed: 09/04/2024] Open
Abstract
As an important biomarker of neural aging, the brain age reflects the integrity and health of the human brain. Accurate prediction of brain age could help to understand the underlying mechanism of neural aging. In this study, a cross-stratified ensemble learning algorithm with staking strategy was proposed to obtain brain age and the derived predicted age difference (PAD) using T1-weighted magnetic resonance imaging (MRI) data. The approach was characterized as by implementing two modules: one was three base learners of 3D-DenseNet, 3D-ResNeXt, 3D-Inception-v4; another was 14 secondary learners of liner regressions. To evaluate performance, our method was compared with single base learners, regular ensemble learning algorithms, and state-of-the-art (SOTA) methods. The results demonstrated that our proposed model outperformed others models, with three metrics of mean absolute error (MAE), root mean-squared error (RMSE), and coefficient of determination (R2) of 2.9405 years, 3.9458 years, and 0.9597, respectively. Furthermore, there existed significant differences in PAD among the three groups of normal control (NC), mild cognitive impairment (MCI) and Alzheimer's disease (AD), with an increased trend across NC, MCI, and AD. It was concluded that the proposed algorithm could be effectively used in computing brain aging and PAD, and offering potential for early diagnosis and assessment of normal brain aging and AD.
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Affiliation(s)
- Xinlin Li
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai 201318, PR China; School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, PR China
| | - Zezhou Hao
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai 201318, PR China; School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, PR China
| | - Di Li
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai 201318, PR China; School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, PR China
| | - Qiuye Jin
- Digital Medical Research Center, School of Basic Medical Science, Fudan University, Shanghai 200032, PR China
| | - Zhixian Tang
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai 201318, PR China
| | - Xufeng Yao
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai 201318, PR China.
| | - Tao Wu
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai 201318, PR China
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Dular L, Špiclin Ž, for the Alzheimer’s Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing. Analysis of Brain Age Gap across Subject Cohorts and Prediction Model Architectures. Biomedicines 2024; 12:2139. [PMID: 39335651 PMCID: PMC11428686 DOI: 10.3390/biomedicines12092139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2024] [Revised: 09/12/2024] [Accepted: 09/16/2024] [Indexed: 09/30/2024] Open
Abstract
Background: Brain age prediction from brain MRI scans and the resulting brain age gap (BAG)-the difference between predicted brain age and chronological age-is a general biomarker for a variety of neurological, psychiatric, and other diseases or disorders. Methods: This study examined the differences in BAG values derived from T1-weighted scans using five state-of-the-art deep learning model architectures previously used in the brain age literature: 2D/3D VGG, RelationNet, ResNet, and SFCN. The models were evaluated on healthy controls and cohorts with sleep apnea, diabetes, multiple sclerosis, Parkinson's disease, mild cognitive impairment, and Alzheimer's disease, employing rigorous statistical analysis, including repeated model training and linear mixed-effects models. Results: All five models consistently identified a statistically significant positive BAG for diabetes (ranging from 0.79 years with RelationNet to 2.13 years with SFCN), multiple sclerosis (2.67 years with 3D VGG to 4.24 years with 2D VGG), mild cognitive impairment (2.13 years with 2D VGG to 2.59 years with 3D VGG), and Alzheimer's dementia (5.54 years with ResNet to 6.48 years with SFCN). For Parkinson's disease, a statistically significant BAG increase was observed in all models except ResNet (1.30 years with 2D VGG to 2.59 years with 3D VGG). For sleep apnea, a statistically significant BAG increase was only detected with the SFCN model (1.59 years). Additionally, we observed a trend of decreasing BAG with increasing chronological age, which was more pronounced in diseased cohorts, particularly those with the largest BAG, such as multiple sclerosis (-0.34 to -0.2), mild cognitive impairment (-0.37 to -0.26), and Alzheimer's dementia (-0.66 to -0.47), compared to healthy controls (-0.18 to -0.1). Conclusions: Consistent with previous research, Alzheimer's dementia and multiple sclerosis exhibited the largest BAG across all models, with SFCN predicting the highest BAG overall. The negative BAG trend suggests a complex interplay of survival bias, disease progression, adaptation, and therapy that influences brain age prediction across the age spectrum.
