Unsupervised deep learning of human brain diffusion magnetic resonance imaging tractography data

L'imagerie par résonance magnétique de diffusion est une technique non invasive permettant de connaître la microstructure organisationnelle des tissus biologiques. Les méthodes computationnelles qui exploitent la préférence orientationnelle de la diffusion dans des structures restreintes pour révéler les voies axonales de la matière blanche du cerveau sont appelées tractographie. Ces dernières années, diverses méthodes de tractographie ont été utilisées avec succès pour découvrir l'architecture de la matière blanche du cerveau. Pourtant, ces techniques de reconstruction souffrent d'un certain nombre de défauts dérivés d'ambiguïtés fondamentales liées à l'information orientationnelle. Cela a des conséquences dramatiques, puisque les cartes de connectivité de la matière blanche basées sur la tractographie sont dominées par des faux positifs. Ainsi, la grande proportion de voies invalides récupérées demeure un des principaux défis à résoudre par la tractographie pour obtenir une description anatomique fiable de la matière blanche. Des approches méthodologiques innovantes sont nécessaires pour aider à résoudre ces questions. Les progrès récents en termes de puissance de calcul et de disponibilité des données ont rendu possible l'application réussie des approches modernes d'apprentissage automatique à une variété de problèmes, y compris les tâches de vision par ordinateur et d'analyse d'images. Ces méthodes modélisent et trouvent les motifs sous-jacents dans les données, et permettent de faire des prédictions sur de nouvelles données. De même, elles peuvent permettre d'obtenir des représentations compactes des caractéristiques intrinsèques des données d'intérêt. Les approches modernes basées sur les données, regroupées sous la famille des méthodes d'apprentissage profond, sont adoptées pour résoudre des tâches d'analyse de données d'imagerie médicale, y compris la tractographie. Dans ce contexte, les méthodes deviennent moins dépendantes des contraintes imposées par les approches classiques utilisées en tractographie. Par conséquent, les méthodes inspirées de l'apprentissage profond conviennent au changement de paradigme requis, et peuvent ouvrir de nouvelles possibilités de modélisation, en améliorant ainsi l'état de l'art en tractographie. Dans cette thèse, un nouveau paradigme basé sur les techniques d'apprentissage de représentation est proposé pour générer et analyser des données de tractographie. En exploitant les architectures d'autoencodeurs, ce travail tente d'explorer leur capacité à trouver un code optimal pour représenter les caractéristiques des fibres de la matière blanche. Les contributions proposées exploitent ces représentations pour une variété de tâches liées à la tractographie, y compris (i) le filtrage et (ii) le regroupement efficace sur les résultats générés par d'autres méthodes, ainsi que (iii) la reconstruction proprement dite des fibres de la matière blanche en utilisant une méthode générative. Ainsi, les méthodes issues de cette thèse ont été nommées (i) FINTA (Filtering in Tractography using Autoencoders), (ii) CINTA (Clustering in Tractography using Autoencoders), et (iii) GESTA (Generative Sampling in Bundle Tractography using Autoencoders), respectivement. Les performances des méthodes proposées sont évaluées par rapport aux méthodes de l'état de l'art sur des données de diffusion synthétiques et des données de cerveaux humains chez l'adulte sain in vivo. Les résultats montrent que (i) la méthode de filtrage proposée offre une sensibilité et spécificité supérieures par rapport à d'autres méthodes de l'état de l'art; (ii) le regroupement des tractes dans des faisceaux est fait de manière consistante; et (iii) l'approche générative échantillonnant des tractes comble mieux l'espace de la matière blanche dans des régions difficiles à reconstruire. Enfin, cette thèse révèle les possibilités des autoencodeurs pour l'analyse des données des fibres de la matière blanche, et ouvre la voie à fournir des données de tractographie plus fiables.Abstract : Diffusion magnetic resonance imaging is a non-invasive technique providing insights into the organizational microstructure of biological tissues. The computational methods that exploit the orientational preference of the diffusion in restricted structures to reveal the brain's white matter axonal pathways are called tractography. In recent years, a variety of tractography methods have been successfully used to uncover the brain's white matter architecture. Yet, these reconstruction techniques suffer from a number of shortcomings derived from fundamental ambiguities inherent to the orientation information. This has dramatic consequences, since current tractography-based white matter connectivity maps are dominated by false positive connections. Thus, the large proportion of invalid pathways recovered remains one of the main challenges to be solved by tractography to obtain a reliable anatomical description of the white matter. Methodological innovative approaches are required to help solving these questions. Recent advances in computational power and data availability have made it possible to successfully apply modern machine learning approaches to a variety of problems, including computer vision and image analysis tasks. These methods model and learn the underlying patterns in the data, and allow making accurate predictions on new data. Similarly, they may enable to obtain compact representations of the intrinsic features of the data of interest. Modern data-driven approaches, grouped under the family of deep learning methods, are being adopted to solve medical imaging data analysis tasks, including tractography. In this context, the proposed methods are less dependent on the constraints imposed by current tractography approaches. Hence, deep learning-inspired methods are suit for the required paradigm shift, may open new modeling possibilities, and thus improve the state of the art in tractography. In this thesis, a new paradigm based on representation learning techniques is proposed to generate and to analyze tractography data. By harnessing autoencoder architectures, this work explores their ability to find an optimal code to represent the features of the white matter fiber pathways. The contributions exploit such representations for a variety of tractography-related tasks, including efficient (i) filtering and (ii) clustering on results generated by other methods, and (iii) the white matter pathway reconstruction itself using a generative method. The methods issued from this thesis have been named (i) FINTA (Filtering in Tractography using Autoencoders), (ii) CINTA (Clustering in Tractography using Autoencoders), and (iii) GESTA (Generative Sampling in Bundle Tractography using Autoencoders), respectively. The proposed methods' performance is assessed against current state-of-the-art methods on synthetic data and healthy adult human brain in vivo data. Results show that the (i) introduced filtering method has superior sensitivity and specificity over other state-of-the-art methods; (ii) the clustering method groups streamlines into anatomically coherent bundles with a high degree of consistency; and (iii) the generative streamline sampling technique successfully improves the white matter coverage in hard-to-track bundles. In summary, this thesis unlocks the potential of deep autoencoder-based models for white matter data analysis, and paves the way towards delivering more reliable tractography data

