Five year iron changes in relapsing-remitting multiple sclerosis deep gray matter compared to healthy controls

Brain microstructure is linked to cognitive fatigue in early multiple sclerosis

Cognitive fatigue is a major symptom of Multiple Sclerosis (MS), from the early stages of the disease. This study aims to detect if brain microstructure is altered early in the disease course and is associated with cognitive fatigue in people with MS (pwMS) compared to matched healthy controls (HC). Recently diagnosed pwMS (N = 18, age < 45 years old) with either a Relapsing-Remitting or a Clinically Isolated Syndrome course of the disease, and HC (N = 19) matched for sex, age and education were analyzed. Quantitative multiparameter maps (MTsat, PD, R1 and R2*) of pwMS and HC were calculated. Parameters were extracted within the normal appearing white matter, cortical grey matter and deep grey matter (NAWM, NACGM and NADGM, respectively). Bayesian T-test for independent samples assessed between-group differences in brain microstructure while associations between score at a cognitive fatigue scale and each parameter in each tissue class were investigated with Generalized Linear Mixed Models. Patients exhibited lower MTsat and R1 values within NAWM and NACGM, and higher R1 values in NADGM compared to HC. Cognitive fatigue was associated with PD measured in every tissue class and to MTsat in NAWM, regardless of group. Disease-specific negative correlations were found in pwMS in NAWM (R1, R2*) and NACGM (R1). These findings suggest that brain microstructure within normal appearing tissues is already altered in the very early stages of the disease. Moreover, additional microstructure alterations (e.g. diffuse and widespread demyelination or axonal degeneration) in pwMS may lead to disease-specific complaint of cognitive fatigue.

Putamen iron quantification in diseases with neurodegeneration: a meta-analysis of the quantitative susceptibility mapping technique

Quantitative susceptibility mapping (QSM) is an MRI technique that accurately measures iron concentration in brain tissues. This meta-analysis synthesized evidence from 30 studies that used QSM to quantify the iron levels in the putamen. The PRISMA statement was adhered to when conducting the systematic reviews and meta-analyses. We conducted a meta-analysis using a random-effects model, as well as subgroup analyses (disease type, geographic region, field strength, coil, disease type, age, and sex) and sensitivity analysis. A total of 1247 patients and 1035 controls were included in the study. Pooled results showed a standardized mean difference (SMD) of 0.41 (95% CI 0.19 to 0.64), with the strongest effect seen in Alzheimer's disease (AD) at 1.01 (95% CI 0.50 to 1.52). Relapsing-remitting multiple sclerosis (RRMS) also showed increased putaminal iron at 0.37 (95% CI 0.177 to 0.58). No significant differences were observed in Parkinson's disease (PD). No significant differences were found between subgroups based on geographic region, field strength, coil, disease type, age, and sex. The studies revealed significant heterogeneity, with field strength as the primary source, while other factors, such as disease type, location, age, sex, and coil type, may have contributed. The sensitivity analysis showed that these factors did not have a significant influence on the overall results. In summary, this meta-analysis supports abnormalities in putaminal iron content across different diseases with neurodegeneration, especially AD and RRMS, as measured by QSM. This highlights the potential of QSM as an imaging biomarker to better understand disease mechanisms involving disturbances in brain iron homeostasis.

Quantitative susceptibility mapping through model-based deep image prior (MoDIP)

The data-driven approach of supervised learning methods has limited applicability in solving dipole inversion in Quantitative Susceptibility Mapping (QSM) with varying scan parameters across different objects. To address this generalization issue in supervised QSM methods, we propose a novel training-free model-based unsupervised method called MoDIP (Model-based Deep Image Prior). MoDIP comprises a small, untrained network and a Data Fidelity Optimization (DFO) module. The network converges to an interim state, acting as an implicit prior for image regularization, while the optimization process enforces the physical model of QSM dipole inversion. Experimental results demonstrate MoDIP's excellent generalizability in solving QSM dipole inversion across different scan parameters. It exhibits robustness against pathological brain QSM, achieving over 32 % accuracy improvement than supervised deep learning methods. It is also 33 % more computationally efficient and runs 4 times faster than conventional DIP-based approaches, enabling 3D high-resolution image reconstruction in under 4.5 min.

