Structural Complexity of Brain MRI reveals age-associated patterns
This provides a reliable tool for multiscale analysis of 3D imaging data and predicting biological age from brain MRI, but it is incremental as it refines an existing framework.
The researchers tackled the problem of analyzing multiscale organization in 3D brain MRI data by adapting structural complexity analysis with a sliding-window coarse-graining scheme to improve robustness at large scales, finding that structural complexity decreases systematically with age, with the strongest effects at coarser scales.
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.