Characterising population variability in brain structure through models of whole-brain structural connectivity

Models of whole-brain connectivity are valuable for understanding neurological function. This thesis seeks to develop an optimal framework for extracting models of whole-brain connectivity from clinically acquired diffusion data. We propose new approaches for studying these models. The aim is to develop techniques which can take models of brain connectivity and use them to identify biomarkers or phenotypes of disease. The models of connectivity are extracted using a standard probabilistic tractography algorithm, modified to assess the structural integrity of tracts, through estimates of white matter anisotropy. Connections are traced between 77 regions of interest, automatically extracted by label propagation from multiple brain atlases followed by classifier fusion. The estimates of tissue integrity for each tract are input as indices in 77x77 ”connectivity” matrices, extracted for large populations of clinical data. These are compared in subsequent studies. To date, most whole-brain connectivity studies have characterised population differences using graph theory techniques. However these can be limited in their ability to pinpoint the locations of differences in the underlying neural anatomy. Therefore, this thesis proposes new techniques. These include a spectral clustering approach for comparing population differences in the clustering properties of weighted brain networks. In addition, machine learning approaches are suggested for the first time. These are particularly advantageous as they allow classification of subjects and extraction of features which best represent the differences between groups. One limitation of the proposed approach is that errors propagate from segmentation and registration steps prior to tractography. This can cumulate in the assignment of false positive connections, where the contribution of these factors may vary across populations, causing the appearance of population differences where there are none. The final contribution of this thesis is therefore to develop a common co-ordinate space approach. This combines probabilistic models of voxel-wise diffusion for each subject into a single probabilistic model of diffusion for the population. This allows tractography to be performed only once, ensuring that there is one model of connectivity. Cross-subject differences can then be identified by mapping individual subjects’ anisotropy data to this model. The approach is used to compare populations separated by age and gender

Statistical Medial Model dor Cardiac Segmentation and Morphometry

In biomedical image analysis, shape information can be utilized for many purposes. For example, irregular shape features can help identify diseases; shape features can help match different instances of anatomical structures for statistical comparison; and prior knowledge of the mean and possible variation of an anatomical structure\u27s shape can help segment a new example of this structure in noisy, low-contrast images. A good shape representation helps to improve the performance of the above techniques. The overall goal of the proposed research is to develop and evaluate methods for representing shapes of anatomical structures. The medial model is a shape representation method that models a 3D object by explicitly defining its skeleton (medial axis) and deriving the object\u27s boundary via inverse-skeletonization . This model represents shape compactly, and naturally expresses descriptive global shape features like thickening , bending , and elongation . However, its application in biomedical image analysis has been limited, and it has not yet been applied to the heart, which has a complex shape. In this thesis, I focus on developing efficient methods to construct the medial model, and apply it to solve biomedical image analysis problems. I propose a new 3D medial model which can be efficiently applied to complex shapes. The proposed medial model closely approximates the medial geometry along medial edge curves and medial branching curves by soft-penalty optimization and local correction. I further develop a scheme to perform model-based segmentation using a statistical medial model which incorporates prior shape and appearance information. The proposed medial models are applied to a series of image analysis tasks. The 2D medial model is applied to the corpus callosum which results in an improved alignment of the patterns of commissural connectivity compared to a volumetric registration method. The 3D medial model is used to describe the myocardium of the left and right ventricles, which provides detailed thickness maps characterizing different disease states. The model-based myocardium segmentation scheme is tested in a heterogeneous adult MRI dataset. Our segmentation experiments demonstrate that the statistical medial model can accurately segment the ventricular myocardium and provide useful parameters to characterize heart function

Orientation matching for diffusion tensor image registration.

