While diffusion MRI promises an insight into white matter microstructure in vivo, the axonal pathways that connect different brain regions together can only partially be segmented using current methods. Here we present a novel method for estimating the tissue composition of each voxel in the brain from diffusion MRI data, thereby providing a foundation for computing the volume of different pathways in both health and disease. With the tissue dependent diffusion model described in this thesis, white matter is segmented by removing the ambiguity caused by the isotropic partial volumes: both grey matter and cerebrospinal fluid. Apart from the volume fractions of all three tissue types, we also obtain estimates of fibre orientations for tractography as well as diffusivity and anisotropy parameters which serve as proxy indices of pathway coherence. We assume Gaussian diffusion of water molecules for each tissue type. The resulting three-tensor model comprises one anisotropic (white matter) compartment modelled by a cylindrical tensor and two isotropic compartments (grey matter and cerebrospinal fluid). We model the measurement noise using a Rice distribution. Markov chain Monte Carlo sampling techniques are used to estimate posterior distributions over the model’s parameters. In particular, we employ a Metropolis Hastings sampler with a custom burn-in and proposal adaptation to ensure good mixing and efficient exploration of the high-probability region. This way we obtain not only point estimates of quantities of interest, but also a measure of their uncertainty (posterior variance). The model is evaluated on synthetic data and brain images: we observe that the volume maps produced with our method show plausible and well delineated structures for all three tissue types. Estimated white matter fibre orientations also agree with known anatomy and align well with those obtained using current methods. Importantly, we are able to disambiguate the volume and anisotropy information thus alleviating partial volume effects and providing measures superior to the currently ubiquitous fractional anisotropy. These improved measures are then applied to study brain differences in a cohort of healthy volunteers aged 25-65 years. Lastly, we explore the possibility of using prior knowledge of the spatial variability of our parameters in the brain to further improve the estimation by pooling information among neighbouring voxels.

BACKGROUND: Sleep deprivation is one of the first symptoms to manifest in schizophrenia patients. An important proponent for both sleep and cognition, sleep spindles have been investigated to understand the connection between sleep and schizophrenia. This thesis aims to conduct a meta-analysis on this topic to conglomerate previous research and come to a definitive conclusion on how sleep and schizophrenia interact. Multiple studies have reported associations between sleep, schizophrenia, and the thalamus. Novel methods have allowed researchers to segment the thalamus into 25 different nuclei. Therefore, this thesis will also attempt to validate these findings and use new segmentation software to investigate which specific nuclei affect schizophrenia. This thesis also extends this thalamic investigation to explore white matter tracts related to the thalamus. Using both arms of this study, we aim to further understand the complex relationship between brain structure, sleep, and schizophrenia. METHODS: The meta-analysis portion of this thesis pooled fifteen studies for a total of 321 patients and 323 healthy controls. The patient population was made up of first-episode psychosis (FEP), family high-risk (FHR), and schizophrenia (SZ) populations. R Studio was utilized to run a meta-analysis on sleep spindle density (SSD) values pulled from each study. This dataset was also used for meta-regressions and funnel plots. The imaging aspect of this thesis pulled subjects from two separate Boston studies for a total of 54 early course patients (EC) and 27 healthy controls (HC). A brain editing software, FreeSurfer, was used to quality control and segment the thalamus. This segmentation provided volumes for each nuclei. A free-water imaging pipeline was used to process diffusion weighted images so that free-water (FW) and corrected fractional anisotropy (FAt) could be collected. These values were run through Tract-Based Spatial Statistics (TBSS) to standardize scans and identify white matter regions of interest. RESULTS: This thesis reports an effect size of -1.24 between HC and the collective subject groups with a confidence interval of -1.63 to -.84. Via meta-regression, we report that illness duration, publication year, and spindle frequency gap (SFG) all are associated with sleep spindles. The thalamic volumetric analysis showed that four nuclei differed between EC and HC and two nuclei differed between EC and FH. The imaging aspect of this thesis discovered no significant FAt findings comparing volume to population, though heat maps revealed a trend where FHR and EC had lower FAt than HC. For FW analysis, we found a similar trend where FHR and EC had higher FW than HC. CONCLUSION: Using both analyses, this thesis connected sleep spindles, schizophrenia, and brain structure. We illuminated consistent reports of schizophrenia populations having lower sleep spindle density compared to healthy controls. This thesis reports a difference in thalamic nuclei volumes between both HC and FHR versus EC as well as FAt and FW differences between both FHR and EC and HC. / 2021-06-07T00:00:00Z