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Affiliation(s)
| | - Žiga Špiclin
- University of Ljubljana, Faculty of Electrical Engineering, Tržaška cesta 25, 1000 Ljubljana, Slovenia
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Wu Y, Gao H, Zhang C, Ma X, Zhu X, Wu S, Lin L. Machine Learning and Deep Learning Approaches in Lifespan Brain Age Prediction: A Comprehensive Review. Tomography 2024; 10:1238-1262. [PMID: 39195728 PMCID: PMC11359833 DOI: 10.3390/tomography10080093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2024] [Revised: 08/09/2024] [Accepted: 08/09/2024] [Indexed: 08/29/2024] Open
Abstract
The concept of 'brain age', derived from neuroimaging data, serves as a crucial biomarker reflecting cognitive vitality and neurodegenerative trajectories. In the past decade, machine learning (ML) and deep learning (DL) integration has transformed the field, providing advanced models for brain age estimation. However, achieving precise brain age prediction across all ages remains a significant analytical challenge. This comprehensive review scrutinizes advancements in ML- and DL-based brain age prediction, analyzing 52 peer-reviewed studies from 2020 to 2024. It assesses various model architectures, highlighting their effectiveness and nuances in lifespan brain age studies. By comparing ML and DL, strengths in forecasting and methodological limitations are revealed. Finally, key findings from the reviewed articles are summarized and a number of major issues related to ML/DL-based lifespan brain age prediction are discussed. Through this study, we aim at the synthesis of the current state of brain age prediction, emphasizing both advancements and persistent challenges, guiding future research, technological advancements, and improving early intervention strategies for neurodegenerative diseases.
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Affiliation(s)
| | | | | | | | | | | | - Lan Lin
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China; (Y.W.); (H.G.); (C.Z.); (X.M.); (X.Z.); (S.W.)
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Hanson JL, Adkins DJ, Bacas E, Zhou P. Examining the reliability of brain age algorithms under varying degrees of participant motion. Brain Inform 2024; 11:9. [PMID: 38573551 PMCID: PMC10994881 DOI: 10.1186/s40708-024-00223-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 03/18/2024] [Indexed: 04/05/2024] Open
Abstract
Brain age algorithms using data science and machine learning techniques show promise as biomarkers for neurodegenerative disorders and aging. However, head motion during MRI scanning may compromise image quality and influence brain age estimates. We examined the effects of motion on brain age predictions in adult participants with low, high, and no motion MRI scans (Original N = 148; Analytic N = 138). Five popular algorithms were tested: brainageR, DeepBrainNet, XGBoost, ENIGMA, and pyment. Evaluation metrics, intraclass correlations (ICCs), and Bland-Altman analyses assessed reliability across motion conditions. Linear mixed models quantified motion effects. Results demonstrated motion significantly impacted brain age estimates for some algorithms, with ICCs dropping as low as 0.609 and errors increasing up to 11.5 years for high motion scans. DeepBrainNet and pyment showed greatest robustness and reliability (ICCs = 0.956-0.965). XGBoost and brainageR had the largest errors (up to 13.5 RMSE) and bias with motion. Findings indicate motion artifacts influence brain age estimates in significant ways. Furthermore, our results suggest certain algorithms like DeepBrainNet and pyment may be preferable for deployment in populations where motion during MRI acquisition is likely. Further optimization and validation of brain age algorithms is critical to use brain age as a biomarker relevant for clinical outcomes.
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Affiliation(s)
- Jamie L Hanson
- Learning, Research & Development Center, University of Pittsburgh, Murdoch Building 3420 Forbes Ave. Rm. 639, Pittsburgh, PA, 15260, USA.
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Dorthea J Adkins
- Learning, Research & Development Center, University of Pittsburgh, Murdoch Building 3420 Forbes Ave. Rm. 639, Pittsburgh, PA, 15260, USA
| | - Eva Bacas
- Learning, Research & Development Center, University of Pittsburgh, Murdoch Building 3420 Forbes Ave. Rm. 639, Pittsburgh, PA, 15260, USA
| | - Peiran Zhou
- Learning, Research & Development Center, University of Pittsburgh, Murdoch Building 3420 Forbes Ave. Rm. 639, Pittsburgh, PA, 15260, USA
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