STRUCTURAL COMPLEXITY OF BRAIN MRI REVEALS AGE-ASSOCIATED PATTERNS
Anzhe Cheng, Italo Ivo Lima Dias Pinto, Paul Bogdan
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.
BibTeX
@inproceedings{icassp2026_structuralcomple,
title = {STRUCTURAL COMPLEXITY OF BRAIN MRI REVEALS AGE-ASSOCIATED PATTERNS},
author = {Anzhe Cheng and Italo Ivo Lima Dias Pinto and Paul Bogdan},
booktitle = {ICASSP 2026},
year = {2026}
}