Noninvasive technique to evaluate the muscle fiber characteristics using q-space imaging

Background Skeletal muscles include fast and slow muscle fibers. The tibialis anterior muscle (TA) is mainly composed of fast muscle fibers, whereas the soleus muscle (SOL) is mainly composed of slow muscle fibers. However, a noninvasive approach for appropriately investigating the characteristics of muscles is not available. Monitoring of skeletal muscle characteristics can help in the evaluation of the effects of strength training and diseases on skeletal muscles. Purpose The present study aimed to determine whether q-space imaging can distinguish between TA and SOL in in vivo mice. Methods In vivo magnetic resonance imaging of the right calves of mice (n = 8) was performed using a 7-Tesla magnetic resonance imaging system with a cryogenic probe. TA and SOL were assessed. q-space imaging was performed with a field of view of 10 mm x 10 mm, matrix of 48 x 48, and section thickness of 1000 mu m. There were ten b-values ranging from 0 to 4244 s/mm(2), and each b-value had diffusion encoding in three directions. Magnetic resonance imaging findings were compared with immunohistological findings. Results Full width at half maximum and Kurtosis maps of q-space imaging showed signal intensities consistent with immunohistological findings for both fast (myosin heavy chain II) and slow (myosin heavy chain I) muscle fibers. With regard to quantification, both full width at half maximum and Kurtosis could represent the immunohistological findings that the cell diameter of TA was larger than that of SOL (P < 0.01). Conclusion q-space imaging could clearly differentiate TA from SOL using differences in cell diameters. This technique is a promising method to noninvasively estimate the fiber type ratio in skeletal muscles, and it can be further developed as an indicator of muscle characteristics.journal articl

Colorectal carcinoma: Ex vivo evaluation using q-space imaging; Correlation with histopathologic findings

BACKGROUND: Although the prognosis of colorectal carcinoma (CRC) patients depends on the histologic grade (HG) and lymph node metastasis (LNM), accurate preoperative assessment of these prognostic factors is often difficult. PURPOSE: To assess the HG and extent of LNM by q-space imaging (QSI) for preoperative diagnosis of CRC. STUDY TYPE: Prospective. SPECIMEN: A total of 20 colorectal tissue samples containing adenocarcinomas and resected lymph nodes (LNs). FIELD STRENGTH/SEQUENCE: QSI was performed with a 3T MRI system using a diffusion-weighted echo-planar imaging sequence: repetition time, 10,000 msec; echo time, 216 or 210 msec; field of view, 113 x 73.45 mm; matrix, 120 x 78; section thickness, 4 mm; and 11 b values ranging from 0 to 9000 s/mm(2) . ASSESSMENT: The mean displacement (MDP; mum), zero-displacement probability (ZDP; arbitrary unit [a.u.]), kurtosis (K; a.u.), and apparent diffusion coefficient (ADC) were analyzed by two observers and compared with histopathologic findings. STATISTICAL TESTS: Spearman\u27s rank correlation coefficient, Mann-Whitney U-test, and ROC curve analyses. RESULTS: For all 20 carcinomas, the MDP, ZDP, K, and ADC were 8.87 +/- 0.37 mum, 82.0 +/- 6.2 a.u., 74.3 +/- 3.0 a.u., and 0.219 +/- 0.040 x 10(-3) mm(2) /s, respectively. The MDP (r = -0.768; P < 0.001), ZDP (r = 0.768; P < 0.001), and K (r = 0.785; P < 0.001) were significantly correlated with the HG of CRC, but not the ADC (r = 0.088; P = 0.712). There were also significant differences in the MDP, ZDP, and K between metastatic and nonmetastatic LNs (all, P < 0.001), but not the ADC (P = 0.082). In the HG of CRC and LNM, the area under the curve was significantly greater for MDP, ZDP, and K than for ADC. DATA CONCLUSION: QSI provides useful diagnostic information to assess the HG and extent of LNM in CRC. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1059-1068

Automated Discrimination of Brain Pathological State Attending to Complex Structural Brain Network Properties: The Shiverer Mutant Mouse Case