This thesis develops a registration algorithm specifically for diffusion-tensor (DT) images. The proposed approach matches the tensor orientations to find the registration transformation. Early results show that local optimisation does not find the global minimum in registration of DT-MR brain images. Therefore, a global optimisation registration technique is also implemented. This thesis proposes several new similarity measures for DT registration and provides a comparison of them along with several others previously proposed in the literature. The thesis also proposes several new performance evaluation measures to assess registration quality and develops a performance evaluation framework that uses directional coherence and landmark separation. Experiments with direct optimisation demonstrate increased local minima in tensor registration objective functions over scalar registration. Using registration with global optimisation, this thesis compares the performance of scalar-derived similarity measures with those derived from the full tensor. Results suggest that similarity measures derived from the full tensor matrix do not find a more accurate registration than those based on the derived scalar indices. Affine and higher-order polynomial registration is not reliable enough to make a firm conclusion about whether diffusion tensor orientation matching improves the accuracy of registration over registration algorithms that ignore orientation. The main problem preventing a firm conclusion is that the local minima problem persists despite the use of global optimisation, causing poor registration of the regions of interest

Correcting for Motion between Acquisitions in Diffusion MR Imaging

The diffusion tensor (DT) and other diffusion models assume that each voxel corresponds to the same anatomical location in all the measurements. Movements and distortions violate this assumption and typically the images are realigned before model fitting. We propose a set of model-based methods to improve motion correction and avoid the errors that the traditional method introduces. The new methods are based on a three-step procedure to register DWI datasets, and use different reference images for DWIs with different gradient directions for registration, so the registrations take into account the contrast differences of measurements. Performance of the model-based registration techniques depends critically on outlier rejection. We develop new methods for fitting the diffusion tensor to diffusion MRI measurements in the presence of outliers by drawing on the RANSAC algorithm from computer vision. We compareone popularly used outlier rejection method RESTORE in the diffusion MRI literature with our new method. Then, we combine outlier rejection methods with model-based registration schemes, and compare the performance of motion correction with other methods. After aligning the dataset, we also update diffusion gradients for the registered datasets from both traditional and our methods, according to the transformations used in registrations. We develop and discuss a variety of registration evaluation methods using both synthetic and human-brain diffusion MRI datasets. Experiments demonstrate both quantitative and qualitative improvements using our new model-based methods

Diffusion tensor imaging and resting state functional connectivity as advanced imaging biomarkers of outcome in infants with hypoxic-ischaemic encephalopathy treated with hypothermia

Therapeutic hypothermia confers significant benefit in term neonates with hypoxic-ischaemic encephalopathy (HIE). However, despite the treatment nearly half of the infants develop an unfavourable outcome. Intensive bench-based and early phase clinical research is focused on identifying treatments that augment hypothermic neuroprotection. Qualified biomarkers are required to test these promising therapies efficiently. This thesis aims to assess advanced magnetic resonance imaging (MRI) techniques, including diffusion tensor imaging (DTI) and resting state functional MRI (fMRI) as imaging biomarkers of outcome in infants with HIE who underwent hypothermic neuroprotection. FA values in the white matter (WM), obtained in the neonatal period and assessed by tract-based spatial statistics (TBSS), correlated with subsequent developmental quotient (DQ). However, TBSS is not suitable to study grey matter (GM), which is the primary site of injury following an acute hypoxic-ischaemic event. Therefore, a neonatal atlas-based automated tissue labelling approach was applied to segment central and cortical grey and whole brain WM. Mean diffusivity (MD) in GM structures, obtained in the neonatal period correlated with subsequent DQ. Although the central GM is the primary site of injury on conventional MRI following HIE; FA within WM tissue labels also correlated to neurodevelopmental performance scores. As DTI does not provide information on functional consequences of brain injury functional sequel of HIE was studied with resting state fMRI. Diminished functional connectivity was demonstrated in infants who suffered HIE, which associated with an unfavourable outcome. The results of this thesis suggest that MD in GM tissue labels and FA either determined within WM tissue labels or analysed with TBSS correlate to subsequent neurodevelopmental performance scores in infants who suffered HIE treated with hypothermia and may be applied as imaging biomarkers of outcome in this population. Although functional connectivity was diminished in infants with HIE, resting state fMRI needs further study to assess its utility as an imaging biomarker following a hypoxic-ischaemic brain injury.Open Acces

