268 research outputs found
Mapping Connectivity Damage in the Case of Phineas Gage
White matter (WM) mapping of the human brain using neuroimaging techniques has gained considerable interest in the neuroscience community. Using diffusion weighted (DWI) and magnetic resonance imaging (MRI), WM fiber pathways between brain regions may be systematically assessed to make inferences concerning their role in normal brain function, influence on behavior, as well as concerning the consequences of network-level brain damage. In this paper, we investigate the detailed connectomics in a noted example of severe traumatic brain injury (TBI) which has proved important to and controversial in the history of neuroscience. We model the WM damage in the notable case of Phineas P. Gage, in whom a âtamping ironâ was accidentally shot through his skull and brain, resulting in profound behavioral changes. The specific effects of this injury on Mr. Gage's WM connectivity have not previously been considered in detail. Using computed tomography (CT) image data of the Gage skull in conjunction with modern anatomical MRI and diffusion imaging data obtained in contemporary right handed male subjects (aged 25â36), we computationally simulate the passage of the iron through the skull on the basis of reported and observed skull fiducial landmarks and assess the extent of cortical gray matter (GM) and WM damage. Specifically, we find that while considerable damage was, indeed, localized to the left frontal cortex, the impact on measures of network connectedness between directly affected and other brain areas was profound, widespread, and a probable contributor to both the reported acute as well as long-term behavioral changes. Yet, while significantly affecting several likely network hubs, damage to Mr. Gage's WM network may not have been more severe than expected from that of a similarly sized âaverageâ brain lesion. These results provide new insight into the remarkable brain injury experienced by this noteworthy patient
Building connectomes using diffusion MRI: why, how and but
Why has diffusion MRI become a principal modality for mapping connectomes in vivo? How do different image acquisition parameters, fiber tracking algorithms and other methodological choices affect connectome estimation? What are the main factors that dictate the success and failure of connectome reconstruction? These are some of the key questions that we aim to address in this review. We provide an overview of the key methods that can be used to estimate the nodes and edges of macroscale connectomes, and we discuss open problems and inherent limitations. We argue that diffusion MRI-based connectome mapping methods are still in their infancy and caution against blind application of deep white matter tractography due to the challenges inherent to connectome reconstruction. We review a number of studies that provide evidence of useful microstructural and network properties that can be extracted in various independent and biologically-relevant contexts. Finally, we highlight some of the key deficiencies of current macroscale connectome mapping methodologies and motivate future developments
Intrahemispheric cortico-cortical connections of the human auditory cortex.
The human auditory cortex comprises the supratemporal plane and large parts of the temporal and parietal convexities. We have investigated the relevant intrahemispheric cortico-cortical connections using in vivo DSI tractography combined with landmark-based registration, automatic cortical parcellation and whole-brain structural connection matrices in 20 right-handed male subjects. On the supratemporal plane, the pattern of connectivity was related to the architectonically defined early-stage auditory areas. It revealed a three-tier architecture characterized by a cascade of connections from the primary auditory cortex to six adjacent non-primary areas and from there to the superior temporal gyrus. Graph theory-driven analysis confirmed the cascade-like connectivity pattern and demonstrated a strong degree of segregation and hierarchy within early-stage auditory areas. Putative higher-order areas on the temporal and parietal convexities had more widely spread local connectivity and long-range connections with the prefrontal cortex; analysis of optimal community structure revealed five distinct modules in each hemisphere. The pattern of temporo-parieto-frontal connectivity was partially asymmetrical. In conclusion, the human early-stage auditory cortical connectivity, as revealed by in vivo DSI tractography, has strong similarities with that of non-human primates. The modular architecture and hemispheric asymmetry in higher-order regions is compatible with segregated processing streams and lateralization of cognitive functions
Improving the Tractography Pipeline: on Evaluation, Segmentation, and Visualization
Recent advances in tractography allow for connectomes to be constructed in vivo. These have applications for example in brain tumor surgery and understanding of brain development and diseases. The large size of the data produced by these methods lead to a variety problems, including how to evaluate tractography outputs, development of faster processing algorithms for tractography and clustering, and the development of advanced visualization methods for verification and exploration. This thesis presents several advances in these fields.
First, an evaluation is presented for the robustness to noise of multiple commonly used tractography algorithms. It employs a MonteâCarlo simulation of measurement noise on a constructed ground truth dataset. As a result of this evaluation, evidence for obustness of global tractography is found, and algorithmic sources of uncertainty are identified.
The second contribution is a fast clustering algorithm for tractography data based on kâmeans and vector fields for representing the flow of each cluster. It is demonstrated that this algorithm can handle large tractography datasets due to its linear time and memory complexity, and that it can effectively integrate interrupted fibers that would be rejected as outliers by other algorithms.
Furthermore, a visualization for the exploration of structural connectomes is presented. It uses illustrative rendering techniques for efficient presentation of connecting fiber bundles in context in anatomical space. Visual hints are employed to improve the perception of spatial relations.
Finally, a visualization method with application to exploration and verification of probabilistic tractography is presented, which improves on the previously presented Fiber Stippling technique. It is demonstrated that the method is able to show multiple overlapping tracts in context, and correctly present crossing fiber configurations
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 rÎle
est central dans la compréhension du genre humain. Le cerveau est composé de
neurones, oĂč leurs dendrites et synapses se retrouvent dans la matiĂšre grise alors que
les axones en constituent la matiĂšre blanche. Lâinformation traitĂ©e dans les diffĂ©rentes
rĂ©gions de la matiĂšre grise est ensuite transmise par lâintermĂ©diaire des axones afin
dâaccomplir diffĂ©rentes fonctions cognitives.