Neuroimaging classification procedures between normal and pathological subjects are sparse and highly dependent of an expert's clinical criterion. Here, we aimed to investigate whether possible brain structural network differences in the shiverer mouse mutant, a relevant animal model of myelin related diseases, can reflect intrinsic individual brain properties that allow the automatic discrimination between the shiverer and normal subjects. Common structural networks properties between shiverer (C3Fe.SWV Mbpshi/Mbpshi, n = 6) and background control (C3HeB.FeJ, n = 6) mice are estimated and compared by means of three diffusion weighted MRI (DW-MRI) fiber tractography algorithms and a graph framework. Firstly, we found that brain networks of control group are significantly more clustered, modularized, efficient and optimized than those of the shiverer group, which presented significantly increased characteristic path length. These results are in line with previous structural/functional complex brain networks analysis that have revealed topologic differences and brain network randomization associated to specific states of human brain pathology. In addition, by means of network measures spatial representations and discrimination analysis, we show that it is possible to classify with high accuracy to which group each subject belongs, providing also a probability value of being a normal or shiverer subject as an individual anatomical classifier. The obtained correct predictions (e.g., around 91.6–100%) and clear spatial subdivisions between control and shiverer mice, suggest that there might exist specific network subspaces corresponding to specific brain disorders, supporting also the point of view that complex brain network analyses constitutes promising tools in the future creation of interpretable imaging biomarkers

Indirect Detection of Axonal Architecture With Q-Space Imaging

Evaluating axon morphology would provide insights into connectivity, maturation, and disease pathology. Conventional diffusion MRI can provide metrics that are related to axon morphology, but cannot measure specific parameters such as mean axon diameter (MAD) and intracellular fraction (ICF). Q-space imaging (QSI) is an advanced diffusion MRI technique that may be able to provide more information on axon morphology. However, QSI has several limitations that affect its implementation and accuracy. The main objective of this dissertation was to address these limitations and to evaluate the potential of QSI to accurately assess axon morphology. First, a custom-built high-amplitude gradient coil was used to address the limitations in the maximum gradient amplitude available with commercial systems. Second, to understand the relationship between axon morphology and QSI, simulations were used to investigate the effects of the presence of both extracellular and intracellular signals (ECS and ICS) as well as variation in cell size and shape. Third, three QSI-based methods were designed provide specific measures of axon morphology which have not been reported before. The maximum amplitude of the custom gradient coil was 50 T/m that, for the first time, allowed for sub-micron displacement resolution while fulfilling the short gradient approximation. This enabled near-ideal QSI experiments to be performed. QSI experiments on excised mouse spinal cords showed good correlation with histology, but overestimated MAD. Simulations showed that axon morphology was the dominant effect on QSI and suggested that the presence of ECS and ICS signals may complicate interpretation. Three methods were designed to account for signal in ECS and ICS: two relied on a two-compartment model of the displacement probability density function and the echo attenuation at low q-values, and a third varied the gradient duration to differentiate diffusion in ECS from ICS. All three methods provided estimates of MAD and ICF that showed better agreement with histology than QSI. The methods were also evaluated implementation on a clinical scanner. This dissertation demonstrated the sensitivity of QSI to axon morphology and showed the feasibility of three methods to accurately estimate MAD and ICF. Further investigation is warranted to study future applications

Automatisation et optimisation de l’analyse de données de résonance magnétique pour les cerveaux de rongeur en développement

