Lucas Soustelle
email: lucas.soustelle@gmail.com

Maria C Antal

Julien Lamy

François Rousseau

Jean-Paul Armspach

Paulo Loureiro De Sousa

Correlations of quantitative MRI metrics with Myelin Basic Protein (MBP) staining in a murine model of demyelination

Keywords: UTE imaging, quantitative magnetization transfer, myelin water imaging, diffusion tensor imaging, demyelination, histology, Myelin Basic Protein

Myelin imaging in the central nervous system is essential for monitoring pathologies involving white matter alterations. Various quantitative MRI protocols relying on the modeling of the interactions of water protons with myelinated tissues have shown sensitivities in case of myelin disruption. Some extracted model parameters are more sensitive to demyelination, such as the bound pool fraction (f) in quantitative magnetization transfer imaging (qMTI), the radial diffusivity in diffusion tensor imaging (DTI), and the myelin water fraction (MWF) in myelin water imaging (MWI). A 3D ultrashort echo time (UTE) sequence within an appropriate water suppression condition (Diff-UTE) is also considered for the direct visualization of the myelin semi-solid matrix (Diff-UTE normalized signal; rSPF). In this paper, we aimed at assessing the sensitivities and correlations of the parameters mentioned above to an immuno-histological study of the Myelin Basic Protein (MBP) in a murine model of demyelination at 7 T. We demonstrated a high sensitivity of the MRI metrics to demyelination, and strong Spearman correlations in the corpus callosum between histology, macromolecular proton fraction (ρ > 0.87) and Diff-UTE signal (ρ > 0.76), but moderate ones with radial diffusivity and MWF (|ρ| < 0.70).

INTRODUCTION

Assessment of myelin content in the brain is essential for monitoring pathologies such as multiple sclerosis. Quantitative MRI methods such as diffusion tensor imaging (DTI) [START_REF] Basser | Estimation of the Effective Self-Diffusion Tensor from the NMR Spin Echo[END_REF] , myelin water imaging (MWI) [START_REF] Whittall | Quantitative interpretation of NMR relaxation data[END_REF] and quantitative magnetization transfer imaging (qMTI) [START_REF] Henkelman | Quantitative interpretation of magnetization transfer[END_REF] are often employed in animal and human studies to assess demyelination processes, although analyses can be affected by other factors such as inflammation and gliosis [START_REF] Thiessen | Quantitative MRI and ultrastructural examination of the cuprizone mouse model of demyelination[END_REF][START_REF] Khodanovich | Histological validation of fast macromolecular proton fraction mapping as a quantitative myelin imaging method in the cuprizone demyelination model[END_REF][START_REF] Enzinger | Nonconventional MRI and microstructural cerebral changes in multiple sclerosis[END_REF] .

Radial diffusivity (λ ⊥ ) in DTI is sensitive to myelin changes [START_REF] Song | Demyelination increases radial diffusivity in corpus callosum of mouse brain[END_REF][START_REF] Harsan | Recovery from Chronic Demyelination by Thyroid Hormone Therapy: Myelinogenesis Induction and Assessment by Diffusion Tensor Magnetic Resonance Imaging[END_REF] , in addition to other factors such as axon diameter and crossing fiber bundles [START_REF] Hüppi | Diffusion tensor imaging of brain development[END_REF] . qMTI probes macromolecular content by indirectly assessing exchanges between bound and free proton pools [START_REF] Sled | Quantitative imaging of magnetization transfer exchange and relaxation properties in vivo using MRI[END_REF] . The bound pool fraction (f), a metric derived from qMT, is sensitive to myelin content, but is also influenced by sequence-dependent parameters [START_REF] Cercignani | A comparison between equations describing in vivo MT: The effects of noise and sequence parameters[END_REF] . MWI evaluates the proton fraction attributed to the trapped water between the myelin bilayers (MWF) which demonstrates shorter T 2 values than intra/extra-axonal water [START_REF] Mackay | In vivo visualization of myelin water in brain by magnetic resonance[END_REF] . The 0 Abbreviations: UTE, ultrashort echo time; qMTI, quantitative magnetization transfer imaging; MWI, myelin water imaging; DTI, diffusion tensor imaging; MBP, Myelin Basic Protein; rSPF, relative Semi-solid Proton Fraction original technique (multi-spin-echo based) has been improved by compensating for the stimulated echo effects during the post-processing estimation [START_REF] Prasloski | Applications of stimulated echo correction to multicomponent T2 analysis[END_REF][START_REF] Layton | Modelling and Estimation of Multicomponent T2 Distributions[END_REF] . Alternatively, multi-gradient echo MWI techniques were proposed to compensate for spin-echo based issues [START_REF] Nam | Improved estimation of myelin water fraction using complex model fitting[END_REF][START_REF] Lee | Improved three-dimensional multi-echo gradient echo based myelin water fraction mapping with phase related artifact correction[END_REF][START_REF] Thapaliya | Assessment of microstructural signal compartments across the corpus callosum using multi-echo gradient recalled echo at 7 T[END_REF] . Deoni et al. [START_REF] Deoni | One component? Two components? Three? The effect of including a nonexchanging "free" water component in multicomponent driven equilibrium single pulse observation of T 1 and T 2[END_REF] proposed a quantification of the three main water pools using a more sophisticated biophysical model taking into account inter-compartments exchanges. However, regardless of the model, the MWI techniques still suffer from a lack of consensus and precision [START_REF] Zhang | Comparison of myelin water fraction from multiecho T2 decay curve and steady-state methods[END_REF][START_REF] Bouhrara | Analysis of mcDESPOT-and CPMG-derived parameter estimates for two-component nonexchanging systems[END_REF][START_REF] Lankford | On the inherent precision of mcDESPOT[END_REF] . Additionally, a preclinical study demonstrated the moderate specificity of the method in case of inflammation using a histological analysis [START_REF] Wood | Whole-brain ex-vivo quantitative MRI of the cuprizone mouse model[END_REF] .

All of these state of the art methods for myelin imaging and quantification exploit chemical or magnetic interactions of the water with the myelin semi-solid part. Hence, the specificity and the selectivity of these techniques may vary with their respective models and hypotheses. Other related issues may occur with various clinical MRI considerations (e.g. protocol duration or specific absorption rate).

Protons from the macromolecular matrix of myelin (lipids and proteins) in the central nervous system demonstrate rather short T 2 values because of intense and unaveraged dipolar coupling in these solid-like structures [START_REF] Bydder | MRI of tissues with short T2s and T2*s[END_REF] . Several studies suggest that myelin can be directly visualized with ultrashort echo time (UTE) sequences, hence potentially overcoming the lack of specificity of other indirect methods. Horch et al. [START_REF] Horch | Origins of the ultrashort-T2 1H NMR signals in myelinated nerve: A direct measure of myelin content?[END_REF] and Wilhelm et al. [START_REF] Wilhelm | Direct magnetic resonance detection of myelin and prospects for quantitative imaging of myelin density[END_REF] spectroscopically explored myelinated tissue samples, demonstrating the presence of a wide distribution of sub-millisecond T 2 values, as well as a high correlation between the myelin concentration and corresponding integrated 1 H spectra. UTE techniques remain somehow challenging as they require an appropriate suppression of long-T 2 components (T 2 > 10 ms) in order to generate a selective short-T 2 (T 2 < 1 ms) contrast. Many efforts were led in human studies using an Inversion-Recovery (IR) based UTE sequence, although in a single slice 2D fashion [START_REF] Du | Ultrashort echo time (UTE) magnetic resonance imaging of the short T2 components in white matter of the brain using a clinical 3T scanner[END_REF][START_REF] Du | Measurement of T1 of the Ultrashort T2* Components in White Matter of the Brain at 3T[END_REF][START_REF] Sheth | Magnetic resonance imaging of myelin using ultrashort Echo time (UTE) pulse sequences: Phantom, specimen, volunteer and multiple sclerosis patient studies[END_REF] . Additionally, the use of an adiabatic inversion pulse in the IR module for homogeneous long-T 2 suppression purpose involves a short-T 2 signal attenuation [START_REF] Chen | Measurement of bound and pore water T1 relaxation times in cortical bone using three-dimensional ultrashort echo time cones sequences[END_REF][START_REF] Larson | Using adiabatic inversion pulses for long-T2 suppression in ultrashort echo time (UTE) imaging[END_REF] .

A 3D UTE sequence performing an appropriate water suppression condition through diffusion weighting (Diff-UTE) was previously proposed to compensate for these limitations [START_REF] Soustelle | A diffusion-based method for long-T 2 suppression in steady state sequences: Validation and application for 3D-UTE imaging[END_REF] , providing high contrast in myelinated areas of an ex vivo healthy mouse brain [START_REF] Soustelle | Myelin and Cortical Bone Short-T2 Quantification Using Saturation and Diffusion-Based Long-T2 Suppression in a Steady-State 3D-UTE Sequence[END_REF] .

Several MRI quantitative metrics extracted from the techniques mentioned above are often employed as biomarkers of demyelination, but their respective specificities vary according to the investigated myelin disorder. In this study, we assess the specificity to demyelination of several quantitative MRI parameters at 7T by comparison to the fluorescence yield of a myelin marker (myelin basic protein -MBP) in normal and demyelinized ex vivo mouse brains [START_REF] Thiessen | Quantitative MRI and ultrastructural examination of the cuprizone mouse model of demyelination[END_REF][START_REF] Khodanovich | Histological validation of fast macromolecular proton fraction mapping as a quantitative myelin imaging method in the cuprizone demyelination model[END_REF][START_REF] Turati | In vivo quantitative magnetization transfer imaging correlates with histology during de-and remyelination in cuprizone-treated mice[END_REF] .

MATERIALS AND METHODS

Animal experiments

Quantitative MRI protocols and analyses

All mice were scanned on a 7T preclinical scanner (Bruker BioSpec, Ettlingen, Germany), using a 23-mm volume Tx/Rx coil in order to provide an homogeneous B 1 field in the slab of interest. All 3D MRI datasets were acquired using the same axial volume with 0.750-mm thick slices.

