140 research outputs found

    Towards ultra-high resolution 3D reconstruction of a whole rat brain from 3D-PLI data

    Full text link
    3D reconstruction of the fiber connectivity of the rat brain at microscopic scale enables gaining detailed insight about the complex structural organization of the brain. We introduce a new method for registration and 3D reconstruction of high- and ultra-high resolution (64 Ό\mum and 1.3 Ό\mum pixel size) histological images of a Wistar rat brain acquired by 3D polarized light imaging (3D-PLI). Our method exploits multi-scale and multi-modal 3D-PLI data up to cellular resolution. We propose a new feature transform-based similarity measure and a weighted regularization scheme for accurate and robust non-rigid registration. To transform the 1.3 Ό\mum ultra-high resolution data to the reference blockface images a feature-based registration method followed by a non-rigid registration is proposed. Our approach has been successfully applied to 278 histological sections of a rat brain and the performance has been quantitatively evaluated using manually placed landmarks by an expert.Comment: 9 pages, Accepted at 2nd International Workshop on Connectomics in NeuroImaging (CNI), MICCAI'201

    Microstructural imaging of the human spinal cord with advanced diffusion MRI

    Get PDF
    The aim of this PhD thesis is to advance the state-of-the-art of spinal cord magnetic resonance imaging (MRI) in multiple sclerosis (MS), a demyelinating, inflammatory and neurodegenerative disease of the central nervous system. Neurite orientation dispersion and density imaging (NODDI) is a recent diffusion-weighted (DW) MRI technique that provides indices of density and orientation dispersion of neuronal processes. These could be new useful biomarkers for the spinal cord, since they could better characterise overall, widespread MS pathology than conventional metrics. In this thesis, we test innovative clinically feasible acquisitions as well as signal analysis methods to study the potential of NODDI for the spinal cord. We also design and run computer simulations that corroborate our in vivo findings. Furthermore, we compare NODDI metrics to quantitative histological features, with the aim of validating their specificity. The thesis is divided in two parts. In the first part, in vivo experiments are described. Specific objectives are: i) to demonstrate the feasibility of performing NODDI in the spinal cord and in clinical settings; ii) to study the possibility of extracting with new approaches such as NODDI more specific microstructural information from standard DW acquisitions; iii) to assess how features typical of spinal cord microstructure, such as presence of large axons, influence NODDI metrics. In the second part of the thesis, ex vivo experiments are discussed. Their objective is the validation of the specificity of NODDI metrics via comparison to quantitative histology in post mortem spinal cord tissue. The experiments required the implementation of high-field DW scans as well as histological procedures and complex analysis pipelines. The results of this thesis contribute to current scientific knowledge. They prove that NODDI offers new opportunities to study how neurodegenerative diseases such as MS alter neural tissue complexity. We showed for the first time that NODDI can be performed in the spinal cord in vivo and in clinical scans. We also demonstrated that NODDI analysis of standard DW data is challenging, and quantified how the presence of large axons in the spinal cord influences NODDI metrics. Lastly, our ex vivo data highlight that unlike routine DW MRI methods, NODDI can detect reliably pathological variations of neurite orientation dispersion. NODDI is also sensitive to the density of axons and dendrites, but can not fully resolve axonal loss and demyelination in MS. We believe that the technique is a key element of a more general multi-modal MRI approach, which is necessary to obtain a complete description of complex diseases such as MS

    Reconstruction de l'activité corticale à partir de données MEG à l'aide de réseaux cérébraux et de délais de transmission estimés à partir d'IRMd