Quantitation in MRI : application to ageing and epilepsy

Multi-atlas propagation and label fusion techniques have recently been developed for segmenting the human brain into multiple anatomical regions. In this thesis, I investigate possible adaptations of these current state-of-the-art methods. The aim is to study ageing on the one hand, and on the other hand temporal lobe epilepsy as an example for a neurological disease. Overall effects are a confounding factor in such anatomical analyses. Intracranial volume (ICV) is often preferred to normalize for global effects as it allows to normalize for estimated maximum brain size and is hence independent of global brain volume loss, as seen in ageing and disease. I describe systematic differences in ICV measures obtained at 1.5T versus 3T, and present an automated method of measuring intracranial volume, Reverse MNI Brain Masking (RBM), based on tissue probability maps in MNI standard space. I show that this is comparable to manual measurements and robust against field strength differences. Correct and robust segmentation of target brains which show gross abnormalities, such as ventriculomegaly, is important for the study of ageing and disease. We achieved this with incorporating tissue classification information into the image registration process. The best results in elderly subjects, patients with TLE and healthy controls were achieved using a new approach using multi-atlas propagation with enhanced registration (MAPER). I then applied MAPER to the problem of automatically distinguishing patients with TLE with (TLE-HA) and without (TLE-N) hippocampal atrophy on MRI from controls, and determine the side of seizure onset. MAPER-derived structural volumes were used for a classification step consisting of selecting a set of discriminatory structures and applying support vector machine on the structural volumes as well as morphological similarity information such as volume difference obtained with spectral analysis. Acccuracies were 91-100 %, indicating that the method might be clinically useful. Finally, I used the methods developed in the previous chapters to investigate brain regional volume changes across the human lifespan in over 500 healthy subjects between 20 to 90 years of age, using data from three different scanners (2x 1.5T, 1x 3T), using the IXI database. We were able to confirm several known changes, indicating the veracity of the method. In addition, we describe the first multi-region, whole-brain database of normal ageing

The impact of aerobic exercise on brain's white matter integrity in the Alzheimer's disease and the aging population

The brain is the most complex organ in the body. Currently, its complicated functionality has not been fully understood. However, in the last decades an exponential growth on research publications emerged thanks to the use of in-vivo brain imaging techniques. One of these techniques pioneered for medical use in the early 1970s was known as nuclear magnetic resonance imaging based (now called magnetic resonance imaging [MRI]). Nowadays, the advances of MRI technology not only allowed us to characterize volumetric changes in specific brain structures but now we could identify different patterns of activation (e.g. functional MRI) or changes in structural brain connectivity (e.g. diffusion MRI). One of the benefits of using these techniques is that we could investigate changes that occur in disease-specific cohorts such as in the case of Alzheimer’s disease (AD), a neurodegenerative disease that affects mainly older populations. This disease has been known for over a century and even though great advances in technology and pharmacology have occurred, currently there is no cure for the disease. Hence, in this work I decided to investigate whether aerobic exercise, an emerging alternative method to pharmacological treatments, might provide neuroprotective effects to slow down the evident brain deterioration of AD using novel in-vivo diffusion imaging techniques. Previous reports in animal and human studies have supported these exercise-related neuro-protective mechanisms. Concurrently in AD participants, increased brain volumes have been positively associated with higher cardiorespiratory fitness levels, a direct marker of sustained physical activity and increased exercise. Thus, the goal of this work is to investigate further whether exercise influences the brain using structural connectivity analyses and novel diffusion imaging techniques that go beyond volumetric characterization. The approach I chose to present this work combined two important aspects of the investigation. First, I introduced important concepts based on the neuro-scientific work in relation to Alzheimer’s diseases, in-vivo imaging, and exercise physiology (Chapter 1). Secondly, I tried to describe in simple mathematics the physics of this novel diffusion imaging technique (Chapter 2) and supported a tract-specific diffusion imaging processing methodology (Chapter 3 and 4). Consequently, the later chapters combined both aspects of this investigation in a manuscript format (Chapter 5-8). Finally, I summarized my findings, include recommendations for similar studies, described future work, and stated a final conclusion of this work (Chapter 9)