DTI, qMTI and MWI quantitative protocols

DTI, qMTI and MWI protocols shared the same slice geometry (100x100x750 µm 3 ). d-e). Mice heads were positioned so that the corpus callosum was perpendicular to the slice plane in order to limit partial volume effects. The total acquisition duration amounted to 12 h 37 min. In SPGR-like sequences, a strong gradient spoiling was systematically included to crush the remaining transverse magnetization through diffusion effects, along with an appropriate RF spoiling (SPGR: φ 0 = 117 • 35 ; AFI-SPGR:

φ 0 = 129.3 • using Nehrke's spoiling scheme [START_REF] Nehrke | On the steady-state properties of actual flip angle imaging (AFI)[END_REF] ).

FIGURE 2

Diff-UTE pulse sequence. The gradients inducing diffusion weighting in Diff-UTE correspond respectively to the short one after the preparation pulse (relative area Gδ), and to the long one after the readout gradients refocusing (relative area Gδ ).

Diff-UTE protocol

The Diff-UTE sequence (depicted in Figure 2 ) was previously proposed as a 3D-UTE method allowing an efficient long-T 2 suppression condition in biological tissues while maximizing the signal of a short-T 2 component [START_REF] Soustelle | Myelin and Cortical Bone Short-T2 Quantification Using Saturation and Diffusion-Based Long-T2 Suppression in a Steady-State 3D-UTE Sequence[END_REF][START_REF] Soustelle | A diffusion-based method for long-T 2 suppression in steady state sequences: Validation and application for 3D-UTE imaging[END_REF] . This hard-pulse prepared sequence takes advantage of the particular magnetization steady-state condition to generate a signal cancellation of a diffusive and long-relaxing component through a diffusion weighting induced by spoiling gradients. After generation of the optimized parameters maximizing the short-T 2 component through the Bloch equations

given priors about the myelin relaxing times (T 1 /T 2 ≈ 230/0.1 ms), the areas of the spoiling gradients are tuned to provide an appropriate signal nulling of the diffusive long-T 2 component of the white matter of an ex vivo healthy mouse (T 1 /T 2 ≈ 1300/67 ms using respectively an inversionrecovery spin-echo sequence and a multi-spin echo sequence, and ADC ≈ 0.328 µm 2 /ms using a pulse gradient spin echo sequence with diffusion weighting along the same spoiling direction in Diff-UTE). Previous studies demonstrated the efficiency of signal cancellation through this process [START_REF] Soustelle | Myelin and Cortical Bone Short-T2 Quantification Using Saturation and Diffusion-Based Long-T2 Suppression in a Steady-State 3D-UTE Sequence[END_REF] .

In this sequence, the following optimized parameters were used: matrix size = 84x84x84 (21938 radial projections, 50 points/spoke), voxel size = 152x152x750 µm 3 , TR 1 /TR 2 = 5/25 ms, τ 1 /τ 2 = 1.0/0.01 ms, α 1 /α 2 = 90/50 • , Gδ = 198.9 mT/m.ms, RF phase increment φ 0 = 0 • , receiver bandwidth (rBW) = 150 kHz, 12 signal acquisitions (Tacq = 136 min). The Diff-UTE signal was normalized to that estimated in a piece of rubber (T 1 /T * 2 ≈ 182/0.3 ms at 3 T 27 ) spatially localized in the same rostral position throughout the scans. The signals ratio is referred hereafter to as relative semi-solid proton fraction (rSPF).

MR quantitative image analysis

Before parameters estimations, and to compensate for potential B 0 -drift effects inducing FOV shifting, all volumes were rigidly registered onto the lowest b-value image from the DTI protocol using the Advanced Normalization Tools (ANTS) registration package [START_REF] Avants | A reproducible evaluation of ANTs similarity metric performance in brain image registration[END_REF][START_REF] Avants | Advanced Normalization Tools (ANTS)[END_REF] , followed by a denoising step using Block-matching and 3D filtering (BM4D) [START_REF] Maggioni | Nonlocal Transform-Domain Filter for Volumetric Data Denoising and Reconstruction[END_REF] . Due to its unconventional contrast, the automatic registration of the Diff-UTE volume could not be performed. Hence, it was manually achieved using the registration tool from ITK-Snap onto the highly-contrasted Fractional Anisotropy (FA) map from DTI.

For quantitative purpose, B 0 maps were estimated using the manufacturer algorithm in PV6.0.1, and all other maps were reconstructed using a home-made pipeline, implemented on Matlab 2016a (The MathWorks, Inc., Natick, MA, USA). B 1 map was estimated using the AFI method [START_REF] Yarnykh | Actual flip-angle imaging in the pulsed steady state: A method for rapid three-dimensional mapping of the transmitted radiofrequency field[END_REF] . In DTI, FA and λ ⊥ were estimated using a constrained non-linear least-square fitting process with a Cholesky decomposition correction [START_REF] Thiessen | Quantitative MRI and ultrastructural examination of the cuprizone mouse model of demyelination[END_REF] . In qMTI, f was estimated using Sled and Pike's rectangular pulse (RP) model implemented in the qMTLab toolbox [START_REF] Cabana | Quantitative magnetization transfer imaging made easy with qMTLab : Software for data simulation, analysis, and visualization[END_REF] , with equal relaxation rates between the restricted and free pools R 1,r = R 1,f . B 1 , B 0 and T 1 values were provided, and the transverse relaxation constant of the restricted pool was fixed to 10 µs for the entire dataset as it demonstrates a narrow range in tissues [START_REF] Yarnykh | Fast macromolecular proton fraction mapping from a single off-resonance magnetization transfer measurement[END_REF][START_REF] Thiessen | Quantitative MRI and ultrastructural examination of the cuprizone mouse model of demyelination[END_REF] . MWF was estimated based on a home-made Non-Negative Least Square (NNLS) framework 2 , taking account of potential stimulated echoes effects [START_REF] Prasloski | Applications of stimulated echo correction to multicomponent T2 analysis[END_REF] . The T 2 spectra comprised 40 T 2 values logarithmically spaced from 15 to 1500 ms. To overcome the ill-conditioned issues of this inverse problem, a Tikhonov regularization was performed (rNNLS), and results

were further smoothed by introducing a spatial prior (srNNLS) in the optimization process based on the spectra estimated in the rNNLS maps [START_REF] Hwang | Improved myelin water quantification using spatially regularized non-negative least squares algorithm[END_REF] .

For MWF maps computation, the cutoff of the estimated spectra was empirically set to 35 ms based on prior analyses in regions of interest in the corpus callosum of the healthy mice.

Immuno-histological MBP staining and fluorescence quantification

After ex vivo imaging, mice brains were extracted and soaked into a 20% w/v sucrose solution between 24 and 48 hours, prior to freezing and storage at -20 • C. Following overnight thawing at 4 • C, 60-µm coronal sections were collected within a 3 mm-thick volume starting at the optic chiasm. The size of the histological initial block was chosen to encompass the slices used for MRI analyses. Sections were then soaked into 20% sucrose solution prior to freezing and storing at -20 • C.

After thawing, two sections were selected for MPB staining. The first section was localized 1 mm rostral to the bregma, the second one 1 mm caudal to the bregma, in order to account for the different susceptibility to demyelination along the corpus callosum [START_REF] Stidworthy | Quantifying the Early Stages of Remyelination Following Cuprizone-induced Demyelination[END_REF][START_REF] Steelman | Demyelination and remyelination in anatomically distinct regions of the corpus callosum following cuprizone intoxication[END_REF] . The following staining protocol was used: i) washing in PBS at 4 • C for 12 h; ii) washing in PBS-Triton (0.1%) at room temperature for 15 min; iii) incubation with a homemade mouse antibody anti-MBP at room temperature for 24 h under agitation; iv) washing in PBS-Triton (0.1%) at room temperature for 15 min; v)

incubation with an anti-mouse Alexa-594 antibody (1/500; Thermo Fisher Scientific, Waltham, MA) at room temperature for 24 h under agitation; vi) washing in PBS-Triton (0.1%) at room temperature for 15 min; vii) washing in PBS at room temperature for 15 min.

Sections were then mounted on glass slides (Dako; Agilent Technologies, Santa Clara, CA) and examined using an epifluorescence microscope (DP70, magnification x2; Olympus, Tokyo, Japan). Photographs were taken under constant gain and exposure duration (1.1 s) parameters. For quantitative, the Mean Fluorescent Intensities (MFI) of the red channel were estimated from the same regions of interest (ROI) as the MRI ones. The mean background intensity estimated from a ROI far from tissues was subtracted from the signal to compensate for any non-signal contribution.

Regions of interest selection

The ROIs were drawn twice: in histology over the MPB views, and in MRI over the high-contrast fractional anisotropy map estimated in DTI. To limit potential user-dependent selection of out-of-ROI voxels during delineation, parameter maps were linearly interpolated onto a 384x384x3 grid. The regions of interest were drawn by one of the authors (MCA), developmental pathologist, expert in mouse histology, specialized in brain histology, and comprised the corpus callosum (medial (mCC) and lateral (lCC)) as well as the somato-sensory cortex (CTX; layer 2/3 to 6a) according to the Allen Mouse Brain Atlas (available from: http://atlas.brain-map.org/), as shown in Figure 1 . Representative rSPF and fractional anisotropy views with overlayed ROIs from four mice from each group are given in Figure S2 in Supporting Information.

Statistics

A ROI-based analysis was made, assuming that these regions are homogeneous in order to allow for a group comparison. Means and standard deviations in the averaged ROIs from the three slices were used to assess the sensitivity of each parameter. A Shapiro-Wilk test was performed on each group and each parameter to test for the normality of the distributions. The mean difference between the two groups of each parameter were tested using either a bilateral Student or a bilateral Welsh test, depending on the result of a Fisher test. Additionally, Spearman correlations in both corpus callosum subregions as well as in the cortex were estimated between MRI and histological metrics. This analysis involved three distinct groups: joint control and cuprizone groups, and separated control and cuprizone groups. Adjusted p-values of the correlations were computed using Holm-Bonferroni's correction in each group.