Comprendre l'organisation de la connectivité structurelle du cerveau ainsi que comment celle-ci contraint sa fonctionnalité est une question fondamentale en neuroscience. L'avènement de l'Imagerie par Résonance Magnétique de diffusion (IRMd) a permis l'estimation de la connectivité des neurones in vivo. Dans cette thèse, nous profitons de ces avancées pour : étudier l'organisation structurelle du cerveau ; étudier la relation entre la connectivité, l'anatomie et la fonction cérébrale ; identifier les régions corticales correspondantes d'un sujet à un autre ; et inférer la connectivité en présence de pathologie. Cette thèse contient trois contributions majeures. La première est un modèle pour la connectivité axonale et une technique efficace pour diviser le cerveau en régions de connectivité homogène. Cette technique de parcellisation permet de diviser le cerveau tant pour un seul sujet que pour une population. Les parcelles résultantes sont en accord avec les parcellations anatomiques, structurelles et fonctionnelles existant dans la littérature. La seconde contribution de cette thèse est une technique qui permet d'identifier les régions correspondantes d'un sujet à un autre. Cette technique, basée sur le transport optimal, offre une meilleure performance que les techniques courantes. La troisième contribution est une technique de segmentation, dite multi-atlas, pour identifier les faisceaux d'axones de la matière blanche de patients atteints d'une pathologie cérébrale. Comme les techniques existantes, notre approche utilise l'information spatiale provenant d'atlas de sujets sains, mais pondère celle-ci avec l'information d'IRMd du patient. Nous montrons que notre technique obtient de meilleurs résultats que les méthodes non pondérées. / Understanding how brain connectivity is organized, and how this constrains brain functionality is a key question of neuroscience. The advent of Diffusion Magnetic Resonance Imaging (dMRI) permitted the in vivo estimation of brain axonal connectivity. In this thesis, we leverage these advances in order to: study how the brain connectivity is organized; study the relationship between brain connectivity, anatomy, and function; find correspondences between structurally-defined regions of different subjects, and infer connectivity in the presence of a brain’s pathology. We present three major contributions. Our first contribution is a model for the long-range axonal connectivity, and an efficient technique to divide the brain in regions with homogeneous connectivity. Our parceling technique can create both single-subject and groupwise structural parcellations of the brain. The resulting parcels are in agreement with anatomical, structural and functional parcellations extant in the literature. Our second contribution is a method to find correspondence between structural parcellations of different subjects. Based on Optimal Transport, it performs significantly better than the state-of-the-art ones. Our third contribution is a multi-atlas technique to infer the location of white-matter bundles in patients with a brain pathology. As existent techniques, our approach aggregates spatial information from healthy subjects, our novelty is to weight such information with the diffusion image of the patient. We show that our technique achieves better results than the non-weighted methods.

Diffusion magnetic resonance imaging (dMRI) allows uniquely the study of the human brain non-invasively and in vivo. Advances in dMRI offer new insight into tissue microstructure and connectivity, and the possibility of investigating the mechanisms and pathology of neurological diseases. The great potential of the technique relies on indirect inference, as modelling frameworks are necessary to map dMRI measurements to neuroanatomical features. However, this mapping can be computationally expensive, particularly given the trend of increasing dataset sizes and/or the increased complexity in biophysical modelling. Limitations on computing can restrict data exploration and even methodology development. A step forward is to take advantage of the power offered by recent parallel computing architectures, especially Graphics Processing Units (GPUs). GPUs are massive parallel processors that offer trillions of floating point operations per second, and have made possible the solution of computationally intensive scientific problems that were intractable before. However, they are not inherently suited for all types of problems, and bespoke computational frameworks need to be developed in many cases to take advantage of their full potential. In this thesis, we propose parallel computational frameworks for the analysis of dMRI using GPUs within different contexts. We show that GPU-based designs can offer accelerations of more than two orders of magnitude for a number of scientific computing tasks with different parallelisability requirements, ranging from biophysical modelling for tissue microstructure estimation to white matter tractography for connectome generation. We develop novel and efficient GPUaccelerated solutions, including a framework that automatically generates GPU parallel code from a user-specified biophysical model. We also present a parallel GPU framework for performing probabilistic tractography and generating whole-brain connectomes. Throughout the thesis, we discuss several strategies for parallelising scientific applications, and we show the great potential of the accelerations obtained, which change the perspective of what is computationally feasible.

