Brain-age prediction improved by modeling stable multifractal patterns
Beyond Feature Reliability: Repeat-Informed Multifractal Curve Regression for Brain-Age Prediction
Machine Learning
Summary
Predicting a person’s brain age using resting brain scans can help understand how brain activity changes with age. The authors introduce a new method called RMCR that looks at complex patterns in brain signals and how these patterns are consistent across repeated scans. This approach not only makes age predictions more accurate but also ensures the predictions are more reliable when scans are repeated. They tested RMCR on two separate brain scan datasets and found it improved prediction accuracy and repeatability compared to existing methods.
What this means in practice
- •For medical imaging analysts: Produce more consistent and accurate brain-age estimates from repeated resting-state fMRI scans using stable multifractal feature modeling.
- •For neuroinformatics software developers: Integrate repeat-informed multifractal curve regression to improve predictive modeling tools for brain aging biomarkers.
Authors
Yu Chang, Anzhe Cheng, Jiahao Chen, Heng Ping, Peiyu Zhang, Puquan Pan, Tamoghna Chattopadhyay, Sophia Thomopoulos, Shahin Nazarian, Paul Thompson, Paul Bogdan
Abstract
Brain-age prediction from resting-state fMRI provides a quantitative framework for characterizing age-related changes in spontaneous brain dynamics and for identifying functional signatures. Existing studies have linked fractal and multifractal scaling to age and examined the reliability of individual features. However, prediction repeatability depends on how features fluctuate jointly and how a predictor combines them, which feature-wise reliability assessments do not capture. To address this problem, we propose Repeat-informed Multifractal Curve Regression (RMCR), a structured framework for learning stable age-predictive patterns from multifractal curves. By jointly modeling curve structure and repeat-scan variability, RMCR learns predictive combinations of fluctuation orders that target both accuracy and within-subject consistency. Relative to a matched run-level ridge baseline, RMCR reduces single-run MAE by 6.1% on HCP-A and 7.9% on an external Cam-CAN cohort, and within-visit repeat absolute difference by 18.5% on HCP-A, using a single scan at inference.