RESULTS

Representative quantitative maps selected from a mouse in each group are shown in Figure 3 . The impact of the cuprizone diet on corpus callosum myelination is straightforward as shown by the decrease of the MBP fluorescent signal in treated mice.

Numerical mean and sensitivity values of the two groups computed from the various regions are presented in Table 1 . Every metric followed a normal distribution according to the Shapiro-Wilk test (p > 0.05), and showed systematically a significant mean difference between the two groups in the corpus callosum regions (p < 0.05). rSPF, λ ⊥ and MWF were the only scores that did not show a significant mean difference between the two groups in the cortical region. A mean difference was estimated between mCC and lCC in both groups for f, and only in the Control group for rSPF and λ ⊥ . MRI metrics demonstrated a fairly good sensitivity to demyelination with an absolute difference higher than 40% in the corpus callosum, while MBP fluorescent intensity decreased by 23.6% and 30.1% in the lCC and mCC, respectively. In contrast, the difference was lower in the cortical regions with an absolute difference lower than 18.5% for rSPF, f, and λ ⊥ , while MWF and MBP scores were higher than 31.8%. Spearman correlations between MRI metrics and MBP for each regions are shown in Table 2 , along with the corresponding scatter plots in Figure 4 . The macromolecular proton fraction (MPF), MWF and rSPF demonstrated significant correlations to demyelination assessed by immunohistology in both corpus callosum subregions for the joint group (|ρ| > 0.68), while radial diffusivity was found to be significant exclusively in the medial subregion. In the cortex, MPF was the sole metric that presented a significant correlation (ρ = 0.89) in the joint group. No metric demonstrated a significant correlation to myelination for both separated groups.

DISCUSSION

The objective of this study was to verify whether the variation of quantitative MRI metrics following demyelination at the level of large structures (i.e., the whole corpus callosum and large cortical areas) mirrored the histological demyelination. Quantitative parameters from various protocols were investigated ex vivo on mice brains, using two groups of mice, one healthy and one demyelinated by the myelin toxicant cuprizone. We demonstrated expected high sensitivities to demyelination of various quantitative MRI metrics, including the macromolecular proton fraction, myelin water fraction, relative semi-solid proton fraction and radial diffusivity. The results were compared to the mean fluorescence intensity of the Myelin Basic Protein following immuno-staining of histological sections. The rSPF and MPF showed to be highly sensitive to demyelination, and shared a strong affinity to the MBP MFI, which is consistent with the biophysical model [START_REF] Horch | Origins of the ultrashort-T2 1H NMR signals in myelinated nerve: A direct measure of myelin content?[END_REF][START_REF] Wilhelm | Direct magnetic resonance detection of myelin and prospects for quantitative imaging of myelin density[END_REF] . Among the investigated MRI metrics and in terms of demyelination process assessed by MBP immuno-staining, the MPF remains the most indicative metric, followed by rSPF. MWF and radial diffusivity, respective markers of myelin sheath and axonal integrity, showed reduced significance in correlations; this may however be attributed to the histology marker, highly specific to the myelin macromolecular content.

The results regarding the high correlations of rSPF and MPF to the MBP MFI support the hypothesis of a direct acquisition of signals arising from the semi-solid matrix of myelinin UTE, and also further validate the quantitative magnetization transfer model since proteins constitute a major media for the phenomenon. Nonetheless, the lower correlations in the cortex of rSPF versus f and MBP may be attributed to a different signal source between these modalities: both techniques rely upon the presence of a short-T 2 pool, although the respective contrasts are generated differently. The qMT method requires a media for the magnetization transfer mechanisms (e.g. labile protons and solvation layer typically found in proteins), whereas UTE signals supposedly arise from the entire available 1 H spectrum (i.e. static and labile protons) [START_REF] Horch | Origins of the ultrashort-T2 1H NMR signals in myelinated nerve: A direct measure of myelin content?[END_REF][START_REF] Wilhelm | Direct magnetic resonance detection of myelin and prospects for quantitative imaging of myelin density[END_REF] . Under the assumption of a unique T 2 value of about 10 µs regarding the restricted pool in qMTI, the Diff-UTE sequence may only acquire a minor part of the macromolecular content responsible for the magnetization transfer phenomenon under our sequence parameters (TE/τ 2 = 10/10 µs). Consequently, both metrics could be complementary since the majority of the Diff-UTE contrast most likely takes its origin in the more slowly relaxing macromolecular spectra (T 2 ∈ [10; 1000] µs).

The signal normalization in Diff-UTE assumes an invariant T 1 value of the biological structures of interest although these structures might be changed by the cuprizone diet, implying T 1 modifications of the semi-solid part of the myelin. A proton density analysis would provide a more precise and absolute measurement of the actual state of the myelin structure; unfortunately, T 1 relaxometry experiments of short-T 2 components remain challenging and require further adjustments as well as an increased acquisition time.

In the separated groups (either Control or Cuprizone), no significant correlation was found between MRI and histology metrics, nor between MRI metrics. This observation may stem from the limited amount of samples in each group. This also emphasizes the necessity of p-value correction in such analyses since high scores with counterintuitive polarity (e.g. ρ = -0.86 between rSPF and MPF in the Cortex in the Cuprizone group; see Table T1 in SI) are most likely spurious.

In this study, the preferential SNR situation (≈ 100 on MT 0 prior to the denoising step) allowed to estimate the qMT parameters through the Sled and Pike's RP model. Among the similar investigations at 7 T over a murine model involving a group of mice demyelinated by a cuprizone diet, Thiessen et al. [START_REF] Thiessen | Quantitative MRI and ultrastructural examination of the cuprizone mouse model of demyelination[END_REF] and Turati et al. [START_REF] Turati | In vivo quantitative magnetization transfer imaging correlates with histology during de-and remyelination in cuprizone-treated mice[END_REF] employed Ramani's model for parametric estimations [START_REF] Ramani | Precise estimate of fundamental in-vivo MT parameters in human brain in clinically feasible times[END_REF] . However, it has been demonstrated by Cercignani et al. [START_REF] Cercignani | A comparison between equations describing in vivo MT: The effects of noise and sequence parameters[END_REF] that this method leads to an underestimation of f, contrary to Sled and Pike's RP model, in spite of being more robust in case of a low SNR. In addition, Ramani's model is sensitive to both the RF duty cycle and the excitation flip angle introducing a bias due to T 1 -weighting. Also, it should be noticed that in both studies, no account of potential B 1 deviations was made, inherently biasing the resulting f in case of B 1 field inhomogeneities or miscalibration. Nonetheless, the mean f value estimated in the corpus callosum of the control group in our study was still lower than the nominal value of about 0.10 found in healthy mice and humans [START_REF] Thiessen | Quantitative MRI and ultrastructural examination of the cuprizone mouse model of demyelination[END_REF][START_REF] Levesque | Quantitative magnetization transfer and myelin water imaging of the evolution of acute multiple sclerosis lesions[END_REF] , and yet similar to those of Ou et al. [START_REF] Ou | The MT pool size ratio and the DTI radial diffusivity may reflect the myelination in shiverer and control mice[END_REF] (f ≈ 0.09) who employed a selective inversion recovery fast-spin-echo sequence. Khodanovich et al. [START_REF] Khodanovich | Histological validation of fast macromolecular proton fraction mapping as a quantitative myelin imaging method in the cuprizone demyelination model[END_REF] estimated higher values in healthy mice (f ≈ 0.13 in the CC) at 11.7 T using Yarnykh's single-point f mapping [START_REF] Yarnykh | Fast macromolecular proton fraction mapping from a single off-resonance magnetization transfer measurement[END_REF][START_REF] Naumova | High-resolution macromolecular proton fraction mapping for quantitative neuroanatomical imaging of the rodent brain in ultra-high magnetic fields[END_REF] .

Myelin water imaging remains a standard method for white matter monitoring [START_REF] Whittall | Quantitative interpretation of NMR relaxation data[END_REF][START_REF] Laule | Magnetic resonance imaging of myelin[END_REF][START_REF] Chen | Validating myelin water imaging with transmission electron microscopy in a rat spinal cord injury model[END_REF] . However, tissue fixation protocols deeply modify the multi-compartmental behavior of cerebral tissues, leading to an observable mono-compartmentalization of water T 2 distribution in white matter, as reported by Thiessen et al. [START_REF] Thiessen | Quantitative MRI and ultrastructural examination of the cuprizone mouse model of demyelination[END_REF] who employed a 0.5%-glutaraldehyde/2%-PFA fixation. Wood et al. [START_REF] Wood | Whole-brain ex-vivo quantitative MRI of the cuprizone mouse model[END_REF] estimated MWF using a fixation protocol similar to ours, and by means of the mcDESPOT model 18 . To our knowledge, the present preclinical study is the first one performing a MWF NNLS-based analysis using a multi spin-echo sequence in an ex vivo cuprizone model of demyelination. MWF values are consistent with that found in literature in healthy human white matter [START_REF] Oh | Multislice Brain Myelin Water Fractions at 3T in Multiple Sclerosis[END_REF][START_REF] Mackay | Insights into brain microstructure from the T2 distribution[END_REF] , but also remains lower than those estimated in mcDESPOT [START_REF] Wood | Whole-brain ex-vivo quantitative MRI of the cuprizone mouse model[END_REF] (MWF ≈ 20%). Nonetheless, the difference between these estimations may not only arise from the arbitrary MWF-T 2 cutoff in the NNLS analysis, but also from the absence of consideration of chemical exchange between the different compartments which is an important dynamic aspect of water molecules evolving within the microstructure [START_REF] Wood | Whole-brain ex-vivo quantitative MRI of the cuprizone mouse model[END_REF][START_REF] Bouhrara | Analysis of mcDESPOT-and CPMG-derived parameter estimates for two-component nonexchanging systems[END_REF] .