La matiĂšre blanche forme une structure dâinterconnections complexe encore dif-
ficile Ă comprendre et Ă Ă©tudier. La relation entre lâarchitecture et la fonction du
cerveau a Ă©tĂ© Ă©tudiĂ©e chez les humains ainsi que pour dâautres espĂšces, croyant que
lâarchitecture des axones dĂ©terminait la dynamique du rĂ©seau fonctionnel.
Dans ce mĂȘme objectif, lâImagerie par rĂ©sonance (IRM) est un outil formidable
qui nous permet de visualiser les tissus cérébraux de façon non-invasive. Plus partic-
uliĂšrement, lâIRM de diffusion permet dâestimer et de sĂ©parer la diffusion libre de celle
restreinte par la structure des tissus. Cette mesure de restriction peut ĂȘtre utilisĂ©e
afin dâinfĂ©rer lâorientation locale des faisceaux de matiĂšre blanche. Lâalgorithme de
tractographie exploite cette carte dâorientation pour reconstruire plusieurs connexions
de la matiĂšre blanche (nommĂ©es âstreamlinesâ).
Cette modĂ©lisation de la matiĂšre blanche permet dâestimer la connectivitĂ© cĂ©rĂ©brale
dite structurelle entre les diffĂ©rentes rĂ©gions du cerveau. Ces rĂ©sultats peuvent ĂȘtre
employĂ©s directement pour la planification chirurgicale ou indirectement pour lâanalyse
ou une Ă©valuation clinique.
Malgré plusieurs de ses limitations, telles que sa variabilité et son imprécision, la
tractographie reste lâunique moyen dâĂ©tudier lâarchitecture de la matiĂšre blanche ainsi
que la connectivité cérébrale de façon non invasive.
Lâobjectif de ce projet de doctorat est de rĂ©pondre spĂ©cifiquement Ă ces limitations
et dâamĂ©liorer la prĂ©cision anatomique des estimations de connectivitĂ© structurelle.
Dans ce but, nous avons dĂ©veloppĂ© un algorithme dâoptimisation 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
dâadapter dynamiquement la position dâun 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 Ćuvre une mĂ©thode pour modĂ©liser des
groupes de lignes de courant basées sur le concept que les axones sont organisés en
fascicules, en adaptant leur forme et leur Ă©tendue en fonction de la microstructure
sous-jacente.Abstract : Human brain has been subject of deep interest for centuries, given itâs 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 Ăš 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 Ăš 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 allâaltra 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
lâarchitettura anatomica determini la dinamica della rete funzionale. In risposta a
ciĂČ, 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 libertĂ di movimento
delle molecole dâacqua, consentendo di mappare lâarchitettura delle fibre neuronali
in vivo e in maniera non invasiva. Basata su dati DW-MRI, la trattografia Ăš in
grado di sfruttare le informazioni sullâorientamento locale delle fibre per ricostruirne
i percorsi a livello globale. Questo, a sua volta, consente di estrarre la connettivitĂ
strutturale della WM, utilizzata in diverse applicazioni cliniche come per diagnosi,
dissezioni virtuali e pianificazione chirurgica. Tuttavia, nonostante questa capacitĂ
unica e promettente, lâacquisizione 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 lâefficacia per lo studio
della connettivitĂ cerebrale. Il focus di questo progetto di dottorato Ăš quello di affrontare in modo specifico
queste limitazioni e di migliorare lâaccuratezza anatomica delle stime di connettivitĂ
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 proprietĂ del tessuto neuronale per guidare la ricostruzione utilizzando
un approccio semi-globale. Successivamente, abbiamo studiato la possibilitĂ
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
Superficial White Matter Analysis: An Efficient Point-cloud-based Deep Learning Framework with Supervised Contrastive Learning for Consistent Tractography Parcellation across Populations and dMRI Acquisitions
Diffusion MRI tractography is an advanced imaging technique that enables in
vivo mapping of the brain's white matter connections. White matter parcellation
classifies tractography streamlines into clusters or anatomically meaningful
tracts. It enables quantification and visualization of whole-brain
tractography. Currently, most parcellation methods focus on the deep white
matter (DWM), whereas fewer methods address the superficial white matter (SWM)
due to its complexity. We propose a novel two-stage deep-learning-based
framework, Superficial White Matter Analysis (SupWMA), that performs an
efficient and consistent parcellation of 198 SWM clusters from whole-brain
tractography. A point-cloud-based network is adapted to our SWM parcellation
task, and supervised contrastive learning enables more discriminative
representations between plausible streamlines and outliers for SWM. We train
our model on a large-scale tractography dataset including streamline samples
from labeled SWM clusters and anatomically implausible streamline samples, and
we perform testing on six independently acquired datasets of different ages and
health conditions (including neonates and patients with space-occupying brain
tumors). Compared to several state-of-the-art methods, SupWMA obtains highly
consistent and accurate SWM parcellation results on all datasets, showing good
generalization across the lifespan in health and disease. In addition, the
computational speed of SupWMA is much faster than other methods.Comment: 12 pages, 7 figures. Extension of our ISBI 2022 paper
(arXiv:2201.12528) (Best Paper Award Finalist
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