    Get PDF
    White matter fibers transfer information between brain regions with delays that are observable with magnetoencephalography and electroencephalography (M/EEG) due to their millisecond temporal resolution. We can represent the brain as a graph where nodes are the cortical sources or areas and edges are the physical connections between them: either local (between adjacent vertices on the cortical mesh) or non-local (long-range white matter fibers). Long-range anatomical connections can be obtained with diffusion MRI (dMRI) tractography which yields a set of streamlines representing white matter fiber bundles. Given the streamlines’ lengths and the information conduction speed, transmission delays can be estimated for each connection. dMRI can thus give an insight into interaction delays of the macroscopicbrain network.Localizing and recovering electrical activity of the brain from M/EEG measurements is known as the M/EEG inverse problem. Generally, there are more unknowns (brain sources) than the number of sensors, so the solution is non-unique and the problem ill-posed. To obtain a unique solution, prior constraints on the characteristics of source distributions are needed. Traditional linear inverse methods deploy different constraints which can favour solutions with minimum norm, impose smoothness constraints in space and/or time along the cortical surface, etc. Yet, structural connectivity is rarely considered and transmission delays almost always neglected.The first contribution of this thesis consists of a multimodal preprocessing pipeline used to integrate structural MRI, dMRI and MEG data into a same framework, and of a simulation procedure of source-level brain activity that was used as a synthetic dataset to validate the proposed reconstruction approaches.In the second contribution, we proposed a new framework to solve the M/EEG inverse problem called Connectivity-Informed M/EEG Inverse Problem (CIMIP), where prior transmission delays supported by dMRI were included to enforce temporal smoothness between time courses of connected sources. This was done by incorporating a Laplacian operator into the regularization, that operates on a time-dependent connectivity graph. Nonetheless, some limitations of the CIMIP approach arised, mainly due to the nature of the Laplacian, which acts on the whole graph, favours smooth solutions across all connections, for all delays, and it is agnostic to directionality.In this thesis, we aimed to investigate patterns of brain activity during visuomotor tasks, during which only a few regions typically get significantly activated, as shown by previous studies. This led us to our third contribution, an extension of the CIMIP approach that addresses the aforementioned limitations, named CIMIP_OML (“Optimal Masked Laplacian”). We restricted the full source space network (the whole cortical mesh) to a network of regions of interest and tried to find how the information is transferred between its nodes. To describe the interactions between nodes in a directed graph, we used the concept of network motifs. We proposed an algorithm that (1) searches for an optimal network motif – an optimal pattern of interaction between different regions and (2) reconstructs source activity given the found motif. Promising results are shown for both simulated and real MEG data for a visuomotor task and compared with 3 different state-of-the-art reconstruction methods.To conclude, we tackled a difficult problem of exploiting delays supported by dMRI for the reconstruction of brain activity, while also considering the directionality in the information transfer, and provided new insights into the complex patterns of brain activity.Les fibres de la matiĂšre blanche permettent le transfert d’information dans le cerveau avec des dĂ©lais observables en MagnĂ©toencĂ©phalographie