Quantitative susceptibility mapping in multiple sclerosis: A systematic review and meta-analysis

BACKGROUND

Quantitative susceptibility mapping (QSM) is a quantitative measure based on magnetic resonance imaging sensitive to iron and myelin content. This makes QSM a promising non-invasive tool for multiple sclerosis (MS) in research and clinical practice.

OBJECTIVE

We performed a systematic review and meta-analysis on the use of QSM in MS.

METHODS

Our review was prospectively registered on PROSPERO (CRD42022309563). We searched five databases for studies published between inception and 30th April 2023. We identified 83 English peer-reviewed studies that applied QSM images on MS cohorts. Fifty-five included studies had at least one of the following outcome measures: deep grey matter QSM values in MS, either compared to healthy controls (HC) (k = 13) or correlated with the score on the Expanded Disability Status Scale (EDSS) (k = 7), QSM lesion characteristics (k = 22) and their clinical correlates (k = 17), longitudinal correlates (k = 11), histological correlates (k = 7), or correlates with other imaging techniques (k = 12). Two meta-analyses on deep grey matter (DGM) susceptibility data were performed, while the remaining findings could only be analyzed descriptively.

RESULTS

After outlier removal, meta-analyses demonstrated a significant increase in the basal ganglia susceptibility (QSM values) in MS compared to HC, caudate (k = 9, standardized mean difference (SDM) = 0.54, 95 % CI = 0.39-0.70, I2 = 46 %), putamen (k = 9, SDM = 0.38, 95 % CI = 0.19-0.57, I2 = 59 %), and globus pallidus (k = 9, SDM = 0.48, 95 % CI = 0.28-0.67, I2 = 60 %), whereas thalamic QSM values exhibited a significant reduction (k = 12, SDM = -0.39, 95 % CI = -0.66--0.12, I2 = 84 %); these susceptibility differences in MS were independent of age. Further, putamen QSM values positively correlated with EDSS (k = 4, r = 0.36, 95 % CI = 0.16-0.53, I2 = 0 %). Regarding rim lesions, four out of seven studies, representing 73 % of all patients, reported rim lesions to be associated with more severe disability. Moreover, lesion development from initial detection to the inactive stage is paralleled by increasing, plateauing (after about two years), and gradually decreasing QSM values, respectively. Only one longitudinal study provided clinical outcome measures and found no association. Histological data suggest iron content to be the primary source of QSM values in DGM and at the edges of rim lesions; further, when also considering data from myelin water imaging, the decrease of myelin is likely to drive the increase of QSM values within WM lesions.

CONCLUSIONS

We could provide meta-analytic evidence for DGM susceptibility changes in MS compared to HC; basal ganglia susceptibility is increased and, in the putamen, associated with disability, while thalamic susceptibility is decreased. Beyond these findings, further investigations are necessary to establish the role of QSM in MS for research or even clinical routine.

Magnetic resonance imaging detection of deep gray matter iron deposition in multiple sclerosis: A systematic review

Multiple sclerosis (MS) is a chronic inflammatory and neurodegenerative disease involving immune-mediated damage. Iron deposition in deep gray matter (DGM) structures like the thalamus and basal ganglia have been suggested to play a role in MS pathogenesis. Magnetic Resonance Imaging (MRI) imaging methods like T2 and T2* imaging, susceptibility-weighted imaging, and quantitative susceptibility mapping can track iron deposition storage in the brain primarily from ferritin and hemosiderin (paramagnetic iron storage proteins) with varying levels of tissue contrast and sensitivity. In this systematic review, we evaluated the role of DGM iron deposition as detected by MRI techniques in relation to MS-related neuroinflammation and its potential as a novel therapeutic target. We searched through PubMed, Embase, and Web of Science databases following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, against predetermined inclusion and exclusion criteria. We included 89 articles (n = 6630 patients), and then grouped them into different categories: i) methodological techniques to measure DGM iron, ii) cross-sectional and group comparison of DGM iron content, iii) longitudinal comparisons of DGM iron, iv) associations between DGM iron and other imaging and neurobiological markers, v) associations with disability, and vi) associations with cognitive impairment. The review revealed that iron deposition in DGM is independent yet concurrent with demyelination, and that these iron deposits contribute to MS-related cognitive impairment and disability. Variability in iron distributions appears to rely on a positive feedback loop between inflammation, and release of iron by oligodendrocytes. DGM iron seems to be a promising prognostic biomarker for MS pathophysiology.

