119 research outputs found

    Pathology Steered Stratification Network for Subtype Identification in Alzheimer's Disease

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    Alzheimer's disease (AD) is a heterogeneous, multifactorial neurodegenerative disorder characterized by beta-amyloid, pathologic tau, and neurodegeneration. There are no effective treatments for Alzheimer's disease at a late stage, urging for early intervention. However, existing statistical inference approaches of AD subtype identification ignore the pathological domain knowledge, which could lead to ill-posed results that are sometimes inconsistent with the essential neurological principles. Integrating systems biology modeling with machine learning, we propose a novel pathology steered stratification network (PSSN) that incorporates established domain knowledge in AD pathology through a reaction-diffusion model, where we consider non-linear interactions between major biomarkers and diffusion along brain structural network. Trained on longitudinal multimodal neuroimaging data, the biological model predicts long-term trajectories that capture individual progression pattern, filling in the gaps between sparse imaging data available. A deep predictive neural network is then built to exploit spatiotemporal dynamics, link neurological examinations with clinical profiles, and generate subtype assignment probability on an individual basis. We further identify an evolutionary disease graph to quantify subtype transition probabilities through extensive simulations. Our stratification achieves superior performance in both inter-cluster heterogeneity and intra-cluster homogeneity of various clinical scores. Applying our approach to enriched samples of aging populations, we identify six subtypes spanning AD spectrum, where each subtype exhibits a distinctive biomarker pattern that is consistent with its clinical outcome. PSSN provides insights into pre-symptomatic diagnosis and practical guidance on clinical treatments, which may be further generalized to other neurodegenerative diseases

    TBSS++: A novel computational method for Tract-Based Spatial Statistics

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    Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to assess the brain white matter. One of the most common computations in dMRI involves cross-subject tract-specific analysis, whereby dMRI-derived biomarkers are compared between cohorts of subjects. The accuracy and reliability of these studies hinges on the ability to compare precisely the same white matter tracts across subjects. This is an intricate and error-prone computation. Existing computational methods such as Tract-Based Spatial Statistics (TBSS) suffer from a host of shortcomings and limitations that can seriously undermine the validity of the results. We present a new computational framework that overcomes the limitations of existing methods via (i) accurate segmentation of the tracts, and (ii) precise registration of data from different subjects/scans. The registration is based on fiber orientation distributions. To further improve the alignment of cross-subject data, we create detailed atlases of white matter tracts. These atlases serve as an unbiased reference space where the data from all subjects is registered for comparison. Extensive evaluations show that, compared with TBSS, our proposed framework offers significantly higher reproducibility and robustness to data perturbations. Our method promises a drastic improvement in accuracy and reproducibility of cross-subject dMRI studies that are routinely used in neuroscience and medical research

    Probabilistische Traktographie des Fasciculus arcuatus in Relation zu funktionellen Zentren der Sprachverarbeitung

