{"title":"MDFNet: a multi-dimensional feature fusion model based on structural magnetic resonance imaging representations for brain age estimation.","authors":"Chenxiao Zhang, Pengzhi Nan, Limei Song, Yuhao Wang, Kaile Su, Qiang Zheng","doi":"10.1007/s10334-025-01294-8","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>Brain age estimation plays a significant role in understanding the aging process and its relationship with neurodegenerative diseases. The aim of the study is to devise a unified multi-dimensional feature fusion model (MDFNet) to enhance the brain age estimation solely on structural MRI but with a diverse representation of whole brain, tissue segmentation of gray matter volume, node message passing of brain network, edge-based graph path convolution of brain connectivity, and demographic data.</p><p><strong>Materials and methods: </strong>The MDFNet was developed by devising and integrating a whole-brain-level Euclidean-Convolution channel (WBEC-channel), a tissue-level Euclidean-convolution channel (TEC-channel), a Graph-convolution channel based on node message passing (nodeGCN-channel) and an edge-based graph path convolution channel on brain connectivity (edgeGCN-channel), and a multilayer perceptron (MLP) channel for demographic data (MLP-channel) to enhance the multi-dimensional feature fusion. The MDFNet was validated on 1872 healthy subjects from four public datasets, and applied to an independent cohort of Alzheimer's Disease (AD) patients. The interpretability analysis and normative modeling of the MDFNet in brain age estimation were also performed.</p><p><strong>Results: </strong>The MDFNet achieved a superior performance of Mean Absolute Error (MAE) of 4.396 ± 0.244 years, a Pearson Correlation Coefficient (PCC) of 0.912 ± 0.002, and a Spearman's Rank Correlation (SRCC) of 0.819 ± 0.015 when comparing with the state-of-the-art deep learning models. The AD group exhibited a significantly greater brain age gap (BAG) than health group (P < 0.05), and the normative modeling also exhibited a significantly higher mean Z-scores of AD patients than healthy subjects (P < 0.05). The interpretability was also visualized at both the group and individual level, enhancing the reliability of the MDFNet.</p><p><strong>Conclusions: </strong>The MDFNet enhanced the brain age estimation solely on structural MRI by employing a multi-dimensional feature integration strategy.</p>","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":" ","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Magnetic Resonance Materials in Physics, Biology and Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s10334-025-01294-8","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0
Abstract
Objectives: Brain age estimation plays a significant role in understanding the aging process and its relationship with neurodegenerative diseases. The aim of the study is to devise a unified multi-dimensional feature fusion model (MDFNet) to enhance the brain age estimation solely on structural MRI but with a diverse representation of whole brain, tissue segmentation of gray matter volume, node message passing of brain network, edge-based graph path convolution of brain connectivity, and demographic data.
Materials and methods: The MDFNet was developed by devising and integrating a whole-brain-level Euclidean-Convolution channel (WBEC-channel), a tissue-level Euclidean-convolution channel (TEC-channel), a Graph-convolution channel based on node message passing (nodeGCN-channel) and an edge-based graph path convolution channel on brain connectivity (edgeGCN-channel), and a multilayer perceptron (MLP) channel for demographic data (MLP-channel) to enhance the multi-dimensional feature fusion. The MDFNet was validated on 1872 healthy subjects from four public datasets, and applied to an independent cohort of Alzheimer's Disease (AD) patients. The interpretability analysis and normative modeling of the MDFNet in brain age estimation were also performed.
Results: The MDFNet achieved a superior performance of Mean Absolute Error (MAE) of 4.396 ± 0.244 years, a Pearson Correlation Coefficient (PCC) of 0.912 ± 0.002, and a Spearman's Rank Correlation (SRCC) of 0.819 ± 0.015 when comparing with the state-of-the-art deep learning models. The AD group exhibited a significantly greater brain age gap (BAG) than health group (P < 0.05), and the normative modeling also exhibited a significantly higher mean Z-scores of AD patients than healthy subjects (P < 0.05). The interpretability was also visualized at both the group and individual level, enhancing the reliability of the MDFNet.
Conclusions: The MDFNet enhanced the brain age estimation solely on structural MRI by employing a multi-dimensional feature integration strategy.
期刊介绍：
MAGMA is a multidisciplinary international journal devoted to the publication of articles on all aspects of magnetic resonance techniques and their applications in medicine and biology. MAGMA currently publishes research papers, reviews, letters to the editor, and commentaries, six times a year. The subject areas covered by MAGMA include:
advances in materials, hardware and software in magnetic resonance technology,
new developments and results in research and practical applications of magnetic resonance imaging and spectroscopy related to biology and medicine,
study of animal models and intact cells using magnetic resonance,
reports of clinical trials on humans and clinical validation of magnetic resonance protocols.
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