Thalamic white matter in multiple sclerosis: A combined diffusion‐tensor imaging and quantitative susceptibility mapping study

Bergsland, Niels; Schweser, Ferdinand; Dwyer, Michael G.; Weinstock‐Guttman, Bianca; Benedict, Ralph H. B.; Zivadinov, Robert

doi:10.1002/hbm.24227

Cited by 21 publications

(10 citation statements)

References 46 publications

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“…The mild trend we observed towards decreased R2* along several of the NAWM tracts, which can result from iron loss and demyelination, is congruent with this. Taken together, our QSM results are consistent with prior imaging and pathology studies that have found evidence of decreased iron content in NAWM [18, 30, 38, 39]. Such depletion could have important implications as iron plays an important role in maintaining cellular functions such as metabolism and lipid synthesis [40, 41].…”

Section: Discussionsupporting

confidence: 90%

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Characterization of normal-appearing white matter in multiple sclerosis using quantitative susceptibility mapping in conjunction with diffusion tensor imaging

Chiang

Stephens

et al. 2018

Neuroradiology

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Purpose Quantitative susceptibility mapping (QSM) is influenced by iron as well as myelin, which makes interpretation of pathologic changes challenging. Concurrent acquisition of MR sequences that are sensitive to axonal/myelin integrity, such as diffusion tensor imaging (DTI), may provide context for interpreting quantitative susceptibility (QS) signal. The purpose of our study was to investigate alterations in normal-appearing white matter (NAWM) in multiple sclerosis (MS) using QSM in conjunction with DTI. Methods Twenty relapsing–remitting MS patients and 20 age-matched healthy controls (HC) were recruited for this prospective study. QS, radial diffusivity (RD), fractional anisotropy (FA), and R2* maps within the whole brain as well as individual tracts were generated for comparison between NAWM and HC white matter (HCWM). Results MS lesions demonstrated significant differences in QS, FA, RD, and R2* compared to HCWM (p < 0.03). These metrics did not show a significant difference between whole-brain NAWM and HCWM. Among NAWM tracts, the cingulate gyri demonstrated significantly decreased QS compared to HCWM (p = 0.004). The forceps major showed significant differences in FA and RD without corresponding changes in QS (p < 0.01). Conclusion We found discordant changes in QSM and DTI metrics within the cingulate gyri and forceps major. This may potentially reflect the influence of paramagnetic substrates such as iron, which could be decreased along these NAWM tracts. Our results point to the potential role of QSM as a unique biomarker, although additional validation studies are needed.

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Section: Discussionsupporting

confidence: 90%

“…Specifically, QS may vary depending on the relative white matter location (i.e., periventricular, deep, subcortical, and perilesional) as well as within different brain regions (i.e., centrum semiovale versus corpus callosum). Along these lines, a separate study focusing on thalamic WM found decreased QS in MS compared to HC [39]. …”

Section: Discussionmentioning

confidence: 99%

Characterization of normal-appearing white matter in multiple sclerosis using quantitative susceptibility mapping in conjunction with diffusion tensor imaging

Chiang

Stephens

et al. 2018

Neuroradiology

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“…Brain iron levels are known to be disturbed in multiple sclerosis (MS) (Filippi et al, 2019; Hagemeier, Geurts, & Zivadinov, 2012; Zecca, Youdim, Riederer, Connor, & Crichton, 2004). Most studies reported increased iron concentrations in the deep gray matter (DGM) (Stankiewicz, Neema, & Ceccarelli, 2014) and around plaques (Craelius, Migdal, Luessenhop, Sugar, & Mihalakis, 1982), and reduced iron concentrations within lesions (Haider et al, 2014; Kutzelnigg et al, 2005; Laule et al, 2013; Yao et al, 2012; Yao et al, 2014), in the normal‐appearing white matter (WM) (Hametner et al, 2013; Paling et al, 2012; Popescu et al, 2017; Yu et al, 2018), and in the thalamus (Bergsland et al, 2018; Burgetova et al, 2017; Khalil et al, 2015; Louapre et al, 2017; Pontillo et al, 2019; Schweser et al, 2018; Uddin, Lebel, Seres, Blevins, & Wilman, 2016; Zivadinov et al, 2018). The literature considers findings of increased region‐average iron concentrations as evidence for iron influx (Bergsland et al, 2019; Ndayisaba, Kaindlstorfer, & Wenning, 2019; Williams, Buchheit, Berman, & LeVine, 2012), whereas it is less clear how to interpret reduced iron concentrations.…”

Section: Introductionmentioning

confidence: 99%

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

Schweser

Hagemeier

Dwyer

et al. 2020

Human Brain Mapping

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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.

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“…Mangeat et al ( 2015 ) used two-dimensional independent components analysis of magnetization transfer, cortical thickness, B 0 orientation, and R2* to extract information about the fraction of myelin in white matter. Bergsland et al ( 2018 ) used QS and diffusion metrics to perform separate univariate statistical analyses of thalamic white matter tracts and used non-parametric correlation analysis to combine these measures and quantify their association with clinical disease metrics.…”

Section: Introductionmentioning

confidence: 99%

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

Pietrosanu

Zhang

Seres

et al. 2021

Front. Hum. Neurosci.

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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 L1 (lasso), total variation (TV), and L2 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.

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Thalamic white matter in multiple sclerosis: A combined diffusion‐tensor imaging and quantitative susceptibility mapping study

Cited by 21 publications

References 46 publications

Characterization of normal-appearing white matter in multiple sclerosis using quantitative susceptibility mapping in conjunction with diffusion tensor imaging

Characterization of normal-appearing white matter in multiple sclerosis using quantitative susceptibility mapping in conjunction with diffusion tensor imaging

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

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

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