ArticleProceedings of SPIE--the International Society for Optical Engineering2024
Predicting Age from White Matter Diffusivity with Residual Learning.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Genetic architecture of white matter microstructure captured by unsupervised deep representation learning of fractional anisotropy maps.Nature communications · 2026Article
- Unveiling genetic architecture of white matter microstructure through unsupervised deep representation learning of fractional anisotropy maps.Research square · 2025Article
- Scalable quality control on processing of large diffusion-weighted and structural magnetic resonance imaging datasets.PloS one · 2025Article
- Brain age identification from diffusion MRI synergistically predicts neurodegenerative disease.Imaging neuroscience (Cambridge, Mass.) · 2025Article
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Abstract
Imaging findings inconsistent with those expected at specific chronological age ranges may serve as early indicators of neurological disorders and increased mortality risk. Estimation of chronological age, and deviations from expected results, from structural magnetic resonance imaging (MRI) data has become an important proxy task for developing biomarkers that are sensitive to such deviations. Complementary to structural analysis, diffusion tensor imaging (DTI) has proven effective in identifying age-related microstructural changes within the brain white matter, thereby presenting itself as a promising additional modality for brain age prediction. Although early studies have sought to harness DTI's advantages for age estimation, there is no evidence that the success of this prediction is owed to the unique microstructural and diffusivity features that DTI provides, rather than the macrostructural features that are also available in DTI data. Therefore, we seek to develop white-matter-specific age estimation to capture deviations from normal white matter aging. Specifically, we deliberately disregard the macrostructural information when predicting age from DTI scalar images, using two distinct methods. The first method relies on extracting only microstructural features from regions of interest (ROIs). The second applies 3D residual neural networks (ResNets) to learn features directly from the images, which are non-linearly registered and warped to a template to minimize macrostructural variations. When tested on unseen data, the first method yields mean absolute error (MAE) of 6.11 ± 0.19 years for cognitively normal participants and MAE of 6.62 ± 0.30 years for cognitively impaired participants, while the second method achieves MAE of 4.69 ± 0.23 years for cognitively normal participants and MAE of 4.96 ± 0.28 years for cognitively impaired participants. We find that the ResNet model captures subtler, non-macrostructural features for brain age prediction.
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