Les lésions de la matière blanche parfois observées chez les enfants prématurés peuvent avoir des conséquences lourdes sur le développement cognitif, comportemental et social de l’enfant. Il est important de réagir tôt pour éviter des conséquences irrémédiables. Malheureusement, à l’heure actuelle, la capacité d’un traitement à protéger les habilités cognitives ou comportementales ne peut être évaluée qu’à un stade développemental avancé, et il est alors généralement trop tard pour un traitement alternatif. L’établissement de biomarqueurs qui corrèlent avec l’issue neuro¬développementale et qui permettent d’évaluer en phase aiguë l’effet du traitement serait très bénéfique. À cet effet, l’imagerie par résonance magnétique (IRM) est un outil de choix. Son caractère non-invasif permet d’étudier sans risques additionnels cette population sensible. Le présent mémoire évalue la capacité des technologies de résonance magnétique à détecter les lésions diffuses de la matière blanche dans un modèle animal. Ce modèle animal reproduit les lésions observées chez l’humain prématuré en induisant une réaction inflammatoire par l’injection de lipopolysaccharides (LPS) directement dans le cerveau au troisième jour postnatal (P3). L’objectif principal du travail présenté est le développement d’outils opérateur-indépendants et optimaux pour l’analyse des données de résonance magnétique du raton. En particulier, ces outils sont conçus pour l’analyse des données d’imagerie du tenseur de diffusion (DTI) et pour l’analyse de données de spectroscopie (MRS). Ces outils sont ensuite appliqués à la caractérisation du modèle animal via l’analyse de deux jeux de données. Le premier est constitué de données DTI acquises à P24 ex vivo sur deux groupes, un sham (témoin) et un ayant subi une injection de LPS. Le second comprend des données MRS et DTI acquises en phase aiguë de la réaction inflammatoire (P4) in vivo dans trois groupes : sham, LPS et un troisième ayant reçu l’injection de LPS et un traitement neuroprotecteur par l’injection de l’antagoniste recombinant de l’IL1-ß (IL1-Ra), une cytokine pro-inflammatoire. Il existe différentes approches à l’analyse des données DTI : par région d’intérêt, par histogrammes, par tractographie et par comparaisons voxel-par-voxel. Principalement, dans ce mémoire, deux méthodes voxel-par-voxel ont été étudiées : "Voxel-Based Analysis" ou VBA et "Tract-Based Spatial Statistics" ou TBSS. VBA compare les populations en calculant une statistique pour chaque point de l’espace. TBSS, développé pour répondre à certaines limitations de VBA, compare les deux groupes en conduisant les tests sur un sous-ensemble des voxels de la matière blanche, le squelette de matière blanche. Ces deux méthodes reposent sur une étape préliminaire majeure : la normalisation spatiale. La normalisation permet de s’assurer, jusqu’à un certain degré, que pour l’ensemble de la population les voxels comparés correspondent à une même région anatomique. Pour ce projet, trois méthodes de normalisation spatiale utilisant chacune un algorithme de recalage différent ont été implémentées : Symmetric Group-Wise Normalization avec l’algorithme Symmetric Normalization (SyN, de la suite Advanced Normalization Tools ou ANTs), la normalisation spatiale non-biaisée du module Diffusion Tensor Toolkit (DTI-TK) et une normalisation spatiale sur base du sujet le plus représentatif de la population avec l’algorithme FMRIB Non-linear Image Registration Tool (FNIRT, de la suite FMRIB Software Library ou FSL). L’analyse automatique des données de diffusion peut donc se faire via différentes combinaisons de normalisation spatiale (ANTs, DTI-TK, FSL) et d’analyse voxel-par-voxel (VBA, TBSS). Il est généralement difficile de déterminer la meilleure combinaison et il n’y a pas de principes établis pour guider ce choix. Ceci est étudié dans l’article « Near equivalence of three automated diffusion tensor analysis pipelines in a neonate rat model of periventricular leukomalacia » où chacune des normalisations spatiales implémentées est testée en combinaison avec VBA et TBSS. L’étude démontre que les