Effects of sex chromosome dosage on corpus callosum morphology in supernumerary sex chromosome aneuploidies.

BackgroundSupernumerary sex chromosome aneuploidies (sSCA) are characterized by the presence of one or more additional sex chromosomes in an individual's karyotype; they affect around 1 in 400 individuals. Although there is high variability, each sSCA subtype has a characteristic set of cognitive and physical phenotypes. Here, we investigated the differences in the morphometry of the human corpus callosum (CC) between sex-matched controls 46,XY (N =99), 46,XX (N =93), and six unique sSCA karyotypes: 47,XYY (N =29), 47,XXY (N =58), 48,XXYY (N =20), 47,XXX (N =30), 48,XXXY (N =5), and 49,XXXXY (N =6).MethodsWe investigated CC morphometry using local and global area, local curvature of the CC boundary, and between-landmark distance analysis (BLDA). We hypothesized that CC morphometry would vary differentially along a proposed spectrum of Y:X chromosome ratio with supernumerary Y karyotypes having the largest CC areas and supernumerary X karyotypes having significantly smaller CC areas. To investigate this, we defined an sSCA spectrum based on a descending Y:X karyotype ratio: 47,XYY, 46,XY, 48,XXYY, 47,XXY, 48,XXXY, 49,XXXXY, 46,XX, 47,XXX. We similarly explored the effects of both X and Y chromosome numbers within sex. Results of shape-based metrics were analyzed using permutation tests consisting of 5,000 iterations.ResultsSeveral subregional areas, local curvature, and BLDs differed between groups. Moderate associations were found between area and curvature in relation to the spectrum and X and Y chromosome counts. BLD was strongly associated with X chromosome count in both male and female groups.ConclusionsOur results suggest that X- and Y-linked genes have differential effects on CC morphometry. To our knowledge, this is the first study to compare CC morphometry across these extremely rare groups

