Susceptibility networks reveal independent patterns of brain iron abnormalities in multiple sclerosis

Abstract

Brain iron homeostasis is necessary for healthy brain function. MRI and histological studies have shown altered brain iron levels in the brains of patients with multiple sclerosis (MS), particularly in the deep gray matter (DGM). Previous studies were able to only partially separate iron-modifying effects because of incomplete knowledge of iron-modifying processes and influencing factors. It is therefore unclear to what extent and at which stages of the disease different processes contribute to brain iron changes. We postulate that spatially covarying magnetic susceptibility networks determined with Independent Component Analysis (ICA) reflect, and allow for the study of, independent processes regulating iron levels. We applied ICA to quantitative susceptibility maps for 170 individuals aged 9-81 years without neurological disease ("Healthy Aging" (HA) cohort), and for a cohort of 120 patients with MS and 120 age- and sex-matched healthy controls (HC; together the "MS/HC" cohort). Two DGM-associated "susceptibility networks" identified in the HA cohort (the Dorsal Striatum and Globus Pallidus Interna Networks) were highly internally reproducible (i.e. "robust") across multiple ICA repetitions on cohort subsets. DGM areas overlapping both robust networks had higher susceptibility levels than DGM areas overlapping only a single robust network, suggesting that these networks were caused by independent processes of increasing iron concentration. Because MS is thought to accelerate brain aging, we hypothesized that associations between age and the two robust DGM-associated networks would be enhanced in patients with MS. However, only one of these networks was altered in patients with MS, and it had a null age association in patients with MS rather than a stronger association. Further analysis of the MS/HC cohort revealed three additional disease-related networks (the Pulvinar, Mesencephalon, and Caudate Networks) that were differentially altered between patients with MS and HCs and between MS subtypes. Exploratory regression analyses of the disease-related networks revealed differential associations with disease duration and T2 lesion volume. Finally, analysis of ROI-based disease effects in the MS/HC cohort revealed an effect of disease status only in the putamen ROI and exploratory regression analysis did not show associations between the caudate and pulvinar ROIs and disease duration or T2 lesion volume, showing the ICA-based approach was more sensitive to disease effects. These results suggest that the ICA network framework increases sensitivity for studying patterns of brain iron change, opening a new avenue for understanding brain iron physiology under normal and disease conditions.

Keywords: Aging; Iron; Multiple sclerosis; Network; QSM.

Figure 1. ICA methodology applied to QSM.

(A) Visual representation of the ICA network decomposition process. (B) Representation of the reconstruction of an original MRI scan from networks and loading coefficients. (C) Interpretation of ICA network spatial patterns.

Figure 2. Sample susceptibility map and subcortical atlas.

(A) Representative template-normalized susceptibility map scaled from −0.1 ppm (black) to 0.2 ppm (white). (B) Subcortical atlas used to determine DGM-associated networks.

Figure 3. Five HA cohort networks.

Axial slices of the five HA cohort networks that were significantly associated with at least one DGM structure. Image threshold: |Z| > 3.3, corresponding to P < 0.001 two-tailed voxel-wise t tests uncorrected.

Figure 4. Robustness of HA Cohort Networks.

Probability maps showing the fraction of the 20 DGM network-matched ICA iterations that were significant (|Z| > 3.3) at each voxel. Transparent areas indicate voxels that were not significant for that network in any of the 20 iterations.

Figure 5. Anatomical overlap of the two robust HA cohort networks and susceptibility values in areas of network overlap.

(A) Bar graph showing the percent volume overlap of the robust DGM-associated networks within anatomical regions (threshold: |Z| > 3.3). Only DGM areas with > 5% overlap by volume are displayed. (B) Violin plots of susceptibility values of voxels in the subcortical atlas. ***P < 0.0001, Bonferroni corrected for two comparisons. Ant. = anterior; Cereb. = cerebellar; CL-LP-MP = central-lateral, lateral-posterior, and medial-pulvinar; Ext. Cap. = external capsule; Front-Occ. Fasc. = fronto-occipital fasciculus; GP = globus pallidus; Int. Cap. = internal capsule; Post. = posterior; Sup. = superior; VL = ventral-lateral.

Figure 6. MS networks and their anatomical representations.

(A) Axial slices of the networks derived from the MS/HC cohort that had a significant main effect of disease status. Image threshold: |Z| > 3.3, corresponding to P < 0.001 two-tailed voxel-wise t tests, uncorrected. (B) Bar graph showing the percent volume overlap of additional disease-related MS/HC networks and anatomical regions (threshold: |Z| > 3.3). Only areas with > 5% overlap by volume are shown. Ant. = anterior; CL-LP-MP = central-lateral, lateral-posterior, and medial-pulvinar;

Figure 7. Diagram of the factors associated with the HA Networks and MS Networks.

Associations are shown separately for the HA cohort regression (blue arrows) and MS-only regression (gold arrows). Green plus sign = positive association; red negative sign = negative association; grey “X” = no association in MS cohort only.