résultats sont très cohérents entre les différentes approches mais met en évidence des limitations de VBA et TBSS. Les résultats suggèrent qu’appliquer la normalisation DTI-TK en combinaison avec TBSS permet une analyse plus robuste, du moins pour les ratons et dans le cadre de ce modèle animal. Des modules d’analyse par histogramme et de parcellisation automatique de la matière blanche sous-corticale ont également été implémentés et testés. L’analyse des données de spectroscopie est plus directe, dans le sens où les paramètres de l’analyse sont moins influents sur le résultat. De ce fait, un pipeline unique et opérateur¬indépendant a été implémenté, incorporant le prétraitement des données et la quantification des métabolites à l’aide du Linear Combination Model (LCModel). Ces pipelines DTI/MRS ont été appliqués à l’étude du modèle animal et ont permis de démontrer la sensibilité des technologies de résonance magnétique à ce type de lésion. En effet, l’analyse des données de diffusion ex vivo a souligné une lésion persistante et diffuse de la matière blanche sous-corticale du côté ipsilatéral. En phase aiguë de l’inflammation, les données de diffusion in vivo indiquent une forte diminution de la diffusivité radiale et axiale. La spectroscopie a également permis de mettre en évidence des changements métaboliques avec notamment une réduction de N-acetylaspartate, glutamate, phosphorylethanolamine et une augmentation de lipides et de macromolécules. Le traitement à l’IL1-Ra a permis de modérer les changements observés en DTI et en MRS. En conclusion, différents outils « état-de-l’art » relatifs à l’analyse de données DTI et MRS ont été développés et appliqués avec succès à l’étude d’un modèle animal des lésions de la matière blanche de l’enfant prématuré. Les résultats permettent de considérer la DTI et la MRS comme technologies prometteuses pour la caractérisation et le suivi de ce type de lésion, celles-ci étant sensibles à la lésion en phase aiguë de la réaction inflammatoire ainsi qu’à un stade développemental plus avancé. Cependant, afin de permettre une interprétation solide des changements observés, il est nécessaire de confronter les observations IRM à d’autres méthodes d’imagerie telles que l’immuno-histologie, la microscopie électronique ou encore l’optique par cohérence tomographique.----------ABSTRACT White matter injuries observed in the preterm infant may have heavy consequences on the cognitive, behavioral and social development of the child and it is imperative to act early in order to avoid definitive repercussions. Unfortunately, for now, the efficacy of neuroprotective treatments can only be assessed at an advanced developmental stage when it is already too late to experiment with an alternative treatment. Finding biomarkers that correlate with the neuro¬developmental outcome and allow to assess the efficacy of the treatment at an early stage would be greatly beneficial. Magnetic resonance imaging (MRI) is a prominent technology with potential for establishing quantitative biomarkers. Moreover, its non-invasive nature allows to study this sensitive population without additional risks. This thesis assesses the use of MRI technologies for the study of diffuse white matter injury in an animal model. This animal model reproduces the lesions observed in human preterms by inducing an inflammatory reaction in the neonate rat brain by an injection of lipopolysaccharides (LPS) at postnatal day 3 (P3). The main goal of this project is to develop user-independent and optimal tools for the analysis of magnetic resonance data of the rat pup brain. Specifically, these tools are designed for the analysis of diffusion tensor imaging (DTI) and magnetic resonance spectroscopy (MRS) data. The secondary goal is to apply these tools to the study of the animal model of white matter injury. The data are made up of two different sets. The first one is constituted of DTI data only acquired ex vivo at P24 on two different groups: one sham and one that underwent an LPS injection. The second set of data comprises both DTI and MRS data. Data were acquired in