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    In dieser Arbeit wurde untersucht, inwiefern probabilistische Traktographie des Fasciculus arcuatus eine Alternative für fMRT in der Lokalisation der funktionellen Sprachareale von Broca und Wernicke darstellen kann. Dafür wurden bei einer Gruppe von Patienten drei Bildgebungs-Datensätze untersucht: ein anatomisches Strukturbild (MP-RAGE), eine fMRT-Sequenz zur Darstellung der Sprachareale und ein DTI- Datensatz. Traktographie wurde orientiert an makro- (anatomische Landmarken) und/oder mikrostrukturellen (DTI-Ellipsoide) Eigenschaften durchgeführt, ohne Vorkenntnis über die Lokalisation von Spracharealen. Diese Herangehensweise war erfolgreich in der Darstellung des Fasciculus arcuatus, sowohl bei Patienten mit Epilepsie als auch bei Patienten mit zum Teil massiven Raumforderungen. Zudem wurde Traktographie auch direkt orientiert an den Ergebnissen der fMRT durchgeführt. So konnten Darstellungen des AF, die ohne Vorkenntnisse über die Lokalisation von Spracharealen erstellt worden waren, mit Darstellungen des AF, die per Definition auf die Verbindung der funktionellen Sprachareale fokussiert waren, verglichen werden. Die Traktographieergebnisse wurden auf ihre strukturellen Eigenschaften und Morphologie hin untersucht und untereinander verglichen, wobei sich eine deutliche Konkordanz feststellen ließ. Ferner wurden die strukturellen Eigenschaften und insbesondere deren asymmetrische Ausprägung mit der Ausprägung der funktionellen Sprachaktivierung und hemisphärischen Sprachdominanz verglichen. Es zeigte sich eine gute Übereinstimmung zwischen der Projektion der AF-Darstellungen und der Lokalisation der funktionellen Sprachareale. Jedoch war keinerlei statistisch signifikante Korrelation zwischen der Lateralität der strukturellen Parameter und der hemisphärischen Sprachdominanz feststellbar, weder für die Traktographie, die ohne Vorkenntnisse über die Lokalisation funktioneller Sprachareale durchgeführt worden war, noch für die Traktographie, die explizit daran ausgerichtet wurde

    On harmonisation of brain MRI data across scanners and sites

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    Magnetic resonance imaging (MRI) of the brain has revolutionised neuroscience by opening unique opportunities for studying unknown aspects of brain organisation, function and pathology-induced dysfunction. Despite the huge potential, MRI measures can be limited in their consistency, reproducibility and accuracy which subsequently restricts their quantifiability. Nuisance non-biological factors, such as hardware, software, calibration differences between scanners and post-processing options can contribute or drive trends in neuroimaging features to an extent that interferes with biological variability and obstructs scientific explorations and clinical applications. Such lack of consistency, or harmonisation across neuroimaging datasets poses a great challenge for our capabilities in quantitative MRI. This thesis contributes to better understanding and addressing it. We specifically build a new resource for comprehensively mapping the extent of the problem and objectively evaluating neuroimaging harmonisation approaches. We use a travelling heads paradigm consisting of multimodal MRI data of 10 travelling subjects, each scanned at 5 different sites on 6 different 3.0T scanners from all the 3 major vendors and using 5 imaging modalities. We use this dataset to explore the between-scanner variability of hundreds of imaging-extracted features and compare these to within-scanner (within-subject) variability and biological (between-subject) variability. We identify subsets of features that are/are not reliable across scanners and use our resource as a testbed to enable new investigations which until now have been relatively unexplored. Specifically, we identify optimal pipeline processing steps that minimise between-scanner variability in extracted features (implicit harmonisation). We also test the performance of post-processing harmonisation tools (explicit harmonisation) and specifically check their efficiency in reducing between-scanner variability against baseline gold standards provided by our data. Our explorations allow us to come up with good practice suggestions on processing steps and sets of features where results are more consistent and reproducible and also set references for future studies in this field

    Diffusion MRI tractography for oncological neurosurgery planning:Clinical research prototype

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    Neuroimaging methods for analysing connectivity in the presence of white matter lesions