Tractographie adaptative basée sur la microstructure pour des analyses précises de la connectivité cérébrale

Le cerveau est un sujet de recherche depuis plusieurs décennies, puisque son rôle est central dans la compréhension du genre humain. Le cerveau est composé de neurones, où leurs dendrites et synapses se retrouvent dans la matière grise alors que les axones en constituent la matière blanche. L’information traitée dans les différentes régions de la matière grise est ensuite transmise par l’intermédiaire des axones afin d’accomplir différentes fonctions cognitives. La matière blanche forme une structure d’interconnections complexe encore dif- ficile à comprendre et à étudier. La relation entre l’architecture et la fonction du cerveau a été étudiée chez les humains ainsi que pour d’autres espèces, croyant que l’architecture des axones déterminait la dynamique du réseau fonctionnel. Dans ce même objectif, l’Imagerie par résonance (IRM) est un outil formidable qui nous permet de visualiser les tissus cérébraux de façon non-invasive. Plus partic- ulièrement, l’IRM de diffusion permet d’estimer et de séparer la diffusion libre de celle restreinte par la structure des tissus. Cette mesure de restriction peut être utilisée afin d’inférer l’orientation locale des faisceaux de matière blanche. L’algorithme de tractographie exploite cette carte d’orientation pour reconstruire plusieurs connexions de la matière blanche (nommées “streamlines”). Cette modélisation de la matière blanche permet d’estimer la connectivité cérébrale dite structurelle entre les différentes régions du cerveau. Ces résultats peuvent être employés directement pour la planification chirurgicale ou indirectement pour l’analyse ou une évaluation clinique. Malgré plusieurs de ses limitations, telles que sa variabilité et son imprécision, la tractographie reste l’unique moyen d’étudier l’architecture de la matière blanche ainsi que la connectivité cérébrale de façon non invasive. L’objectif de ce projet de doctorat est de répondre spécifiquement à ces limitations et d’améliorer la précision anatomique des estimations de connectivité structurelle. Dans ce but, nous avons développé un algorithme d’optimisation globale qui exploite les informations de micro et macrostructure, en introduisant une procédure itéra- tive qui utilise les propriétés sous-jacentes des tissus pour piloter la reconstruction en utilisant une approche semi-globale. Ensuite, nous avons étudié la possibilité d’adapter dynamiquement la position d’un ensemble de lignes de courant candidates tout en intégrant le préalable anatomique de la douceur des trajectoires et en adap- tant la configuration en fonction des données observées. Enfin, nous avons introduit le concept de bundle-o-graphy en mettant en œuvre une méthode pour modéliser des groupes de lignes de courant basées sur le concept que les axones sont organisés en fascicules, en adaptant leur forme et leur étendue en fonction de la microstructure sous-jacente.Abstract : Human brain has been subject of deep interest for centuries, given it’s central role in controlling and directing the actions and functions of the body as response to external stimuli. The neural tissue is primarily constituted of neurons and, together with dendrites and the nerve synapses, constitute the gray matter (GM) which plays a major role in cognitive functions. The information processed in the GM travel from one region to the other of the brain along nerve cell projections, called axons. All together they constitute the white matter (WM) whose wiring organization still remains challenging to uncover. The relationship between structure organization of the brain and function has been deeply investigated on humans and animals based on the assumption that the anatomic architecture determine the network dynamics. In response to that, many different imaging techniques raised, among which diffusion-weighted magnetic resonance imaging (DW-MRI) has triggered tremendous hopes and expectations. Diffusion-weighted imaging measures both restricted and unrestricted diffusion, i.e. the degree of movement freedom of the water molecules, allowing to map the tissue fiber architecture in vivo and non-invasively. Based on DW-MRI data, tractography is able to exploit information of the local fiber orien- tation to