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. Author manuscript; available in PMC: 2020 Dec 3.
Published in final edited form as: Neuroimage. 2019 Dec 4;208:116415. doi: 10.1016/j.neuroimage.2019.116415

Longitudinal evaluation of demyelinated lesions in a multiple sclerosis model using ultrashort echo time magnetization transfer (UTE-MT) imaging

Caroline Guglielmetti 1,2,3, Tanguy Boucneau 2, Peng Cao 2, Annemie Van der Linden 3, Peder EZ Larson 2,4, Myriam M Chaumeil 1,2,4
PMCID: PMC6953605  NIHMSID: NIHMS1546729  PMID: 31811900

Abstract

Alterations in myelin integrity are involved in many neurological disorders and demyelinating diseases, such as multiple sclerosis (MS). Although magnetic resonance imaging (MRI) is the gold standard method to diagnose and monitor MS patients, clinically available MRI protocols show limited specificity for myelin detection, notably in cerebral grey matter areas. Ultrashort echo time (UTE) MRI has shown great promise for direct imaging of lipids and myelin sheaths, and thus holds potential to improve lesion detection.

In this study, we used a sequence combining magnetization transfer (MT) with UTE (“UTE-MT”, TE = 76 μs) and with short TE (“STE-MT”, TE = 3000 μs) to evaluate spatial and temporal changes in brain myelin content in the cuprizone mouse model for MS on a clinical 7 Tesla scanner. During demyelination, UTE-MT ratio (UTE-MTR) and STE-MT ratio (STE-MTR) values were significantly decreased in most white matter and grey matter regions. However, only UTE-MTR detected cortical changes. After remyelination in subcortical and cortical areas, UTE-MTR values remained lower than baseline values, indicating that UTE-MT, but not STE-MT, imaging detected long-lasting changes following a demyelinating event.

Next, we evaluated the potential correlations between imaging values and underlying histopathological markers. The strongest correlation was observed between UTE-MTR and percent coverage of myelin basic protein (MBP) immunostaining (r2 = 0.71). A significant, although lower, correlation was observed between STE-MTR and MBP (r2 = 0.48), and no correlation was found between UTE-MTR or STE-MTR and gliosis immunostaining. Interestingly, correlations varied across brain substructures

Altogether, our results demonstrate that UTE-MTR values significantly correlate with myelin content as measured by histopathology, not only in white matter, but also in subcortical and cortical grey matter regions in the cuprizone mouse model for MS. Readily implemented on a clinical 7 Tesla system, this approach thus holds great potential for detecting demyelinating/remyelinating events in both white and grey matter areas in humans. When applied to patients with neurological disorders, including MS patient populations, UTE-MT methods may improve the non-invasive longitudinal monitoring of brain lesions, not only during disease progression but also in response to next generation remyelinating therapies.

Keywords: Ultrashort echo time, Magnetization transfer, Myelin, Demyelination, Multiple sclerosis, Cuprizone

Graphical Abstract

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1. Introduction

Myelin is an essential component of the central nervous system (CNS) that ensures proper neuronal function and neural transmission (Baumann and Pham-Dinh, 2001). Alterations and damage of the myelin sheaths have been implicated in various neurological disorders, particularly multiple sclerosis (MS) (Love, 2006; Popescu and Lucchinetti, 2012; Reich et al., 2018). MS is an autoimmune disease that leads to demyelination in the CNS, both in white and grey matter areas, resulting in motor and cognitive disabilities. Patients suffering from MS present brain lesions characterized by demyelination and presence of inflammatory cells (Reich et al., 2018). Magnetic resonance imaging (MRI) is the preferred imaging tool for diagnosis of MS and monitoring of disease activity and progression. Although conventional MRI techniques have demonstrated very high sensitivity for the detection of lesions in white matter and active lesions associated with a leaky blood brain barrier (Filippi and Rocca, 2011; Filippi et al., 2016; Traboulsee et al., 2016), to date, there is still a lack of robust methods that allow detecting lesions located in grey matter and cortical areas. Therefore, development and validation of novel MRI methods enabling non-invasive evaluation of myelin integrity would not only provide new tools to improve our understanding of MS pathology, but are also becoming a necessity as novel drugs promoting tissue repair and remyelination are underway (Green et al., 2017; Plemel et al., 2017).

In the CNS, myelin is a specialized structure sheathing the axons, and is constituted by spirally wrapped membranes originating from oligodendrocytes. This multilamellar structure is principally composed of lipids (70% of dry weight) and myelin-associated proteins (Baumann and Pham-Dinh, 2001). Due to this unique arrangement, myelin lipid protons and protons in water molecules that are tightly bound to myelin possess very short T2 relaxation times, in the order of 50-1000μs, that cannot be detected with conventional clinical magnetic resonance (MR) sequences (Horch et al., 2011; Laule et al., 2007; Wilhelm et al., 2012).

Several MR-based strategies have been developed to indirectly image myelin, such as T2 relaxometry and measure of myelin water fraction, diffusion tensor and diffusion kurtosis MR imaging, and magnetization transfer (MT) imaging. For extensive review of these MR techniques and their applications to myelin imaging, we refer the reader to (Heath et al., 2018; Laule et al., 2007; Ontaneda and Fox, 2017). Over the past decade, a number of studies have brought evidences that very short T2 components associated with myelin lipids could be imaged using ultra-short echo time (UTE) MRI sequences (Boucneau et al., 2018; Du et al., 2014; Fan et al., 2017; Fan et al., 2018; He et al., 2017; Horch et al., 2011; Robson et al., 2003; Sheth et al., 2016; Sheth et al., 2017; Wilhelm et al., 2012). Recently, UTE sequences have been combined with magnetization transfer (MT) techniques for the assessment of tissue possessing extremely short T2 relaxation times (Carl et al., 2018; Chang et al., 2015a; Du et al., 2009; Hodgson et al., 2011; Wei et al., 2018). In short, an off-resonance radiofrequency pulse is applied before the standard UTE sequence in order to sensitize the MR signal to proton pools with extremely short T2 relaxation times. From these UTE-MT sequences, quantitative UTE-MT ratio (UTE-MTR) values can be obtained, that reflect both direct saturation of ultrashort T2 components as well as MT saturation of longer T2 components. To date, UTE-MTR measures have shown great promises for differentiating between normal control and pathological abnormalities of tendons, cortical bones, and more recently, for improving the characterization of tissue microstructure in the human brain (Chang et al., 2015a; Chang et al., 2015b; Grosse et al., 2013; Syha et al., 2014; Wei et al., 2018).

The cuprizone (CPZ) mouse model is a well-described and very reproducible animal model of MS, which makes it highly suitable for validation of imaging techniques (Guglielmetti et al., 2017a; Matsushima and Morell, 2001; Nathoo et al., 2014; Praet et al., 2014). Supplementation of regular rodent diet with 0.2% of the copper chelator CPZ has been shown to induce oligodendrocyte cell death, demyelination and important reactive gliosis in the forebrain and cerebellum (Groebe et al., 2009; Kipp et al., 2009; Skripuletz et al., 2010; Skripuletz et al., 2008). Both white and grey matter, including subcortical and cortical grey areas, undergo severe demyelination within a few weeks of CPZ administration. Interestingly, when CPZ is withdrawn from the diet, spontaneous remyelination occurs (Matsushima and Morell, 2001). Additionally, studies have reported that the degree of demyelination varies within white matter areas, in particular along the rostro-caudal plane, thus enabling to evaluate the relative sensitivity of imaging tools to disease severity (Guglielmetti et al., 2016b; Steelman et al., 2012; Tagge et al., 2016; Wu et al., 2008; Xie et al., 2010).

