Structural Complexity of Brain MRI reveals age-associated patterns

TL;DR

This work introduces a sliding-window coarse-graining scheme that provides smoother estimates and improved robustness at large scales and finds that structural complexity decreases systematically with age, with the strongest effects emerging at coarser scales.

Abstract

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.

Figures (2)

• Figure 1: Illustration of the structural complexity method for scalar fields. Left panels show the mid-slice of the original volume ("Before"), its progressively coarse-grained representations at scales $\lambda_{1}=2$, $\lambda_{2}=4$, $\lambda_{3}=8$, $\lambda_{4}=16$, and $\lambda_{5}=32$ ("After"), and the corresponding pixel-wise overlaps between successive scales. The right panel shows the per-scale overlap values $O(\lambda_i)$ as a function of the scale $\lambda_i$, displayed on logarithmic axes. The cumulative contribution of these overlaps across scales defines the structural complexity measure.
• Figure 2: Association between Age and Structural Complexity at Scales 4 and 5. Scatter plots show the relationship between $\log C(\lambda)$, the structural complexity of the brain signal at scale $\lambda$, and $\log$ Age (years). Linear fits (orange lines) were estimated using least-squares regression in log--log space. Pearson correlation coefficients ($r$) and FDR-corrected $p$-values are reported within each panel, indicating significant negative associations between age and complexity at both scales ($p < 0.001$ after correction). These results suggest that structural complexity decreases systematically with aging at larger scales.