Diffusion-weighted (DW) magnetic resonance imaging is an important neuroimaging technique that has successful applications in diagnosis of ischemic stroke and methods based on diffusion tensor imaging (DTI). Tensor measures have been used for detecting changes in tissue microstructure and for non-invasively tracing white matter connections in vivo. The most common image acquistion strategy is to use a DW single-shot echo-planar imaging (ss-EPI) pulse sequence, which is attractive due to its robustness to motion artefacts and high imaging speed. However, this sequence has limited achievable spatial resolution and suffers from geometric distortion and blurring artefacts. Readout-segmented echo-planar imaging (rs-EPI) is a DW sequence that is capable of acquiring high-resolution images by segmenting the acquisition of k- space into multiple shots. The fast, short readouts reduce distortion and blurring and the problem of artefacts due to motion-induced phase changes between shots can be overcome with navigator techniques. The rs-EPI sequence has two main shortcomings. (i) The method is slow to produce image volumes, which is limiting for clinical scans due to patient welfare and prevents us from acquiring very many directions in DTI. (ii) The sequence (like other diffusion techniques) is far from the optimum repetition time (TR) for acquiring data with the highest possible signal-to-noise ratio (SNR) in a given time. The work in this thesis seeks to address both of these important issues using a range of approaches. In Chapter 4 a partial Fourier extension is presented, which addresses point (i) by reducing the number of readout segments acquired and estimating the missing data. This allows reductions in scan time by approximately 40% and the reliability of the images is demonstrated in comparisons with the original images. The application of a simultaneous multi-slice scheme to rs-EPI, to address points (i) and (ii), is described in Chapter 5. Using the slice-accelerated rs-EPI sequence, tractography data were compared to ss-EPI data and high-resolution trace-weighted data were acquired in clinically relevant scan times. Finally, a 3D multi-slab extension that addresses point (i) is presented in Chapter 6. A 3D sequence could also allow higher resolution in the slice direction than 2D multi-slice methods, which are limited by the difficulties in exciting thin, accurate slices. A 3D version of rs-EPI was simulated and implemented and a k-space acquisition synchronised to the cardiac cycle showed substantial improvements in image artefacts compared to a conventional k-space acquisition.

