Cross-sectional and Longitudinal Assessment of Brain Iron Level in Alzheimer Disease Using 3-T MRI

Damulina, Anna; Pirpamer, Lukas; Soellradl, Martin; Sackl, Maximilian; Tinauer, Christian; Hofer, Edith; Enzinger, Christian; Gesierich, Benno; Duering, Marco; Ropele, Stefan; Schmidt, Reinhold; Langkammer, Christian

doi:10.1148/radiol.2020192541

Cited by 89 publications

(99 citation statements)

References 36 publications

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“…Why iron increases with age and even more profoundly in neurodegenerative diseases is still largely unknown [ 8 , 48 ]. It is hypothesized to be caused by several factors including increased blood–brain barrier permeability and disorganization of the iron-dense myelin sheaths [ 49 – 51 ].…”

Section: Discussionmentioning

confidence: 99%

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Iron loading is a prominent feature of activated microglia in Alzheimer’s disease patients

Kenkhuis

Somarakis

Haan

et al. 2021

acta neuropathol commun

114

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Brain iron accumulation has been found to accelerate disease progression in amyloid-β(Aβ) positive Alzheimer patients, though the mechanism is still unknown. Microglia have been identified as key players in the disease pathogenesis, and are highly reactive cells responding to aberrations such as increased iron levels. Therefore, using histological methods, multispectral immunofluorescence and an automated in-house developed microglia segmentation and analysis pipeline, we studied the occurrence of iron-accumulating microglia and the effect on its activation state in human Alzheimer brains. We identified a subset of microglia with increased expression of the iron storage protein ferritin light chain (FTL), together with increased Iba1 expression, decreased TMEM119 and P2RY12 expression. This activated microglia subset represented iron-accumulating microglia and appeared morphologically dystrophic. Multispectral immunofluorescence allowed for spatial analysis of FTL+Iba1+-microglia, which were found to be the predominant Aβ-plaque infiltrating microglia. Finally, an increase of FTL+Iba1+-microglia was seen in patients with high Aβ load and Tau load. These findings suggest iron to be taken up by microglia and to influence the functional phenotype of these cells, especially in conjunction with Aβ.

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Section: Discussionmentioning

confidence: 99%

“…Iron accumulation, irrespective of microglial activation, on the other hand, has been reported in disease-affected areas in Alzheimer’s disease, using both in-vivo and post-mortem human MRI [ 8 ]. Several MRI and histology studies found high correlations between iron accumulation and cortical Aβ and tau spreading [ 9 – 11 ].…”

Section: Introductionmentioning

confidence: 99%

Iron loading is a prominent feature of activated microglia in Alzheimer’s disease patients

Kenkhuis

Somarakis

Haan

et al. 2021

acta neuropathol commun

114

View full text Add to dashboard Cite

show abstract

“…Why iron increases with age and even more profoundly in neurodegenerative diseases is still largely unknown [8,47]. It is hypothesized to be caused by several factors including increased blood-brain barrier permeability and disorganization of the iron-dense myelin sheaths [48][49][50].…”

Section: Discussionmentioning

confidence: 99%

“…Iron accumulation, irrespective of microglial activation, on the other hand, has been reported in disease-affected areas in Alzheimer's disease, using both in-vivo and post-mortem human MRI [8]. Several MRI and histology studies found high correlations between iron accumulation and cortical Aβ and tau spreading [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Iron-loading is a prominent feature of activated microglia in Alzheimer’s disease patients

Kenkhuis

Somarakis

Haan

et al. 2021

Preprint

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Brain iron accumulation has been found to accelerate disease progression in Amyloid β-positive Alzheimer patients, though the mechanism is still unknown. Microglia have been identified as key-players in the disease pathogenesis, and are highly reactive cells responding to aberrations such as increased iron levels. Therefore, using histological methods, multispectral immunofluorescence and an automated in-house developed microglia segmentation and analysis pipeline, we studied the occurrence of iron-accumulating microglia and the effect on its activation state in human Alzheimer brains. We identified a subset of microglia with increased expression of the iron storage protein ferritin light chain (FTL), together with increased Iba1 expression, decreased TMEM119 and P2RY12 expression. This activated microglia subset represented iron-accumulating microglia and appeared morphologically dystrophic. Multispectral immunofluorescence allowed for spatial analysis of FTL+Iba1+-microglia, which were found to be the predominant Aβ-plaque infiltrating microglia. Finally, an increase of FTL+Iba1+-microglia was seen in patients with high Amyloid-β load and Tau load. These findings suggest iron to be taken up by microglia and to influence the functional phenotype of these cells, especially in conjunction with Aβ.

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“…For the latter applications, relaxation and susceptibility values in the form of, e.g., lesions in strategic locations can be easily added to the current phantom. Simulated data might be also relevant in the case of group studies of diseases, where differences in the deep grey matter nuclei were found (88,89) and the optimum QSM reconstruction parameters might improve the limits to detect those changes. Such question can be readily addressed with the digital phantom by changing the parameters of Eq.…”

Section: Discussionmentioning

confidence: 99%

QSM Reconstruction Challenge 2.0: a realistic in silico head phantom for MRI data simulation and evaluation of susceptibility mapping procedures

Marques

Meineke

Milovic

et al. 2020

Preprint

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Purpose: To create a realistic in-silico head phantom for the second QSM Reconstruction Challenge and for future evaluations of processing algorithms for Quantitative Susceptibility Mapping (QSM). Methods: We created a whole-head tissue property model by segmenting and post-processing high-resolution, multi-parametric MRI data acquired from a healthy volunteer. We simulated the steady-state magnetization using a Bloch simulator and mimicked a Cartesian sampling scheme through Fourier-based post-processing. We demonstrated some of the phantom properties, including the possibility of generating phase data that do not evolve linearly with echo time due to partial volume effects or complex distributions of frequency shifts within the voxel. Computer code for generating the phantom and performing the MR simulation was designed to facilitate flexible modifications of the model, such as the inclusion of pathologies, as well as the simulation of a wide range of acquisition protocols. Results: The brain-part of the phantom features realistic morphology combined with realistic spatial variations in relaxation and susceptibility values. Simulation code allows adjusting the following parameters and effects: repetition time and echo time, voxel size, background fields, and RF phase biases. Additionally, diffusion weighted imaging data of the phantom is provided allowing future investigations of tissue microstructure effects in phase and QSM algorithms. Conclusion: The presented phantom and computer programs are publicly available and may serve as a ground truth in future assessments of the faithfulness of quantitative MRI reconstruction algorithms.

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Cross-sectional and Longitudinal Assessment of Brain Iron Level in Alzheimer Disease Using 3-T MRI

Cited by 89 publications

References 36 publications

Iron loading is a prominent feature of activated microglia in Alzheimer’s disease patients

Iron loading is a prominent feature of activated microglia in Alzheimer’s disease patients

Iron-loading is a prominent feature of activated microglia in Alzheimer’s disease patients

QSM Reconstruction Challenge 2.0: a realistic in silico head phantom for MRI data simulation and evaluation of susceptibility mapping procedures

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