vivo at the acute phase of injury (P4) on three different groups: sham, LPS and a third group that was injected with LPS and received a neuroprotective treatment by administration of the recombinant antagonist of IL1¬ß (IL1-Ra), a pro-inflammatory cytokine. There are several ways to conduct the analysis of DTI data: by region of interest, by histograms, by tractography or by conducting voxelwise comparisons. Primarily, in this thesis, two voxelwise methods were studied: “Voxel-Based Analysis” or VBA and “Tract-Based Spatial Statistics” or TBSS. VBA compares populations by computing a statistic at every voxel of the image. TBSS, which has been developed to alleviate some limitations of VBA, runs the statistical tests on a subset of voxels in the white matter, the white matter skeleton. Both these methods strongly rely on a specific image processing step: the spatial normalization. The normalization ensures, to a certain extent, that the voxels correspond to a same anatomical region across the subjects. Here, three normalization approaches, each using a different registration algorithm, were implemented: Symmetric Group-Wise Normalization using the Symmetric Normalization (SyN) algorithm of the Advanced Normalization Tools (ANTs) toolbox; the unbiased normalization of the Diffusion Tensor Toolkit (DTI-TK); and a normalization based on the population most representative subject using FMRIB Non-linear Image Registration Tool (FNIRT) algorithm of the FMRIB Software Library (FSL). Automatic diffusion data analysis can therefore be performed using combinations of a certain spatial normalization (ANTs, DTI-TK, FSL) and a voxelwise analysis (VBA, TBSS). Determining the best combination is not straight-forward and there are no principled ways to choose one combination over another. This was studied in the submitted paper “Near equivalence of three automated diffusion tensor analysis pipelines in a neonate rat model of periventricular leukomalacia” in which each of the implemented normalization methods were tested in combination with VBA and TBSS. Results demonstrate great coherence among the tested pipelines but also underlines both VBA and TBSS limitations. The study also suggests that, for the rat pup data of this animal model, combining DTI-TK normalization with TBSS might yield a more robust analysis. Other analysis modules implemented for the study of DTI data include analysis by histogram and by automatic parcelling of sub-cortical white matter. Magnetic spectroscopy data analysis does not depend as strongly to the processing pipeline as diffusion data. Therefore, a unique and user-independent pipeline was implemented. This pipeline incorporates data preprocessing operations and automatic metabolites quantification using the Linear Combination Model (LCModel). These pipelines were applied to the study of the animal model and the results demonstrated that magnetic resonance technologies are sensitive to these injuries. The ex vivo diffusion data exhibited a persistent and diffuse injury of the sub-cortical white matter on the ipsilateral side. At the acute phase, the in vivo diffusion data showed a strong decrease of axial and radial diffusivities. The spectroscopy data also underlined metabolic perturbations with essentially a decrease of N-acetylaspartate, glutamate, phosphorylethanolamine and an increase of lipids and macromolecules. The IL1-Ra neuroprotective treatment seemed effective and moderated the amplitude of these changes in both DTI and MRS. In conclusion, various state-of-the-art analysis tools for DTI and MRS data were developed and successfully applied to the study of an animal model of diffuse white matter injury of the preterm baby. Results indicate that DTI and MRS are potential tools for the characterisation and monitoring of this pathology, these being sensitive to the injury in the acute and chronic stages. However, in order to further strengthen the interpretation of these results, it is necessary that these be supported by other imaging technologies such as immuno-histology, electronic microscopy or optical coherence tomography