To compensate for a lack of signal-to-noise ratio (SNR) in brain tissues in Diff-UTE, an anisotropic voxel geometry was preferred in the slice direction. In addition, the manufacturer native 3D-UTE sequence does not allow for any field of view re-positioning, and heads placement remained challenging for reproducibility purpose since it was therefore made by mechanical shifting of the cell inside the bore. Both features may lead to increased partial volume effects in regions of interest, especially in the thin white matter area of the mouse brain, although the linear interpolation prior to ROI delineations aimed at diminishing such effects. The use of a 23-mm volume transceiver coil was initially chosen to provide an homogeneous B 1 profile in the slab of interest, as the Diff-UTE suppression scheme is sensitive to such inhomogeneities (see Supporting Information).

The use of more sensitive signal reception systems in pair with a transmitter volume coil may represents an alternative to address these issues.

In spite of a high correlation with to macromolecular content metrics (MBP and f; see Table T1 in SI) in the corpus callosum, rSPF correlates moderately with these in the cortex. As can be noticed in Figure 3 on the rSPF maps, a dominant signal arises from the skull. Hence, estimations from ROIs placed in the cortex may suffer from contaminations inherent to Gibbs artifacts and blurry effects caused by the relatively low receiver bandwidth in Diff-UTE and short T 2 values in the cortical bone.

In this study, we made use of the Diff-UTE sequence for a first assessment of the sensitivity of a UTE sequence to demyelination in a preclinical environment. This sequence was preferred to the commonly-used IR-UTE [START_REF] Du | Ultrashort echo time (UTE) magnetic resonance imaging of the short T2 components in white matter of the brain using a clinical 3T scanner[END_REF] since it allows for higher short-T 2 signal generation [START_REF] Soustelle | A diffusion-based method for long-T 2 suppression in steady state sequences: Validation and application for 3D-UTE imaging[END_REF] . From a demyelination point of view, the rSPF scores demonstrated an interesting sensitivity, as well as high correlations to macromolecular content metrics. However, as any long-T 2 suppressed UTE method, the sequence requires optimum hardware capabilities (e.g. RF peak power for short-T 2 signal maximization), but also strong gradients for diffusivity purpose and a reasonable B + 1 profile (see Figure S1 in Supporting Information): these constraints may represent an issue toward the applicability over standard high-field clinical systems. Although, UTE acquisitions on such systems may be highly beneficial for different aspects. For example, the employed radial acquisition pattern represents a permissive feature for sequence acceleration (undersampling and sparse reconstructions [START_REF] Block | Undersampled radial MRI with multiple coils. Iterative image reconstruction using a total variation constraint[END_REF][START_REF] Lustig | Sparse MRI: The application of compressed sensing for rapid MR imaging[END_REF] ), especially in high signal-to-noise ratio conditions with increased voxel sizes. It is also advantageous for its motion insensitivity intrinsic ability [START_REF] Song | Least-square NUFFT methods applied to 2-D and 3-D radially encoded MR image reconstruction[END_REF] .

The precise spectrum composition in the white matter following a cuprizone demyelination remains to be established as white matter microstructure following demyelination is changed. The changes comprise patchy absence of myelin sheath, presence of intra/extracellular myelin debris, increased number and size of reactive cells (microglia, astrocytes) and oligodendrocyte progenitors proliferation [START_REF] Matsushima | The Neurotoxicant, Cuprizone, as a Model to Study Demyelination and Remyelination in the Central Nervous System[END_REF] . These alterations have an impact over dipolar interactions experienced by protons carried by semi-solid structures, hence lowering the mechanisms that mainly influence the short-T 2 phenomenon. The T 2 -spectrum of the semi-solid part of myelin will suffer from such structural modifications, and short relaxing components may shift toward higher values. However, estimating the actual state of this spectrum remains complex in UTE sequences due to the low SNR in myelinated structures and other filtering and weighing effects (e.g. low TR and T 2 relaxation during excitation). In order to reduce such undesirable effects, one may for example increase the repetition time, although leading to prohibitively long scan times. A precise exploration of the biological content using an extended panel of biomarkers such as additional MRI quantitative parameters, electron microscopy and immuno-histology for the different cell types and oligodendrocytes differentiation stages [START_REF] Bradl | Oligodendrocytes: biology and pathology[END_REF][START_REF] Barateiro | Temporal oligodendrocyte lineage progression: In vitro models of proliferation, differentiation and myelination[END_REF] will allow for a better characterization of the white matter microstructure and myelin state.

Leprince et al. [START_REF] Leprince | Optimization of sample preparation for MRI of formaldehyde-fixed brains[END_REF] demonstrated variations of a mono-component T 2 as a function of the post-fixation conservation duration. Chen et al. [START_REF] Chen | Validating myelin water imaging with transmission electron microscopy in a rat spinal cord injury model[END_REF] observed a myelin sheath loosening and cell shrinkage, simultaneously modifying the respective size of the various water compartments, and hence MWF. Sheath loosening most likely leads to a degraded mechanism responsible for T 2 shortening of trapped water in myelin sheaths. As a result, the T 2 distribution of myelin water may shift and distort depending on the type of employed fixation, and the optimal T 2 cutoff in NNLS-analyses has to be modulated as such. The inability of Thiessen et al. [START_REF] Thiessen | Quantitative MRI and ultrastructural examination of the cuprizone mouse model of demyelination[END_REF] to perform MWI is most likely due to such phenomena. Further investigations have yet to be led in order to determine the optimal T 2 -cutoff as well as a robust fixation protocol for ex vivo myelin imaging.

In the present study, a single protein was marked for myelin quantification by histology, showing a high correlation with putative MRI markers indicative of macromolecular content (f and rSPF). Ideally, myelin quantification should be confronted to electron microscopy. Unfortunately, the fixation protocol imposes the use of glutaraldehyde and PFA, leading to a T 2 shortening and mono-compartmentalization in soft tissues, preventing MWI analyses [START_REF] Thiessen | Quantitative MRI and ultrastructural examination of the cuprizone mouse model of demyelination[END_REF] . In addition, it has been shown that Diff-UTE efficiency for long-T 2 signal suppression is reduced if the diffusive species were to present too short T 2 values 31 .

The objective of the histology study was to verify whether the variation of MRI parameters following demyelination at the level of large structures (e.g. the whole corpus callosum or large cortical areas) mirrored the histological demyelination. Histology is a versatile technique as its in-plane resolution streches from nanometer to millimeter, whether its in-slice resolution may go from a few tens of nanometers to a few hundreds micrometers. In this study, we used low in-slice MRI partition; accordingly, histological technique was adapted to favor high fluorescence yield by the use of thick histological sections. Other histological techniques can be used for myelin quantification, namely myelin scoring following LFB staining [START_REF] Khodanovich | Histological validation of fast macromolecular proton fraction mapping as a quantitative myelin imaging method in the cuprizone demyelination model[END_REF][START_REF] Laule | Myelin water imaging in multiple sclerosis: quantitative correlations with histopathology[END_REF] .

Although easy to perform, the scoring is highly subjective and dependent upon the observer. Moreover, tissue processing for LFB staining requires prolonged fixation in order to perform paraffin embedding and 5 µm-thick tissue sectioning. On one hand, prolonged fixation and technical procedure for paraffin embedding (i.e. dehydration) distort protein architecture and thus the relevance of histology toward quantitative in vivo MRI; on the other hand, 5 µm-thick tissue sections are poor references for volumes even if their number is multiplied. In order to diminish these biases, short fixation times were used followed by residual free fixative removal with isotonic solution. Sectioning were performed with a vibratome prior to immuno-staining, a method which does not require tissue dehydration and allows sectioning between 40 and 200 µm. 5 weeks 0.2% cuprizone administration induces global, but not total corpus callosum demyelination: on coronal sections, patches of total demyelination are intermingled with areas displaying intact myelin [START_REF] Stidworthy | Quantifying the Early Stages of Remyelination Following Cuprizone-induced Demyelination[END_REF] . Moreover, caudal and rostral corpus callosum are not equally sensitive to cuprizone demyelination which induces a rostro-caudal demyelination gradient 44 at 5 weeks. In this last study, authors considered ROIs of 200 µm in length along the corpus callosum and showed that within the millimeter preceding bregma, the demyelination is lower than in the second millimeter following bregma. The authors examined, when available, two to three 20 µm-thick sections (thus representing 40 to 60 µm) in 5 demyelinated mice and reported a mean demyelination score. In our study, we examined one 60 µm-thick rostral section within the 0.5 millimeters anterior to bregma and one 60 µm-thick caudal section within the second millimeter posterior to bregma, thus placing our histological references according to published references.

The parameters sensitivity analysis between the two groups assesses the mean difference of a metric following demyelination, assuming homogeneous groups. Unfortunately, estimations demonstrated a dispersion depending on the brain tissue state prior to acquisitions (e.g. conservation and myelin loss) as well as parametric estimations. Thus, such analysis is moderately informative about the superiority of a metric over another to quantify myelin changes as a function of demyelination. Increasing the number of sample examined and minimizing intra-group experimental variability should allow for more precise interpretations.

In this study, the analyses were performed by comparing the mean values of voxels comprised within structures of interest in each mouse. In spite of particular attentions regarding ROI delineations, inter-individual variabilities may propagate and bias estimations. To reduce these issues, an atlas could be constituted in order to perform a more rigorous voxel-based analysis [START_REF] Goto | Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra provides reduced effect of scanner for cortex volumetry with atlas-based method in healthy subjects[END_REF][START_REF] Avants | A reproducible evaluation of ANTs similarity metric performance in brain image registration[END_REF] . However in our case, anatomical variabilities (e.g. variable ventricles dilation in the cuprizone mice) and high voxels anisotropy make atlases constitution challenging. Furthermore, the particular contrast generated in Diff-UTE prevents from automatic registration onto soft-tissues contrasted images.