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    White Matter Hyperintensities (WMHs) are often observed in the MRI scans of the ageing brain. Previous studies show that the WMH load correlates with cognitive decline, as well as with an increased risk of stroke and dementia. Most of the studies use a global WMH load across the whole brain as the only metric. This thesis proposes new insight into such data by introducing a methodology to analyse the WMH impact on the structural and functional brain connectivity in a localised manner, using white matter tracts. The thesis also supports other studies by exploring the potential impact of WMHs presence on tractography modelling, as well as adapts the method to Multiple Sclerosis (MS) lesion data. In the first part of the thesis, we presented and evaluated two possible measures of the WMHs impact on the WM tracts from the structural perspective. We showed that despite the different distributions of the two measures, both show a similar relationship with the functional connectivity measure. We explored several possible options for quantifying the resting state functional connectivity between the endpoints of the WM tract in the presence of WMHs. We showed that the choice of connectivity measure (full or partial correlation), as well as gray matter parcellation, made a substantial difference in the results. We also observed variability in the results among the tracts and compared our findings to the results from the literature. The majority of this work depends on the correct definition of the white matter tracts. Therefore, the second part of the thesis focuses on evaluating the impact of the presence of WMHs on tractography modelling. Despite changes in the microstructural parameters within the WMHs and their proximity, we found no meaningful alterations in the shape of the WM tracts in the presence of WMHs on the tract. Finally, we explored the possibility to apply some of this methodology to data from patients with Multiple Sclerosis, which is the most common cause of neurological disability in young adults. MS lesions have different aetiology to the WMHs but may have a similar appearance. We showed that directly applying the method between those two conditions may not be possible, and we proposed an alternative approach, suited to the MS data

    Diffusion MRI tractography for oncological neurosurgery planning:Clinical research prototype

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    On noise, uncertainty and inference for computational diffusion MRI

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    Diffusion Magnetic Resonance Imaging (dMRI) has revolutionised the way brain microstructure and connectivity can be studied. Despite its unique potential in mapping the whole brain, biophysical properties are inferred from measurements rather than being directly observed. This indirect mapping from noisy data creates challenges and introduces uncertainty in the estimated properties. Hence, dMRI frameworks capable to deal with noise and uncertainty quantification are of great importance and are the topic of this thesis. First, we look into approaches for reducing uncertainty, by de-noising the dMRI signal. Thermal noise can have detrimental effects for modalities where the information resides in the signal attenuation, such as dMRI, that has inherently low-SNR data. We highlight the dual effect of noise, both in increasing variance, but also introducing bias. We then design a framework for evaluating denoising approaches in a principled manner. By setting objective criteria based on what a well-behaved denoising algorithm should offer, we provide a bespoke dataset and a set of evaluations. We demonstrate that common magnitude-based denoising approaches usually reduce noise-related variance from the signal, but do not address the bias effects introduced by the noise floor. Our framework also allows to better characterise scenarios where denoising can be beneficial (e.g. when done in complex domain) and can open new opportunities, such as pushing spatio-temporal resolution boundaries. Subsequently, we look into approaches for mapping uncertainty and design two inference frameworks for dMRI models, one using classical Bayesian methods and another using more recent data-driven algorithms. In the first approach, we build upon the univariate random-walk Metropolis-Hastings MCMC, an extensively used sampling method to sample from the posterior distribution of model parameters given the data. We devise an efficient adaptive multivariate MCMC scheme, relying upon the assumption that groups of model parameters can be jointly estimated if a proper covariance matrix is defined. In doing so, our algorithm increases the sampling efficiency, while preserving accuracy and precision of estimates. We show results using both synthetic and in-vivo dMRI data. In the second approach, we resort to Simulation-Based Inference (SBI), a data-driven approach that avoids the need for iterative model inversions. This is achieved by using neural density estimators to learn the inverse mapping from the forward generative process (simulations) to the parameters of interest that have generated those simulations. By addressing the problem via learning approaches offers the opportunity to achieve inference amortisation, boosting efficiency by avoiding the necessity of repeating the inference process for each new unseen dataset. It also allows inversion of forward processes (i.e. a series of processing steps) rather than only models. We explore different neural network architectures to perform conditional density estimation of the posterior distribution of parameters. Results and comparisons obtained against MCMC suggest speed-ups of 2-3 orders of magnitude in the inference process while keeping the accuracy in the estimates

    25th Annual Computational Neuroscience Meeting: CNS-2016

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    Abstracts of the 25th Annual Computational Neuroscience Meeting: CNS-2016 Seogwipo City, Jeju-do, South Korea. 2–7 July 201
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