Ferdinand Schweser1,2, Nicola Bertolino1, Michael G Dwyer1, Jesper Hagemeier1, Paul Polak1, Niels P Bergsland1,3, Andreas Deistung4, Bianca Weinstock-Guttman5, Jürgen R Reichenbach4,6, and Robert Zivadinov1,2
1Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, The State University of New York at Buffalo, Buffalo, NY, United States, 2MRI Molecular and Translational Research Center, Jacobs School of Medicine and Biomedical Sciences, The State University of New York at Buffalo, Buffalo, NY, United States, 3MR Research Laboratory, IRCCS Don Gnocchi Foundation ONLUS, Milan, Italy, 4Medical Physics Group, Department of Diagnostic and Interventional Radiology, Jena University Hospital - Friedrich Schiller University Jena, Jena, Germany, 5Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, The State University of New York at Buffalo, Buffalo, NY, United States, 6Michael Stifel Center for Data-driven and Simulation Science Jena, Friedrich Schiller University Jena, Jena, Germany
Synopsis
Quantitative susceptibility mapping (QSM) is the most sensitive technique currently available to study brain iron in vivo. The technique opens the door to a longitudinal assessment of brain iron, bearing the potential to understand and disentangle factors resulting in the large scatter of reported iron concentrations in later decades of life.
In the present work, we investigated longitudinal changes of brain magnetic susceptibility in a cohort of 40 healthy controls (HCs) and 160 multiple sclerosis (MS) patients over a period of 2 years.
Introduction
A variety of MRI-based imaging methods have been used to study the trajectory of aging-related iron concentrations in the human brain. In a recent study, Li et al.1 applied quantitative susceptibility mapping (QSM), the most sensitive technique currently available to study brain iron in vivo, to 191 subjects between 1 and 83 years of age, nicely confirming the 1958 landmark study by Hallgren and Sourander2.
So far, most in vivo brain iron research relied on cross-sectional imaging data, providing little additional insights compared to post mortem studies. However, the ability to study brain iron in vivo opens the door to a longitudinal assessment of brain iron, bearing the potential to understand and disentangle factors resulting in the large scatter of reported iron concentrations in later decades of life2.
In the present work, we investigated longitudinal changes of brain magnetic susceptibility in a cohort of 40 healthy controls (HCs) and 160 multiple sclerosis (MS) patients over a period of 2 years.
Methods
Subjects: 160 MS patients and 40 age- and sex-matched HCs were recruited. Table 1 summarizes demographic and clinical characteristics of the study group.
Data acquisition: Participants were scanned on a 3T GE Signa Excite HD 12.0 with a multi-channel head-neck coil using a 3D flow-compensated gradient echo sequence (matrix 512x192x64, 256x192x128mm3, TE/TR=22ms/40ms, BW=13.9kHz, flip=12°). The average time between scans was 1.9±1.2 years for HCs and 2.1±1.1 for MS patients. No major hard- or software-upgrades of the MRI system occurred between baseline and follow-up. Magnetic susceptibility maps were reconstructed from raw k-space data using scalar-phase-matching3, gradient unwarping4, best-path unwrapping5, V-SHARP6,7, and HEIDI8.
Analysis: DGM regions were automatically segmented using FSL FIRST based on an additional magnetization-prepared spoiled gradient-echo scan with 1mm isotropic resolution, and average susceptibility values were calculated for all segmented regions. Temporal (baseline to follow-up) and cross-sectional (MS vs. HC) differences were tested using Mixed Factorial ANOVA analysis and independent samples t-test and paired t-test. Associations between susceptibility, other MRI measures and clinical measures were explored using Spearman’s correlation coefficient. A p-value below 0.05 was considered as statistically significant.
Results
At both time points, MS patients had significantly lower susceptibility in the thalamus (-5.6 parts per billion [ppb] at baseline and -6.7ppb at follow-up). In the caudate nucleus and globus pallidus of MS patients, higher susceptibility was found (p<0.001). These results are in line with a recent study in more than 1000 MS patients10.