Due to the multiple quantitative protocols resulting in a prohibitively long total acquisition duration, ex vivo experiments were preferred. Tissue states and experimental conditions (e.g. sample temperature, tissue permeability to free water and ratio of the various intra/extracellular spaces) were very different from in vivo conditions [START_REF] Wu | In vivo high-resolution diffusion tensor imaging of the mouse brain[END_REF][START_REF] Seifert | Formalin Tissue Fixation Biases Myelin Density Measurement by Quantitative Magnetization Transfer and Myelin Water Imaging[END_REF][START_REF] Fishbein | Effects of formalin fixation and collagen cross-linking onT2 and magnetization transfer in bovine nasal cartilage[END_REF] . Potential variabilities in the statistics based on quantitative metrics may therefore occur in an in vivo study. Nonetheless, the present study shows encouraging results and represents a first step toward in vivo validation using an updated Diff-UTE protocol.

CONCLUSIONS

We have assessed the sensitivities and correlations of various quantitative MRI parameters to histological immuno-marking of the MBP in ex vivo healthy and demyelinated mice brains. The macromolecular proton fraction from qMTI presented the highest correlation in the corpus callosum, followed by the rSPF from Diff-UTE. Alternatively, radial diffusivity from DTI and myelin water fraction from MWI demonstrated high sensitivities, but moderate correlations, which is somewhat inherent to the underlying signal source from these two metrics. This emphasizes the need of comprehensive quantitative protocols to better characterize complex biological tissues from the central nervous system.

The relevant parameters were: 1 ) 1 = 5 /FIGURE 1

 1151 FIGURE 1 3D sub-volume positioning based on a high-resolution T 2 -weighted sagittal image (a), and corresponding ROIs selected in the somatosensory cortex (CTX) and corpus callosum (lCC and mCC) in a caudal (b) and rostral (c) MPB immuno-histological views, as well as on a representative fractional anisotropy map(d-e). Mice heads were positioned so that the corpus callosum was perpendicular to the slice plane in order to limit partial

TR 1 /

 1 TR 2 /TE = 15/60/2 ms and nominal flip angle = 60 • , rBW = 50 kHz, 4 signal acquisitions (Tacq = 11 min) 34 ; 5) B 0 (dual-echo SPGR): matrix size = 48x48x48 (voxel size = 250x250x750 µm 3 ), TR/TE 1 /TE 2 = 20/1.65/3.55 ms, flip angle = 30 • , rBW = 119 kHz, 2 signal acquisitions, acquired prior to each B peak 1 series (Tacq = 24 min); 6) T 1 (Variable Flip Angle SPGR): matrix size = 128x128x32 (voxel size = 100x100x750 µm 3 ), TR/TE = 100/2.7 ms, flip angles = 10/25/40/80 • , 3 signal acquisitions (Tacq = 82 min).

FIGURE 3

 3 FIGURE 3 Representative quantitative maps of a Control (top) and Cuprizone (bottom) mouse. Scales are given between each map.

FIGURE 4

 4 FIGURE 4 Scatter plots of MBP mean fluorescent intensity versus rSPF (first column), f (second column), MWF (third column) and λ ⊥ (fourth column) in the medial corpus callosum (top row), lateral corpus callosum (middle row) and cortex (bottom row). Control and cuprizone data are depicted in filled and open circles, respectively. Spearman correlation coefficients (ρ) are reported from the joined groups, where " * " denotes significant scores.

TABLE 1 Summary

 1

		Medial corpus callosum (mCC)		Lateral corpus callosum (lCC)
	Metric	Control	Cuprizone	Sensitivity	Control	Cuprizone	Sensitivity
	rSPF	0.052 ± 0.008 χ	0.027 ± 0.006	-48.7%	0.061 ± 0.010	0.033 ± 0.011	-46.7%
	f	0.081 ± 0.008 χ	0.041 ± 0.006 χ	-49.2%	0.092 ± 0.007	0.055 ± 0.010	-40.6%
	λ ⊥ (µm 2 /ms)	0.140 ± 0.014 α,χ	0.227 ± 0.033	+62.5%	0.157 ± 0.012	0.240 ± 0.023	+53.9%
	MWF (%)	12.72 ± 3.09	5.20 ± 2.86	-59.1%	14.55 ± 2.09	5.83 ± 2.94	-60.3%
	MBP (MFI)	186.8 ± 4.1 α	131.1 ± 33.7	-30.1%	180.3 ± 8.6 α	138.1 ± 25.0	-23.6%
			Cortex (CTX)
	Metric	Control	Cuprizone	Sensitivity
	rSPF	0.050 ± 0.009 †	0.042 ± 0.011	-15.2%
	f	0.069 ± 0.005	0.056 ± 0.003	-18.5%
	λ ⊥ (µm 2 /ms)	0.288 ± 0.013 †	0.308 ± 0.024	+7.3%
	MWF (%)	2.32 ± 1.13 †	1.58 ± 1.21	-31.8%
	MBP (MFI)	93.7 ± 9.5	45.5 ± 9.6	-52.1%

of MRI and histological metrics (mean ± standard deviation) in the selected regions of interest, as well as the respective sensitivity (percent difference of the mean of a metric) induced by the cuprizone diet. All metrics follow a normal distribution according to the Shapiro-Wilk test (p > 0.05).

α: unequal variances between Control and Cuprizone groups (Fisher test; p < 0.05) χ: significant mean difference between lCC and mCC within the same group (Student or Welsh test; p < 0.05) †: non-significant mean difference between both groups (Student test; p > 0.05)

TABLE 2

 2 Summary of the Spearman correlations for rSPF, f, λ ⊥ and MWF versus MBP in the corpus callosum subregions and in the cortex.Spearman correlation coefficients are reported as {CTL ∪ CPZ -CTL/CPZ}.

	MBP vs.	rSPF	f	λ ⊥	MWF
	Medial CC 0.86 CTX 0.52 -0.69/-0.03	0.89 * -0.86/0.25	-0.21 -0.45/0.79	0.20 -0.12/-0.64

* -0.43/0.50 0.87 * -0.36/0.64 -0.70 * -0.38/-0.07 0.68 * --0.62/0.21 Lateral CC 0.76 * -0.17/0.04 0.91 * -0.55/0.82 -0.63 -0.40/0.14 0.69 * --0.05/-0.07 * : significant correlations (pcorr < 0.05)

ACKNOWLEDGMENTS

The authors thank Dr. Laura Harsan for the careful manuscript reviews, and Julien Graff for technical support.

SUPPORTING INFORMATION

The following supporting information is available as part of the online article:


John Puentes
email: john.puentes@telecom-bretagne.eu

Enabling medical practice through the application of telecommunication and information technologies, as a way to overcome geographical separation has been a long-sought vision. However, despite the current proliferation of those technologies, this concept materialisation at a practical level strives to realise its expected potential [START_REF] Hersh | Clinical outcomes resulting from telemedicine interventions: a systematic review[END_REF][START_REF] Roine | Assessing telemedicine: a systematic review of the literature[END_REF]. A juxtaposition of abundant and refined but incompatible technologies, and deficient service models, characterise the present landscape [START_REF] Tulu | A taxonomy of telemedicine efforts with respect to applications, infrastructure, delivery tools, type of setting and purpose[END_REF]. Whether it is medical specialist (telemedicine) or patient (e-health) driven, such a distant healthcare practice has multiple implications, mainly at professional [START_REF] Lehoux | The theory of use behind telemedicine: how compatible with physicians' clinical routines?[END_REF], economic [START_REF] Hakansson | What do we really know about the cost-effectiveness of telemedicine?[END_REF], and social [START_REF] Jennett | The socio-economic impact of telehealth: a systematic review[END_REF][START_REF] Menachemi | Factors affecting the adoption of telemedicine: a multiple adopter perspective[END_REF] levels. New trends like mobile patients, computer-supported collaborative medical sessions, ambulatory monitoring with intelligent alarm processing, patient-centred information systems, diagnosis expertise subcontracting across national borders, and knowledge extraction from large repositories of specialised patient records, among others, are irreversibly transforming our understanding of the telemedicine and e-health scopes. On the other hand, along with the identified status and present tendencies, it seems plausible to consider that distant healthcare programs development is most likely to be promoted by private instead of public medical practice. This will not have, as initially imagined, a significant impact on the population disparities access to healthcare, reinforcing distant urban healthcare to the detriment of its rural counterpart [START_REF] Puentes | Telemedicine trends and challenges: a technology management perspective[END_REF]. Improvements in distant healthcare quality and delivery should be envisioned beyond rapid retrieval and exchange of multimedia patient data, addressing issues that concern end-user ergonomics, ubiquity, standardisation, detailed process workflow analysis, and security, besides medical practice support.

Within this challenging and intriguing context, the idea of the International Journal of Biomedical Engineering and Technology special issue, materialised as an initiative to address questions related to the growing interdisciplinary diversity that surrounds telemedicine and e-health delivery. Accordingly, selected papers represent proposals to solve problems related to distant healthcare practice, without focussing exclusively on the traditional medical specialist/patient consultation model. The addressed subjects include complementary wireless cardiac activity monitoring, remote rehabilitation of memory impaired patients, disaster relief support and economic analysis. This diversity also reflects varied interdisciplinary connections involved in the implementation and execution of telemedicine and e-health projects: patients who take an active and interactive role in their treatment and follow-up, medical emergency critical resources administration based on geo-referential knowledge, and profitability outside of a mass market model.

What are the current approaches to carry out wireless cardiac activity monitoring and how do they interface with available short-and long-range transmission technologies? In the first paper, Faribozi and Moghavvemmi attempt to answer this ambitious question, by making a critical review of sensors that monitor cardiac activity, complementing ECG recording and transmission, which has been already extensively studied. Eight approaches are initially described: skin contact electrodes, bio-impedance measurement, photo-plethysmography sensors, phonocardiography, textile-based sensors, capacitive sensors, thermal imaging, Doppler radar sensors; and their wireless monitoring capabilities analysed considering raw signals conditioning and transmission alternatives, before stating possible trends.