In this study, we evaluated the potential of a UTE-MT MR sequence to generate images with high contrast for cerebral structures in the mouse brain using a clinical 7 Tesla MRI scanner. We also acquired images using a short TE (STE) within the same MR acquisition scheme, for comparison and paired assessment of the effect of TE on the image contrast. Next, we conducted a longitudinal imaging study to examine the temporal changes of UTE-MTR and STE-MTR following CPZ-mediated brain demyelination, gliosis, and subsequent remyelination. Our results indicated that UTE-MTR and STE-MTR can detect CPZ-induced alterations in white matter and subcortical grey matter, while only UTE-MTR detected alterations in cortical grey matter during the demyelination phase. At the end of the recovery period, UTE-MTR could detect spontaneous remyelination in white matter and persistent changes to tissue microstructure in subcortical and cortical grey areas, while STE-MTR only detected spontaneous remyelination in the white matter. Importantly, we also showed that UTE-MTR was more strongly associated with myelin content as assessed by histopathology, as compared to STE-MTR. Altogether, our findings demonstrated that UTE-MT imaging can detect brain tissue alterations in white and grey matter in the CPZ model for MS. This MR approach is readily clinically translatable, and thus has great potential to improve characterization of damaged tissue at acute and chronic time points in MS patients.

2. Materials and Methods

2.1. Animals and experimental outline

All animal research was approved by the Institutional Animal Care and Use Committee of the University of California, San Francisco. B6(Cg)-Tyrc-2J/J (stock no. 000058) albino female mice (n=30) were purchased from Jackson Laboratories (Bar Harbor, ME, USA). At eight weeks of age, all mice received a 0.2% cuprizone (CPZ, Sigma-Aldrich) diet for up to six weeks to induce cerebral demyelination. At the end of the six-week period, mice were returned to a standard rodent chow for an additional period of six weeks to allow for recovery and remyelination, as previously described (Guglielmetti et al., 2017b; Guglielmetti et al., 2016b).

A total of six mice underwent longitudinal MRI exams at eight weeks of age, prior to CPZ administration (W0, Control), after four weeks of CPZ diet (W4 CPZ), after six weeks of CPZ diet (W6 CPZ), and after six weeks of CPZ diet followed by six weeks of recovery (W6 CPZ + W6 recovery). A separate group of mice (n=24 mice) that was not subject to any MRI was euthanized at each corresponding imaging time point (n=6 mice per time point) in order to perform quantitative immunofluorescence analyses.

2.2. MRI acquisitions

In vivo imaging experiments were conducted on a 7 Tesla human imaging system (MR950, GE Healthcare, Waukesha, WI) equipped with 50 mT/m, 200 mT/m/ms gradients and a 1H home-build transmit/receive surface coil (Ø = 20 mm). For each imaging session, mice were anesthetized using isoflurane (1.5 – 2% in O2) and placed in a dedicated water-heated cradle allowing for reproducible positioning of the mouse head.

Acquisition of the 3D UTE and STE gradient-echo MRI data were performed within the same sequence with the following parameters: repetition time (TR) = 20 ms, echo time (TE1) = 76 μs (for UTE), TE2 = 3000 μs (for STE), flip angle = 10°, non-selective hard pulse excitation, field-of-view (FOV) 17 × 12.5 × 22 mm3, total readout length 1320 μs, 4 averages, voxel sixe = 200 × 200 × 500 μm3. The readout bandwidth was ±125 kHz, and approximately 40,000 spokes were acquired per image. The sequence used a 3D radial k-space trajectory supporting anisotropic field-of-view and resolution. An analytic density compensation was applied based on the k-space trajectory (Larson et al., 2008). Images were reconstructed using gridding. To create the magnetization transfer contrast, UTE and STE images were acquired two ways: with an off-resonance radio frequency (RF) pulse incorporated into the sequence (saturated), and without the off-resonance RF pulse (unsaturated). The RF saturation scheme used a pair of adiabatic hyperbolic secant (sech) 180° pulses with a total pulse length = 20 ms, pulse bandwidth = 1 kHz, and nominal peak power = 0.15 G, which was applied at the offset frequency of −1800Hz from water; each saturation pulse was followed by 8 repetitions. The total scan time for both saturated and unsaturated acquisitions was approximately 40 minutes.

2.3. MRI post-processing and analysis

Magnetization transfer ratio (MTR) maps were reconstructed with custom-built programs written in MATLAB (Matlab 2015b, The MathWorks) using the following formula based on the unsaturated and saturated images: MMTR = (∣Munsaturated∣ - ∣Msaturated∣) / ∣Munsaturated∣. UTE-MTR maps were generated using images acquired with TE1 = 76 μs, and STE-MTR maps using images acquired with TE2 = 3000 μs, as illustrated in Supplementary Figure 1.

Regions of interest (ROIs) were manually delineated using AMIRA software (Mercury Computer systems, San Diego, USA) on the UTE-MTR maps, and the mean UTE-MTR value was calculated for each ROI. The ROIs delineated on the UTE-MTR maps were then propagated to the STE-MTR maps to obtain the corresponding mean STE-MTR values. In order to minimize partial volume effect, we delineated small ROIs over four to five consecutives axial UTE-MTR map slices, so the total volume per area was equal or exceeded 0.36 mm3. ROIs were delineated at two distinct brain locations (Bregma +0.6mm and Bregma −2mm) as indicated in Supplementary Figure 2. The following ROIs were delineated: 1) three white matter tracts regions: splenium of the corpus callosum, genu of the corpus callosum, and internal capsule; 2) three subcortical areas: thalamus, caudate/putamen, and hippocampus; 3) three cortical areas: retro-spinal cortex, somatosensory cortex and motor cortex.

2.4. Histological analyses

Histological analyses were performed as previously described (Guglielmetti et al., 2017b). Briefly, mice were intracardially perfused with a phosphate buffered saline (PBS) solution followed by a 4% paraformaldehyde (PFA) solution. Next, brains were dissected and postfixed in a 4% PFA solution for 24 h, then dehydrated through a sucrose gradient (2 h at 5%, 2 h at 10%, and overnight at 20%). Afterwards, brain tissue was snap-frozen in liquid nitrogen and kept at − 80°C until further processing. Ten μm-thick cryosections were collected using a cryostat microtome (Leica, CM1850). The presence of myelin was qualitatively assessed with Black-Gold II histochemical staining, as Black-Gold II stain distributes in the inner, compact and outer myelin layers (Savaskan et al., 2009; Schmued et al., 2008). Quantitative analyses of myelin content were evaluated using myelin basic protein (MBP) immunofluorescence, as previously described (Guglielmetti et al., 2016b). For Black-Gold II staining, the sections were incubated in Black-Gold II solution at 60°C for twelve to fourteen minutes, rinsed in H2O, and incubated in 1% sodium thiosulfate solution at 60°C for three minutes, according to manufacturer’s instructions (Millipore, AG105). MBP immunofluorescence staining was performed on brain slides using the following antibody combination: a primary chicken anti-MBP antibody (Millipore, AB9348; 1:200 dilution) with a secondary rabbit anti-chicken Cy3 antibody (Jackson, 303-166-003, 1:1000 dilution). Glial fibrillary acidic protein (GFAP) immunofluorescence was used to evaluate the degree of astrogliosis using: a primary rabbit anti-GFAP (Dako, Z0334, 1:500 dilution) with a secondary goat anti-rabbit FITC (Jackson, 111-096-144,1:1000 dilution).

Fluorescence and bright field image acquisitions were performed using a conventional widefield system designed for epifluorescent and bright field imaging (Nikon Ti Microscope) equipped with Nikon DS-Qi2 monochrome and DS-Ri2 color cameras. The NIS-Elements 5.02 (Build 1266) software was used for wide field image acquisition. Quantitative analyses of immunofluorescence images were performed using NIH ImageJ analysis software (v1.51n). The levels of MBP and GFAP were determined based on the image-covering staining and expressed as percentage of the total area, as previously described (Guglielmetti et al., 2016a; Guglielmetti et al., 2017b; Guglielmetti et al., 2016b).

2.5. Statistical analyses

Results are expressed as mean ± standard deviation (SD). Statistical analyses of UTE-MTR, STE-MTR and MBP of the control mouse brain were performed using a One-Way ANOVA. A repeated measures ANOVA was used to evaluate statistical significance of the UTE-MTR and STE-MTR following CPZ diet. A One-Way ANOVA was used to assess statistical significance of the MBP and GFAP stainings following CPZ administration. The correlation between MR parameters and myelin content (MBP) or gliosis (GFAP) was assessed using linear regression. All given p-values obtained from One-Way ANOVA and repeated measures ANOVA were corrected for multiple testing using the Tukey HSD post-hoc test (*p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, ****p ≤ 0.0001).