et ÉlectroencĂ©phalographie (M/EEG) grĂące Ă  leur haute rĂ©solution temporelle. Le cerveau peut ĂȘtre reprĂ©sentĂ© comme un graphe oĂč les nƓuds sont les rĂ©gions corticales et les liens sont les connexions physiques entre celles-ci: soit locales (entre sommets adjacents sur le maillage cortical), soit non locales (fibres de la matiĂšre blanche). Les connexions non-locales peuvent ĂȘtre reconstruites avec la tractographie de l’IRM de diffusion (IRMd) qui gĂ©nĂšre un ensemble de courbes («streamlines») reprĂ©sentant des fibres de la matiĂšre blanche. Sachant les longueurs des fibres et la vitesse de conduction de l’information, les dĂ©lais de transmission peuvent ĂȘtre estimĂ©s. L’IRMd peut donc donner un aperçu des dĂ©lais d’interaction du rĂ©seau cĂ©rĂ©bral macroscopique.La localisation et la reconstruction de l’activitĂ© Ă©lectrique cĂ©rĂ©brale Ă  partir des mesures M/EEG est un problĂšme inverse. En gĂ©nĂ©ral, il y a plus d’inconnues (sources cĂ©rĂ©brales) que de capteurs. La solution n’est donc pas unique et le problĂšme est dit mal posĂ©. Pour obtenir une solution unique, des hypothĂšses sur les caractĂ©ristiques des distributions de sources sont requises. Les mĂ©thodes inverses linĂ©aires traditionnelles utilisent diffĂ©rentes hypothĂšses qui peuvent favoriser des solutions de norme minimale, imposer des contraintes de lissage dans l’espace et/ou dans le temps, etc. Pourtant, la connectivitĂ© structurelle est rarement prise en compte et les dĂ©lais de transmission sont presque toujours nĂ©gligĂ©s.La premiĂšre contribution de cette thĂšse est un pipeline de prĂ©traitement multimodal utilisĂ© pour l’intĂ©gration des donnĂ©es d’IRM, IRMd et MEG dans un mĂȘme cadre, et d’une mĂ©thode de simulation de l’activitĂ© corticale qui a Ă©tĂ© utilisĂ©e comme jeu de donnĂ©es synthĂ©tiques pour valider les approches de reconstruction proposĂ©es. Nous proposons Ă©galement une nouvelle approche pour rĂ©soudre le problĂšme inverse M/EEG appelĂ©e «ProblĂšme Inverse M/EEG InformĂ© par la Connectivité» (CIMIP pour Connectivity-Informed M/EEG Inverse Problem), oĂč des dĂ©lais de transmission provenant de l’IRMd sont inclus pour renforcer le lissage temporel entre les dĂ©cours des sources connectĂ©es. Pour cela, un opĂ©rateur Laplacien, basĂ© sur un graphe de connectivitĂ© en fonction du temps, a Ă©tĂ© intĂ©grĂ© dans la rĂ©gularisation. Cependant, certaines limites de l’approche CIMIP sont apparues en raison de la nature du Laplacien qui agit sur le graphe entier et favorise les solutions lisses sur toutes les connexions, pour tous les dĂ©lais, et indĂ©pendamment de la directionnalitĂ©.Lors de tĂąches visuo-motrices, seules quelques rĂ©gions sont gĂ©nĂ©ralement activĂ©es significativement. Notre troisiĂšme contribution est une extension de CIMIP pour ce type de tĂąches qui rĂ©pond aux limitations susmentionnĂ©es, nommĂ©e CIMIP_OML («Optimal Masked Laplacian») ou Laplacien MasquĂ© Optimal. Nous essayons de trouver comment l’information est transfĂ©rĂ©e entre les nƓuds d’un sous-rĂ©seau de rĂ©gions d’intĂ©rĂȘt du rĂ©seau complet de l’espace des sources. Pour dĂ©crire les interactions entre nƓuds dans un graphe orientĂ©, nous utilisons le concept de motifs de rĂ©seau. Nous proposons un algorithme qui 1) cherche un motif de rĂ©seau optimal- un modĂšle optimal d’interaction entre rĂ©gions et 2) reconstruit l’activitĂ© corticale avec le motif trouvĂ©. Des rĂ©sultats prometteurs sont prĂ©sentĂ©s pour des donnĂ©es MEG simulĂ©es et rĂ©elles (tĂąche visuo-motrice) et comparĂ©s avec 3 mĂ©thodes de l’état de l’art. Pour conclure, nous avons abordĂ© un problĂšme difficile d’exploitation des dĂ©lais de l’IRMd lors l’estimation de l’activitĂ© corticale en tenant compte de la directionalitĂ© du transfert d’information, fournissant ainsi de nouvelles perspectives sur les patterns complexes de l’activitĂ© cĂ©rĂ©brale