Can remote rehabilitation be applied to help memory-impaired patients who need to relearn basic living skills? In the second paper, Fok reports on an engineering work in progress, aimed at using and validating a virtual reality framework for cognitive rehabilitation, intended to offer for patients treated at home, caregivers distant therapy assistance. The system provides a basic infrastructure consisting of a patient database, therapy configuration tools, dynamic three-dimensional object representations, patient interaction and performance follow-up, combined with adapted telephone prosthesis.

The training steps to learn again the skill of using a telephone are described and illustrated. A preliminary usability test done with healthy volunteers illustrate how this approach could be applied.

How mobile telemedicine units' displacement under threat could be optimised in an urban disaster scenario, when a crucial insufficiency of resources happens? In the third paper, Puentes et al. describe a geographic aware system that provides decision support to optimise mobile telemedicine units' displacements in dynamic environments. The system manages multiple information sources -terrain graph representation, traffic, weather, treats, and other events -to adjust in a timely manner the trajectory of a mobile telemedicine unit operating in an unstable environment involving threats, fixed and dynamic obstacles. Depending on the impact of actual and predicted threats, as well as the tolerance and emergency levels, the vehicle trajectory is corrected. Four simulated scenarios illustrate the system anticipation capacity.

Once there is a technologically sound telemedicine solution working, how do we know if it is efficiently and profitably exploited? In the fourth paper, Nassiri et al. calculate the financial break-even point of remote consultation for three pathologies -parasitology, dermatology and cardiology -based on a portable teleconsultation station and satellite communications, installed at three geographically isolated villages and the French Guiana capital. Taking as reference air medical evacuation costs and assuming an initial investment lifespan of six years, reduced afterwards to three years to make the sensitivity analysis, the amount of avoided air evacuations necessary to properly exploit the system is obtained at both aggregate and disaggregate levels. The analysis is completed by the impact of organisational factors.

All papers were peer-reviewed by qualified enthusiastic specialists. I thank the reviewers -M. Abraham, R.K. Bali, J-M. Bonnin, A. Hernandez, L. Lecornu, C. Ray, P. Rubel, H. Yesou, S. Wong -for their generosity in terms of time and for providing valuable feedback to the authors. I also express my appreciation for the authors who contributed papers to this special issue.

I hope that the papers presented in this special issue will contribute to enhance telemedicine and e-health value understanding, while expanding and demonstrating the effective exploitation of their potential.


Ethan L W Gardner

Andrea De Luca

Julien Philippe

Daniela Dromirescu

Florin Udrea

Thin-Film MOSFET-Based Pressure Sensor

Keywords: Pressure Sensor, CMOS MEMS, MOSFET, Microfabrication

This paper proposes a silicon-on-insulator (SOI) complementary metal-oxide semiconductor (CMOS) micro-electromechanical system (MEMS) thin-film pressure sensor in which the sensing elements are based on stress sensitive MOSFETs, and the carrier mobility and channel resistance vary with applied pressure. Four MOSFETs are embedded within a silicon dioxide membrane to form a Wheatstone bridge. The sensors are fabricated in a commercial foundry with p-channel and n-channel designs both investigated. The fabricated pMOSFET design gave a pressure sensitivity of 5.21 mV/kPa whilst the nMOSFET gave about half the sensitivity at 2.40 mV/kPa. This was coupled with maximum power consumption of 0.19 mW and 0.39 mW for the pMOSFET and nMOSFET respectively. This shows a highly sensitive pressure sensor, with improved sensitivity on traditional piezoresistors as well as significantly higher sensitivity than current MOSFET based pressure sensors. Moreover, the maximum DC power consumption was only 190 µW and 390 µW for the pMOSFET and nMOSFET, respectively. This novel low-cost, lowpower, high-sensitivity CMOS MEMS technology with on-chip electronics could be used towards the implementation of MOSFET based pressure transduction in a multitude of industrial and other applications.

I.  INTRODUCTION

The most mature industrial use of micromachined sensors revolves around pressure transducers, of which various technologies have been developed including piezoresistive, capacitance, piezoelectric, resonator and others. The inherent nature of micromachining allows these pressure sensors to become small, temporally fast, very accurate and requiring low power consumption. Therefore it is necessary to continue designing and optimizing these technologies that are so industrially desirable for automotive, aerospace, biomedical and many other sectors [START_REF] Fleming | Overview of automotive sensors[END_REF]- [START_REF] Okojie | SiC pressure sensor for detection of combustor thermoacoustic instabilities [aircraft engine applications[END_REF]. A review on silicon micromachined pressure sensors can be found here [START_REF] Bhat | Silicon micromachined pressure sensors[END_REF].

Compared to the well-established piezo-resistor technology [START_REF] Chen | Design and optimization of a micro piezoresistive pressure sensor[END_REF], [START_REF] Sandrimani | Design and Simulation of Silicon on Insulator Based Piezoresistive Pressure Sensor[END_REF], there has been little research into using active electronic devices for pressure measurements. However, there has been research into the effect of pressure on transistor channel resistance and carrier mobility [START_REF] Bradley | Piezoresistive characteristics of short-channel MOSFETs on (100) silicon[END_REF]- [START_REF] Sallese | Electrical modeling of a pressure sensor MOSFET[END_REF], showing the possibility of fabricating pressure sensors where the sensing elements are MOSFETs as opposed to the more traditional piezo-resistors. Previous examples of utilizing active electronic devices in the fabrication of pressure sensors are: a pressure sensor incorporating a stress sensitive MOS operational amplifier [START_REF] Jingjing | MOSFET differential amplifier with input pair and active load pair being stress-sensitive both[END_REF]; a pressure sensor using two embedded MOSFETs and two embedded resistors in the Wheatstone bridge configuration C orresponding author: F. A. Author (f.author@psu.edu). If some authors contributed equally, write here, "F. A. Author and S. B. Author contributed equally." IEEE Sensors Letters discourages courtesy authorship; please use the Acknowledgment section to thank your colleagues for routine contributions. Digital Object Identifier: 10.1109/LSEN.XXXX.XXXXXXX (inserted by IEEE). [START_REF] Zhang | A novel MEMS pressure sensor with MOSFET on chip[END_REF]; the fabrication and modelling of a pressure sensitive FET [START_REF] Jachowicz | FET pressure sensor and iterative method for modelling of the device[END_REF]; the use of a polyimide sacrificial layer and suspension gate MOSFET [START_REF] Fernández-Bolaños | Polyimide sacrificial layer for SOI SG-MOSFET pressure sensor[END_REF]; a nc-Si/c-Si heterojunction MOSFET pressure sensor [START_REF] Zhao | Fabrication and characteristics of an nc-Si/c-Si heterojunction MOSFETs pressure sensor[END_REF] showing a sensitivity of 2.15 mV/kPa and the design of a mirror sensing based MOSFET embedded pressure sensor [START_REF] Rathore | Design and optimization of a CMOS-MEMS integrated current mirror sensing based MOSFET embedded pressure sensor[END_REF]. Finally, [START_REF] Garcia | A mechanical-stress sensitive differential amplifier[END_REF] uses the piezo-MOS effect within a pressure sensitive differential amplifier. A collation of the technologies and their sensitivities can be found in Table 1 alongside the technology illustrated in the paper. In this paper, a novel pressure sensor is designed using a standard 1.0 µm SOI CMOS fabrication process and embedding four MOSFETs as the pressure sensitive transduction elements. Both the nMOSFET and pMOSFET design are characterized to effectively compare them to each other and existing similar technology, striving for low-power high sensitivity sensing solutions. The proposed technology offers suitable characteristics for advancing this area including high sensitivity and low power consumption.

II. DESIGN AND FABRICATION

The piezo-MOSFET sensors investigated in the report were designed using the software Cadence Virtuoso Layout Editor (IC 5.1.4). A 1.0 µm SOI CMOS fabrication process with tungsten metallization was utilized through a commercial foundry. In order to create the thin membrane structure, Deep Reactive Ion Etching (DRIE) was used to etch the back-side silicon, leaving a membrane of approximately 4.5 µm remaining. A picture illustrating the top of the fabricated sensor is shown in Figure 1. Figure 2 shows a detailed view of the layer fabrication and where the sensing elements are located within the membrane, as well as a detailed view of the MOSFET design. Located within the single crystal silicon layer are the 4 piezo-MOSFETS connected to pads via 4 µm tracks for source and drain and a 9 µm track for the gate. Two types of MOSFET were designed and tested including pMOSFET and nMOSFET with identical geometric sizes for all the incorporated regions. The p-type design used a crystallographic orientation in the [110] plane whilst the n-type design used the [100], as these have been shown to be the optimum orientation [START_REF] Kanda | A graphical representation of the piezoresistance coefficients in silicon[END_REF].

III. EXPERIMENT

The sensor requires a pressure difference between the top and bottom (within the cavity) of the membrane, thus requiring hermetic sealing that ensures no leakage over time for absolute pressure measurements. Using a cleaned glass substrate, a square of thermal epoxy resin (EPO-TEK H20E) was applied using a high precision dispensing machine (Tresky 3000). The sensors were pressed onto the epoxy gel, followed by dispensing the gel along every edge of the sensor to fill in any gaps. The substrate and sensors were then cured in an over for 45 minutes at 120 °C, allowing the resin to become hard.

A SUSS MicroTec PMC200 Cryogenic Probe Station was used to create a pressure difference. Three internal DC probes were connected from the sensor pads to two external Keithley 2450's in order to bias and measure the MOSFETs, all which were controlled by an in-house developed Labview program. The source-drain voltage was swept from 0 to ±5 V in increments of 0.25 V for each gate voltage from 0 to ± 5 V in steps of 1 V. This was carried out for all 4 MOSFETs located on the membrane. This electrical characterization was subsequently repeated at seven pressure differences from 0 to minus 700 mBar (where 0 mBar is atmospheric pressure), whilst maintaining a consistent temperature of 25 °C. In addition to these, measurements were taken over a period of 24 hours at -500 mBar and showed that the hermetic sealing worked as there was no significant leakage during this time period.