3. Results

3.1. UTE-MTR and STE-MTR of the control mouse brain

We first evaluated the potential of UTE-MTR and STE-MTR to generate MR contrast of cerebral substructures in the control mouse brain at 7 Tesla. As shown in Figure 1.A, white matter tract regions were visible as bright, hyper-intense MR signals on both UTE-MTR maps (TE1 = 76 μs) and STE-MTR maps (TE2 = 3000 μs). In contrast, ventricles and dorsal hippocampus appeared as regions of hypointense MR signal. ROIs from brain regions of various myelin content and arrangement were selected for subsequent analysis of MRI data within the white matter tracts, subcortical areas, and cortical areas (Figure 1.B).

Figure 1: UTE-MTR, STE-MTR and myelin immunofluorescence of the control mouse brain.

Figure 1:

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(A) Representative UTE-MTR and STE-MTR maps (grey scale) obtained from a control (W0) mouse brain. The white matter tracts appeared as hyper-intense structures, while the ventricles appeared as hypointense regions. (B) Schematic representation of ROIs delineations of the white matter tracts (orange), subcortical grey matter (blue) and cortical grey matter (green) areas. (C) UTE-MTR and (D) STE-MTR values from all ROIs. (E) Representative MBP immunofluorescence images from a control mouse brain, demonstrating the heterogeneity in myelin load, compaction and arrangement between the selected ROIs (Scale bar = 200 μm). (F) Corresponding percent coverage of MBP staining. Correlations between (G) UTE-MTR and MBP, and (H) STE-MTR and MBP for all the ROIs, demonstrating a higher coefficient of correlation between UTE-MTR and MBP (r2 = 0.76, p < 0.0001) compared to STE-MTR and MBP (r2 = 0.46, p < 0.0001). Dark line shows the best-fit straight line from the linear regression analysis ± 95% confidence intervals. Abbreviations: Hippoc.: Hippocampus, Cau./put.: Caudate/putamen, Somatosensory. : Somatosensory cortex. All values are reported as mean ± SD. One-Way ANOVA, Tukey HSD post hoc test, *p ≤ 0.05, **p ≤ 0.01, and ***p ≤ 0.001, ****p ≤ 0.0001 (n = 6 mice per group).

In white matter tracks, UTE-MTR values from the internal capsule were significantly higher than values from the genu and the splenium (Figure 1.C, p = 0.0002 and p < 0.0001, respectively), while STE-MTR only detected a significant difference between the splenium and the internal capsule (Figure 1.D, p = 0.0089). Quantification of immunofluorescence images revealed a denser myelin content in the internal capsule compared to the splenium and genu (Figure 1.E and 1.F, p ≤ 0.0269).

In subcortical areas, UTE-MTR and STE-MTR from the thalamus and caudate/putamen were significantly higher than in the hippocampus (Figure 1.C, p < 0.0001 and p < 0.0001, respectively for UTE-MTR, and Figure 1.D, p = 0.0008 and p = 0.0002, respectively for STE-MTR). However, only UTE-MTR was able to differentiate between the caudate/putamen and the thalamus (Figure 1.C, p < 0.0001). MBP quantification revealed higher myelin load in the thalamus and caudate/putamen compared to hippocampus (Figure 1.F, p < 0.0001 and p = 0.0168, respectively), and between thalamus and caudate/putamen (p < 0.0001).

In cortical areas, only UTE-MTR was able to detect significant differences between the ROIs. UTE-MTR of the retro spinal cortex were higher than those of motor and somatosensory cortices (Figure 1.C, p = 0.0089 and p = 0.0065, respectively), in line with higher myelin content in the retro spinal cortex region compared to motor and somatosensory cortices (Figure 1.F, p = 0.0139 and p = 0.0082, respectively).

Last, we found a strong positive correlation between UTE-MTR and MBP (Figure 1. G, r2 = 0.76, p < 0.0001) as well as between STE-MTR and MBP (Figure 1. H, r2 = 0.46, p < 0.0001).

3.2. UTE-MTR and STE-MTR following CPZ-induced demyelination, remyelination and reactive gliosis

In this study, demyelination was achieved through the administration of 0.2% CPZ diet for a period of six weeks. Mice underwent longitudinal MRI exams prior to CPZ diet (W0), at W4 and W6 CPZ diet and after six week of recovery (W6 CPZ + W6 recovery). Histological analyses were performed at each corresponding MRI timepoints (Figure 2.A).

Figure 2: Longitudinal UTE-MTR, STE-MTR and histological characterization of the CPZ mouse brain: representative datasets.

Figure 2:

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(A) Experimental outline of the study indicating the time points of MRI and histological analyses, prior to CPZ diet (W0), after four (W4 CPZ) and six (W6 CPZ) weeks of CPZ diet, and after six weeks of recovery (W6 CPZ + W6 recovery). (B) Representative UTE-MTR maps and STE-MTR maps (grey scale) at all time points of interest. Decreased contrast can be seen on both UTE-MTR and STE-MTR maps for example in the splenium of the corpus callosum (white arrows) at W4 CPZ, followed by reappearance of contrast at W6 and W6 CPZ + W6 recovery. On the UTE-MTR maps, hypointensity can be observed in the thalamic area at W4 and W6 CPZ and W6 CPZ + W6 recovery. (C) Representative images of Black-Gold II and MBP staining demonstrated the spatial and temporal modulations in myelin levels, as seen by purple color or black contrast, respectively. Following CPZ diet, severe demyelination can be seen in the forebrain, as indicated by faint stainings for both Black-Gold II and MBP, particularly in the splenium at W4 CPZ, and thalamus and cortical areas at W6 CPZ. Reappearance of Black-Gold II and MBP staining can be observed at W6 CPZ + W6 recovery, demonstrating remyelination. (D) Representative GFAP immunofluorescence images showed important astrogliosis (GFAP, black contrast) in the splenium, internal capsule, thalamus and cortex following CPZ diet. At the end of the recovery period, astrogliosis was overall reduced but was still present in the splenium.

Representative UTE-MTR and STE-MTR maps, displayed as greyscale, clearly showed the hypointense splenium of the corpus callosum at W4 CPZ compared to W0, and its increased contrast back to hyperintense at W6 CPZ and W6 CPZ + W6 recovery. Additionally, decrease of UTE-MTR can be visually observed in the thalamic area following CPZ diet (Figure 2.B).

At W0, myelin appears as a red-purple coloration when stained with Black-Gold II, or as a dark grey-black contrast when stained for MBP (Figure 2.C). Both Black-Gold II and MBP staining confirmed demyelination of the splenium of the corpus callosum at W4 CPZ and widespread demyelination of the cortical and subcortical areas at W6 CPZ. At the end of the recovery period, remyelination was observed as Black-Gold II and MBP coloration displayed more intense staining.

Demyelination is often accompanied by neuro-inflammatory processes and reactive gliosis. As shown in Figure 2.D, astrogliosis, depicted by an increase of dark grey-black contrast from GFAP staining, occurred in the entire brain following W4 and W6 CPZ diet, and was particularly noticeable in the splenium, internal capsule, thalamus and cerebral cortex. At the end of the recovery period, astrogliosis is strongly reduced but persisted in the splenium.

UTE-MTR, STE-MTR, percent MBP and GFAP coverage were calculated for each ROIs defined in Figure 1.B and Supplementary Figure 2. Results are presented in the three following subsections. Percent changes relative to Baseline and all p-values are shown in Supplementary Tables 1 - 4.

