Structural Complexity of Brain MRI reveals age-associated patterns

TL;DR

This paper introduces a multiscale complexity analysis method for 3D brain MRI data, revealing age-related decreases in structural complexity, especially at larger scales, and demonstrating its potential for biological age prediction.

Contribution

It presents a novel sliding-window coarse-graining approach for robust multiscale complexity analysis of 3D MRI data, improving stability over traditional methods.

Findings

Structural complexity decreases with age, especially at coarse scales.

The method reliably predicts biological age from brain MRI.

The sliding-window scheme enhances robustness at large 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.