标题： IRONMAP：检测神经系统疾病中随时间变化的脑铁异常的铁网络映射和分析协议 显示英文标题

标题： IRONMAP: Iron Network Mapping and Analysis Protocol for Detecting Over-Time Brain Iron Abnormalities in Neurological Disease

摘要： 病理学改变的铁水平，使用铁敏感的MRI技术如定量磁化率成像（QSM）检测到，在神经疾病如多发性硬化症（MS）中可见，并可能在疾病病理生理学中起关键作用。 然而，即使在神经系统疾病中，大脑铁变化发生得很慢，并可能受到饮食等生理因素的影响。 因此，需要新的分析方法，以提高对与传统基于区域分析方法相比的疾病相关铁变化的敏感性。 本研究介绍了IRONMAP，即铁网络映射和分析协议，这是一种新的基于网络的分析方法，用于评估磁化率随时间的变化。 通过这种新方法，我们分析了多发性硬化症患者（pwMS）和健康对照组（HCs）队列中的短期（<1年）纵向QSM数据，并评估了与疾病相关的网络模式，将新方法与传统的基于区域的变化率方法进行了比较。 IRONMAP分析能够检测到使用变化率方法无法检测到的时间相关、MS相关的脑铁异常。 IRONMAP适用于个体层面，与仅使用变化率数据相比，提高了pwMS与HCs的二分类性能（曲线下面积：0.773 vs 0.636，p = 0.024）。 进一步分析显示，所观察到的IRONMAP衍生的HC网络结构与基于健康衰老相关磁化率数据的模拟网络高度一致，这表明正常衰老相关的铁变化中断可能导致pwMS中观察到的网络差异。 IRONMAP可推广到任何神经系统疾病，包括阿尔茨海默病和帕金森病，并可能允许研究比以前更短时间范围内的脑铁异常。 显示英文摘要

摘要： Pathologically altered iron levels, detected using iron-sensitive MRI techniques such as quantitative susceptibility mapping (QSM), are observed in neurological disorders such as multiple sclerosis (MS) and may play a crucial role in disease pathophysiology. However, brain iron changes occur slowly, even in neurological diseases, and can be influenced by physiological factors such as diet. Therefore, novel analysis methods are needed to improve sensitivity to disease-related iron changes as compared to conventional region-based analysis methods. This study introduces IRONMAP, Iron Network Mapping and Analysis Protocol, which is a novel network-based analysis method to evaluate over-time changes in magnetic susceptibility. With this novel methodology, we analyzed short-term (<1 year) longitudinal QSM data from a cohort of individuals with MS (pwMS) and healthy controls (HCs) and assessed disease-related network patterns, comparing the new approach to a conventional per-region rate-of-change method. IRONMAP analysis was able to detect over-time, MS-related brain iron abnormalities that were undetectable using the rate-of-change approach. IRONMAP was applicable on the per-subject level, improving binary classification of pwMS vs HCs compared to rate-of-change data alone (areas under the curve: 0.773 vs 0.636, p = 0.024). Further analysis revealed that the observed IRONMAP-derived HC network structure closely aligned with simulated networks based on healthy aging-related susceptibility data, suggesting that disruptions in normal aging-related iron changes may contribute to the network differences seen in pwMS. IRONMAP is generalizable to any neurological disease, including Alzheimer's disease and Parkinson's disease, and may allow for study of brain iron abnormalities over shorter timeframes than previously possible.