    Experimental and computational study of vascular access for hemodialysis

    Get PDF

    Vascular Segmentation Algorithms for Generating 3D Atherosclerotic Measurements

    Get PDF
    Atherosclerosis manifests as plaques within large arteries of the body and remains as a leading cause of mortality and morbidity in the world. Major cardiovascular events may occur in patients without known preexisting symptoms, thus it is important to monitor progression and regression of the plaque burden in the arteries for evaluating patient\u27s response to therapy. In this dissertation, our main focus is quantification of plaque burden from the carotid and femoral arteries, which are major sites for plaque formation, and are straight forward to image noninvasively due to their superficial location. Recently, 3D measurements of plaque burden have shown to be more sensitive to the changes of plaque burden than one-/two-dimensional measurements. However, despite the advancements of 3D noninvasive imaging technology with rapid acquisition capabilities, and the high sensitivity of the 3D plaque measurements of plaque burden, they are still not widely used due to the inordinate amount of time and effort required to delineate artery walls plus plaque boundaries to obtain 3D measurements from the images. Therefore, the objective of this dissertation is developing novel semi-automated segmentation methods to alleviate measurement burden from the observer for segmentation of the outer wall and lumen boundaries from: (1) 3D carotid ultrasound (US) images, (2) 3D carotid black-blood magnetic resonance (MR) images, and (3) 3D femoral black-blood MR images. Segmentation of the carotid lumen and outer wall from 3DUS images is a challenging task due to low image contrast, for which no method has been previously reported. Initially, we developed a 2D slice-wise segmentation algorithm based on the level set method, which was then extended to 3D. The 3D algorithm required fewer user interactions than manual delineation and the 2D method. The algorithm reduced user time by ≈79% (1.72 vs. 8.3 min) compared to manual segmentation for generating 3D-based measurements with high accuracy (Dice similarity coefficient (DSC)\u3e90%). Secondly, we developed a novel 3D multi-region segmentation algorithm, which simultaneously delineates both the carotid lumen and outer wall surfaces from MR images by evolving two coupled surfaces using a convex max-flow-based technique. The algorithm required user interaction only on a single transverse slice of the 3D image for generating 3D surfaces of the lumen and outer wall. The algorithm was parallelized using graphics processing units (GPU) to increase computational speed, thus reducing user time by 93% (0.78 vs. 12 min) compared to manual segmentation. Moreover, the algorithm yielded high accuracy (DSC \u3e 90%) and high precision (intra-observer CV \u3c 5.6% and inter-observer CV \u3c 6.6%). Finally, we developed and validated an algorithm based on convex max-flow formulation to segment the femoral arteries that enforces a tubular shape prior and an inter-surface consistency of the outer wall and lumen to maintain a minimum separation distance between the two surfaces. The algorithm required the observer to choose only about 11 points on its medial axis of the artery to yield the 3D surfaces of the lumen and outer wall, which reduced the operator time by 97% (1.8 vs. 70-80 min) compared to manual segmentation. Furthermore, the proposed algorithm reported DSC greater than 85% and small intra-observer variability (CV ≈ 6.69%). In conclusion, the development of robust semi-automated algorithms for generating 3D measurements of plaque burden may accelerate translation of 3D measurements to clinical trials and subsequently to clinical care

    Dynamic electrophysiological connectomics

    Get PDF
    The human brain can be divided into multiple areas, each responsible for different aspects of behaviour. For a century we have been developing techniques to non-invasively map these areas and their associated functions, a discipline now known as neuroimaging. In recent years the field has undergone a paradigm shift to investigate how the brain communicates with itself; it is widely regarded that healthy brain function relies upon efficient connectivity between different functional areas, and the neuroimaging field has been revolutionised by our ability to estimate this connectivity. Studies into communication between spatially separate locations in the brain have revealed a series of robust functional networks which govern mental processes. However these studies have been based on the temporal averaging of minutes or even hours of data to give us a generalised ’snapshot’ of connectivity. Increasing evidence shows us that these connections are dynamic in space, time and frequency and so the next generation of of neuroimaging methods, which capture this 5-dimensional connectivity will prove to be key tools in the investigation of brain networks and ultimately their breakdown in disease. In this thesis we introduce novel methods to capture non-stationarity using magnetoencephalography (MEG), an imaging modality which measures the changes in extracranial magnetic fields associated with neuronal current flow. MEG is a direct measurement of neural activity and has an excellent temporal resolution, which makes it attractive for non-invasively tracking dynamic functional connections. However there are many technical limitations which can confound assessment of functional connectivity which have to be addressed. In Chapters 2 and 3 we introduce the theory behind MEG; specifically how it is possible to measure the femtoTelsa changes in magnetic field generated by the brain and how to project these data to generate a 3-dimensional picture of current in the brain. Chapter 4 reviews some of popular methods of assessing functional connectivity and how to control for the influence of artefactual functional connections erroneously produced during source projection. Chapter 5 introduces a pipeline to assess functional connections across time, space and frequency and in Chapter 6 we apply this pipeline to show that resting state networks, measured using ’static’ metrics are in fact comprised of a series of rapidly forming and dissolving subnetwork connections. Finally, Chapter 7 introduces a pipeline to track dynamic network behaviour simultaneously across the entire brain volume and shows that networks can be characterised by their temporal signatures of connectivity