3.2.1. White matter tracts

The splenium of the corpus callosum showed the strongest decrease of UTE-MTR values at W4 CPZ, followed by a subsequent increase at W6 CPZ and W6 CPZ +W6 recovery (Figure 3.A). Similar changes were observed with STE-MTR, although just reaching significance level (Figure 3.B). MBP immunostaining of the splenium mirrored the changes detected with UTE-MTR and STE-MTR. Severe demyelination was observed at W4 CPZ, followed by remyelination at W6 CPZ and W6 CPZ + W6 recovery (Figure 3.C. and Supplementary Figure 3.A, top row). Interestingly, astrogliosis was noted at W4 but reached its maximum at W6 CPZ. After recovery, astrocyte levels were decreased compared to W6 CPZ, but persistent astrogliosis was observed (Figure 3.D and Supplementary Figure 4.A, top row). In the genu of the corpus callosum, UTE-MTR was decreased at W4 CPZ and W6 CPZ, and increased at the end of the recovery period (Figure 3.E). In contrast, STE-MTR was only decreased at W6 CPZ (Figure 3.F). Demyelination and astrogliosis were observed at W4 and W6 CPZ. At W6 CPZ + W6 recovery remyelination had occurred, however astrocyte levels remained significantly higher compared to W0 (Figure 3.G. and Figure 3.H; Supplementary Figure 3.A and Supplementary Figure 4.A, middle rows).

Figure 3: Longitudinal UTE-MTR, STE-MTR, MBP and GFAP immunofluorescence of white matter tracts following CPZ diet.

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(A) UTE-MTR and (B) STE-MTR values were significantly decreased in the splenium after W4 CPZ, when (C) MBP staining indicated maximum demyelination. UTE-MTR and STE-MTR values from the splenium were increased at W6 CPZ and at W6 CPZ + W6 recovery, mirroring the MBP immunostaining outcome which indicated remyelination. (D) Astrogliosis was observed at W4 and W6 CPZ and remained after recovery. (E) UTE-MTR values from the genu were reduced at W4 and W6 CPZ, while (F) STE-MTR was only decreased at W6 CPZ. At W6 CPZ + W6 recovery, both UTE-MTR and STE-MTR values rose back to W0 values. (G) Demyelination was observed at W4 and W6 CPZ, and remyelination occurred by the end of the recovery period. Similarly as for the splenium, (H) astrogliosis was present at W4 and W6 CPZ and remained after recovery. (I) UTE-MTR values in the internal capsule were significantly decreased only at W6 CPZ while (J) STE-MTR did not detect any changes. (K) Demyelination and (L) astrocyte levels reached their maximum at W6 CPZ. All values are reported as mean ± SD. The gray lines represent mean value from each mouse and the colored lines represent mean ±SD of mean values from the individual subjects. One-Way ANOVA or repeated measures ANOVA, Tukey HSD post hoc test, *p ≤ 0.05, **p ≤ 0.01, and ***p ≤ 0.001, ****p ≤ 0.0001 (n = 6 mice).

In the internal capsule, UTE-MTR was significantly decreased only at W6 CPZ (Figure 3.I), and no differences in STE-MTR values were observed (Figure 3.J). Demyelination and astrogliosis were observed at W6 CPZ, followed by remyelination at W6 CPZ + W6 recovery (Figure 3.K. and 3.L; Supplementary Figure 3.A and Supplementary Figure 4.A, bottom rows).

3.2.2. Subcortical areas

In the thalamic area, UTE-MTR were lower following CPZ diet, (Figure 4.A), at W4 CPZ, W6 CPZ and W6 CPZ + W6 recovery. In contrast, STE-MTR values remained unchanged (Figure 4.B). MBP immunostaining was decreased, and astrogliosis was observed at W4 CPZ, W6 CPZ and after recovery (Figure 4.C and 4.D; Supplementary Figure 3.B, and Supplementary Figure 4.B, top rows).

Figure 4: Longitudinal UTE-MTR, STE-MTR and MBP and GFAP immunofluorescence of subcortical grey matter regions following CPZ diet.

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(A) UTE-MTR values of the thalamus were significantly decreased at W4 and W6 CPZ, and after recovery, while (B) STE-MTR remained unchanged. (C) MBP and (D) GFAP staining indicated demyelination at W4 and W6 CPZ and at W6 CPZ + W6 recovery. The strongest decrease of UTE-MTR corresponded to the maximal demyelination at W6 CPZ. (E) UTE-MTR values in the caudate/putamen were significantly decreased at W4 and W6 CPZ, and after recovery, while (F) STE-MTR was decreased only at W4 and W6 CPZ. (G) Demyelination and (H) astrogliosis were observed at every time point following CPZ diet, with astrogliosis reaching its highest level at W4 and W6 CPZ. (I) UTE-MTR and (J) STE-MTR of the hippocampus remained non significantly changed during the whole experimental period, despite significant (K) demyelination and (L) astrogliosis at W4 and W6 CPZ. All values are reported as mean ± SD. The gray lines represent mean value from each mouse and the colored lines represent mean ±SD of mean values from the individual subjects. One-Way ANOVA or repeated measures ANOVA, Tukey HSD post hoc test, *p ≤ 0.05, **p ≤ 0.01, and ***p ≤ 0.001, ****p ≤ 0.0001 (n = 6 mice).

In the caudate/putamen, UTE-MTR was decreased at W4 CPZ, W6 CPZ and W6 CPZ + W6 recovery (Figure 4.E), while STE-MTR was significantly decreased only at W4 and W6 CPZ (Figure 4.F). Demyelination and astrogliosis were observed at W4 CPZ, W6 CPZ and W6 CPZ + W6 recovery (Figure 4.G and Figure 4.H; Supplementary Figure 3.B, and Supplementary Figure 4.B, middle rows).

In the hippocampus, UTE-MTR and STE-MTR could not detect any changes following CPZ diet (Figure 4.I-J). However, demyelination was detected after W4 CPZ and W6 CPZ. At the end of the recovery period, remyelination was observed (Figure 4.K and Supplementary Figure 3.B, bottom row). The hippocampus showed increased astrocyte immunostaining at W4 CPZ (Figure 4.L and Supplementary Figure 4.B, bottom row), which returned to control levels by W6 CPZ + W6 recovery.

3.2.3. Cortical areas

UTE-MTR of the retro spinal cortex was decreased at W4 CPZ, further decreased at W6 CPZ and remained significantly lower at the end of the recovery period (Figure 5.A). STE-MTR remained unchanged over the entire experimental period (Figure 5.B). MBP immunostaining mirrored the changes observed with UTE-MTR. Myelin level was decreased at W4 CPZ, followed by an additional decrease at W6 CPZ diet (Figure 5.C and Supplementary Figure 3.C, top row). While spontaneous remyelination occurred during the six weeks recovery period, myelin content remained significantly lower compared to control mice. Astrocytes levels significantly increased at W4 and W6 CPZ and returned to nearly control levels after recovery (Figure 5.L and Supplementary Figure 4.C, top row).

Figure 5: Longitudinal UTE-MTR, STE-MTR and MBP and GFAP immunofluorescence of cortical grey matter regions following CPZ diet.

Figure 5:

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(A) UTE-MTR values of the retro spinal cortex were significantly decreased at W4 and W6 CPZ, and after W6 CPZ + W6 recovery, while (B) STE-MTR remained unchanged. (C) MBP staining mirrored the UTE-MTR results, indicating demyelination at W4 and W6 CPZ and after recovery, with the strongest decrease of UTE-MTR corresponding to the maximal demyelination at W6 CPZ. (D) Astrogliosis was present at W4 and W6 CPZ. (E) UTE-MTR of the somatosensory cortex was decreased at W6 CPZ and after recovery, while (F) STE-MTR remained unchanged. (G) Demyelination was observed at W4, W6 CPZ and persisted after W6 CPZ + W6 recovery, while (H) astrogliosis was only observed at W4 and W6 CPZ. (I) UTE-MTR of the motor cortex was decreased at W6 CPZ and after recovery, while (J) STE-MTR remained unchanged. (K) Demyelination was observed at W4, W6 CPZ and after recovery. (H) Astrogliosis was present at W4 and W6 CPZ. All values are reported as mean ± SD. The gray lines represent mean value from each mouse and the colored lines represent mean ±SD of mean values from the individual subjects. One-Way ANOVA or repeated measures ANOVA, Tukey HSD post hoc test, *p ≤ 0.05, **p ≤ 0.01, and ***p ≤ 0.001, ****p ≤ 0.0001 (n=6 mice).