Affine transformation edited and refined deep neural network for quantitative susceptibility mapping

Deep neural networks have demonstrated great potential in solving dipole inversion for Quantitative Susceptibility Mapping (QSM). However, the performances of most existing deep learning methods drastically degrade with mismatched sequence parameters such as acquisition orientation and spatial resolution. We propose an end-to-end AFfine Transformation Edited and Refined (AFTER) deep neural network for QSM, which is robust against arbitrary acquisition orientation and spatial resolution up to 0.6 mm isotropic at the finest. The AFTER-QSM neural network starts with a forward affine transformation layer, followed by a Unet for dipole inversion, then an inverse affine transformation layer, followed by a Residual Dense Network (RDN) for QSM refinement. Simulation and in-vivo experiments demonstrated that the proposed AFTER-QSM network architecture had excellent generalizability. It can successfully reconstruct susceptibility maps from highly oblique and anisotropic scans, leading to the best image quality assessments in simulation tests and suppressed streaking artifacts and noise levels for in-vivo experiments compared with other methods. Furthermore, ablation studies showed that the RDN refinement network significantly reduced image blurring and susceptibility underestimation due to affine transformations. In addition, the AFTER-QSM network substantially shortened the reconstruction time from minutes using conventional methods to only a few seconds.

Quantitative MRI in Multiple Sclerosis: From Theory to Application

Quantitative MR imaging techniques allow evaluating different aspects of brain microstructure, providing meaningful information about the pathophysiology of damage in CNS disorders. In the study of patients with MS, quantitative MR imaging techniques represent an invaluable tool for studying changes in myelin and iron content occurring in the context of inflammatory and neurodegenerative processes. In the first section of this review, we summarize the physics behind quantitative MR imaging, here defined as relaxometry and quantitative susceptibility mapping, and describe the neurobiological correlates of quantitative MR imaging findings. In the second section, we focus on quantitative MR imaging application in MS, reporting the main findings in both the gray and white matter compartments, separately addressing macroscopically damaged and normal-appearing parenchyma.

Multisite reproducibility of quantitative susceptibility mapping and effective transverse relaxation rate in deep gray matter at 3 T using locally optimized sequences in 24 traveling heads

Iron concentration in the human brain plays a crucial role in several neurodegenerative diseases and can be monitored noninvasively using quantitative susceptibility mapping (QSM) and effective transverse relaxation rate (R₂ *) mapping from multiecho T₂ *-weighted images. Large population studies enable better understanding of pathologies and can benefit from pooling multisite data. However, reproducibility may be compromised between sites and studies using different hardware and sequence protocols. This work investigates QSM and R₂ * reproducibility at 3 T using locally optimized sequences from three centers and two vendors, and investigates possible reduction of cross-site variability through postprocessing approaches. Twenty-four healthy subjects traveled between three sites and were scanned twice at each site. Scan-rescan measurements from seven deep gray matter regions were used for assessing within-site and cross-site reproducibility using intraclass correlation coefficient (ICC) and within-subject standard deviation (SDw) measures. In addition, multiple QSM and R₂ * postprocessing options were investigated with the aim to minimize cross-site sequence-related variations, including: mask generation approach, echo-timing selection, harmonizing spatial resolution, field map estimation, susceptibility inversion method, and linear field correction for magnitude images. The same-subject cross-site region of interest measurements for QSM and R₂ * were highly correlated (R²  ≥ 0.94) and reproducible (mean ICC of 0.89 and 0.82 for QSM and R₂ *, respectively). The mean cross-site SDw was 4.16 parts per billion (ppb) for QSM and 1.27 s^-1 for R₂ *. For within-site measurements of QSM and R₂ *, the mean ICC was 0.97 and 0.87 and mean SDw was 2.36 ppb and 0.97 s^-1 , respectively. The precision level is regionally dependent and is reduced in the frontal lobe, near brain edges, and in white matter regions. Cross-site QSM variability (mean SDw) was reduced up to 46% through postprocessing approaches, such as masking out less reliable regions, matching available echo timings and spatial resolution, avoiding the use of the nonconsistent magnitude contrast between scans in field estimation, and minimizing streaking artifacts.