    Dynamic electrophysiological connectomics

    Get PDF
    The human brain can be divided into multiple areas, each responsible for different aspects of behaviour. For a century we have been developing techniques to non-invasively map these areas and their associated functions, a discipline now known as neuroimaging. In recent years the field has undergone a paradigm shift to investigate how the brain communicates with itself; it is widely regarded that healthy brain function relies upon efficient connectivity between different functional areas, and the neuroimaging field has been revolutionised by our ability to estimate this connectivity. Studies into communication between spatially separate locations in the brain have revealed a series of robust functional networks which govern mental processes. However these studies have been based on the temporal averaging of minutes or even hours of data to give us a generalised ’snapshot’ of connectivity. Increasing evidence shows us that these connections are dynamic in space, time and frequency and so the next generation of of neuroimaging methods, which capture this 5-dimensional connectivity will prove to be key tools in the investigation of brain networks and ultimately their breakdown in disease. In this thesis we introduce novel methods to capture non-stationarity using magnetoencephalography (MEG), an imaging modality which measures the changes in extracranial magnetic fields associated with neuronal current flow. MEG is a direct measurement of neural activity and has an excellent temporal resolution, which makes it attractive for non-invasively tracking dynamic functional connections. However there are many technical limitations which can confound assessment of functional connectivity which have to be addressed. In Chapters 2 and 3 we introduce the theory behind MEG; specifically how it is possible to measure the femtoTelsa changes in magnetic field generated by the brain and how to project these data to generate a 3-dimensional picture of current in the brain. Chapter 4 reviews some of popular methods of assessing functional connectivity and how to control for the influence of artefactual functional connections erroneously produced during source projection. Chapter 5 introduces a pipeline to assess functional connections across time, space and frequency and in Chapter 6 we apply this pipeline to show that resting state networks, measured using ’static’ metrics are in fact comprised of a series of rapidly forming and dissolving subnetwork connections. Finally, Chapter 7 introduces a pipeline to track dynamic network behaviour simultaneously across the entire brain volume and shows that networks can be characterised by their temporal signatures of connectivity

    Gaze-Based Human-Robot Interaction by the Brunswick Model

    Get PDF
    We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered

    Activation of the pro-resolving receptor Fpr2 attenuates inflammatory microglial activation

    Get PDF
    Poster number: P-T099 Theme: Neurodegenerative disorders & ageing Activation of the pro-resolving receptor Fpr2 reverses inflammatory microglial activation Authors: Edward S Wickstead - Life Science & Technology University of Westminster/Queen Mary University of London Inflammation is a major contributor to many neurodegenerative disease (Heneka et al. 2015). Microglia, as the resident immune cells of the brain and spinal cord, provide the first line of immunological defence, but can become deleterious when chronically activated, triggering extensive neuronal damage (Cunningham, 2013). Dampening or even reversing this activation may provide neuronal protection against chronic inflammatory damage. The aim of this study was to determine whether lipopolysaccharide (LPS)-induced inflammation could be abrogated through activation of the receptor Fpr2, known to play an important role in peripheral inflammatory resolution. Immortalised murine microglia (BV2 cell line) were stimulated with LPS (50ng/ml) for 1 hour prior to the treatment with one of two Fpr2 ligands, either Cpd43 or Quin-C1 (both 100nM), and production of nitric oxide (NO), tumour necrosis factor alpha (TNFα) and interleukin-10 (IL-10) were monitored after 24h and 48h. Treatment with either Fpr2 ligand significantly suppressed LPS-induced production of NO or TNFα after both 24h and 48h exposure, moreover Fpr2 ligand treatment significantly enhanced production of IL-10 48h post-LPS treatment. As we have previously shown Fpr2 to be coupled to a number of intracellular signaling pathways (Cooray et al. 2013), we investigated potential signaling responses. Western blot analysis revealed no activation of ERK1/2, but identified a rapid and potent activation of p38 MAP kinase in BV2 microglia following stimulation with Fpr2 ligands. Together, these data indicate the possibility of exploiting immunomodulatory strategies for the treatment of neurological diseases, and highlight in particular the important potential of resolution mechanisms as novel therapeutic targets in neuroinflammation. References Cooray SN et al. (2013). Proc Natl Acad Sci U S A 110: 18232-7. Cunningham C (2013). Glia 61: 71-90. Heneka MT et al. (2015). Lancet Neurol 14: 388-40
    • 

    corecore