Non-invasive assessment of normal and impaired iron homeostasis in living human brains

Filo, Shir; Shaharabani, Rona; Hanin, Daniel Bar; Adam, Masha; Ben-David, Eliel; Schoffman, Hanan; Margalit, Nevo; Shahar, Tal; Mezer, Aviv

doi:10.1101/2022.05.02.490254

Cited by 4 publications

(3 citation statements)

References 98 publications

(370 reference statements)

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“…A reduction of glial cell density, including astrocytes and oligodendrocytes could lead to a reduction of ferritin leading to a decreased iron 141 . Further, we know that T2* mostly reflects the amount of ferritin 142 Therefore, we postulate that our findings principally captures a reduction in hippocampal tissue integrity, decreased myelin, increased water content and iron reduction.…”

Section: Specificity Of Qmri Metricsmentioning

confidence: 68%

Joint signatures of morphological and microstructural inter-individual variation in the Alzheimer’s spectrum

Bussy,

Patel,

Parent

et al. 2024

Preprint

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Alzheimer's disease (AD) is primarily characterized by the accumulation of amyloid and tau pathologies. However, alterations in the detailed organization and composition of neural tissue also contribute to the disease's early stages. Here, we sought to explore whether hippocampal and cortical microstructural changes, such as myelin alterations and inflammation-mediated increases in iron, could serve as indices of AD-related pathophysiology. In this study, we included 158 participants across the AD spectrum: from individuals without cognitive impairment, at high risk for AD, in the prodromal phase with mild cognitive impairment, and suffering from clinical dementia. We measured atrophy using structural magnetic resonance imaging (MRI) and estimated myelin and iron content using quantitative MRI (qMRI) metrics derived from T1 and T2* relaxation, times respectively. We integrated these contrasts to estimate a joint multivariate signature of tissue alterations across the cortex and hippocampus using non-negative matrix factorization. The relevance of these signatures to AD-spectrum measures of medical history, lifestyle, and cognition were further explored using partial least squares correlation. Our results reveal lower disease-related cortical thickness over large areas of the cortex while T2* provided specific variation across the brain (lower in dorsomedial and superior temporal areas, superior frontal cortex, and premotor cortex, and higher in the occipital lobe). Additionally, we observed longer T1 and T2* times in the hippocampus associated with specific lifestyle risk factors like past smoking, high blood pressure, high cholesterol levels, and higher anxiety. These patterns were significantly related to older age, associated with AD progression, being female, and being an APOE-ϵ4 carrier. Taken together, our results suggest that qMRI metrics could serve as a valuable non-invasive tool for exploring the role of myelin and inflammation in AD-related pathophysiology and could be sensitive to modifiable risk factors related to lifestyle and medical history. Future studies may use these signatures to investigate their relationship in investigations related to lifestyle interventions or novel therapeutics.

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Section: Specificity Of Qmri Metricsmentioning

confidence: 68%

Joint signatures of morphological and microstructural inter-individual variation in the Alzheimer’s spectrum

Bussy,

Patel,

Parent

et al. 2024

Preprint

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“…In this study, the 3D FLASH sequence was used twice: once to reconstruct the T 1 maps (single-echo acquisition, Sequence #6) and once to calculate T 2 * maps (multi-echo acquisition, Sequence #4). It is possible to merge the two sequences into a single scan [128], in which multi-echo measurements for several flip angles will allow to reduce noise by averaging multiple T 2 * and QSM maps across different flip-angles. In this study we chose to acquire the two FLASH data-sets separately, offering a more modular protocol design, which allows to map specific parameters at a minimal scan time, i.e., investigators who are interested in only T 2 * or QSM maps are not required to repeat the FLASH sequence for different flip angles.…”

Section: Plos Onementioning

confidence: 99%

A comprehensive protocol for quantitative magnetic resonance imaging of the brain at 3 Tesla

