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

TL;DR

IRONMAP is a new network-based analysis method that improves detection of brain iron abnormalities over time in neurological diseases like MS, outperforming traditional region-based approaches and applicable to other disorders.

Contribution

The paper introduces IRONMAP, a novel network analysis protocol that enhances sensitivity to disease-related brain iron changes in longitudinal MRI data.

Findings

IRONMAP detects MS-related iron abnormalities undetectable by conventional methods.

IRONMAP improves classification accuracy of MS vs healthy controls.

Disrupted aging-related iron networks may contribute to MS pathology.

Abstract

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.