发表机构
University of Southern California; University of Toronto（南加州大学; 多伦多大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对脑龄预测中特征可靠性评估无法捕捉联合波动的问题，提出重复信息感知的多重分形曲线回归（RMCR），联合建模曲线结构与重复扫描变异性，在HCP-A和Cam-CAN上分别将单次运行MAE降低6.1%和7.9%，并提升受试者内一致性。
AI 中文摘要
基于静息态功能磁共振成像的脑龄预测提供了一个定量框架，用于表征自发性脑动力学的年龄相关变化并识别功能特征。现有研究已将分形和多重分形标度与年龄联系起来，并考察了单个特征的可靠性。然而，预测的重复性取决于特征如何联合波动以及预测器如何组合它们，而基于特征层面的可靠性评估无法捕捉这一点。为解决此问题，我们提出了重复信息感知的多重分形曲线回归（RMCR），这是一个从多重分形曲线中学习稳定年龄预测模式的结构化框架。通过联合建模曲线结构和重复扫描变异性，RMCR学习预测性的波动阶组合，同时针对准确性和受试者内一致性。相对于匹配的运行级岭回归基线，RMCR在HCP-A上将单次运行的平均绝对误差降低了6.1%，在外部Cam-CAN队列上降低了7.9%，并在HCP-A上将访问内重复绝对差异降低了18.5%，且推理时仅使用单次扫描。
英文摘要
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.
