Beyond Crossing Fibers: Tractography Exploiting Sub-voxel Fibre Dispersion and Neighbourhood Structure

In this paper we propose a novel algorithm which leverages models of white matter fibre dispersion to improve tractography. Tractography methods exploit directional information from diffusion weighted magnetic resonance (DW-MR) imaging to infer connectivity between different brain regions. Most tractography methods use a single direction (e.g. the principal eigenvector of the diffusion tensor) or a small set of discrete directions (e.g. from the peaks of an orientation distribution function) to guide streamline propagation. This strategy ignores the effects of within-bundle orientation dispersion, which arises from fanning or bending at the sub-voxel scale, and can lead to missing connections. Various recent DW-MR imaging techniques estimate the fibre dispersion in each bundle directly and model it as a continuous distribution. Here we introduce an algorithm to exploit this information to improve tractography. The algorithm further uses a particle filter to probe local neighbourhood structure during streamline propagation. Using information gathered from neighbourhood structure enables the algorithm to resolve ambiguities between converging and diverging fanning structures, which cannot be distinguished from isolated orientation distribution functions. We demonstrate the advantages of the new approach in synthetic experiments and in vivo data. Synthetic experiments demonstrate the effectiveness of the particle filter in gathering and exploiting neighbourhood information in recovering various canonical fibre configurations and experiments with in vivo brain data demonstrate the advantages of utilising dispersion in tractography, providing benefits in practical situations. © 2013 Springer-Verlag

Utilising measures of fiber dispersion in white matter tractography

This paper proposes a new tractography algorithm utilising measures of fiber dispersion derived from diffusion weighted magnetic resonance (DW-MR) imaging. Tractography estimates connectivity by integrating a pathway from a seed point following directional information derived from DW-MR images. Current tractography techniques follow a discrete set of directions given in each voxel of a DW-MR image and probabilistic techniques account for noise induced uncertainty on those discrete directions. Histological evidence suggests that fiber orientation dispersion exists in areas of white matter such as the centrum semiovale, representing a continuum of potential fiber orientations which cannot be accurately summarised by a limited set of discrete directions. Recent studies have shown that measures of fiber dispersion in brain white matter can be directly measured from DW-MR imaging data and explicitly represented in the orientation distribution function (ODF) of a voxel, but such measures have yet to be used in guiding tractography algorithms. We present a tracking algorithm which makes use of ODFs which account for underlying fiber dispersion to trace potential fiber pathways, we compare this method with traditional tracking methods on simulated data and in vivo human data, showing that measures of fiber dispersion can aid tractography in finding connectivity commonly missed by current tractography methods

Small-World Brain Networks Revisited.

It is nearly 20 years since the concept of a small-world network was first quantitatively defined, by a combination of high clustering and short path length; and about 10 years since this metric of complex network topology began to be widely applied to analysis of neuroimaging and other neuroscience data as part of the rapid growth of the new field of connectomics. Here, we review briefly the foundational concepts of graph theoretical estimation and generation of small-world networks. We take stock of some of the key developments in the field in the past decade and we consider in some detail the implications of recent studies using high-resolution tract-tracing methods to map the anatomical networks of the macaque and the mouse. In doing so, we draw attention to the important methodological distinction between topological analysis of binary or unweighted graphs, which have provided a popular but simple approach to brain network analysis in the past, and the topology of weighted graphs, which retain more biologically relevant information and are more appropriate to the increasingly sophisticated data on brain connectivity emerging from contemporary tract-tracing and other imaging studies. We conclude by highlighting some possible future trends in the further development of weighted small-worldness as part of a deeper and broader understanding of the topology and the functional value of the strong and weak links between areas of mammalian cortex.DSB acknowledges support from the John D. and Catherine T. MacArthur Foundation, the Alfred P. Sloan Foundation, the Army Research Laboratory and the Army Research Office through contract numbers W911NF-10-2-0022 and W911NF-14-1-0679, the National Institute of Health (2-R01-DC-009209-11, 1R01HD086888-01, R01-MH107235, R01-MH107703, and R21-M MH-106799), the Office of Naval Research, and the National Science Foundation (BCS-1441502, CAREER PHY-1554488, and BCS-1631550).This is the final version of the article. It first appeared from Sage at http://dx.doi.org/10.1177/1073858416667720

Proyecto visualizador de imágenes cerebrales a través de tractografías

En este trabajo se presenta un proyecto de investigación con el fin de ayudar a estandarizar el problema de la construcción de tractografías a partir de las imágenes ponderada por difusión (DWI) el cual normalmente es un problema al que se enfrentan los neurólogos y neurocirujanos para identificar tractos cerebrales. Con el fin de solucionar el problema, la investigación se plantea desde la identificación y comparación de las herramientas y algoritmos utilizados hasta la elaboración de una herramienta web de prueba la cual permite la reconstrucción completa desde la imagen DWI hasta la tractografía