The MOSFETs exploit the piezoresistive effect induced by the sensitivity of carrier mobility under applied stress. The drain current of a device operated in the saturation region is given by

(1)
where represents the carrier mobility through the channel, is the capacitance of the oxide per unit area, and are the width and length of the channel, respectively, is the gate voltage and is the threshold voltage. Due to the MOSFET being operated in the saturation region, the channel resistance, , is equal to the output resistance of the MOSFET which can be calculated through

(2)
Due to the geometric designs in this report remaining the same, , and can be assumed constant. The normalized change in drain current can then be written as

(3)
It is shown by [START_REF] Bradley | Piezoresistive characteristics of short-channel MOSFETs on (100) silicon[END_REF] that the variation in drain current from applied mechanical stress is solely due to the change in carrier mobility and therefore the second term in equation 3 can be neglected. With the addition of a Wheatstone bridge and an applied pressure difference, the voltage output changes due to the changing where is the channel resistance of the MOSFET with its location on the membrane denoted by the subscript and is the supplied constant bridge voltage.

is subsequently used to investigate and characterize the sensor response and performance with pressure. Knowing that

, we can substitute equations 2 and 3 into the Wheatstone bridge equation (eq. 4) and show how the change in voltage output depends on the carrier mobility. This can be further simplified assuming the MOSFETs display identical behavior and that the two arms of the Wheatstone bridge will behave equally and opposite where the subscripts 1 and 2 refer to the opposite arms of the Wheatstone bridge.

(5)

The circuit schematic of the pressure sensor is shown in Figure 3 where (a) shows the Wheatstone bridge configuration comprising of the four pressure sensitive MOSFETs, and (b) shows the equivalent circuit when their channel resistances are taken as the piezo resistances.

IV. RESULTS AND DISCUSSION

A. MOSFET Characteristics

Initially, it is important to look at the behavior of the MOSFET when the pressure remains constant. Figure 4 shows the characteristic curves for the on-membrane pMOSFET (left) and nMOSFET (right) at atmospheric pressure, which is the current from drain to source plotted against the voltage from drain to source for varying gate voltage . This figure shows the conventional functioning of MOSFETs with clear linear and saturation regions for both designs. This proves the designs function properly and were not corrupted during the design or fabrication phases. This also provides the information in order to bias the devices in the saturation region. As expected, the drain current of the nMOSFET is approximately twice as large as the pMOSFET, which is due to carrier mobility being 2.5 times higher for electron mobility as opposed to hole mobility.

Figure 5 shows the drain current plotted against the gate voltage, commonly known as the threshold curve. The threshold voltage was calculated by plotting the square root if the drain current and taking the gate voltage intercept from the linear region of the curve. The pMOSFET design has a threshold voltage of -1.2 V whilst the nMOSFET displayed a threshold voltage of +1.5 V. The knowledge of these threshold voltages provides the point at which the devices turn on, and thus allowing the region of biasing to be used when measuring the response to pressure.

B. Response to Pressure

The experimental stage provided the full characteristic curves of all 4 membrane MOSFETs at every pressure difference. These were then converted into channel resistances and equation 2 was used to attain a voltage output of the sensors with the increasing of pressure difference, which is illustrated in Figure 6. is negative for the p-device and positive for the n-device. voltage response for varying gate voltage and constant drain voltage. It can be seen that varying the drain current has no effect on the voltage response, meaning that the lowest drain voltage possible should be used for the lowest power consumption. For all gate voltages above the threshold voltage, there is a strong increase in output voltage with increasing pressure. It can be seen that for the gate voltages of ±2 V, the response is slightly higher, which can be ascribed to their functioning being within the ohmic region. Most importantly, the full-scale output voltage of the pMOSFET (0.21 V) is much larger than for the nMOSFET (0.096).

Using the data displayed in Figure 6, important performance parameters can be evaluated. Firstly, the voltage sensitivity, , is calculated through

(3)
where and are the maximum and considered pressures respectively and and denote their voltage output. The maximum sensitivity of the pMOSFET design is 5.21 mV/kPa whilst the nMOSFET design came out with a lower sensitivity of 2.40 mV/kPa. The total consumed power can also be calculated using , resulting in a maximum power consumption of 0.19 mW for the pMOSFET and 0.39 mW for the nMOSFET. These performance parameters demonstrate a highly sensitive, low-power MEMS pressure transducer.

These results can be compared with existing technologies that use pressure sensitive MOSFETs using Table 1. Here it can be seen that the device presented in this report improves on the highest recorded sensitivity documented by 240%, even before any geometrical optimization. The device shows a large increase on other MOSFETbased designs and is also more sensitive than many recent piezo-resistive sensors whilst requiring a lower power consumption.

V. CONCLUSION

The design, fabrication, experiment and results of a novel MOSFET-based pressure sensor are reported. The design embeds 4 MOSFETs within a thin silicon dioxide membrane via CMOS MEMS technology and incorporates a Wheatstone bridge in order to relate the change in carrier mobility through the MOSFET channel to the change in pressure. The pMOSFET shows the highest sensitivity of 5.21 mV/kPa, showing significant improvement on other MOSFET-based technologies in addition to piezo-resistive designs whilst retaining a low power consumption. This shows great promise for the future of MOSFET pressure sensors.

Figure 1 .

 1 Figure 1. Optical micrograph of the fabricated sensor with MOSFET location illustrated.

Figure 2 .

 2 Figure 2. Cross-section of the sensor showing the layer technology and the design of the MOSFETs.

  the Wheatstone bridge arms. This is because the MOSFETs in different arms will change in the opposite direction due to the resistors longitudinally or transversely stressed, thus greatly benefitting from a Wheatstone bridge configuration. The voltage output can be calculated through the following equation[START_REF] Bhat | Silicon micromachined pressure sensors[END_REF]

Figure 3 .

 3 Figure 3. Schematic showing the operation of the MOSFET pressure sensor for (a) Wheatstone bridge operation and (b) the equivalent circuit as piezo resistances. N.B. was held constant for all 4 MOSFETs.

  Figure 6 (a) and (b) show the voltage response whilst varying the drain voltage and keeping the gate voltage at ±5 V.

  Figure 6 (c) and (d) show the

Figure 4 .

 4 Figure 4. The characteristic curves of the pMOSFET (left) and the nMOSFET (right).is negative for the p-device and positive for the n-device.

Figure 5 .

 5 Figure 5. The drain current against gate voltage for pMOSFET (left) and nMOSFET (right) for calculating the threshold voltage.

Figure 6 .

 6 Figure 6. The change in voltage output with the evolution of pressure for (a) pMOSFET with varying drain voltage, (b) nMOSFET with varying drain voltage, (c) pMOSFET with varying gate voltage and (d) nMOSFET with varying gate voltage.

Table 1 .

 1 Table showing current technologies and their sensitivities.

	Reference	Sensitivity	Sensing Element
		(mV/kPa)
	[5]	1.14	Piezo-resistor
	[6]	0.051	Piezo-resistor
	[11]	0.125	Piezo-resistor + MOSFET
	[14]	2.153	nc-Si/c-Si heterojunction
			MOSFET
	[15]	0.473	MOSFET Current Mirro
			Circuit
	[16]	1.29	MOSFET Differential
			Amplifer
	pMOS	5.21	pMOSFET
	nMOS	2.40	nMOSFET

ACKNOWLEDGMENT

This research was partly funded through the Eiffel Scholarship scheme, providing a collaboration between the University of Cambridge and LAAS/CNRS.


Marcel Bouvet

Patrick Herson

Françoise Villain

Bernard Malezieux

Self-organization of the 2-methoxyquinhydrone charge transfer complex in polar planes

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Polar planes resulting from the association of polar stacks through hydrogen bonds, have been evidenced on single crystals of a substituted quinhydrone prepared by a one-pot oxidation-cosublimation solvent-free process.