Evaluating the accuracy of diffusion MRI models in white matter

Models of diffusion MRI within a voxel are useful for making inferences about the properties of the tissue and inferring fiber orientation distribution used by tractography algorithms. A useful model must fit the data accurately. However, evaluations of model-accuracy of some of the models that are commonly used in analyzing human white matter have not been published before. Here, we evaluate model-accuracy of the two main classes of diffusion MRI models. The diffusion tensor model (DTM) summarizes diffusion as a 3-dimensional Gaussian distribution. Sparse fascicle models (SFM) summarize the signal as a linear sum of signals originating from a collection of fascicles oriented in different directions. We use cross-validation to assess model-accuracy at different gradient amplitudes (b-values) throughout the white matter. Specifically, we fit each model to all the white matter voxels in one data set and then use the model to predict a second, independent data set. This is the first evaluation of model-accuracy of these models. In most of the white matter the DTM predicts the data more accurately than test-retest reliability; SFM model-accuracy is higher than test-retest reliability and also higher than the DTM, particularly for measurements with (a) a b-value above 1000 in locations containing fiber crossings, and (b) in the regions of the brain surrounding the optic radiations. The SFM also has better parameter-validity: it more accurately estimates the fiber orientation distribution function (fODF) in each voxel, which is useful for fiber tracking

Topographic organization of V1 projections through the corpus callosum in humans.

The visual cortex in each hemisphere is linked to the opposite hemisphere by axonal projections that pass through the splenium of the corpus callosum. Visual-callosal connections in humans and macaques are found along the V1/V2 border where the vertical meridian is represented. Here we identify the topography of V1 vertical midline projections through the splenium within six human subjects with normal vision using diffusion-weighted MR imaging and probabilistic diffusion tractography. Tractography seed points within the splenium were classified according to their estimated connectivity profiles to topographic subregions of V1, as defined by functional retinotopic mapping. First, we report a ventral-dorsal mapping within the splenium with fibers from ventral V1 (representing the upper visual field) projecting to the inferior-anterior corner of the splenium and fibers from dorsal V1 (representing the lower visual field) projecting to the superior-posterior end. Second, we also report an eccentricity gradient of projections from foveal-to-peripheral V1 subregions running in the anterior-superior to posterior-inferior direction, orthogonal to the dorsal-ventral mapping. These results confirm and add to a previous diffusion MRI study (Dougherty et al., 2005) which identified a dorsal/ventral mapping of human splenial fibers. These findings yield a more detailed view of the structural organization of the splenium than previously reported and offer new opportunities to study structural plasticity in the visual system

Topography of the Chimpanzee Corpus Callosum

The corpus callosum (CC) is the largest commissural white matter tract in mammalian brains, connecting homotopic and heterotopic regions of the cerebral cortex. Knowledge of the distribution of callosal fibers projecting into specific cortical regions has important implications for understanding the evolution of lateralized structures and functions of the cerebral cortex. No comparisons of CC topography in humans and great apes have yet been conducted. We investigated the topography of the CC in 21 chimpanzees using high-resolution magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). Tractography was conducted based on fiber assignment by continuous tracking (FACT) algorithm. We expected chimpanzees to display topographical organization similar to humans, especially concerning projections into the frontal cortical regions. Similar to recent studies in humans, tractography identified five clusters of CC fibers projecting into defined cortical regions: prefrontal; premotor and supplementary motor; motor; sensory; parietal, temporal and occipital. Significant differences in fractional anisotropy (FA) were found in callosal regions, with highest FA values in regions projecting to higher-association areas of posterior cortical (including parietal, temporal and occipital cortices) and prefrontal cortical regions (p<0.001). The lowest FA values were seen in regions projecting into motor and sensory cortical areas. Our results indicate chimpanzees display similar topography of the CC as humans, in terms of distribution of callosal projections and microstructure of fibers as determined by anisotropy measures

Anatomical connections in the human visual cortex: validation and new insights using a DTI Geodesic Connectivity Mapping method

Various approaches have been introduced to infer the organization of white matter connectivity using Diffusion Tensor Imaging (DTI). In this study, we validate a recently introduced geometric tractography technique, Geodesic Connectivity Mapping (GCM), able to overcome the main limitations of geometrical approaches. Using the GCM technique, we could successfully characterize anatomical connections in the human low-level visual cortex. We reproduce previous findings regarding the topology of optic radiations linking the LGN to V1 and the regular organization of splenium fibers with respect to their origin in the visual cortex. Moreover, our study brings further insights regarding the connectivity of the human MT complex (hMT+) and the retinotopic areas

Neuroimaging Evidence of Major Morpho-Anatomical and Functional Abnormalities in the BTBR T+TF/J Mouse Model of Autism

