Low-Rank Tensor Fusion for Enhanced Deep Learning-Based Multimodal Brain Age Estimation
<b>Background/Objectives:</b> A multimodal brain age estimation model could provide enhanced insights into brain aging. However, effectively integrating multimodal neuroimaging data to enhance the accuracy of brain age estimation remains a challenging task. <b>Methods:</b> In...
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MDPI AG
2024-12-01
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| Series: | Brain Sciences |
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| Online Access: | https://www.mdpi.com/2076-3425/14/12/1252 |
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| author | Xia Liu Guowei Zheng Iman Beheshti Shanling Ji Zhinan Gou Wenkuo Cui |
| author_facet | Xia Liu Guowei Zheng Iman Beheshti Shanling Ji Zhinan Gou Wenkuo Cui |
| author_sort | Xia Liu |
| collection | DOAJ |
| description | <b>Background/Objectives:</b> A multimodal brain age estimation model could provide enhanced insights into brain aging. However, effectively integrating multimodal neuroimaging data to enhance the accuracy of brain age estimation remains a challenging task. <b>Methods:</b> In this study, we developed an innovative data fusion technique employing a low-rank tensor fusion algorithm, tailored specifically for deep learning-based frameworks aimed at brain age estimation. Specifically, we utilized structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), and magnetoencephalography (MEG) to extract spatial–temporal brain features with different properties. These features were fused using the low-rank tensor algorithm and employed as predictors for estimating brain age. <b>Results:</b> Our prediction model achieved a desirable prediction accuracy on the independent test samples, demonstrating its robust performance. <b>Conclusions:</b> The results of our study suggest that the low-rank tensor fusion algorithm has the potential to effectively integrate multimodal data into deep learning frameworks for estimating brain age. |
| format | Article |
| id | doaj-art-5923ad0879f54d9dae784efc1a813b7d |
| institution | OA Journals |
| issn | 2076-3425 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Brain Sciences |
| spelling | doaj-art-5923ad0879f54d9dae784efc1a813b7d2025-08-20T02:00:24ZengMDPI AGBrain Sciences2076-34252024-12-011412125210.3390/brainsci14121252Low-Rank Tensor Fusion for Enhanced Deep Learning-Based Multimodal Brain Age EstimationXia Liu0Guowei Zheng1Iman Beheshti2Shanling Ji3Zhinan Gou4Wenkuo Cui5School of Management Science and Information Engineering, Hebei University of Economics and Businesses, Shijiazhuang 050061, ChinaSchool of Computer Science and Technology, Harbin Institute of Technology, Weihai 264209, ChinaDepartment of Human Anatomy and Cell Science, University of Manitoba, Winnipeg, MB R3T 2N2, CanadaInstitute of Mental Health, Jining Medical University, Jining 272111, ChinaSchool of Management Science and Information Engineering, Hebei University of Economics and Businesses, Shijiazhuang 050061, ChinaSchool of Management Science and Information Engineering, Hebei University of Economics and Businesses, Shijiazhuang 050061, China<b>Background/Objectives:</b> A multimodal brain age estimation model could provide enhanced insights into brain aging. However, effectively integrating multimodal neuroimaging data to enhance the accuracy of brain age estimation remains a challenging task. <b>Methods:</b> In this study, we developed an innovative data fusion technique employing a low-rank tensor fusion algorithm, tailored specifically for deep learning-based frameworks aimed at brain age estimation. Specifically, we utilized structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), and magnetoencephalography (MEG) to extract spatial–temporal brain features with different properties. These features were fused using the low-rank tensor algorithm and employed as predictors for estimating brain age. <b>Results:</b> Our prediction model achieved a desirable prediction accuracy on the independent test samples, demonstrating its robust performance. <b>Conclusions:</b> The results of our study suggest that the low-rank tensor fusion algorithm has the potential to effectively integrate multimodal data into deep learning frameworks for estimating brain age.https://www.mdpi.com/2076-3425/14/12/1252brain agespatial–temporalmultimodallow-rank tensor fusionmachine learningdeep learning |
| spellingShingle | Xia Liu Guowei Zheng Iman Beheshti Shanling Ji Zhinan Gou Wenkuo Cui Low-Rank Tensor Fusion for Enhanced Deep Learning-Based Multimodal Brain Age Estimation Brain Sciences brain age spatial–temporal multimodal low-rank tensor fusion machine learning deep learning |
| title | Low-Rank Tensor Fusion for Enhanced Deep Learning-Based Multimodal Brain Age Estimation |
| title_full | Low-Rank Tensor Fusion for Enhanced Deep Learning-Based Multimodal Brain Age Estimation |
| title_fullStr | Low-Rank Tensor Fusion for Enhanced Deep Learning-Based Multimodal Brain Age Estimation |
| title_full_unstemmed | Low-Rank Tensor Fusion for Enhanced Deep Learning-Based Multimodal Brain Age Estimation |
| title_short | Low-Rank Tensor Fusion for Enhanced Deep Learning-Based Multimodal Brain Age Estimation |
| title_sort | low rank tensor fusion for enhanced deep learning based multimodal brain age estimation |
| topic | brain age spatial–temporal multimodal low-rank tensor fusion machine learning deep learning |
| url | https://www.mdpi.com/2076-3425/14/12/1252 |
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