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

Yu Chang; Anzhe Cheng; Jiahao Chen; Heng Ping; Peiyu Zhang; Puquan Pan; Tamoghna Chattopadhyay; Sophia Thomopoulos

cs.LGSep 24, 2026

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

Authors: Yu Chang, Anzhe Cheng, Jiahao Chen, Heng Ping, Peiyu Zhang, Puquan Pan, Tamoghna Chattopadhyay, Sophia Thomopoulos, +3 more

Organizations: University of Southern California, USA · University of Toronto, Canada

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.

Figures & tables

	HCP-A ( $n=480$ )			Cam-CAN ( $n=320$ )
Method	MAE $\downarrow$	$R^{2}\uparrow$	RAD $\downarrow$	MAE $\downarrow$	$R^{2}\uparrow$
Mean age	9.19	.000	0.00	9.81	$-.049$
Level	6.34 †	.486	2.76	8.12 †	.185
Level + summaries	5.93 †	.554	2.92	7.56 †	.307
Level + spectral	5.71 †	.583	2.79	7.24 †	.346
MSE + Ridge	5.84 †	.559	3.06	7.43 †	.339

Table 1: Single-run prediction. MAE and RAD in years; Level:

h(2)

. Bold: best among learned models. † : paired bootstrap 95% CI of the MAE difference from RMCR excludes zero; for RAD, this holds for all learned baselines.

Variant	$\lambda_{q}$	$\lambda_{\mathrm{rep}}$	MAE $\downarrow$	RAD $\downarrow$	Slope
Mean Ridge	0	0	5.87	3.65	0.56
Run Ridge	0	1	5.43	2.86	0.62
Smooth-only	$\star$	0	5.56	3.20	0.60
Smooth Run Ridge	$\star$	1	5.38	2.68	0.63
Repeat-only	0	$\star$	5.42	2.61	0.62
Diagonal	$\star$	$\star$	5.40	2.55	0.63

Table 2: HCP-A component comparisons (identical inputs and constraints).

\star

: inner-validation selection (

\lambda_{0}

always selected). MAE and RAD in years; Slope: visit-mean predicted age on chronological age. Diagonal keeps only the diagonal of

R_{\mathrm{rep}}

. All MAE and RAD differences from RMCR have paired bootstrap 95% CIs excluding zero.

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A Two-Stage Multi-Modal MRI Framework for Lifespan Brain Age Prediction

Dingyi Zhang, Ruiying Liu, Yun Wang

Department of Computer Science, Emory University, Atlanta, GA, USA · Department of Biomedical Informatics, Emory University, Atlanta, GA, USA

Brain Age Multimodal Neuroimaging Magnetic Resonance Imaging Age Developmental Trajectories

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The accurate quantification of brain age from MRI has emerged as an important biomarker of brain health. However, existing approaches are often restricted to narrow age ranges and single-modality MRI data, limiting their capacity to capture the coordinated macro- and microstructural changes that unfold across the human lifespan. To address these limitations, we develop a multi-modal brain age framework to characterize the integrated evolution of brain morphology and white matter organization. Our model adopts a two-stage architecture, where modalities are processed independently and integrated via late fusion in both stages: first to estimate a probability distribution over six developmental stages, and then to predict age via probability-weighted stage-specialized experts. Experiments on nine datasets spanning fetal to elderly stages demonstrate competitive in-domain performance and out-of-domain generalization, with our method reducing MAE by 13% and 78% over existing baselines and multi-modal integration yielding 12-13% gains. Analysis of ADNI clinical groups further suggests the potential of the predicted brain age gap to characterize Alzheimer's-related brain aging.

Abstract

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