In somatosensory and motor cortices, UTE-MTR was significantly decreased only after W6 CPZ (Figure 5.E and 5.I) and UTE-MTR of the somatosensory cortex remained lower at W6 CPZ + W6 recovery. STE-MTR did not detect any changes following CPZ diet (Figure 5.F and 5.J). Demyelination and astrogliosis were observed in somatosensory and motor cortices at W4 and W6 CPZ. At the end of the recovery period, remyelination was incomplete but astrocyte levels returned to nearly control levels (Figure 5.G, 5.H, 5.K and 5.L; Supplementary Figure 3.C and Supplementary Figure 4.C, middle and bottom rows).

3.3. Correlations between MR and immunohistological parameters

Potential associations between UTE-MTR/STE-MTR values and MBP/GFAP levels were explored using linear regression analyses (Figure 8, Table 1 and Table 2). When considering all ROIs together, the strongest correlation was observed between UTE-MTR and MBP (r2 = 0.71, p < 0.0001). Although lower, a significant correlation was also found between STE-MTR and MBP (r2 = 0.48, p < 0.0001). No correlations between UTE-MTR and GFAP or STE-MTR and GFAP were observed.

Table 1.

UTE-MTR and MBP, and UTE-MTR and GFAP correlation parameters

Region of interest MBP GFAP
Linear regression
equation		 r2 p-value Linear regression
equation		 r2 p-value
All Y = 0.0016*X + 0.31 0.71 <0.0001 (****) Y = 0.0003*X + 0.38 0.01 0.2231
White Matter Tracts
 Splenium Y = 0.0016*X + 0.29 0.78 <0.0001 (****) Y = −0.0014*X + 0.44 0.18 0.0410 (*)
 Genu Y = 0.0021*X + 0.26 0.59 <0.0001 (****) Y = −0.0022*X + 0.48 0.61 <0.0001 (****)
 Internal capsule Y = 0.0010*X + 0.39 0.21 0.0257 (*) Y = −0.0024*X + 0.51 0.24 0.0150 (*)
Subcortical Grey Matter
 Thalamus Y = 0.0015*X + 0.31 0.51 0.0001 (***) Y = −0.0078*X + 0.43 0.47 0.0002 (***)
 Caudate/Putamen Y = 0.0016*X + 0.33 0.35 0.0021 (***) Y = −0.0014*X + 0.38 0.17 0.0453 (*)
 Hippocampus Y = 0.0002*X + 0.34 0.01 0.6140 Y = −0.0001*X + 0.34 0.00 0.8598
Cortical Grey Matter
 Retro spinal cortex Y = 0.0009*X + 0.32 0.28 0.0085 (**) Y = −0.0015*X + 0.37 0.09 0.1553
 Somatosensory cortex Y = 0.0005*X + 0.33 0.19 0.0322 (*) Y = −0.0010*X + 0.36 0.10 0.1302
 Motor cortex Y = 0.0008*X + 0.32 0.19 0.0309 (*) Y = −0.0011*X + 0.36 0.05 0.3092
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Parameters from regression analysis between UTE-MTR and myelin content (MBP) or astrogliosis (GFAP).

Table 2.

MTR and MBP, and MTR and GFAP correlation parameters

Region of interest MBP GFAP
Linear regression
equation		 r2 p-value Linear regression
equation		 r2 p-value
All Y = 0.0011*X + 0.33 0.48 <0.0001 (****) Y = 0.0004*X + 0.38 0.01 0.1444
White Matter Tracts
 Splenium Y = 0.0009*X + 0.33 0.42 0.0006 (***) Y = −0.0004*X + 0.40 0.02 0.4851
 Genu Y = 0.0013*X + 0.30 0.20 0.0300 (*) Y = −0.0016*X + 0.45 0.30 0.0090 (**)
 Internal capsule Y = 0.0005*X + 0.42 0.03 0.4252 Y = −0.0010*X + 0.47 0.03 0.4181
Subcortical Grey Matter
 Thalamus Y = 0.0011*X + 0.34 0.23 0.0186 (*) Y = −0.0051*X + 0.42 0.19 0.0355 (*)
 Caudate/Putamen Y = 0.0026*X + 0.31 0.50 0.0001 (***) Y = −0.0030*X + 0.40 0.41 0.0007 (***)
 Hippocampus Y = −0.0003*X + 0.36 0.02 0.4715 Y = 0.0013*X + 0.33 0.05 0.2738
Cortical Grey Matter
 Retro spinal cortex Y = 0.0006*X + 0.33 0.07 0.2047 Y = −0.0003*X + 0.36 0.00 0.8511
 Somatosensory cortex Y = 0.0001*X + 0.35 0.00 0.8713 Y = 0.0005*X + 0.35 0.01 0.5886
 Motor cortex Y = 0.0005*X + 0.33 0.05 0.3065 Y = 0.0001*X + 0.35 0.00 0.9254
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Parameters from regression analysis between MTR and myelin content (MBP) or astrogliosis (GFAP).

When considering each ROI separately, we observed a significant correlation between UTE-MTR and MBP in the splenium, genu, internal capsule, thalamus, caudate/putamen, retro spinal, somatosensory and motor cortices (Table 1, Supplementary Figure 5). Significant correlations were also found between UTE-MTR and GFAP in the splenium, genu, internal capsule, thalamus and caudate/putamen. No significant correlations were found between STE-MTR and GFAP in the cortical areas (Table 1, Supplementary Figure 6).

As for STE-MTR values, we found significant correlations between STE-MTR and MBP in the splenium, genu, thalamus and caudate/putamen (Table 2, Supplementary Figure 7). Significant correlation between STE-MTR and GFAP were found in the genu, thalamus and caudate/putamen (Table 2, Supplementary Figure 8).

4. Discussion

In this study, 3D UTE-MT and STE-MT imaging sequences were implemented on a clinical 7 Tesla scanner and used to obtain MR images of the mouse brain. We first demonstrated that STE-MTR and UTE-MTR maps enabled the visualization of anatomically distinct regions, including white matter tracts, subcortical grey matter areas and cortical grey matter areas in the normal mouse brain. We then showed that both STE-MTR and UTE-MTR measures could detect cerebral tissue alterations following the administration of the neurotoxic compound CPZ. Importantly, UTE-MTR values were more strongly correlated with myelin immunostaining than STE-MTR, in both white and grey matter regions. Furthermore, UTE-MTR showed significantly higher discrimination between brain regions than STE-MTR in the healthy mouse brain, and additionally enabled the detection of CPZ-induced alterations in multiple white and grey matter regions, including in the cerebral cortex.

Inflammatory demyelination and spontaneous remyelination were achieved by feeding mice a diet supplemented with 0.2% CPZ for a period of six weeks, followed by a recovery period of six additional weeks. While the CPZ mouse model does not fully reflect the heterogeneity of lesions found in MS patients, CPZ-induced lesions have been shown to resemble pattern III and IV of MS lesions, with primary oligodendrocyte damage and demyelination, highlighting its clinical relevance (Lucchinetti et al., 2000; Reich et al., 2018; Torkildsen et al., 2008). Furthermore and importantly, the CPZ model presents many advantages over other demyelinating models, in particular a well-defined pathology in time, a high reproducibility in the location of lesions in the brain, and an extensive histological characterization (Goldberg et al., 2015; Gudi et al., 2009; Praet et al., 2014). Such features make this model highly suitable for validation of the proposed imaging techniques while requiring low number of animals (Fjaer et al., 2013; Guglielmetti et al., 2017b; Guglielmetti et al., 2016b; Jelescu et al., 2016; Khodanovich et al., 2017; Petiet et al., 2016).

In this study, differences between UTE-MTR and STE-MTR are expected to be due to two main biophysical-related phenomena: (1) the potential of UTE to directly capture signals from bound protons in the myelin phospholipid membranes (Horch et al., 2011; Wilhelm et al., 2012) as well as (2) increased sensitivity of UTE to myelin water protons that may experience MT saturation. Other expected sources of signal (intra and extra cellular water) have much longer T2* ~ 30 ms at 7 Tesla (van Gelderen et al., 2012), and thus should be approximately equivalent for UTE- and STE-MTR and not induce major differences.