New tractography methods based on parametric models of white matter fibre dispersion

Diffusion weighted magnetic resonance imaging (DW-MRI) is a powerful imaging technique that can probe the complex structure of the body, revealing structural trends which exist at scales far below the voxel resolution. Tractography utilises the information derived from DW-MRI to examine the structure of white matter. Using information derived from DW-MRI, tractography can estimate connectivity between distinct, functional cortical and sub-cortical regions of grey matter. Understanding how seperate functional regions of the brain are connected as part of a network is key to understanding how the brain works. Tractography has been used to deliniate many known white matter structures and has also revealed structures not fully understood from anatomy due to limitations of histological examination. However, there still remain many shortcomings of tractography, many anatomical features for which tractography algorithms are known to fail, which leads to discrepancies between known anatomy and tractography results. With the aim of approaching a complete picture of the human connectome via tractography, we seek to address the shortcomings in current tractography techniques by exploiting new advances in modelling techniques used in DW-MRI, which provide more accurate representation of underlying white matter anatomy. This thesis introduces a methodology for fully utilising new tissue models in DWMRI to improve tractography. It is known from histology that there are regions of white matter where fibres disperse or curve rapidly at length scales below the DW-MRI voxel resolution. One area where dispersion is particularly prominent is the corona radiata. New DW-MRI models capture dispersion utilising specialised parametric probability distributions. We present novel tractography algorithms utilising these parametric models of dispersion in tractography to improve connectivity estimation in areas of dispersing fibres. We first present an algorithm utilising the the new parametric models of dispersion for tractography in a simple Bayesian framework. We then present an extension to this algorithm which introduces a framework to pool neighbourhood information from multiple voxels in the neighbournhood surrounding the tract in order to better estimate connectivity, introducing the new concept of the neighbourhood-informed orientation distribution function (NI-ODF). Specifically, using neighbourhood exploration we address the ambiguity arising in ’fanning polarity’. In regions of dispersing fibres, the antipodal symmetry inherent in DW-MRI makes it impossible to resolve the polarity of a dispersing fibre configuration from a local voxel-wise model in isolation, by pooling information from neighbouring voxels, we show that this issue can be addressed. We evaluate the newly proposed tractography methods using synthetic phantoms simulating canonical fibre configurations and validate the ability to effectively navigate regions of dispersing fibres and resolve fanning polarity. We then validate that the algorithms perform effectively in real in vivo data, using DW-MRI data from 5 healthy subjects. We show that by utilising models of dispersion, we recover a wider range of connectivity compared to other standard algorithms when tracking through an area of the brain known to have significant white fibre dispersion - the corona radiata. We then examine the impact of the new algorithm on global connectivity estimates in the brain. We find that whole brain connectivity networks derived using the new tractography method feature strong connectivity between frontal lobe regions. This is in contrast to networks derived using competing tractography methods which do not account for sub-voxel fibre dispersion. We also compare thalamo-cortical connectivity estimated using the newly proposed tractography method and compare with a compteing tractography method, finding that the recovered connectivity profiles are largely similar, with some differences in thalamo-cortical connections to regions of the frontal lobe. The results suggest that fibre dispersion is an important structural feature to model in the basis of a tractography algorithm, as it has a strong effect on connectivity estimation

Adaptive microstructure-informed tractography for accurate brain connectivity analyses

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 orientation 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 acquisition 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

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

Think Global, Act Local; Projectome Estimation with BlueMatter

Abstract. Estimating the complete set of white matter fascicles (the projectome) from diffusion data requires evaluating an enormous number of potential pathways; consequently, most algorithms use computation-ally efficient greedy methods to search for pathways. The limitation of this approach is that critical global parameters- such as data prediction error and white matter volume conservation- are not taken into account. We describe BlueMatter, a parallel algorithm for global projectome eval-uation, which uniquely accounts for global prediction error and volume conservation. Leveraging the BlueGene/L supercomputing architecture, BlueMatter explores a massive database of 180 billion candidate fasci-cles. The candidates are derived from several sources, including atlases and mutliple tractography algorithms. Using BlueMatter we created the highest resolution, volume-conserved projectome of the human brain.

Changes in psychological and biological signals after completing an adaptive training program requiring working memory related cognitive processes

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Psicología, Departamento de Psicología Biológica y de la Salud. Fecha de lectura: 11-12-201