BTBR T+tf/J (BTBR) mice display prominent behavioural deficits analogous to the defining symptoms of autism, a feature that has prompted a widespread use of the model in preclinical autism research. Because neuro-behavioural traits are described with respect to reference populations, multiple investigators have examined and described the behaviour of BTBR mice against that exhibited by C57BL/6J (B6), a mouse line characterised by high sociability and low self-grooming. In an attempt to probe the translational relevance of this comparison for autism research, we used Magnetic Resonance Imaging (MRI) to map in both strain multiple morpho-anatomical and functional neuroimaging readouts that have been extensively used in patient populations. Diffusion tensor tractography confirmed previous reports of callosal agenesis and lack of hippocampal commissure in BTBR mice, and revealed a concomitant rostro-caudal reorganisation of major cortical white matter bundles. Intact inter-hemispheric tracts were found in the anterior commissure, ventro-medial thalamus, and in a strain-specific white matter formation located above the third ventricle. BTBR also exhibited decreased fronto-cortical, occipital and thalamic gray matter volume and widespread reductions in cortical thickness with respect to control B6 mice. Foci of increased gray matter volume and thickness were observed in the medial prefrontal and insular cortex. Mapping of resting-state brain activity using cerebral blood volume weighted fMRI revealed reduced cortico-thalamic function together with foci of increased activity in the hypothalamus and dorsal hippocampus of BTBR mice. Collectively, our results show pronounced functional and structural abnormalities in the brain of BTBR mice with respect to control B6 mice. The large and widespread white and gray matter abnormalities observed do not appear to be representative of the neuroanatomical alterations typically observed in autistic patients. The presence of reduced fronto-cortical metabolism is of potential translational relevance, as this feature recapitulates previously-reported clinical observations

structural connectivity analysis in children with segmental callosal agenesis

BACKGROUND AND PURPOSE: Segmental callosal agenesis is characterized by the absence of the intermediate callosal portion. We aimed to evaluate the structural connectivity of segmental callosal agenesis by using constrained spherical deconvolution tractography and connectome analysis. MATERIALS AND METHODS: We reviewed the clinical-radiologic features of 8 patients (5 males; mean age, 3.9 years). Spherical deconvolution and probabilistic tractography were performed on diffusion data. Structural connectivity analysis, including summary network metrics, modularity analysis, and network consistency measures, was applied in 5 patients and 10 age-/sex-matched controls. RESULTS: We identified 3 subtypes based on the position of the hippocampal commissure: beneath the anterior callosal remnant in 3 patients (type I), beneath the posterior callosal remnant in 3 patients (type II), and between the anterior and posterior callosal remnants in 2 patients (type III). In all patients, the agenetic segment corresponded to fibers projecting to the parietal lobe, and segmental Probst bundles were found at that level. Ectopic callosal bundles were identified in 3 patients. Topology analysis revealed reduced global connectivity in patients compared with controls. The network topology of segmental callosal agenesis was more variable across patients than that of the control connectomes. Modularity analysis revealed disruption of the structural core organization in the patients. CONCLUSIONS: Three malformative subtypes of segmental callosal agenesis were identified. Even the absence of a small callosal segment may impact global brain connectivity and modularity organization. The presence of ectopic callosal bundles may explain the greater interindividual variation in the connectomes of patients with segmental callosal agenesis

Microstructural differences in white matter tracts across middle to late adulthood : a diffusion MRI study on 7167 UK Biobank participants

Acknowledgements This research was approved by the UK Biobank (application number: 24089) and was supported by the Roland Sutton Academic Trust (grant number: 0039/R/16) and Taiwan National Health Research Institute (NHRI-EX109-10928NI). We acknowledge the valuable contributions of members of the UK Biobank Imaging Working Group and the UK Biobank coordinating center. The UK Biobank (including the imaging enhancement) was supported by the UK Medical Research Council and the Wellcome Trust. The authors are grateful for the provision of simultaneous multislice (multiband) pulse sequence and reconstruction algorithms by the Center for Magnetic Resonance Research, University of Minnesota. Finally, the authors are extremely grateful to all UK Biobank study participants, who have generously donated their time to make this resource possible. This article was edited by Wallace Academic Editing.Peer reviewedPostprin

Parametric spherical deconvolution: Inferring anatomical connectivity using diffusion MR imaging

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