L'IRM quantitative a un potentiel énorme pour fournir une valeur intrinsèque et indirecte aux propriétés des tissus utiles au diagnostic, au pronostic et aux essais cliniques de la sclérose en plaques (SEP), qui est une maladie inflammatoire du système nerveux central. Complémentaire à l’imagerie cérébrale, étudier l’impact de la maladie sur la moelle épinière grâce à l’imagerie quantitative, en particulier l’IRM de diffusion, devient un véritable défi. L'acquisition et le traitement de ce type de données posent des problèmes inhérents en raison de la distorsion de susceptibilité, de la petite section transversale de la moelle et l’absence de repères anatomiques visibles qui permettant d'identifier des voies ou du niveau vertébral. Dans ce contexte, nous proposons plusieurs contributions pour le traitement et l'analyse statistique de ces données. Tout d'abord, nous proposons de nouvelles métriques géométriques pour évaluer et comparer différentes méthodes de correction de distorsion en mesurant l'alignement du modèle de diffusion reconstruit avec l'axe central apparent de la moelle épinière. Deuxièmement, en utilisant une cohorte de patients atteints de SEP et de témoins sains, nous étudions le lien entre les mesures de diffusion et la présence ou l'absence de lésion dans un niveau vertébral donné et nous montrons que nous pouvons prédire ce dernier avec une bonne précision en utilisant un apprentissage linéaire multivarié. Enfin, nous montrons la faisabilité d’une étude longitudinale de l’évolution des métriques d'IRM de diffusion en réalisant une étude de reproductibilité à l’aide d’un ensemble de données test-retest, et l’appliquons aux 2 premières acquisitions (M0 et M12) de notre cohorte de patients. / Quantitative MRI has huge potential to provide intrinsic and normative value to tissue properties useful for diagnosis, prognosis and ultimately clinical trials in multiple sclerosis (MS) which is an inflammatory disorder of the central nervous system. Complementary to brain imaging, investigating how the spinal cord is damaged using quantitative imaging, and in particular diffusion MRI, becomes an acute challenge. Acquiring and processing this type of data present inherent challenges due to the susceptibility distortion, the small crosssectional area of the spine and the lack of visible anatomical landmarks to help identification of tracts or vertebral level. In this context, we propose several contributions for the processing and statistical analysis of this data. First, we propose novel geometric metrics to evaluate and compare different distortion correction methods by measuring the alignment of the reconstructed diffusion model with the apparent centerline of the spine. Second, using a cohort of MS patients and healthy controls, we study the link between diffusion measures and the presence or absence of lesion in a given vertebral level and we show that we can predict the latter with good accuracy by learning a multivariate linear classifier. Last, we show the feasibility of longitudinal study of the evolution of diffusion MRI metrics by performing a reproducibility study using a test-retest dataset and apply it to the 2 first timepoints (M0 and M12) of our cohort of MS patients.

No description available.

Diffusion MRI provides a unique opportunity to study the brain tissue architecture at a microscopic level. More specifically, it allows to infer biophysical properties of the axons in the white matter in-vivo. Microstructural parameters are widely used in multi-subject studies to track pathological processes, follow normal development and aging, or investigate behaviour. This thesis aims to identify and potentially address the limitations and pitfalls in voxelwise comparison of diffusion MRI parameters across subjects. To allow for accurate matching of brain structures across subjects, non-linear transformation that spatially aligns the data is required. We demonstrate that using advanced registration methods, we can outperform the standard registration-projection approach both in terms of sensitivity and specificity. The coarse resolution of the data typically causes partial volume effects that bias the diffusion parameters and potentially mislead the interpretation of a group study outcome. We provide evidence that these effects can be addressed by constraining the diffusion model parameter space, which leads to marginally lower sensitivity, but allows an accurate interpretation of the results. Additionally, we suggest that additional information inferred with a data driven approach might mitigate the loss in sensitivity. Finally, we design an original tract-specific modelling framework that enables to estimate microstructural parameters unbiased by the presence of foreign fibre populations or tissues. We demonstrate the sensitivity of our method in a study relating microstructure and behaviour.