Radunsky,

Solomon,

Stern

et al. 2024

PLoS ONE

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Quantitative MRI (qMRI) has been shown to be clinically useful for numerous applications in the brain and body. The development of rapid, accurate, and reproducible qMRI techniques offers access to new multiparametric data, which can provide a comprehensive view of tissue pathology. This work introduces a multiparametric qMRI protocol along with full postprocessing pipelines, optimized for brain imaging at 3 Tesla and using state-of-the-art qMRI tools. The total scan time is under 50 minutes and includes eight pulse-sequences, which produce range of quantitative maps including T1, T2, and T2* relaxation times, magnetic susceptibility, water and macromolecular tissue fractions, mean diffusivity and fractional anisotropy, magnetization transfer ratio (MTR), and inhomogeneous MTR. Practical tips and limitations of using the protocol are also provided and discussed. Application of the protocol is presented on a cohort of 28 healthy volunteers and 12 brain regions-of-interest (ROIs). Quantitative values agreed with previously reported values. Statistical analysis revealed low variability of qMRI parameters across subjects, which, compared to intra-ROI variability, was x4.1 ± 0.9 times higher on average. Significant and positive linear relationship was found between right and left hemispheres’ values for all parameters and ROIs with Pearson correlation coefficients of r>0.89 (P<0.001), and mean slope of 0.95 ± 0.04. Finally, scan-rescan stability demonstrated high reproducibility of the measured parameters across ROIs and volunteers, with close-to-zero mean difference and without correlation between the mean and difference values (across map types, mean P value was 0.48 ± 0.27). The entire quantitative data and postprocessing scripts described in the manuscript are publicly available under dedicated GitHub and Figshare repositories. The quantitative maps produced by the presented protocol can promote longitudinal and multi-center studies, and improve the biological interpretability of qMRI by integrating multiple metrics that can reveal information, which is not apparent when examined using only a single contrast mechanism.

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“…Assuming for now that cortical myelin density is predictive of MEG signal strength, we must further appreciate that R1 maps (like other qMRI measures) can only serve as a proxy for myelin content. Quantitative MRI parameter maps are sensitive to multiple tissue components to a varying extent 76 and thus lack the specificity to directly infer the abundance of any single microstructural tissue component.…”

Section: Limitationsmentioning

confidence: 99%

Myelin-informed forward models for M/EEG source reconstruction

Helbling,

Meyer,

Weiskopf

2024

Preprint

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Magnetoencephalography (MEG) and Electroencephalography (EEG) provide direct electrophysiological measures at an excellent temporal resolution, but the spatial resolution of source-reconstructed current activity is limited to several millimetres. Here we show, using simulations of MEG signals and Bayesian model comparison, that non-invasive myelin estimates from high-resolution quantitative magnetic resonance imaging (MRI) can enhance MEG/EEG source reconstruction. Our approach assumes that MEG/EEG signals primarily arise from the synchronised activity of pyramidal cells, and since most of the myelin in the cortical sheet originates from these cells, myelin density can predict the strength of cortical sources measured by MEG/EEG. Leveraging recent advances in quantitative MRI, we exploit this structure-function relationship and scale the leadfields of the forward model according to the local myelin density estimates from in vivo quantitative MRI to inform MEG/EEG source reconstruction. Using Bayesian model comparison and dipole localisation errors (DLEs), we demonstrate that adapting local forward fields to reflect increased local myelination at the site of a simulated source explains the simulated data better than models without such leadfield scaling. Our model comparison framework proves sensitive to myelin changes in simulations with exact coregistration and moderate-to-high sensor-level signal-to-noise ratios (≥10 dB) for the multiple sparse priors (MSP) and empirical Bayesian beamformer (EBB) approaches. Furthermore, we sought to infer the microstructure giving rise to specific functional activation patterns by comparing the myelin-informed model which was used to generate the activation with a set of test forward models incorporating different myelination patterns. We found that the direction of myelin changes, however not their magnitude, can be inferred by Bayesian model comparison. Finally, we apply myelin-informed forward models to MEG data from a visuo-motor experiment. We demonstrate improved source reconstruction accuracy using myelin estimates from a quantitative longitudinal relaxation (R1) map and discuss the limitations of our approach.HighlightsWe use quantitative MRI to implement myelin-informed forward models for M/EEGLocal myelin density was modelled by adapting the local leadfieldsMyelin-informed forward models can improve source reconstruction accuracyWe can infer the directionality of myelination patterns, but not their strengthWe apply our approach to MEG data from a visuo-motor experiment

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Non-invasive assessment of normal and impaired iron homeostasis in living human brains

Cited by 4 publications

References 98 publications

Joint signatures of morphological and microstructural inter-individual variation in the Alzheimer’s spectrum

Joint signatures of morphological and microstructural inter-individual variation in the Alzheimer’s spectrum

A comprehensive protocol for quantitative magnetic resonance imaging of the brain at 3 Tesla

Myelin-informed forward models for M/EEG source reconstruction

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