# Beyond Feature Reliability: Repeat-Informed Multifractal Curve Regression for Brain-Age Prediction

arXiv: https://arxiv.org/abs/2609.29307 · HTML: https://mlsanity.com/paper/2609.29307/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 20 references that are in the map.
- First posted on arXiv 24 Sep 2026.

## Builds on (most cited first, 1 of 1)

- [Interpretable brain age prediction using linear latent variable models of functional connectivity](https://mlsanity.com/paper/1908.01555/) (2019)

## Abstract (arXiv)

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.