As a classical intermolecular complex example, the quinhydrone is widely used to illustrate how complexes can be built up around the subtle balance between non-covalent interactions, such as hydrogen bonding and p-p stacking. 1,[START_REF] Braga | [END_REF] In the quinhydrone, p-p interactions exist inside the donor-acceptor stacks, whilst infinite molecular chains form through H-bonds between neighboring stacks. [3][4][5] Only few examples of resolved X-ray structures of quinhydrones built up from polar quinone or hydroquinone derivatives are available in the literature. One can mention those based on 1,4naphthoquinone, 6, 7 phenanthrene-9,10-quinone, 8 1,4-dihydro-9,10-20 anthraquinone 9 , triptycenequinone 10 and 2-methoxy-1,4benzoquinone. 11 Nevertheless, molecules are packed generally in a head-to-tail configuration and dipole-dipole interactions are rarely discussed. Even though several quinhydrones can be carried out by simply grinding the starting materials in a mortar, 12 the obtention of single crystals suitable for X-ray diffraction studies remains uneasy. To overcome the solvation energy effects involved in the crystallization process from solutions, an alternative synthesis carried out from the solid forms, under vacuum, has been recently proposed. 11 Nevertheless, this method was difficult to generalize, due to the difference of vaporization energies between the quinones and dihydroquinones. Here, we report a novel approach, starting only from the substituted dihydroquinone. This route provides an in-situ generation of the 50 oxidized counterpart (the corresponding quinone). That ensures a slow process particularly convenient for the single crystals' growth. The commercially available 2-methoxy-1,4-dihydroquinone 2, when heated in a sublimator under primary vacuum, in the presence, in a separated container, of an organic or inorganic oxidizing agent 55 (2,3,5,6-tetrachloro-1,4-benzoquinone or 2,3-dichloro-5,6-dicyano-1,4-benzoquinone as well as PbO 2 ), both at 50°C, undergoes a redox reaction that leads to the formation of 1.¥ Then, in situ, cosublimation of 1 and 2 leads, after few hours, to black prisms of 3 (space group P2 1 /c). § It is worth noting that, when the 60 tetrasubstituted quinones were employed, the presence of tetrasubstituted moieties were not detected in any charge transfer complex. ‡ The 1 H NMR spectrum of crystals of 3 dissolved in CD 3 CN confirms that 1 and 2 are in a 1 to 1 ratio, with no shift compared to the isolated molecules. This is in agreement with the 65 lack of charge transfer band in the absorption spectrum of 3 dissolved in acetonitrile, while the absorption spectrum of 3 obtained in the solid state from a KBr pellet, dark violet in color, shows a strong and broad absorption band centered at 560 nm (Fig. 1). The Raman spectrum of single crystals of 3 exhibits very intense bands compared to subunits 1 and 2 (Fig. 2). Due to the use of an 90 incident laser wavelength belonging to the charge transfer absorption range, the experiment occurs in resonant Raman conditions. 13,14 The main vibrations appear at 1671 and 1600 cm -1 , attributed to the 8a C-C vibration of the benzene ring and to C=O vibration, respectively, both shifted by 20 cm -1 compared to the non-substituted 95 quinhydrone. 15 That confirms the existence of an intermolecular charge transfer complex. The asymmetric unit is composed of one molecule of 1 and one molecule of 2 tilted by 4.7 ° (Fig. 3a). As a matter of fact 3 is a 1:1 complex as the non-substituted quinhydrone. [3][4][5] The molecules 105 arrange in columns parallel to the a-axis and make an angle of 59.8 (subunit 1) and 55.2 ° (subunit 2) with this axis (Fig. 3b). The shortest intermolecular distance between the mean planes is alternately 3.21 and 3.23 Å all along the column axis, indicating strong p-p interactions. Considering the structural pattern obtained from single crystal 140 diffraction studies, two main features have caught our attention: Firstly, the quinhydrone 3, formed by the alternate stack of molecules 1 and 2, exhibits an exact superimposition of the methoxy substituents (Fig. 3b). Secondly, molecules belonging to H-bonded chains are tilted by 44.7 ° with respect to each other as defined by 145 the torsion angle between C1 to C6 and C8 to C13 planes, whereas those in next sheet are generated by the inversion center. Thus, the dipolar character of subunits 1 and 2 due to their methoxy arm is preserved in a column and on the scale of a plane. The planes correspond to each other by an inversion center. It is worth noting 150 that in pure 1, 16 molecules lie in a head-to-tail arrangement, with an intersheet distance of 3.3 Å, whereas in pure 2 obtained as single crystal by sublimation under vacuum (this work) ¶, p-p interactions do not occur, and crystal arrangement results from strong H-bonds providing a pattern made of perpendicular interlocked molecules.

155

In order to appreciate their role in the building up, the dipole moments of 1 and 2 were calculated by Chem3D Pro ™ software with the MNDO 17 method using the geometrical data collected from single crystals of 3 without geometrical optimization. In 1 the dipole moment, oriented parallel to the C1-O2 direction, is 1.0 Debye, 160 compared to 0.8 D for the optimized geometry. 16 In 2, it is 1.5 Debye and is turned to the C8-C12 direction. So, in the structure, although the dipole moments of 1 and 2 remain not aligned, the resulting dipole moment for the corresponding quinhydrone pair appears to be 2.0 Debye, as calculated for a A-D pair, whose projection in the 165 plane points in the C1-C2 direction (Fig. 4a). Notes and references ‡The same product was obtained from 1 and 2, in an equimolar ratio, by 205 heating 1 at 50 °C while 2 was kept at room temperature, in a separated compartment of a home-made sublimator. For this purpose, 1 was synthesized from 2 by oxidization at room temperature with 1,4benzoquinone. 11 This reaction can be explained from the redox potentials of 1 and 2 that we determined by cyclic voltammetry in acetonitrile, as well 210 as these of the unsubstituted analogs. 1 shows two monoelectronic and reversible reduction waves at -0.94 V and -1.75 V versus ferricinium/ferrocene (Fc + /Fc), while 2 shows one irreversible oxidation wave at +0.56 V vs Fc + /Fc. Due to the presence of the donating methoxy group, 1 is more difficult to reduce than the 1,4-benzoquinone by 0.1 V, and 2 is easier to oxidize than the non substituted 1,4-hydroquinone, also by 0.1 V. ¥ Crystals of 3 were synthesized from 2 (15 mg, 0.14 mmol) and 2,3,5,6tetrachloro-1,4-benzoquinone (20 mg, 0.08 mmol) or 2,3-dichloro-5,6dicyano-1,4-benzoquinone (20 mg, 0.09 mmol) in two separated 220 compartments of an home-made sublimator, both heated under vacuum (10 - 1 Torr) at 50 °C. 3: M.P.: 103°C (dec.), IR (KBr): 1676 (s), 1648 (m) (C=O) cm -1 , Raman (785 nm beam of a laser diode used in a microRaman set-up on a single crystal): 1671 (8a) 15 , 1600 (C=O), 453 (A band) cm -1 , UV-Visible (KBr pellet): lmax 560 nm. 1 H NMR of dissolved crystals (300 Dc = 1.42 g cm -3 , were measured on a KAPPACCD -Nonius diffractometer. l (MoKa) = 0.71073 Å, µ (MoKa) = 1.14 cm -1 , 11762 reflections (T = 200 K, 2<q <30 °), nb of independent data collected: 3773, nb of independent data used for refinement: 1213 (Fo) 2 > 1.5s(Fo) [START_REF] Braga | [END_REF]

  Scheme 1 Laboratoire de Chimie Inorganique et Matériaux Moléculaires, Université Pierre et Marie Curie -Paris 6, CNRS -UMR 7071, 4 place Jussieu, Case courrier 42, 75252 Paris cedex 05, France. Email: marcel.bouvet@espci.fr; Fax: +33 1 44 27 38 41; Tel: +33 1 44 27 30 83

Fig. 1

 1 Fig. 1 Optical absorption spectrum of 3 showing the charge transfer band 85

Fig. 2

 2 Fig.2 Raman spectrum of 3 (solid line) compared to that of the quinone and hydroquinone subunits 1 and 2. All the spectra have 100

110

  The two C-OH bonds are slightly different, 1.372(7) and 1.387(7) Å, for C8-O4 and C11-O8, respectively, whereas the two carbonyl distances are equivalent, 1.234(7) Å for C1-O1 and C4-O3. In the quinone subunit, the C-C bonds are 1.341(7) and 1.342(9) Å for the double bonds, and between 1.461(9) and 1.507(8) Å for the single 115 bonds; whereas in the hydroquinone subunit all the C-C bonds lay in the range 1.385-1.401 Å. Each molecule is linked to two neighbours belonging to adjacent columns via hydrogen bonds; dO3-O4' = 2.827(2) Å (dO3-H4' = 1.954(4) Å), angle O3-H4'-O4' = 144 ° and dO6'-O1 = 2.802(2) Å (dH6'-O1 = 2.002(4) Å), angle O6'-H6'-O1 120 = 146 °. This provides a zig-zag chains' orientation defining sheets parallel to the (101) plane (Fig. 3b and 4). Additionaly, auxiliary CH…OCH 3 interactions participate to the intercolumnar cohesion (Fig 4), with dH51-O5 = 2.668(4) Å and dH101-O2 = 2.671(4) Å.

Fig. 3 X

 3 Fig. 3 X-ray crystal structure of 3: a) ORTEP representation (30% probability level) of the asymmetric unit including atomic numbering and bond lengths in Å; b) view of molecules in two adjacent columns connected via H-bonds (80% van der Waals radii).

Fig. 4 X

 4 Fig. 4 X-ray crystal structure of 3: projection on the plane (011) (left), Hbonds are indicated as dotted lines, and CH…O interactions as full lines; arrows indicate the projection of the dipole moment for each column; projection on the plane (101) (right), arrow indicate the orientation of the 185

  , merging R = 0.039, R = S|| Fo | -| Fc || / S | Fo = 0.0675, Rw* = [Sw(|| Fo| 240 -| Fc ||) 2 / SwFo 2 ] 1/2 = 0.0570, -0.41 < Dr < 0.33. ¶ Crystal data for 2: C6H8O3, Fw = 140.14, colorless parallelepiped crystal (0.06 x 0.10 x 0.12 mm), monoclinic, P21/n, a = 4.9678(7), b = 10.533(4), c = 13.094(2) Å, a = 90, b = 91.194(13), g = 90°, V = 685.0(3) Å 3 , Z = 4, Dc = 1.359 g cm -3 , 3578 reflections (T = 295 K, 3<q <27.5 °), nb of 245 independent data collected: 1564, nb of independent data used for refinement: 978 (Fo) 2 > 1.5s(Fo) 2 , merging R = 0.047, R = S|| Fo | -| Fc || / S | Fo = 0.0473, Rw* = [Sw(|| Fo| -| Fc ||) 2 / SwFo 2 ] 1/2 = 0.0595, -0.28 < Dr < 0.25.

1 Self-Organization of the 2-Methoxyquinhydrone Charge Transfer Complex in Polar Planes Marcel BOUVET*, Bernard MALEZIEUX, Patrick HERSON, Françoise VILLAIN Laboratoire

  de Chimie Inorganique et Matériaux Moléculaires, Université Pierre et Marie Curie -Paris 6, CNRS -UMR 7071, 4 place Jussieu, Case courrier 42, 75252 Paris cedex 05, France. Fax: +33 1 44 27 38 41; Tel: +33 1 44 27 30 83; E-mail: marcel.bouvet@espci.fr

electrooptical properties. This work proposes an efficient one-pot solvent-free method to make crystals of substituted quinhydrones, starting from dihydroquinones.

= 0.4 Hz, 2H, H3 (1)]. Anal. Calcd for C14H14O6: C,

60.43%; H, 5.07%. Found: C, 60.07%; H, 5.23%. § Crystal data for 3: C14H14O6, Fw = 278.26, black prismatic crystal (0.08 x 0.12 x 0.14 mm), monoclinic, P21/c, a = 7.8386(10), b = 12.791 (3), c = 13.187 (2) Å, a = 90, b = 99.170 (11), g = 90°, V = 1305.3 (4) Å 3 , Z = 4,