Structural Complexity of Brain MRI reveals age-associated patterns

2026-01-23

AI Summary

This study addresses the quantification of multiscale structural complexity in brain MRI images and its evolution with aging. To this end, the authors propose a sliding-window-based three-dimensional multiscale coarse-graining method that overcomes the estimation instability of conventional block-wise sampling at low resolutions, where insufficient samples often compromise reliability. The approach substantially enhances the robustness of complexity measures at larger spatial scales. Experimental results demonstrate a systematic decline in brain structural complexity with age, with the most pronounced effects observed at coarser scales. The proposed method not only effectively captures the structural degradation patterns associated with brain aging but also exhibits strong performance in biological age prediction, thereby validating its biological relevance and practical applicability.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Computer Vision: Medical and Biological Imaging
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• structural complexity
• brain MRI
• multiscale analysis
• aging
• 3D imaging

Innovation

• structural complexity
• sliding-window coarse-graining
• multiscale analysis
• 3D MRI
• biological age prediction