Structural Complexity of Brain MRI reveals age-associated patterns
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866911395907371008 |
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| author | Cheng, Anzhe Pinto, Italo Ivo Lima Dias Bogdan, Paul |
| author_facet | Cheng, Anzhe Pinto, Italo Ivo Lima Dias Bogdan, Paul |
| contents | We adapt structural complexity analysis to three-dimensional signals, with an emphasis on brain magnetic resonance imaging (MRI). This framework captures the multiscale organization of volumetric data by coarse-graining the signal at progressively larger spatial scales and quantifying the information lost between successive resolutions. While the traditional block-based approach can become unstable at coarse resolutions due to limited sampling, we introduce a sliding-window coarse-graining scheme that provides smoother estimates and improved robustness at large scales. Using this refined method, we analyze large structural MRI datasets spanning mid- to late adulthood and find that structural complexity decreases systematically with age, with the strongest effects emerging at coarser scales. These findings highlight structural complexity as a reliable signal processing tool for multiscale analysis of 3D imaging data, while also demonstrating its utility in predicting biological age from brain MRI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_17211 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Structural Complexity of Brain MRI reveals age-associated patterns Cheng, Anzhe Pinto, Italo Ivo Lima Dias Bogdan, Paul Computer Vision and Pattern Recognition We adapt structural complexity analysis to three-dimensional signals, with an emphasis on brain magnetic resonance imaging (MRI). This framework captures the multiscale organization of volumetric data by coarse-graining the signal at progressively larger spatial scales and quantifying the information lost between successive resolutions. While the traditional block-based approach can become unstable at coarse resolutions due to limited sampling, we introduce a sliding-window coarse-graining scheme that provides smoother estimates and improved robustness at large scales. Using this refined method, we analyze large structural MRI datasets spanning mid- to late adulthood and find that structural complexity decreases systematically with age, with the strongest effects emerging at coarser scales. These findings highlight structural complexity as a reliable signal processing tool for multiscale analysis of 3D imaging data, while also demonstrating its utility in predicting biological age from brain MRI. |
| title | Structural Complexity of Brain MRI reveals age-associated patterns |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.17211 |