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

Yu, Fang; Chiang, Florence L.; Stephens, Nicholas R.; Huang, Susie Y.; Bilgic̦, Berkin; Tantiwongkosi, Bundhit; Romero, Rebecca

doi:10.1007/s00234-018-2137-7

Cited by 29 publications

(18 citation statements)

References 49 publications

(83 reference statements)

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“…For example, myelin estimation based on simultaneous T1/T2 relaxometry and proton density mapping correlated strongly with histological myelin as seen in post-mortem MS brain samples (Ouellette et al, 2020). Another study demonstrated that quantitative susceptibility provided additional myelin information in brain NAWM of MS patients, independent of FA and RD (Yu et al, 2019). Further, pre-operative myelin mapping using T1/T2 ratio showed the potential to predict outcomes of trigeminal neuralgia following Gamma knife radiosurgery, while FA and RD demonstrated similar values in this prediction as estimators of pre-operative axons (Li et al, 2020).…”

Section: Discussionmentioning

confidence: 88%

Advanced Analysis of Diffusion Tensor Imaging Along With Machine Learning Provides New Sensitive Measures of Tissue Pathology and Intra-Lesion Activity in Multiple Sclerosis

et al. 2021

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Tissue pathology in multiple sclerosis (MS) is highly complex, requiring multi-dimensional analysis. In this study, our goal was to test the feasibility of obtaining high angular resolution diffusion imaging (HARDI) metrics through single-shell modeling of diffusion tensor imaging (DTI) data, and investigate how advanced measures from single-shell HARDI and DTI tractography perform relative to classical DTI metrics in assessing MS pathology. We examined 52 relapsing-remitting MS patients who had 3T anatomical brain MRI and DTI. Single-shell HARDI modeling yielded 5 sub-voxel-based metrics, totalling 11 diffusion measures including 4 DTI and 2 tractography metrics. Based on machine learning of 3-dimensional regions of interest, we evaluated the importance of the measures through several tissue classification tasks. These included two within-subject comparisons: lesion versus normal appearing white matter (NAWM); and lesion core versus shell. Further, by stratifying patients as having high (above 75%ile) and low (below 25%ile) number of MS lesions, we also performed 2 classifications between subjects for lesions and NAWM respectively. Results showed that in lesion-NAWM analysis, HARDI orientation distribution function (ODF) energy, DTI fractional anisotropy (FA), and HARDI orientation dispersion index were the top three metrics, which together achieved 65.2% accuracy and 0.71 area under the receiver operating characteristic curve (AUROC). In core-shell analysis, DTI mean diffusivity (MD), radial diffusivity, and FA were the top three metrics, and MD dominated the classification, which achieved 59.3% accuracy and 0.59 AUROC alone. Between patients, FA was the leading feature in lesion comparisons, while ODF energy was the best in NAWM separation. Collectively, single-shell modeling of common diffusion data can provide robust orientation measures of lesion and NAWM pathology, and DTI metrics are most sensitive to intra-lesion abnormality. Combined analysis of both advanced and classical diffusion measures may be critical for improved understanding of MS pathology.

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Section: Discussionmentioning

confidence: 88%

Advanced Analysis of Diffusion Tensor Imaging Along With Machine Learning Provides New Sensitive Measures of Tissue Pathology and Intra-Lesion Activity in Multiple Sclerosis

et al. 2021

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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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“…Based on such low resolution, it is difficult for a conventional brain MRI to accurately reflect the state of damage of a relatively small number of brain cells. Furthermore, it has been shown that brain lesions of several brain diseases other than DAI also cannot be detected by conventional MRI [29,30].…”

Section: Problem 2: the Low Sensitivity Of Conventional Brain Mri In mentioning

confidence: 99%

Diagnostic Problems in Diffuse Axonal Injury

Jang

2020

Diagnostics

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In this study, three problems associated with diagnosing diffuse axonal injury (DAI) in patients with traumatic brain injury are reviewed: the shortage of scientific evidence supporting the 6-hour loss of consciousness (LOC) diagnostic criterion to discriminate concussion and DAI, the low sensitivity of conventional brain MRI in the detection of DAI lesions, and the inappropriateness of the term diffuse in DAI. Pathological study by brain biopsy is required to confirm DAI; however, performing a brain biopsy for the diagnosis of DAI in a living patient is impossible. Therefore, the diagnosis of DAI in a living patient is clinically determined based on the duration of LOC, clinical manifestations, and the results of conventional brain MRI. There is a shortage of scientific evidence supporting the use of the 6-hour LOC criterion to distinguish DAI from concussion, and axonal injuries have been detected in many concussion cases with a less than 6-hour LOC. Moreover, due to the low sensitivity of conventional brain MRI, which can only detect DAI lesions in approximately half of DAI patients, diagnostic MRI criteria for DAI are not well established. In contrast, diffusion tensor imaging (DTI) has been shown to have high sensitivity for the detection of DAI lesions. As DTI is a relatively new method, further studies aimed at the establishment of diagnostic criteria for DAI detection using DTI are needed. On the other hand, because DAI distribution is not diffuse but multifocal, and because axonal injury lesions have been detected in concussion patients, steps to standardize the use of terms related to axonal injury in both concussion and DAI are necessary.

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

Cited by 29 publications

References 49 publications

Advanced Analysis of Diffusion Tensor Imaging Along With Machine Learning Provides New Sensitive Measures of Tissue Pathology and Intra-Lesion Activity in Multiple Sclerosis

Advanced Analysis of Diffusion Tensor Imaging Along With Machine Learning Provides New Sensitive Measures of Tissue Pathology and Intra-Lesion Activity in Multiple Sclerosis

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

Diagnostic Problems in Diffuse Axonal Injury

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