hMRI - A toolbox for quantitative MRI in neuroscience and clinical research

Neuroscience and clinical researchers are increasingly interested in quantitative magnetic resonance imaging (qMRI) due to its sensitivity to micro-structural properties of brain tissue such as axon, myelin, iron and water concentration. We introduce the hMRI-toolbox, an open-source, easy-to-use tool available on GitHub, for qMRI data handling and processing, presented together with a tutorial and example dataset. This toolbox allows the estimation of high-quality multi-parameter qMRI maps (longitudinal and effective transverse relaxation rates and , proton density and magnetisation transfer saturation) that can be used for quantitative parameter analysis and accurate delineation of subcortical brain structures. The qMRI maps generated by the toolbox are key input parameters for biophysical models designed to estimate tissue microstructure properties such as the MR g-ratio and to derive standard and novel MRI biomarkers. Thus, the current version of the toolbox is a first step towards in vivo histology using MRI (hMRI) and is being extended further in this direction. Embedded in the Statistical Parametric Mapping (SPM) framework, it benefits from the extensive range of established SPM tools for high-accuracy spatial registration and statistical inferences and can be readily combined with existing SPM toolboxes for estimating diffusion MRI parameter maps. From a user's perspective, the hMRI-toolbox is an efficient, robust and simple framework for investigating qMRI data in neuroscience and clinical research

A travelling heads study investigating qMRI metrics on cortical regions

Technological advances in magnetic resonance imaging (MRI) have facilitated numerous studies on neural architecture, such as studies addressing pathology, behaviour or individual differences in brain activity. It is important, however, to first ascertain what variation can arise due to site-specific scanner properties (hard- and software). A certain amount of noise in MR images can indeed be attributable to such properties, even when the same scanner is used across different sites. Reproducibility across sites is possible with the use of quantitative MRI metrics (qMRI), where physical properties assigned to voxels allow for non-invasive analysis of brain tissue including sensitivity to iron and myelin content. Leutritz et al. (2020) investigated intra-site (scan-rescan) and intersite (between sites) variability on Siemens and Philips scanners through multi-parameter mapping techniques (MPM). The authors found intra-site scan-rescan coefficients of variance (CoV) ranging between 4% and 16% across parameters, with similar results for inter-site CoV. The current study implements a similar strategy to Leutritz et al. (2020) in that it investigates inter-site and interscanner variability in a "travelling heads" type of study. Using scanners by the same manufacturer (but two different models), the study investigates qMRI metrics for inter-site and inter-scanner differences and their corresponding effects on cortical regions.peer-reviewe

Investigating demyelination, iron accumulation, and synaptic loss in Alzheimer’s disease using multimodal imaging techniques

Alzheimer’s disease (AD), the most common type of dementia, is associated with neuronal death and synaptic loss [1], [2]. Pathological aggregation of amyloid-beta and tau protein are key elements of AD pathophysiology. Myelin loss and iron accumulation in the brain are also fundamental features of aging and dementia [3], [4], but are less frequently investigated. Quantitative MRI (qMRI) enables us to determine the brain tissue parameters such as magnetization transfer (MT) and effective transverse relaxation (R2*), which leads to the detection of microstructural tissue-related alterations in aging and neurodegenerative diseases [5]. Here we investigate the association of neurodegeneration (as indexed by loss of synaptic density), increased iron accumulation, and decreased myelination in Alzheimer's disease in cohorts of 24 amyloid-positive patients (AD, 11 males and 13 females) and 19 healthy controls (HC, 9 males, and 10 females). All participants underwent a multiparameter qMRI protocol, which was processed to generate probability maps for MTsat and R2* [5]. Synaptic density was evaluated by the total volume distribution (Vt)maps, representing the distribution of the [18F] UCB-H PET radiotracer in the brain [6]. The data is organized according to the Brain Imaging Data Structure (BIDS) [7]. MRI data processing was performed in MATLAB (The MathWorks Inc., Natick, MA, USA) using the SPM12 framework (www.fil.ion.ucl.ac.uk/spm) and the hMRI toolbox [8] after modifications to make MR data compatible with the BIDS format [9]. Each multi-parameter map presents a different tissue-related (semi-)quantitative property, and therefore the qMRI maps have specific units. Therefore, all maps were z-transformed to ensure the comparability of the maps in a multivariate model. Then, we used General Linear Model (GLM) to test the groups against each other using age and sex as the covariates. Also, a multivariate GLM (mGLM) was performed on all modalities using the MSPM toolbox (https://github.com/LREN-CHUV/MSPM) to test differences in groups controlling for the age and sex of the participants [10]. Univariate group analysis of MTsat data resulted in a significant difference at the cluster level in the right hippocampus with p_cluster<0.05 FWE corrected and p_voxel<.001 uncorrected as cluster forming threshold (Figure1.A). In contrast, the same analysis for R2* modality reveals no difference between the groups. PET_Vt maps showed a difference between AD and HC at p_voxel<0.05 (FWE corrected) in the right amygdala and hippocampus (Figure1.B), which agrees with previously reported results in [6]. See table.1 for more information. Multimodal analysis combining R2*, MTsat, and PET_Vt shows a bilateral difference in hippocampus between patients and healthy controls for voxel-wise analysis with corrected FWE P-voxel < 0.05 (Figure1.C). The canonical analysis suggests that AD patients had combined decreased myelination, decreased synaptic density, and increased iron in the hippocampus compared to controls. To conclude, in the case of AD, there is an interaction between neuropathological risk factors, therefore, to restrain the true multivariate nature of the data and better control for the false positive rate, one should use the multivariate model over multiple univariate models