Caractérisation de la microstructure des voies spinales humaines par IRM multiparamétrique

RÉSUMÉ La routine clinique en IRM pour le diagnostic ou le suivi de pathologies neurodégénératives telles que la sclérose en plaques ou la dégénérescence wallérienne ne se fait actuellement que par une évaluation visuelle basée sur des différences de contraste dans des images anatomiques. Il est donc difficile de déterminer précisément le degré des lésions. L’IRM quantitative (IRMq) se propose de quantifier l’évolution du tissu par des métriques sensibles et spécifiques aux différentes caractéristiques microstructurales. Très développé dans le cerveau, sa faisabilité et ses applications ont été démontrées dans la moelle. Toutefois, l’acquisition de telles métriques prend généralement trop de temps et est souvent trop exigeant en termes de force de gradients magnétiques pour entrer dans un cadre clinique. De plus, plusieurs sources d’erreurs sont susceptibles de baiser les mesures. Ce mémoire vise à mettre en place un protocole complet (de l’acquisition au traitement des données) permettant l’estimation de métriques IRMq spécifiquement à chaque voie spinale. Les principales métriques issues de ce protocole sont le Ratio de Transfert de Magnétisation (MTR), le temps de relaxation T1, le Volume de Tissu Macromoléculaire (MTV), les indices des modèles de diffusion NODDI (Neurite Orientation Dispersion and Density Imaging) et DTI (Imagerie par Tenseur de Diffusion), le g-ratio (ratio du diamètre axonal sur celui de la fibre incluant la gaine de myéline) et l’aire de section axiale. Le protocole développé est applicable en clinique et prend en compte les différentes sources d’erreurs connues qui peuvent s’introduire dans les mesures durant l’acquisition. De plus, basé sur le recalage d’un atlas des voies spinales sur chaque métrique, le protocole de traitement de données, rapide et quasi-automatique, permet de s’affranchir du biais lié à l’opérateur lors de la délimitation manuelle des régions d’intérêt. Quant à la méthode d’estimation, elle emploie des estimateurs tels que les estimateurs des moindres carrés et du maximum a posteriori permettant d’atténuer l’effet de volume partiel et du bruit ; elle est par ailleurs validée sur un fantôme synthétique. Finalement, le protocole complet est appliqué à une cohorte de 16 jeunes adultes (de 21 à 33 ans) et 14 adultes âgés (de 61 à 73 ans) sains afin d’évaluer sa sensibilité aux différentes microstructures dans la matière blanche de la moelle épinière. Pour toutes les métriques les estimations montrent des valeurs en accord avec la littérature. Toutes les métriques – excepté les fractions de volume intracellulaire, de volume----------ABSTRACT The current clinical MRI routine for the diagnosis or the screening of neurodegenerative pathologies such as multiple sclerosis or Wallerian degeneration, consists of a simple visual assessment based on contrast differences in anatomical images. Therefore it is hard to precisely assess the stage of the lesions. Quantitative MRI (qMRI) proposes to quantify the evolution of the tissue using metrics sensitive and specific to the different microstructural characteristics. Widely developed in the brain, its feasibility and applications have been demonstrated in the spinal cord. However, the acquisition of such metrics is too time-consuming and demanding in terms of magnetic gradient strength to apply in a clinical framework. Moreover, several sources of error are likely to bias the measures. This thesis aims to develop a comprehensive protocol (from the acquisition to the processing of the data) allowing the estimation of qMRI metrics specifically in each spinal pathway. The main metrics resulting from this protocol are the Magnetization Transfer Ratio (MTR), the relaxation time T1, the Macromolecular Tissue Volume (MTV), the diffusion indices from NODDI (Neurite Orientation Dispersion and Density Imaging) and DTI (Diffusion Tensor Imaging) models, the g-ratio (ratio of the inner diameter over the outer diameter of a fiber, including its myelin sheath) and the cross-sectional area. This protocol is applicable in a clinical framework and takes into account the different sources of error that are likely to affect the measures during acquisition. In addition, since it is based on the registration of a white matter atlas to each metric, the fast and almost automatic data processing pipeline allows to get rid of the usual user-related bias induced by the manual drawing of regions of interest. Moreover, the estimation method uses estimators such as the least square and the maximum a posteriori estimators allowing to mitigate the effect of partial volume and of noise; furthermore, a validation of the method is performed on a synthetic phantom. Finally, the whole protocol is applied to a cohort of 16 young (aged 21 to 33) and 14 elderly (aged 61 to 73) healthy adults in order to assess its sensitivity to different microstructures in the spinal cord white matter. For all metrics the estimations show values in agreement with the literature. All metrics – except the intracellular, the axonal and the fiber volume fractions – showed significant difference between the dorsal column and the corticospinal tract, as suggested by histology. However, only the MTR showed a significant decrease between young and elderly, in agreement wit