Instant tissue field and magnetic susceptibility mapping from MRI raw phase using Laplacian enhanced deep neural networks

Quantitative susceptibility mapping (QSM) is an MRI post-processing technique that produces spatially resolved magnetic susceptibility maps from phase data. However, the traditional QSM reconstruction pipeline involves multiple non-trivial steps, including phase unwrapping, background field removal, and dipole inversion. These intermediate steps not only increase the reconstruction time but accumulates errors. This study aims to overcome existing limitations by developing a Laplacian-of-Trigonometric-functions (LoT) enhanced deep neural network for near-instant quantitative field and susceptibility mapping (i.e., iQFM and iQSM) from raw MRI phase data. The proposed iQFM and iQSM methods were compared with established reconstruction pipelines on simulated and in vivo datasets. In addition, experiments on patients with intracranial hemorrhage and multiple sclerosis were also performed to test the generalization of the proposed neural networks. The proposed iQFM and iQSM methods in healthy subjects yielded comparable results to those involving the intermediate steps while dramatically improving reconstruction accuracies on intracranial hemorrhages with large susceptibilities. High susceptibility contrast between multiple sclerosis lesions and healthy tissue was also achieved using the proposed methods. Comparative studies indicated that the most significant contributor to iQFM and iQSM over conventional multi-step methods was the elimination of traditional Laplacian unwrapping. The reconstruction time on the order of minutes for traditional approaches was shortened to around 0.1 seconds using the trained iQFM and iQSM neural networks.

Cerebral Iron Deposition in Neurodegeneration

Disruption of cerebral iron regulation appears to have a role in aging and in the pathogenesis of various neurodegenerative disorders. Possible unfavorable impacts of iron accumulation include reactive oxygen species generation, induction of ferroptosis, and acceleration of inflammatory changes. Whole-brain iron-sensitive magnetic resonance imaging (MRI) techniques allow the examination of macroscopic patterns of brain iron deposits in vivo, while modern analytical methods ex vivo enable the determination of metal-specific content inside individual cell-types, sometimes also within specific cellular compartments. The present review summarizes the whole brain, cellular, and subcellular patterns of iron accumulation in neurodegenerative diseases of genetic and sporadic origin. We also provide an update on mechanisms, biomarkers, and effects of brain iron accumulation in these disorders, focusing on recent publications. In Parkinson’s disease, Friedreich’s disease, and several disorders within the neurodegeneration with brain iron accumulation group, there is a focal siderosis, typically in regions with the most pronounced neuropathological changes. The second group of disorders including multiple sclerosis, Alzheimer’s disease, and amyotrophic lateral sclerosis shows iron accumulation in the globus pallidus, caudate, and putamen, and in specific cortical regions. Yet, other disorders such as aceruloplasminemia, neuroferritinopathy, or Wilson disease manifest with diffuse iron accumulation in the deep gray matter in a pattern comparable to or even more extensive than that observed during normal aging. On the microscopic level, brain iron deposits are present mostly in dystrophic microglia variably accompanied by iron-laden macrophages and in astrocytes, implicating a role of inflammatory changes and blood–brain barrier disturbance in iron accumulation. Options and potential benefits of iron reducing strategies in neurodegeneration are discussed. Future research investigating whether genetic predispositions play a role in brain Fe accumulation is necessary. If confirmed, the prevention of further brain Fe uptake in individuals at risk may be key for preventing neurodegenerative disorders.

Accelerating quantitative susceptibility and R2* mapping using incoherent undersampling and deep neural network reconstruction

Quantitative susceptibility mapping (QSM) and R2* mapping are MRI post-processing methods that quantify tissue magnetic susceptibility and transverse relaxation rate distributions. However, QSM and R2* acquisitions are relatively slow, even with parallel imaging. Incoherent undersampling and compressed sensing reconstruction techniques have been used to accelerate traditional magnitude-based MRI acquisitions; however, most do not recover the full phase signal, as required by QSM, due to its non-convex nature. In this study, a learning-based Deep Complex Residual Network (DCRNet) is proposed to recover both the magnitude and phase images from incoherently undersampled data, enabling high acceleration of QSM and R2* acquisition. Magnitude, phase, R2*, and QSM results from DCRNet were compared with two iterative and one deep learning methods on retrospectively undersampled acquisitions from six healthy volunteers, one intracranial hemorrhage and one multiple sclerosis patients, as well as one prospectively undersampled healthy subject using a 7T scanner. Peak signal to noise ratio (PSNR), structural similarity (SSIM), root-mean-squared error (RMSE), and region-of-interest susceptibility and R2* measurements are reported for numerical comparisons. The proposed DCRNet method substantially reduced artifacts and blurring compared to the other methods and resulted in the highest PSNR, SSIM, and RMSE on the magnitude, R2*, local field, and susceptibility maps. Compared to two iterative and one deep learning methods, the DCRNet method demonstrated a 3.2% to 9.1% accuracy improvement in deep grey matter susceptibility when accelerated by a factor of four. The DCRNet also dramatically shortened the reconstruction time of single 2D brain images from 36-140 seconds using conventional approaches to only 15-70 milliseconds.