For (1) the bound protons, we have measured a T2 * of ≈ 0.2 ms in vivo at 7 Tesla (Boucneau et al., 2018). This relaxation time is notably shorter than our readout time of 1320 μs, and thus the detection of this bound pool using our sequence is limited. For (2) myelin water, previous studies have reported a T2* of ~ 6 ms at 7 Tesla (van Gelderen et al., 2012). STE should thus have less myelin water signal, approximately exp(−TE/T2*) = exp(−3 ms/6 ms) ~ 0.6, or 60% of the myelin water signal compared to the UTE acquisition. Based on these T2* measurements and our TE values, we speculate the main difference between UTE- and STE-MTR is due to myelin water that is experiencing magnetization transfer. Other potential differences between UTE- and STE-MTR values might due to differences in signal-to-noise ratio (SNR) and/or variance of the images. UTE images displayed a higher SNR than STE images (Supplementary Figure 9.A-B, p = 0.0021 and p = 0.0077 for unsaturated and saturated images, respectively). However, as the SNR in the brain ROIs when averaging across voxels is very high for both UTE and STE images (> 100), the noise effect is much smaller than the differences observed between UTE and STE. Furthermore, we found no significant differences between the variance of UTE-MTR and STE-MTR (Supplementary Figure 9.C, p = 0.1295). Therefore, the differences we observed between UTE- and STE-MTR are unlikely due to noise or variance effects.

The results we obtained with STE-MTR are in full agreement with a longitudinal study by Fjaer et al. (Fjaer et al., 2013), in which MTR was performed on a preclinical 7 Tesla system. In that study, MTR values were significantly decreased in the corpus callosum and the caudate/putamen following CPZ administration in mice, but no changes were detected in cortical areas. A few other studies focusing on the corpus callosum of CPZ-treated mice have associated MTR values to oligodendrocytes numbers and myelin (Boretius et al., 2012; Guglielmetti et al., 2016a; Merkler et al., 2005; Thiessen et al., 2013; Turati et al., 2015), and to markers of gliosis (Guglielmetti et al., 2016a; Zaaraoui et al., 2008).

In line with these observations, we also showed that both UTE- and STE-MTR values correlate with myelin content as measured by MBP immunostaining in healthy control mice. Interestingly, these correlations between UTE-MTR or STE-MTR and MBP immunostaining were also observed during toxic-induced demyelination following CPZ diet (Table 1 and Table 2), demonstrating that modulations in both UTE- and STE-MTR values were associated with longitudinal changes in myelin content. However, it is important to note that demyelination is often accompanied by changes in the brain microenvironement, such as immune response, reactive gliosis, neurodegeneration and/or edema (Gudi et al., 2009; Lucchinetti et al., 2011; Praet et al., 2014; Reich et al., 2018). In particular, gliosis and swelling of astrocyte processes, as indicated by an increase of GFAP immunostaining, may contribute to an increase of non-myelin associated water and thus might influence the measured UTE-MTR and STE-MTR values (Guglielmetti et al., 2016a; Zaaraoui et al., 2008). In this study, although GFAP staining was increased following CPZ administration, we did not find any correlations between UTE-MTR or STE-MTR values and GFAP immunostaining (Table 1 and Table 2). However, it is still difficult to completely disentangle demyelination and gliosis as these processes mostly occur concomitantly (Supplementary Table 5). Consequently, when we performed correlations on individual ROIs, we observed a strong positive correlation of UTE-MTR and STE-MTR with MBP and an inverse correlation with GFAP. In this analysis, two areas are of particular interest: (1) the splenium, which displays a distinct temporal pattern of demyelination and astrogliosis (Figure 3, maximal demyelination at W4 CPZ and maximal astrogliosis at W6 CPZ), showed that UTE-MTR was more significantly correlated to MBP than GFAP, and that STE-MTR was solely significantly correlated with MBP; (2) the cortical grey matter ROIs show strong correlation between MBP and GFAP immunostaining (Supplementary Table 5) but only UTE-MTR was able to detect association with MBP at the ROIs level (Table 1), indicating that the strongest correlate of UTE-MTR was MBP immunostaining.

Cortical demyelination has been linked with disease progression and cognitive impairment in MS, but remains invisible using conventional MRI (Kutzelnigg and Lassmann, 2006; Kutzelnigg et al., 2005; Lucchinetti et al., 2011; Stadelmann et al., 2008). Therefore, there is an urgent need to develop new imaging methods that would enable the detection and longitudinal follow up of cortical alterations. The cortex of CPZ-fed mice undergoes severe demyelination upon a few weeks of CPZ administration, as confirmed by post-mortem histological analyses (Gudi et al., 2009; Guglielmetti et al., 2016b; Hibbits et al., 2009; Skripuletz et al., 2008); however only a few MR imaging studies have been able to detect cortical changes in this model in vivo. Specifically, diffusion kurtosis imaging (Guglielmetti et al., 2016b), and macromolecular proton fraction (MPF) imaging (Khodanovich et al., 2017) reported cortical changes following CPZ administration. In this study, we showed that UTE-MTR was significantly decreased in cortical areas and only correlated with myelin levels as measured by MBP immunostaining. It is important to note that while every cortical areas undergo remyelination once the CPZ diet is removed, complete remyelination is not reached by the end of the recovery period (Figure 5, W6 CPZ + W6 recovery) and UTE-MTR remained significantly decreased. The observation of these long-lasting changes may be due to incomplete remyelination at the time of MR exams, or reflect neurodegeneration and subsequent lower number of axonal projections that can be remyelinated. It is also possible that changes to the extra cellular matrix after remyelination and tissue remodeling play a role in the observed decreased UTE-MTR values at the end of the recovery period.

The hippocampal formation is an important region of the mouse brain due to its crucial role in cognitive processes and memory formation. The dorsal hippocampal formation undergo severe demyelination upon CPZ administration (Koutsoudaki et al., 2009; Norkute et al., 2009), followed by spontaneous remyelination once CPZ is removed. Unfortunately, neither UTE-MTR or STE-MTR were able to detect changes at any time points, which could be explained by the particular myelin arrangement in this structure, with low myelin content overall except for the densely myelinated stratum lacunosum molecular region (Figure 1.E-F). Another possibility is that the spatial resolution achieved in this study (200 × 200 × 500 × μm3) is on the rather low side for mouse brain imaging (due to clinical hardware use), and therefore partial volume effect from the ventricles or from voxels containing both white and grey matter might influence our outcome, particularly for this specific ROI of complex myelin arrangement. Partial volume effect may also affect our findings from white matter tracts, which show a thickness of 250 to 500μm in coronal section according to Paxinos mouse atlas, but much less for the other ROIs within cortical and subcortical areas that cover larger brain regions. To minimize this effect we carefully delineated small ROIs over several consecutives MR slices, as indicated in Supplementary Figure 2. It should be noted that the resolution used in this study is comparable to other studies performed in the cuprizone model using diffusion kurtosis imaging and investigating changes in white matter tracts and grey matter regions (Falangola et al., 2014; Guglielmetti et al., 2016b). Last, our goal is the clinical translation of the proposed sequences on the clinical 7 Tesla for future patient’s studies (Wei et al., 2018). While this comes with limited spatial resolution due to clinical hardware setting, it still remains of utmost importance to evaluate the association of UTE-MTR and STE-MTR values to histological markers in a very reproducible animal model, as such approach is not possible in clinical studies.

In this study, we used MT imaging to generate MR images with pool-selective contrast and opted for an offset frequency of −1800Hz based on a prior study that investigated a range of offset frequencies (−1200Hz to −3000 Hz) and showed adequate grey/white matter contrast at an offset frequency of −1800Hz (Wei et al., 2018). Alternative methods to generate MR images with pool-selective contrast include suppression of long-T2 components by subtracting two echoes with different echo times (Du et al., 2009), by using specially designed long-T2 suppression RF pulses (Larson et al., 2006), by applying an inversion recovery UTE acquisition scheme (Du et al., 2014; Piedzia et al., 2014), or, more recently, by appropriate water suppression through diffusion weighting (UTE-Diff) (Soustelle et al., 2019).