Over two years, magnetic susceptibility increased significantly in the caudates of both HC (+2.6ppb) and MS (+1.6ppb). The normalized volumes of all DGM structures decreased between the two scans (p<0.05). Among MS patients, susceptibility of the caudate was significantly associated with volume loss (atrophy) of the caudate (R=-0.247, p=0.010). The Expanded Disability Status Scale (EDSS) was significantly correlated with higher total DGM susceptibility (p=.027).
Table 2 summarizes the findings.
Discussion and Conclusion
Our study presents the first longitudinal assessment of magnetic susceptibility in the human brain.
Comparison with post mortem reports: For the period between 44 and 46 years of age (average of our cohort at the two timepoints), Hallgren and Sourander's landmark brain iron study2 predicted the strongest increase of the (normal) iron concentration in the putamen (+0.17 mg per 100g tissue wet weight [mg/100g]), followed by the caudate (+0.08mg/100g), and globus pallidus (+0.04mg/100g). Considering that the putamen should accumulate iron twice as fast as the caudate, it is surprising that we did not detect any susceptibility change in putamen.
Converting these iron concentrations to magnetic susceptibility (based on the post mortem validation of QSM in Ref. 9) yields an expected (normal) iron-related susceptibility increase of +0.712ppb in the caudate. This is one third of the detected change in HCs and half of the change in MS.
Interpretation: In principle, increased susceptibility can be explained by either decreased abundance of diamagnetc substances (e.g. myelin) or increased abundance of paramagnetic substances (e.g. iron). A strong effect of demyelination is unlikely in DGM, because gray matter is not heavily myelinated. Deoxyheme is paramagnetic and an increased presence of deoxyhemoglobin, e.g. due to increased metabolic demand at advanced ages, is a more reasonable explanation. However, noting that Hallgren and Sourander's experiments date back about 60 years, the most likely explanation is that changed environmental factors, including lifestyle changes, affect the (normal) iron accumulation in the brain, rendering a direct comparison with this study problematic. However, all these interpretations are entirely hypothetical at this point and require further investigation.
Acknowledgements
No acknowledgement found.References
[1] Li W, Wu B, Batrachenko A, Bancroft-Wu V, Morey RA, Shashi V, Liu C. Differential developmental trajectories of magnetic susceptibility in human brain gray and white matter over the lifespan. Hum Brain Mapp 2014, 35(6), 2698–713.
[2] Hallgren B & Sourander P. The effect of age on the non-haemin iron in the human brain. J Neurochem 1958, 3, 41–51.
[3] Hammond KE, Lupo JM, Xu D, Metcalf M, Kelley DAC., Pelletier D, Chang SM, Mukherjee P, Vigneron DB, NelsonSJ. Development of a robust method for generating 7.0 T multichannel phase images of the brain with application to normal volunteers and patients with neurological diseases. NeuroImage 2008, 39(4), 1682–1692.
[4] Polak P, Zivadinov R & Schweser F. Gradient Unwarping for Phase Imaging Reconstruction. ISMRM 2015, p1279. Toronto, CA.
[5] Abdul-Rahman HS, Gdeisat MA, Burton DR, Lalor MJ, Lilley F & Moore CJ. Fast and robust three-dimensional best path phase unwrapping algorithm. Appl Opt 2007, 46(26), 6623–35.
[6] Schweser F, Deistung A, Lehr BW & Reichenbach JR. Quantitative imaging of intrinsic magnetic tissue properties using MRI signal phase: An approach to in vivo brain iron metabolism? NeuroImage 2011, 54(4), 2789–2807.
[7] Wu B, Li W, Guidon A & Liu C. Whole brain susceptibility mapping using compressed sensing. Magn Reson Med 2011, 24, 1129–36.
[8] Schweser F, Sommer K, Deistung A & Reichenbach JR. Quantitative susceptibility mapping for investigating subtle susceptibility variations in the human brain. NeuroImage 2012, 62(3), 2083–2100.
[9] Langkammer C, Schweser F, Krebs N, Deistung A, Goessler W, Scheurer E, Reichenbach JR. Quantitative susceptibility mapping (QSM) as a means to measure brain iron? A post mortem validation study. NeuroImage 2012, 62(3), 1593–1599.
[10] submitted to ISMRM 16 by our group