xQSM: quantitative susceptibility mapping with octave convolutional and noise-regularized neural networks

Quantitative susceptibility mapping (QSM) provides a valuable MRI contrast mechanism that has demonstrated broad clinical applications. However, the image reconstruction of QSM is challenging due to its ill-posed dipole inversion process. In this study, a new deep learning method for QSM reconstruction, namely xQSM, was designed by introducing noise regularization and modified octave convolutional layers into a U-net backbone and trained with synthetic and in vivo datasets, respectively. The xQSM method was compared with two recent deep learning (QSMnet⁺ and DeepQSM) and two conventional dipole inversion (MEDI and iLSQR) methods, using both digital simulations and in vivo experiments. Reconstruction error metrics, including peak signal-to-noise ratio, structural similarity, normalized root mean squared error and deep gray matter susceptibility measurements, were evaluated for comparison of the different methods. The results showed that the proposed xQSM network trained with in vivo datasets achieved the best reconstructions of all the deep learning methods. In particular, it led to, on average, 32.3%, 25.4% and 11.7% improvement in the accuracy of globus pallidus susceptibility estimation for digital simulations and 39.3%, 21.8% and 6.3% improvements for in vivo acquisitions compared with DeepQSM, QSMnet⁺ and iLSQR, respectively. It also exhibited the highest linearity against different susceptibility intensity scales and demonstrated the most robust generalization capability to various spatial resolutions of all the deep learning methods. In addition, the xQSM method also substantially shortened the reconstruction time from minutes using MEDI to only a few seconds.

Stable Anatomy Detection in Multimodal Imaging Through Sparse Group Regularization: A Comparative Study of Iron Accumulation in the Aging Brain

Multimodal neuroimaging provides a rich source of data for identifying brain regions associated with disease progression and aging. However, present studies still typically analyze modalities separately or aggregate voxel-wise measurements and analyses to the structural level, thus reducing statistical power. As a central example, previous works have used two quantitative MRI parameters-R2* and quantitative susceptibility (QS)-to study changes in iron associated with aging in healthy and multiple sclerosis subjects, but failed to simultaneously account for both. In this article, we propose a unified framework that combines information from multiple imaging modalities and regularizes estimates for increased interpretability, generalizability, and stability. Our work focuses on joint region detection problems where overlap between effect supports across modalities is encouraged but not strictly enforced. To achieve this, we combine L ₁ (lasso), total variation (TV), and L ₂ group lasso penalties. While the TV penalty encourages geometric regularization by controlling estimate variability and support boundary geometry, the group lasso penalty accounts for similarities in the support between imaging modalities. We address the computational difficulty in this regularization scheme with an alternating direction method of multipliers (ADMM) optimizer. In a neuroimaging application, we compare our method against independent sparse and joint sparse models using a dataset of R2* and QS maps derived from MRI scans of 113 healthy controls: our method produces clinically-interpretable regions where specific iron changes are associated with healthy aging. Together with results across multiple simulation studies, we conclude that our approach identifies regions that are more strongly associated with the variable of interest (e.g., age), more accurate, and more stable with respect to training data variability. This work makes progress toward a stable and interpretable multimodal imaging analysis framework for studying disease-related changes in brain structure and can be extended for classification and disease prediction tasks.