BACKGROUND: A number of epilepsy syndromes are characterized by sleep-activated epileptiform discharges, however drivers of this process are not well understood. Previous research has found that thalamic injury in early life may increase the odds of sleep-activated spikes. Benign childhood epilepsy with centrotemporal spikes (BECTS) is among the most common pediatric-onset epilepsy syndromes, characterized by sleep-potentiated spike activity, a focal sensorimotor seizure semiology, and deficits in language, attention, and behavioral functioning. Though ictal and interictal electro-clinical activity resolves during mid-adolescence, adverse psychosocial outcomes may persist. Previous findings from monozygotic twin and neuroimaging studies suggest a multifactorial pattern of disease and raise suspicion for structural changes in thalamocortical connectivity focal to the seizure onset zone, though this has not been explored. OBJECTIVE: This research aims to (1) assess white matter differences in focal thalamocortical connectivity between BECTS cases and healthy controls using validated probabilistic tractography methods, (2) assess the association between spike burden and white matter connectivity focal to the seizure onset zone, and (3) evaluate longitudinal changes in thalamocortical connectivity across four cases. METHODS: 42 subjects ages 6-15 years were recruited between November 2015 and February 2018, including 23 BECTS cases and 19 healthy controls. Subjects underwent 3 Tesla structural and diffusion-weighted magnetic resonance imaging (2mm x 2mm x 2mm) with 64 gradient directions (b-value=2000) and 72 electrode sleep-deprived electroencephalographic (EEG) recordings. Seed and target regions of interest (ROIs) were created within each hemisphere using the Desikan-Killiany atlas, with the thalamus set as a seed ROI, and SOZ cortex and non-SOZ (NSOZ) cortex as target ROIs. Probabilistic tractography was executed using PROBTRACKX2 with 500 streamlines per seed voxel, 0.5 millimeter steps, and a curvature threshold of 0.2. All streamlines reaching the target ROI were summed and normalized by seed voxel count. Results for BECTS and healthy controls were plotted by age. The slope of thalamocortical connectivity versus age was computed for each group and compared between groups using nonparametric bootstrap analysis. Additionally, the association between SOZ connectivity and spike burden was assessed in a subgroup analysis using a linear regression model, controlling for age. RESULTS: A significant difference in the developmental trajectory of thalamocortical connectivity to the SOZ in BECTS cases compared to healthy controls was found (p=0.014), where the increase in connectivity with age observed in healthy controls was not present in BECTS children. These results did not extend to NSOZ thalamocortical connections (p=0.192). Longitudinal results support these observations, where all BECTS cases who underwent repeat imaging (N=4) showed a decrease in thalamocortical connectivity to the SOZ over the follow-up period. No relationship was found between thalamocortical connectivity and spike burden (p=0.840). CONCLUSIONS: These findings suggest that children with BECTS show subtle alterations in thalamocortical white matter development focal to the seizure onset zone. Thalamocortical connectivity to the SOZ does not appear to directly mediate non-REM sleep spike potentiation in BECTS. Limitations of this study include the potential for selection bias and limited power to detect sample differences. Additional research is needed to further characterize thalamocortical network changes and electrographic and neuropsychological correlates.

Preterm birth (defined as birth before 37 weeks) is a leading cause of neurocognitive impairment in childhood, including difficulties in social cognition and executive function. Microstructural divergence from typical brain development in the preterm brain can be quantified using diffusion magnetic resonance imaging (dMRI) tractography during the neonatal period. The relationship between dMRI tractography metrics and later cognitive difficulties remains inconclusive. A general measure of white matter microstructure (gWM) offers a neural basis for cognitive processes in adults, however it remains unclear when gWM is first detectable in the developmental trajectory. Eye-tracking is a technique which assesses eye-gaze behaviour in response to visual stimuli, which permits inference about underlying cognitive processes, such as social cognition and executive function in infancy. The primary aims of this thesis were to test the hypotheses: dMRI tractography reveals significant differences in tract-average fractional anisotropy (FA) and mean diffusivity (MD) between preterm and term infants, and variance in tract-average FA and MD is shared across major tracts. Secondly, infants born preterm have altered social cognition and executive function compared to term born peers, assessed by eye-tracking and finally, neonatal MRI gWM is associated with cognitive function in infancy. Preterm (birth weight ≤ 1500g) and term infants (born ≥ 37 weeks’ post-menstrual age [PMA]) were recruited and underwent a MRI scan at term equivalent age (between 38 - 42 weeks’ PMA) and an eye-tracking assessment six to nine months later. Preterm infants were assessed at two years using the Bayley Scales of Infant and Toddler Development, Third Edition (BSID-III). dMRI tractography metrics were generated using probabilistic neighbourhood tractography (PNT) in eight pre-defined tracts-of-interest. Principal component analyses (PCA) were used to determine the correlations between the eight tracts-of-interest for four tract-averaged water diffusion parameters. dMRI metrics were compared to the eye-tracking performance and two year outcome data. Quantitative microstructural changes were identifiable within the preterm brain when compared to infants born at term. PCA revealed a single variable that accounts for nearly 50% of shared variance between tracts-of-interest, and all tracts showed positive loadings. Eye-tracking revealed group-wise differences in infant social cognition, attributable to preterm birth, but executive functions inferred from eye-tracking did not differ between groups. dMRI tractography metrics within the neonatal period did not relate to later outcome measures. This thesis shows that variance in dMRI parameters is substantially shared across white matter tracts of the developing brain and suggests that anatomical foundations of later intelligence are present by term equivalent age. Social cognition is altered by preterm birth, however social cognitive ability in infancy is independent of gWM.