Quantitative MTR (qMT) enables the estimation of the bound protons fraction (f), a metric that has been shown to detect changes associated with myelin content in cortical areas, though only ex vivo in fixed mouse brains (Soustelle et al., 2019; Thiessen et al., 2013). However, post-processing of qMT may be challenging and other studies performed in vivo following CPZ administration have only reported changes in the corpus callosum (white matter) (Turati et al., 2015). Comparing our method to qMT and other myelin imaging methods would be interesting. However, such studies are beyond the scope of our paper.

Last, this study also highlights the feasibility of 3D UTE-MT imaging on a 7 Tesla clinical scanner within scan times suitable for human studies. We were able to obtain high-resolution UTE-MTR maps using clinical gradients and a dedicated mouse brain surface coil in 40 minutes of scan time. This same sequence and system has also used for human imaging studies, achieving 1 × 1 × 3 mm3 resolution and whole brain coverage for 3D UTE-MT in under 10 minutes (Wei et al., 2018). An advantage of UTE-MTR compared to conventional T2 relaxometry, UTE T2* relaxometry or IR sequences is the potential for shorter imaging times, as it relies on only two acquisitions. Furthermore, UTE-MTR is a quantitative metric that has remarkably improved MR contrast compared to MT imaging at conventional TEs.

5. Conclusions

We demonstrated that UTE-MTR and STE-MTR values correlated with myelin content in the control mouse brain and in a MS murine model. Both STE-MTR and UTE-MTR values were decreased following CPZ-induced brain alterations. UTE-MTR was able to detect changes in the highest number of regions (8 out of 9) compared to STE-MTR (3 out of 9), and only UTE-MTR was able to detect alterations in the cortical areas. Comparing the methods in terms of their statistical sensitivity to lesions, UTE-MTR demonstrated the highest coefficients of correlation to myelin immunostaining, suggesting that UTE-MTR is more sensitive to MT saturation of the myelin water than STE-MTR.

Altogether, we showed that UTE-MT imaging can detect brain tissue alterations in white and grey matter regions in the healthy and CPZ mouse brain on a clinical 7 Tesla MR system. Furthermore, our results show that UTE-MTR values correlate with changes in myelination levels, thus demonstrating the potential of UTE-MTR as a clinically translatable sequence that may improve characterization of damaged brain tissue in neurological disorders.

Supplementary Material

Supplementary Figure 1

Supplementary figure 1: UTE-MTR and STE-MTR maps. Representative MR images of a control (W0) mouse brain acquired with TE1 = 76 μs and TE2 = 3000 μs without off-resonance RF pulse (Munsaturated) and with off-resonance RF pulse (Msaturated). Maps were reconstructed using the following formula: MMTR = (∣Munsaturated∣ - ∣Msaturated∣) / ∣Munsaturated∣. UTE-MTR maps were generated using images acquired with TE1 = 76 μs, and STE-MTR maps using images acquired with TE2 = 3000 μ8. The white matter tracts can be seen as hyper-intense structures while the ventricles appear as dark regions.

Supplementary Figure 2

Supplementary figure 2: Delineation of ROIs. (A) Schematic representation of ROIs delineations of the white matter tracts (orange), subcortical grey matter (blue) and cortical grey matter (green) areas. (B) Delineations of white matter tracts (orange), subcortical grey matter (blue) and cortical grey matter (green) areas ROIs overlaid with UTE-MTR maps. The same ROIs were propagated on the STE-MTR maps to obtain the corresponding mean UTE-MTR and STE-MTR values, respectively.

Supplementary Figure 3

Supplementary figure 3: MBP immunofluorescence following CPZ diet. Representative immunofluorescence images of MBP staining (white) of a control mouse (W0), a mouse that received four weeks (W4 CPZ) or six weeks (W6 CPZ) of CPZ diet, and a mouse that received six weeks of CPZ diet followed by a period of six weeks of recovery (W6 CPZ + W6 recovery). Demyelination can be observed through a decrease of MBP coverage following CPZ diet in (A) the white matter tracts (orange), (B) the subcortical grey matter regions (blue) and (C) the cortical grey matter regions (green). Scale bar = 200 μm.

Supplementary Figure 4

Supplementary figure 4: GFAP immunofluorescence following CPZ diet. Representative immunofluorescence images of GFAP staining (white) of a control mouse (W0), a mouse that received four weeks (W4 CPZ) or six weeks (W6 CPZ) of CPZ diet, and a mouse that received six weeks of CPZ diet followed by a period of six weeks of recovery (W6 CPZ + W6 recovery). Astrogliosis can be observed through an increase of GFAP coverage following CPZ diet in (A) the white matter tracts (orange), (B) the subcortical grey matter regions (blue) and (C) the cortical grey matter regions (green). Scale bar = 200 μm.

Supplementary Figure 5

Supplementary figure 5: Correlations between UTE-MTR values and MBP immunostaining for all individual ROIs. Correlations between UTE-MTR and myelin content (MBP) for the white matter tracts (A-C), the subcortical grey matter areas (D-F), and the cortical areas (G-I). All values are reported as mean ± SD for each time point of interest: W0 (black), W4 CPZ (light brown), W6 CPZ (purple) and W6 CPZ + W6 recovery (dark blue). Dark line shows the best-fit linear regression analysis ± 95% confidence intervals.

Supplementary Figure 6

Supplementary figure 6: Correlations between UTE-MTR values and GFAP immunostaining for all individual ROIs. Correlation between UTE-MTR and astrogliosis (GFAP) for the white matter tracts (A-C), the subcortical grey matter areas (D-F), and the cortical areas (G-I). All values are reported as mean ± SD for each time point of interest: W0 (black), W4 CPZ (light brown), W6 CPZ (purple) and W6 CPZ + W6 recovery (dark blue). Dark line shows the best-fit linear regression analysis ± 95% confidence intervals.

Supplementary Figure 7

Supplementary figure 7: Correlations between STE-MTR values and MBP immunostaining for all individual ROIs. Correlation between STE-MTR and myelin content (MBP) for the white matter tracts (A-C), the subcortical grey matter areas (D-F), and the cortical areas (G-I). All values are reported as mean ± SD for each time point of interest: W0 (black), W4 CPZ (light brown), W6 CPZ (purple) and W6 CPZ + W6 recovery (dark blue). Dark line shows the best-fit linear regression analysis ± 95% confidence intervals.

Supplementary Figure 8

Supplementary figure 8: Correlations between STE-MTR values and GFAP immunostaining for all individual ROIs. Correlation between STE-MTR and astrogliosis (GFAP) for the white matter tracts (A-C), the subcortical grey matter areas (D-F), and the cortical areas (G-I). All values are reported as mean ± SD for each time point of interest: W0 (black), W4 CPZ (light brown), W6 CPZ (purple) and W6 CPZ + W6 recovery (dark blue). Dark line shows the best-fit linear regression analysis ± 95% confidence intervals.

Supplementary Figure 9

Supplementary Figure 9: Signal-to-noise ratio of UTE and STE images and variance of UTE- and STE-MTR The signal-to-noise ratio (SNR) was calculated using the following formula: SNR = [[Signal intensity × √ number of voxels] / Standard deviation of the noise]. (A) The SNR of UTE is significantly higher than the SNR of STE unsaturated images (Paired t-test, p = 0.0021). (B) Similarly, the SNR of UTE is significantly higher than the SNR of STE saturated images (Paired t-test, p = 0.0077). (C) The variance of the MTR was calculated using the following formula: Var(MTR) = [[Mean(Y)^2 Var(X) / Mean(X)^4] + [1 / Mean(X)^2 Var(Y)] + [Var(Y) Var(X) / Mean(X)^4]]. With X referring to the unsaturated image and Y to the saturated image. Using this expression, we found no significant differences between the variance of the UTE-MTR and the variance of the STE-MTR (Paired t-test, p = 0.1295).