Mechanisms of spinal cord degeneration and repair in multiple sclerosis: A 3T MRI study of the spinal cord

The spinal cord is a clinically eloquent structure, commonly affected in multiple sclerosis (MS) and spinal neuroaxonal loss is an important cause of non-remitting, disability progression. Neuroaxonal loss in MS is likely to be multifactorial and caused by several disease pathways. In contrast, repair and adaptive mechanisms can ameliorate disability following clinical relapses. This thesis has explored some of these clinically relevant disease mechanisms by combining single-voxel proton spectroscopy (MRS) and Q-space imaging (QSI), two advanced MRI techniques, which have increased pathological specificity for neurodegeneration and myelin, and allow quantification of metabolites that reflect biological mechanisms, to study spinal neurodegeneration and repair in MS. In persons with early primary progressive MS (PPMS), spinal MRS and QSI exhibited increased sensitivity for detection of early disease changes than more conventional measures such as spinal cord atrophy and correlated with clinical disability measures suggesting these measures are functionally relevant. Region of interest analysis of the relationship between QSI indices in spinal white matter tracts and clinical scores which reflect the motor or sensory functions conveyed within those tracts, suggests a strong structure-function relationship exists between axonal integrity and disability. In persons with relapsing remitting MS (RRMS), with recent (within 4 weeks) symptoms suggestive of spinal cord relapse, serial imaging with spinal MRS and QSI over 6 months reflected clinical changes over that time. Specifically, rising spinal concentrations of total N-acetyl-aspartate (tNAA) and restriction of QSI-derived perpendicular diffusivity, which I hypothesise reflect, restoration of mitochondrial function and remyelination, respectively, underlie clinical recovery. Within the RRMS cohort, MRS and QSI measures at baseline were predictive of clinical outcomes at 6 months; elevated baseline spinal glutamate-glutamine (Glx), myo-inositol (Ins) and total creatine (tCr) concentrations and increased QSI-derived perpendicular diffusivity predicted poor outcomes and may reflect important mechanisms of disability progression such as; demyelination, neurodegeneration, astrogliosis and altered neuronal metabolism. Taken together the results suggest that mechanisms of disability following spinal cord relapse are complex and glutamate excitotoxicity, gliosis and axonal metabolic dysfunction may be important determinants of residual disability following relapses. This work suggests that newer, quantitative MRI techniques when applied to the spinal cord are sensitive markers of disease activity and progression and could be useful in monitoring therapies that aim to prevent neurodegeneration and enhance remyelination in MS

Principles of organisation within the pathways in the brainstem and thalamus

There are few detailed studies on the pathways through the human brainstem and even fewer on those through the pons. This thesis aims to address this lack of fine detail, and used ultra-high-field magnetic resonance imaging (MRI) of human and macaque brains to identify and characterise fibre tracts connecting cortical and spinal areas as they traverse through brainstem and thalamic structures. The material in this thesis is based on a unique dataset of ultra-high-field (7 Tesla – Duke and 11, 7 Tesla – Johns Hopkins) MRI scans on postmortem specimens, on which deterministic tractography has been applied based on high-angular-resolution diffusion imaging (HARDI) and subsequently higher order tensor glyph models. The first results section of the thesis (Chapter 3) maps the descending fibre bundles associated with movement. From the motor cortical areas, the fibres of the internal capsule are traced through the crus cerebri, basilar pons and pyramids in three dimensions to reveal their organisation into functional and topographic subdivisions. While human cortico-pontine, -bulbar and -spinal tracts were traditionally considered to be dispersed, or a “melange”, I show here a much more discrete and defined organisation of these descending fibre bundles. Nine descending fibre bundles are identified and their anatomical location and terminations are described. A hitherto unknown pathway at the midline of the pons has been discovered and named herein as the Stria Pontis which connects the neocortex to the pontine tegmentum. Ten transverse fibre bundles connecting the pontine nuclei to the cerebellum are also identified. The second results section (Chapter 4) analyses the sensory pathways; the dorsal column - medial lemniscus pathway, the spinothalamic tract, the spinal trigeminal tract and the trigeminothalamic tracts. The third results section (Chapter 5) analyses the dentato-rubro-thalamic tract. The mapping identifies the superior cerebellar peduncle, the patterning of the fibres within the superior cerebellar decussation, the patterning of the fibres within the red nucleus and finally the projection of the dentato-rubro-thalamic tract from the red nucleus to the ventral lateral nucleus of the thalamus. Finally, I characterised 117 already known anatomical parts, areas and structures of the brainstem and thalamus in 3D