recover global fiber pathways, called streamlines, that represent groups of axons. This, in turn, allows to infer the WM structural connectivity, becoming widely used in many different clinical applications as for diagnoses, virtual dissections and surgical planning. However, despite this unique and compelling ability, data acqui- sition still suffers from technical limitations and recent studies have highlighted the poor anatomical accuracy of the reconstructions obtained with this technique and challenged its effectiveness for studying brain connectivity. The focus of this Ph.D. project is to specifically address these limitations and to improve the anatomical accuracy of the structural connectivity estimates. To this aim, we developed a global optimization algorithm that exploits micro and macro- structure information, introducing an iterative procedure that uses the underlying tissue properties to drive the reconstruction using a semi-global approach. Then, we investigated the possibility to dynamically adapt the position of a set of candidate streamlines while embedding the anatomical prior of trajectories smoothness and adapting the configuration based on the observed data. Finally, we introduced the concept of bundle-o-graphy by implementing a method to model groups of streamlines based on the concept that axons are organized into fascicles, adapting their shape and extent based on the underlying microstructure.Sommario : Il cervello umano è oggetto di profondo interesse da secoli, dato il suo ruolo centrale nel controllare e dirigere le azioni e le funzioni del corpo in risposta a stimoli esterno. Il tessuto neurale è costituito principalmente da neuroni che, insieme ai dendriti e alle sinapsi nervose, costituiscono la materia grigia (GM), la quale riveste un ruolo centrale nelle funzioni cognitive. Le informazioni processate nella GM viaggiano da una regione all’altra del cervello lungo estensioni delle cellule nervose, chiamate assoni. Tutti insieme costituiscono la materia bianca (WM) la cui organizzazione strutturale rimane tuttora sconosciuta. Il legame tra struttura e funzione del cervello sono stati studiati a fondo su esseri umani e animali partendo dal presupposto che l’architettura anatomica determini la dinamica della rete funzionale. In risposta a ciò, sono emerse diverse tecniche di imaging, tra cui la risonanza magnetica pesata per diffusione (DW-MRI) ha suscitato enormi speranze e aspettative. Questa tecnica misura la diffusione sia libera che ristretta, ovvero il grado di libertà di movimento delle molecole d’acqua, consentendo di mappare l’architettura delle fibre neuronali in vivo e in maniera non invasiva. Basata su dati DW-MRI, la trattografia è in grado di sfruttare le informazioni sull’orientamento locale delle fibre per ricostruirne i percorsi a livello globale. Questo, a sua volta, consente di estrarre la connettività strutturale della WM, utilizzata in diverse applicazioni cliniche come per diagnosi, dissezioni virtuali e pianificazione chirurgica. Tuttavia, nonostante questa capacità unica e promettente, l’acquisizione dei dati soffre ancora di limitazioni tecniche e recenti studi hanno messo in evidenza la scarsa accuratezza anatomica delle ricostruzioni ottenute con questa tecnica, mettendone in dubbio l’efficacia per lo studio della connettività cerebrale. Il focus di questo progetto di dottorato è quello di affrontare in modo specifico queste limitazioni e di migliorare l’accuratezza anatomica delle stime di connettività strutturale. A tal fine, abbiamo sviluppato un algoritmo di ottimizzazione globale che sfrutta le informazioni sia micro che macrostrutturali, introducendo una procedura iterativa che utilizza le proprietà del tessuto neuronale per guidare la ricostruzione utilizzando un approccio semi-globale. Successivamente, abbiamo studiato la possibilità di adattare dinamicamente la posizione di un insieme di streamline candidate incorporando il prior anatomico per cui devono seguire traiettorie regolari e adattando la configurazione in base ai dati osservati. Infine, abbiamo introdotto il concetto di bundle-o-graphy implementando un metodo per modellare gruppi di streamline basato sul concetto che gli assoni sono organizzati in fasci, adattando la loro forma ed estensione in base alla microstruttura sottostante