Supplementary Table 1
Supplementary Table 2
Supplementary Table 3
Supplementary Table 4
Supplementary Table 5

Figure 6: Correlations between UTE-MTR/STE-MTR values and MBP/GFAP immunofluorescence levels for all ROIs at all time points of CPZ diet.

Figure 6:

Open in a new tab

Correlations between (A) UTE-MTR and myelin content (MBP), (B) UTE-MTR and astrocyte levels (GFAP), (C) STE-MTR and MBP and (D) STE-MTR and GFAP for all time points combined and all the ROIs: white matter tracts (orange), subcortical (blue) and cortical (green) grey matter areas. All values are reported as mean ± SD. Dark lines show the best-fit linear regression analysis ± 95% confidence intervals.

Acknowledgments

This work was supported by research grants: NIH R21NS089004, R01NS102156, NMSS research grants PP3360 and RG-1701-26630, fellowship from the NMSS (FG-1507-05297) and from the Flemish Institute for Science and Technology.

Footnotes

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Figure 1

Supplementary figure 1: UTE-MTR and STE-MTR maps. Representative MR images of a control (W0) mouse brain acquired with TE1 = 76 μs and TE2 = 3000 μs without off-resonance RF pulse (Munsaturated) and with off-resonance RF pulse (Msaturated). Maps were reconstructed using the following formula: MMTR = (∣Munsaturated∣ - ∣Msaturated∣) / ∣Munsaturated∣. UTE-MTR maps were generated using images acquired with TE1 = 76 μs, and STE-MTR maps using images acquired with TE2 = 3000 μ8. The white matter tracts can be seen as hyper-intense structures while the ventricles appear as dark regions.

NIHMS1546729-supplement-Supplementary_Figure_1.jpg (441.7KB, jpg)
Supplementary Figure 2

Supplementary figure 2: Delineation of ROIs. (A) Schematic representation of ROIs delineations of the white matter tracts (orange), subcortical grey matter (blue) and cortical grey matter (green) areas. (B) Delineations of white matter tracts (orange), subcortical grey matter (blue) and cortical grey matter (green) areas ROIs overlaid with UTE-MTR maps. The same ROIs were propagated on the STE-MTR maps to obtain the corresponding mean UTE-MTR and STE-MTR values, respectively.

NIHMS1546729-supplement-Supplementary_Figure_2.jpg (2.5MB, jpg)
Supplementary Figure 3

Supplementary figure 3: MBP immunofluorescence following CPZ diet. Representative immunofluorescence images of MBP staining (white) of a control mouse (W0), a mouse that received four weeks (W4 CPZ) or six weeks (W6 CPZ) of CPZ diet, and a mouse that received six weeks of CPZ diet followed by a period of six weeks of recovery (W6 CPZ + W6 recovery). Demyelination can be observed through a decrease of MBP coverage following CPZ diet in (A) the white matter tracts (orange), (B) the subcortical grey matter regions (blue) and (C) the cortical grey matter regions (green). Scale bar = 200 μm.

NIHMS1546729-supplement-Supplementary_Figure_3.jpg (15.3MB, jpg)
Supplementary Figure 4

Supplementary figure 4: GFAP immunofluorescence following CPZ diet. Representative immunofluorescence images of GFAP staining (white) of a control mouse (W0), a mouse that received four weeks (W4 CPZ) or six weeks (W6 CPZ) of CPZ diet, and a mouse that received six weeks of CPZ diet followed by a period of six weeks of recovery (W6 CPZ + W6 recovery). Astrogliosis can be observed through an increase of GFAP coverage following CPZ diet in (A) the white matter tracts (orange), (B) the subcortical grey matter regions (blue) and (C) the cortical grey matter regions (green). Scale bar = 200 μm.

NIHMS1546729-supplement-Supplementary_Figure_4.jpg (10MB, jpg)
Supplementary Figure 5

Supplementary figure 5: Correlations between UTE-MTR values and MBP immunostaining for all individual ROIs. Correlations between UTE-MTR and myelin content (MBP) for the white matter tracts (A-C), the subcortical grey matter areas (D-F), and the cortical areas (G-I). All values are reported as mean ± SD for each time point of interest: W0 (black), W4 CPZ (light brown), W6 CPZ (purple) and W6 CPZ + W6 recovery (dark blue). Dark line shows the best-fit linear regression analysis ± 95% confidence intervals.

NIHMS1546729-supplement-Supplementary_Figure_5.jpg (1.9MB, jpg)
Supplementary Figure 6

Supplementary figure 6: Correlations between UTE-MTR values and GFAP immunostaining for all individual ROIs. Correlation between UTE-MTR and astrogliosis (GFAP) for the white matter tracts (A-C), the subcortical grey matter areas (D-F), and the cortical areas (G-I). All values are reported as mean ± SD for each time point of interest: W0 (black), W4 CPZ (light brown), W6 CPZ (purple) and W6 CPZ + W6 recovery (dark blue). Dark line shows the best-fit linear regression analysis ± 95% confidence intervals.

NIHMS1546729-supplement-Supplementary_Figure_6.jpg (1.9MB, jpg)
Supplementary Figure 7

Supplementary figure 7: Correlations between STE-MTR values and MBP immunostaining for all individual ROIs. Correlation between STE-MTR and myelin content (MBP) for the white matter tracts (A-C), the subcortical grey matter areas (D-F), and the cortical areas (G-I). All values are reported as mean ± SD for each time point of interest: W0 (black), W4 CPZ (light brown), W6 CPZ (purple) and W6 CPZ + W6 recovery (dark blue). Dark line shows the best-fit linear regression analysis ± 95% confidence intervals.

NIHMS1546729-supplement-Supplementary_Figure_7.jpg (1.9MB, jpg)
Supplementary Figure 8

Supplementary figure 8: Correlations between STE-MTR values and GFAP immunostaining for all individual ROIs. Correlation between STE-MTR and astrogliosis (GFAP) for the white matter tracts (A-C), the subcortical grey matter areas (D-F), and the cortical areas (G-I). All values are reported as mean ± SD for each time point of interest: W0 (black), W4 CPZ (light brown), W6 CPZ (purple) and W6 CPZ + W6 recovery (dark blue). Dark line shows the best-fit linear regression analysis ± 95% confidence intervals.

NIHMS1546729-supplement-Supplementary_Figure_8.jpg (1.9MB, jpg)
Supplementary Figure 9

Supplementary Figure 9: Signal-to-noise ratio of UTE and STE images and variance of UTE- and STE-MTR The signal-to-noise ratio (SNR) was calculated using the following formula: SNR = [[Signal intensity × √ number of voxels] / Standard deviation of the noise]. (A) The SNR of UTE is significantly higher than the SNR of STE unsaturated images (Paired t-test, p = 0.0021). (B) Similarly, the SNR of UTE is significantly higher than the SNR of STE saturated images (Paired t-test, p = 0.0077). (C) The variance of the MTR was calculated using the following formula: Var(MTR) = [[Mean(Y)^2 Var(X) / Mean(X)^4] + [1 / Mean(X)^2 Var(Y)] + [Var(Y) Var(X) / Mean(X)^4]]. With X referring to the unsaturated image and Y to the saturated image. Using this expression, we found no significant differences between the variance of the UTE-MTR and the variance of the STE-MTR (Paired t-test, p = 0.1295).

NIHMS1546729-supplement-Supplementary_Figure_9.jpg (1.4MB, jpg)
Supplementary Table 1
NIHMS1546729-supplement-Supplementary_Table_1.docx (18.8KB, docx)
Supplementary Table 2
NIHMS1546729-supplement-Supplementary_Table_2.docx (18.7KB, docx)
Supplementary Table 3
NIHMS1546729-supplement-Supplementary_Table_3.docx (18.8KB, docx)
Supplementary Table 4
NIHMS1546729-supplement-Supplementary_Table_4.docx (18.9KB, docx)
Supplementary Table 5
NIHMS1546729-supplement-Supplementary_Table_5.docx (17KB, docx)

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