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Ren B, Wu Y, Huang L, Zhang Z, Huang B, Zhang H, Ma J, Li B, Liu X, Wu G, Zhang J, Shen L, Liu Q, Ni J. Deep transfer learning of structural magnetic resonance imaging fused with blood parameters improves brain age prediction. Hum Brain Mapp 2021; 43:1640-1656. [PMID: 34913545 PMCID: PMC8886664 DOI: 10.1002/hbm.25748] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 11/14/2021] [Accepted: 12/01/2021] [Indexed: 12/27/2022] Open
Abstract
Machine learning has been applied to neuroimaging data for estimating brain age and capturing early cognitive impairment in neurodegenerative diseases. Blood parameters like neurofilament light chain are associated with aging. In order to improve brain age predictive accuracy, we constructed a model based on both brain structural magnetic resonance imaging (sMRI) and blood parameters. Healthy subjects (n = 93; 37 males; aged 50–85 years) were recruited. A deep learning network was firstly pretrained on a large set of MRI scans (n = 1,481; 659 males; aged 50–85 years) downloaded from multiple open‐source datasets, to provide weights on our recruited dataset. Evaluating the network on the recruited dataset resulted in mean absolute error (MAE) of 4.91 years and a high correlation (r = .67, p <.001) against chronological age. The sMRI data were then combined with five blood biochemical indicators including GLU, TG, TC, ApoA1 and ApoB, and 9 dementia‐associated biomarkers including ApoE genotype, HCY, NFL, TREM2, Aβ40, Aβ42, T‐tau, TIMP1, and VLDLR to construct a bilinear fusion model, which achieved a more accurate prediction of brain age (MAE, 3.96 years; r = .76, p <.001). Notably, the fusion model achieved better improvement in the group of older subjects (70–85 years). Extracted attention maps of the network showed that amygdala, pallidum, and olfactory were effective for age estimation. Mediation analysis further showed that brain structural features and blood parameters provided independent and significant impact. The constructed age prediction model may have promising potential in evaluation of brain health based on MRI and blood parameters.
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Affiliation(s)
- Bingyu Ren
- Shenzhen Key Laboratory of Marine Biotechnology and Ecology, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China
| | - Yingtong Wu
- Medical AI Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Liumei Huang
- Shenzhen Key Laboratory of Marine Biotechnology and Ecology, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China
| | - Zhiguo Zhang
- MIND Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Bingsheng Huang
- Medical AI Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.,Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China
| | - Huajie Zhang
- Shenzhen Key Laboratory of Marine Biotechnology and Ecology, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China
| | - Jinting Ma
- Medical AI Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Bing Li
- Medical AI Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Xukun Liu
- Shenzhen Key Laboratory of Marine Biotechnology and Ecology, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China
| | - Guangyao Wu
- Radiology Department, Shenzhen University General Hospital and Shenzhen University Clinical Medical Academy, Shenzhen University, Shenzhen, China
| | - Jian Zhang
- Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China.,Health Science Center, Shenzhen University, Shenzhen, China
| | - Liming Shen
- Shenzhen Key Laboratory of Marine Biotechnology and Ecology, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China
| | - Qiong Liu
- Shenzhen Key Laboratory of Marine Biotechnology and Ecology, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China.,Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China.,Shenzhen Bay Laboratory, Shenzhen, China
| | - Jiazuan Ni
- Shenzhen Key Laboratory of Marine Biotechnology and Ecology, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China
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