Multivariate Age-related Analysis of Variance in quantitative MRI maps: Widespread age-related differences revisited

AbstractThis study utilized multivariate ANOVA analysis to investigate age-related microstructural changes in the brain tissues driven primarily by myelin, iron, and water content. Voxel-wise analyses were performed on gray matter (GM) and white matter (WM), in addition to region of interest (ROI) analyses. The multivariate approach identified brain regions showing coordinated alterations in multiple tissue properties and demonstrated bidirectional correlations between age and all examined modalities in various brain regions, including the caudate nucleus, putamen, insula, cerebellum, lingual gyri, hippocampus, and olfactory bulb. The multivariate model was more sensitive than univariate analyses as evidenced by detecting a larger number of significant voxels within clusters in the supplementary motor area, frontal cortex, hippocampus, amygdala, occipital cortex, and cerebellum bilaterally. The examination of normalized, smoothed, and z-transformed maps within the ROIs revealed age-dependent differences in myelin, iron, and water content. These findings contribute to our understanding of age-related brain differences and provide insights into the underlying mechanisms of aging. The study emphasizes the importance of multivariate analysis for detecting subtle microstructural changes associated with aging that may motivate interventions to mitigate cognitive decline in older adults

Correction of FLASH-based MT saturation in human brain for residual bias of B1-inhomogeneity at 3T

Background: Magnetization transfer (MT) saturation reflects the additional saturation of the MRI signal imposed by an MT pulse and is largely driven by the saturation of the bound pool. This reduction of the bound polarization by the MT pulse is less efficient than predicted by the differential B1-square law of absorption. Thus, B1 inhomogeneities lead to a residual bias in the MT saturation maps. We derive a heuristic correction to reduce this bias for a widely used multi-parameter mapping protocol at 3T. Methods: The amplitude of the MT pulse was varied via the nominal flip angle to mimic variations in B1. The MT saturation's dependence on the actual flip angle features a linear correction term, which was determined separately for gray and white matter. Results: The deviation of MT saturation from differential B1-square law is well described by a linear decrease with the actual flip angle of the MT pulse. This decrease showed no significant differences between gray and white matter. Thus, the post hoc correction does not need to take different tissue types into account. Bias-corrected MT saturation maps appeared more symmetric and highlighted highly myelinated tracts. Discussion:Our correction involves a calibration that is specific for the MT pulse. While it can also be used to rescale nominal flip angles, different MT pulses and/or protocols will require individual calibration. Conclusion: The suggested B1 correction of the MT maps can be applied post hoc using an independently acquired flip angle map

Correction of FLASH-based MT saturation in human brain for residual bias of B1-inhomogeneity at 3T

Background: Magnetization transfer (MT) saturation reflects the additionalsaturation of the MRI signal imposed by an MT pulse and is largely driven bythe saturation of the bound pool. This reduction of the bound polarization bythe MT pulse is less efficient than predicted by the differential B1-square lawof absorption. Thus, B1 inhomogeneities lead to a residual bias in the MTsaturation maps. We derive a heuristic correction to reduce this bias for awidely used multi-parameter mapping protocol at 3T. Methods: The amplitude ofthe MT pulse was varied via the nominal flip angle to mimic variations in B1.The MT saturation's dependence on the actual flip angle features a linearcorrection term, which was determined separately for gray and white matter.Results: The deviation of MT saturation from differential B1-square law is welldescribed by a linear decrease with the actual flip angle of the MT pulse. Thisdecrease showed no significant differences between gray and white matter. Thus,the post hoc correction does not need to take different tissue types intoaccount. Bias-corrected MT saturation maps appeared more symmetric andhighlighted highly myelinated tracts. Discussion: Our correction involves acalibration that is specific for the MT pulse. While it can also be used torescale nominal flip angles, different MT pulses and/or protocols will requireindividual calibration. Conclusion: The suggested B1 correction of the MT mapscan be applied post hoc using an independently acquired flip angle map.<br

In vivo multi-parameter mapping of the habenula using MRI

The habenula is a small, epithalamic brain structure situated between the mediodorsal thalamus and the third ventricle. It plays an important role in the reward circuitry of the brain and is implicated in psychiatric conditions, such as depression. The importance of the habenula for human cognition and mental health make it a key structure of interest for neuroimaging studies. However, few studies have characterised the physical properties of the human habenula using magnetic resonance imaging because its challenging visualisation in vivo, primarily due to its subcortical location and small size. To date, microstructural characterization of the habenula has focused on quantitative susceptibility mapping. In this work, we complement this previous characterisation with measures of longitudinal and effective transverse relaxation rates, proton density and magnetisation transfer saturation using a high-resolution quantitative multi-parametric mapping protocol at 3T, in a cohort of 26 healthy participants. The habenula had consistent boundaries across the various parameter maps and was most clearly visualised on the longitudinal relaxation rate maps. We have provided a quantitative multi-parametric characterisation that may be useful for future sequence optimisation to enhance visualisation of the habenula, and additionally provides reference values for future studies investigating pathological differences in habenula microstructure