Patterns of Brain Iron Accumulation in Vascular Dementia and Alzheimer’s Dementia Using Quantitative Susceptibility Mapping Imaging

Moon, Yeonsil; Han, Seol-Heui; Moon, Won‐Jin

doi:10.3233/jad-151037

Cited by 117 publications

(91 citation statements)

References 43 publications

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“…Then, we selected iron-rich subcortical nuclei as regions of interest (ROI) to assess the association between the susceptibility values of these ROIs with cognitive performance. ROIs were selected in accordance with previous studies and included the pulvinar nucleus of the thalamus, head of caudate nucleus, globus pallidus, putamen, hippocampus, substantia nigra, and red nucleus (Liu et al, 2015a; Moon et al, 2016). Figure 2 presents examples of the seven ROIs.…”

Section: Methodsmentioning

confidence: 99%

“…The significance levels were set at p < 0.05 for all analyses. To assess the reliability of measurement in segmenting ROI, we selected the MR images of 10 normal subjects in accordance with a previous study (Moon et al, 2016). Another rater independently measured the ROIs for each anatomical target.…”

Section: Methodsmentioning

confidence: 99%

“…Thus, QSM can be used in future studies of predictive value for cognitive performance. Additionally, movement and neurodegenerative disorders, such as Parkinson’s disease (PD), VaD, and AD, are associated with iron overload in the brain (Acosta-Cabronero et al, 2013; He et al, 2015; Moon et al, 2016). Although it is unclear whether iron accumulation is the cause or consequence of these diseases, monitoring the spatial distribution and the temporal dynamics of iron deposition may offer important insights on the pathogenesis of these diseases.…”

Section: Introductionmentioning

confidence: 99%

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Characterizing Brain Iron Deposition in Patients with Subcortical Vascular Mild Cognitive Impairment Using Quantitative Susceptibility Mapping: A Potential Biomarker

Sun

Han

et al. 2017

Front. Aging Neurosci.

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The presence and pattern of iron accumulation in subcortical vascular mild cognitive impairment (svMCI) and their effects on cognition have rarely been investigated. We aimed to examine brain iron deposition in svMCI subjects using quantitative susceptibility mapping (QSM). Moreover, we aimed to investigate the correlation between brain iron deposition and the severity of cognitive impairment as indicated by z-scores. We recruited 20 subcortical ischemic vascular disease (SIVD) patients who fulfilled the criteria for svMCI. The control group comprised 19 SIVD patients without cognitive impairment. The SIVD and control groups were matched based on age, gender, and years of education. Both groups underwent QSM using a 3.0T MRI system. Susceptibility maps were reconstructed from in vivo data, which were acquired with a three-dimensional spoiled gradient recalled sequence. Then, regions of interest were drawn manually on the map of each subject. The inter-group differences of susceptibility values were explored in deep gray matter nuclei, including the bilateral pulvinar nucleus of the thalamus, head of caudate nucleus, globus pallidus, putamen, hippocampus, substantia nigra, and red nucleus. The correlations between regional iron deposition and composite z-score, memory z-score, language z-score, attention-executive z-score and visuospatial z-score were assessed using partial correlation analysis, with patient age and gender as covariates. Compared with the control, the svMCI group had elevated susceptibility values within the bilateral hippocampus and right putamen. Furthermore, the susceptibility value in the right hippocampus was negatively correlated with memory z-score and positively correlated with language z-score. The susceptibility value in the right putamen was negatively correlated with attention-executive z-score in the svMCI group. However, composite z-score were unrelated to susceptibility values. Our results suggest that brain iron deposition has clinical relevance as a biomarker for cognition. In addition, our results highlight the importance of iron deposition in understanding svMCI-associated cognitive deficits in addition to conventional MRI markers.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Characterizing Brain Iron Deposition in Patients with Subcortical Vascular Mild Cognitive Impairment Using Quantitative Susceptibility Mapping: A Potential Biomarker

Sun

Han

et al. 2017

Front. Aging Neurosci.

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“…Another study reported the increase of susceptibility (iron load) in vascular dementia as well as AD (Fig. 12) (141). …”

Section: Structural Imagingmentioning

confidence: 99%

Structural MR Imaging in the Diagnosis of Alzheimer's Disease and Other Neurodegenerative Dementia: Current Imaging Approach and Future Perspectives

Park

Moon

2016

Korean J Radiol

Self Cite

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With the rise of aging population, clinical concern and research attention has shifted towards neuroimaging of dementia. The advent of 3T, magnetic resonance imaging (MRI) has permitted the anatomical imaging of neurodegenerative disease, specifically dementia, with improved resolution. Furthermore, more powerful techniques such as diffusion tensor imaging, quantitative susceptibility mapping, and magnetic transfer imaging have successfully emerged for the detection of micro-structural abnormalities. In the present review article, we provide a brief overview of Alzheimer's disease and explore recent neuroimaging developments in the field of dementia with an emphasis on structural MR imaging in order to propose a simple and easily applicable systematic approach to the imaging diagnosis of dementia.

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“…The potential clinical usefulness of QSM also has been shown in multiple sclerosis [6, 43], lupus erythematosus [44], Alzheimer's disease [45], Parkinson's disease [46], multifocal leukoencephalopathy [47], cerebral perfusion [41], hemorrhage [9, 48], function MRI [12, 42] in brain, and some applications in aorta, breast, extremity, kidney, and so forth [1–4, 42, 49]. Improving accuracy of QSM will profit these approaches.…”

Section: Discussionmentioning

confidence: 99%

Structure Prior Effects in Bayesian Approaches of Quantitative Susceptibility Mapping

Wang

Chen

Wang

et al. 2016

BioMed Research International

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Quantitative susceptibility mapping (QSM) has shown its potential for anatomical and functional MRI, as it can quantify, for in vivo tissues, magnetic biomarkers and contrast agents which have differential susceptibilities to the surroundings substances. For reconstructing the QSM with a single orientation, various methods have been proposed to identify a unique solution for the susceptibility map. Bayesian QSM approach is the major type which uses various regularization terms, such as a piece-wise constant, a smooth, a sparse, or a morphological prior. Six QSM algorithms with or without structure prior are systematically discussed to address the structure prior effects. The methods are evaluated using simulations, phantom experiments with the given susceptibility, and human brain data. The accuracy and image quality of QSM were increased when using structure prior in the simulation and phantom compared to same regularization term without it, respectively. The image quality of QSM method using the structure prior is better comparing, respectively, to the method without it by either sharpening the image or reducing streaking artifacts in vivo. The structure priors improve the performance of the various QSMs using regularized minimization including L1, L2, and TV norm.

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Patterns of Brain Iron Accumulation in Vascular Dementia and Alzheimer’s Dementia Using Quantitative Susceptibility Mapping Imaging

Abstract: Increased iron deposition in the putamen and caudate nucleus in VaD and AD patients was not associated with age or the severity of cognitive deficits. Further evaluations are needed to determine the temporal changes in iron load and their diagnostic role in dementia pathology.

Cited by 117 publications

References 43 publications

Characterizing Brain Iron Deposition in Patients with Subcortical Vascular Mild Cognitive Impairment Using Quantitative Susceptibility Mapping: A Potential Biomarker

Characterizing Brain Iron Deposition in Patients with Subcortical Vascular Mild Cognitive Impairment Using Quantitative Susceptibility Mapping: A Potential Biomarker

Structural MR Imaging in the Diagnosis of Alzheimer's Disease and Other Neurodegenerative Dementia: Current Imaging Approach and Future Perspectives

Structure Prior Effects in Bayesian Approaches of Quantitative Susceptibility Mapping

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