Decreasing brain iron in multiple sclerosis: The difference between concentration and content in iron MRI

Increased brain iron concentration is often reported concurrently with disease development in multiple sclerosis (MS) and other neurodegenerative diseases. However, it is unclear whether the higher iron concentration in patients stems from an influx of iron into the tissue or a relative reduction in tissue compartments without much iron. By taking into account structural volume, we investigated tissue iron content in the deep gray matter (DGM) over 2 years, and compared findings to previously reported changes in iron concentration. 120 MS patients and 40 age‐ and sex‐matched healthy controls were included. Clinical testing and MRI were performed both at baseline and after 2 years. Overall, iron content was calculated from structural MRI and quantitative susceptibility mapping in the thalamus, caudate, putamen, and globus pallidus. MS patients had significantly lower iron content than controls in the thalamus, with progressive MS patients demonstrating lower iron content than relapsing–remitting patients. Over 2 years, iron content decreased in the DGM of patients with MS, while it tended to increase or remain stable among controls. In the thalamus, decreasing iron content over 2 years was associated with disability progression. Our study showed that temporally increasing magnetic susceptibility in MS should not be considered as evidence for iron influx because it may be explained, at least partially, by disease‐related atrophy. Declining DGM iron content suggests that, contrary to the current understanding, iron is being removed from the DGM in patients with MS.

Voxel-Based quantitative MRI reveals spatial patterns of grey matter alteration in multiple sclerosis

Despite robust postmortem evidence and potential clinical importance of gray matter (GM) pathology in multiple sclerosis (MS), assessing GM damage by conventional magnetic resonance imaging (MRI) remains challenging. This prospective cross‐sectional study aimed at characterizing the topography of GM microstructural and volumetric alteration in MS using, in addition to brain atrophy measures, three quantitative MRI (qMRI) parameters—magnetization transfer (MT) saturation, longitudinal (R1), and effective transverse (R2*) relaxation rates, derived from data acquired during a single scanning session. Our study involved 35 MS patients (14 relapsing–remitting MS; 21 primary or secondary progressive MS) and 36 age‐matched healthy controls (HC). The qMRI maps were computed and segmented in different tissue classes. Voxel‐based quantification (VBQ) and voxel‐based morphometry (VBM) statistical analyses were carried out using multiple linear regression models. In MS patients compared with HC, three configurations of GM microstructural/volumetric alterations were identified. (a) Co‐localization of GM atrophy with significant reduction of MT, R1, and/or R2*, usually observed in primary cortices. (b) Microstructural modifications without significant GM loss: hippocampus and paralimbic cortices, showing reduced MT and/or R1 values without significant atrophy. (c) Atrophy without significant change in microstructure, identified in deep GM nuclei. In conclusion, this quantitative multiparametric voxel‐based approach reveals three different spatially‐segregated combinations of GM microstructural/volumetric alterations in MS that might be associated with different neuropathology.

Targeting Iron Dyshomeostasis for Treatment of Neurodegenerative Disorders

While iron has an important role in the normal functioning of the brain owing to its involvement in several physiological processes, dyshomeostasis has been found in many neurodegenerative disorders, as evidenced by both histopathological and imaging studies. Although the exact causes have remained elusive, the fact that altered iron levels have been found in disparate diseases suggests that iron may contribute to their development and/or progression. As such, the processes involved in iron dyshomeostasis may represent novel therapeutic targets. There are, however, many questions about the exact interplay between neurodegeneration and altered iron homeostasis. Some insight can be gained by considering the parallels with respect to what occurs in healthy aging, which is also characterized by increased iron throughout many regions in the brain along with progressive neurodegeneration. Nevertheless, the exact mechanisms of iron-mediated damage are likely disease specific to a certain degree, given that iron plays a crucial role in many disparate biological processes, which are not always affected in the same way across different neurodegenerative disorders. Moreover, it is not even entirely clear yet whether iron actually has a causative role in all of the diseases where altered iron levels have been noted. For example, there is strong evidence of iron dyshomeostasis leading to neurodegeneration in Parkinson's disease, but there is still some question as to whether changes in iron levels are merely an epiphenomenon in multiple sclerosis. Recent advances in neuroimaging now offer the possibility to detect and monitor iron levels in vivo, which allows for an improved understanding of both the temporal and spatial dynamics of iron changes and associated neurodegeneration compared to post-mortem studies. In this regard, iron-based imaging will likely play an important role in the development of therapeutic approaches aimed at addressing altered iron dynamics in neurodegenerative diseases. Currently, the bulk of such therapies have focused on chelating excess iron. Although there is some evidence that these treatment options may yield some benefit, they are not without their own limitations. They are generally effective at reducing brain iron levels, as assessed by imaging, but clinical benefits are more modest. New drugs that specifically target iron-related pathological processes may offer the possibility to prevent, or at the least, slow down irreversible neurodegeneration, which represents an unmet therapeutic target.