2,265 research outputs found
Same brain, different look? The impact of scanner, sequence and preprocessing on diffusion imaging outcome parameters
In clinical diagnostics and longitudinal studies, the reproducibility of MRI assessments is of high importance in order to detect pathological changes, but developments in MRI hard- and software often outrun extended periods of data acquisition and analysis. This could potentially introduce artefactual changes or mask pathological alterations. However, if and how changes of MRI hardware, scanning protocols or preprocessing software affect complex neuroimaging outcomes from, e.g., diffusion weighted imaging (DWI) remains largely understudied. We therefore compared DWI outcomes and artefact severity of 121 healthy participants (age range 19–54 years) who underwent two matched DWI protocols (Siemens product and Center for Magnetic Resonance Research sequence) at two sites (Siemens 3T Magnetom Verio and Skyrafit). After different preprocessing steps, fractional anisotropy (FA) and mean diffusivity (MD) maps, obtained by tensor fitting, were processed with tract-based spatial statistics (TBSS). Inter-scanner and inter-sequence variability of skeletonised FA values reached up to 5% and differed largely in magnitude and direction across the brain. Skeletonised MD values differed up to 14% between scanners. We here demonstrate that DTI outcome measures strongly depend on imaging site and software, and that these biases vary between brain regions. These regionally inhomogeneous biases may exceed and considerably confound physiological effects such as ageing, highlighting the need to harmonise data acquisition and analysis. Future studies thus need to implement novel strategies to augment neuroimaging data reliability and replicability
Deviations from normative brain white and gray matter structure are associated with psychopathology in youth
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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
Regional variation models of white matter microstructure
Diffusion-weighted MRI (DW-MRI) is a powerful in vivo imaging technique that is particularly sensitive to the underlying microstructure of white matter tissue in the brain. Many models of the DW-MRI signal exist that allow us to relate the signals we measure to various aspects of the tissue structure, including measures of diffusivity, cellularity and even axon size. From histology, we know that many of these microstructure measures display distinct patterns of variation on length scales greater than the average voxel size. However very few methods exist that use this spatial coherence to inform and guide parameter estimation. Instead, most techniques treat each voxel of data independently. This is particularly problematic when estimating parameters such as axon radius which only weakly influence the signal, as the resulting estimates are noisy. Several methods have been proposed that spatially smooth parameter estimates after fitting the model in each voxel. However if the parameter estimates are very noisy, the underlying trend is likely to be obscured. These methods are also unable to account for spatial coupling that may exist between the various parameters. This thesis introduces a novel framework, the Regional Variation Model (RVM), which exploits the underlying spatial coherence within white matter tracts to estimate trends of microstructure variation across large regions of interest. We fit curves describing parameter variation directly to the diffusion-weighted signals which should capture spatial changes in a more natural way as well as reducing the effects of noise. This allows for more precise estimates of a range of microstructure indices, including axon radius. The resulting curves, which show how microstructure parameters vary spatially through white matter regions, can also be used to detect groupwise differences with potentially greater power than traditional methods
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
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
Joint Total Variation ESTATICS for Robust Multi-Parameter Mapping
Quantitative magnetic resonance imaging (qMRI) derives tissue-specific
parameters -- such as the apparent transverse relaxation rate R2*, the
longitudinal relaxation rate R1 and the magnetisation transfer saturation --
that can be compared across sites and scanners and carry important information
about the underlying microstructure. The multi-parameter mapping (MPM) protocol
takes advantage of multi-echo acquisitions with variable flip angles to extract
these parameters in a clinically acceptable scan time. In this context,
ESTATICS performs a joint loglinear fit of multiple echo series to extract R2*
and multiple extrapolated intercepts, thereby improving robustness to motion
and decreasing the variance of the estimators. In this paper, we extend this
model in two ways: (1) by introducing a joint total variation (JTV) prior on
the intercepts and decay, and (2) by deriving a nonlinear maximum \emph{a
posteriori} estimate. We evaluated the proposed algorithm by predicting
left-out echoes in a rich single-subject dataset. In this validation, we
outperformed other state-of-the-art methods and additionally showed that the
proposed approach greatly reduces the variance of the estimated maps, without
introducing bias.Comment: 11 pages, 2 figures, 1 table, conference paper, accepted at MICCAI
202
Studying neuroanatomy using MRI
The study of neuroanatomy using imaging enables key insights into how our brains function, are shaped by genes and environment, and change with development, aging, and disease. Developments in MRI acquisition, image processing, and data modelling have been key to these advances. However, MRI provides an indirect measurement of the biological signals we aim to investigate. Thus, artifacts and key questions of correct interpretation can confound the readouts provided by anatomical MRI. In this review we provide an overview of the methods for measuring macro- and mesoscopic structure and inferring microstructural properties; we also describe key artefacts and confounds that can lead to incorrect conclusions. Ultimately, we believe that, though methods need to improve and caution is required in its interpretation, structural MRI continues to have great promise in furthering our understanding of how the brain works
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