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

Empirical comparison of diffusion kurtosis imaging and diffusion basis spectrum imaging using the same acquisition in healthy young adults

As diffusion tensor imaging gains widespread use, many researchers have been motivated to go beyond the tensor model and fit more complex diffusion models, to gain a more complete description of white matter microstructure and associated pathology. Two such models are diffusion kurtosis imaging (DKI) and diffusion basis spectrum imaging (DBSI). It is not clear which DKI parameters are most closely related to DBSI parameters, so in the interest of enabling comparisons between DKI and DBSI studies, we conducted an empirical survey of the interrelation of these models in 12 healthy volunteers using the same diffusion acquisition. We found that mean kurtosis is positively associated with the DBSI fiber ratio and negatively associated with the hindered ratio. This was primarily driven by the radial component of kurtosis. The axial component of kurtosis was strongly and specifically correlated with the restricted ratio. The joint spatial distributions of DBSI and DKI parameters are tissue-dependent and stable across healthy individuals. Our contribution is a better understanding of the biological interpretability of the parameters generated by the two models in healthy individuals

Methods for improved mapping of brain lesion connectivity

Recent advances over the past two decades in neuroimaging methods have enabled us to map the connectivity of the brain. In parallel, pathophysiological models of brain disease have shifted from an emphasis on understanding pathology in specific brain regions to characterizing disruptions to interconnected neural networks. Nevertheless, these recent methods for mapping brain connectivity are still under development. Every step of the mapping process becomes a potential source for additional error due to noise or artifacts that could impact final analyses. Segmentation, parcellation, registration, and tractography are some of the steps where this occurs. Moreover, mapping the connectivity in a brain lesion is even more susceptible to errors in these steps. In this body of work, I describe multiple new methods for improving the accuracy of mapping lesion connectivity by reducing errors at the tractography stage which is the most error prone stage. First, we develop an approach for directly normalizing streamlines into a template space that avoids performing tractography in the normalized template space, reducing the error of connectomes constructed in the template space with respect to the ground truth native space connectome. Second, we develop a rapid approach for performing shortest path tractography and constructing shortest path probability weighted connectomes which increases the connection specificity relative to local streamline tracking approaches. We then demonstrate how our shortest path tractography approach can be used construct a disconnectome, a connectivity map of the proportion of connections lost due to intersecting a lesion. We then develop a fast, greedy graph-theoretic algorithm that extracts the maximally disconnected subgraph containing brain regions with the greatest shared loss of connectivity. Finally, we demonstrate how combining methods from diffusion based image inpainting and optimal estimation can be used to restore or inpaint corrupted fiber diffusion models in lesioned white matter tissue, enabling tractography and the study of lesion connectivity and modeling of microstructural measures in the patient’s native space

Development and characterization of methodology and technology for the alignment of fMRI time series

This dissertation has developed, implemented and tested a novel computer based system (AUTOALIGN) that incorporates an algorithm for the alignment of functional Magnetic Resonance Image (fMRI) time series. The algorithm assumes the human brain to be a rigid body and computes a head coordinate system on the basis of three reference points that lie on the directions correspondent to two of the eigenvectors of inertia of the volume, at the intersections with the head boundary. The eigenvectors are found weighting the inertia components with the voxel\u27s intensity values assumed as mass. The three reference points are found in the same position, relative to the origin of the head coordinate system, in both test and reference brain images. Intensity correction is performed at sub-voxel accuracy by tri-linear interpolation. A test fMR brain volume in which controlled simulations of rigid-body transformations have been introduced has preliminarily assessed system performance. Further experimentation has been conducted with real fMRI time series. Rigid-body transformations have been retrieved automatically and the values of the motion parameters compared to those obtained by the Statistical Parametric Mapping (SPM99), and the Automatic Image Registration (AIR 3.08). Results indicated that AUTOALIGN offers subvoxel accuracy in correcting both misalignment and intensity among time points in fMR images time series, and also that its performance is comparable to that of SPM99 and AIR3.08

Deep and superficial amygdala nuclei projections revealed in vivo by probabilistic tractography

Copyright © 2011 Society for Neuroscience and the authors. The The Journal of Neuroscience uses a Creative Commons Attribution-NonCommercial-ShareAlike licence: http://creativecommons.org/licenses/by-nc-sa/4.0/.Despite a homogenous macroscopic appearance on magnetic resonance images, subregions of the amygdala express distinct functional profiles as well as corresponding differences in connectivity. In particular, histological analysis shows stronger connections for superficial (i.e., centromedial and cortical), compared with deep (i.e., basolateral and other), amygdala nuclei to lateral orbitofrontal cortex and stronger connections of deep compared with superficial, nuclei to polymodal areas in the temporal pole. Here, we use diffusion weighted imaging with probabilistic tractography to investigate these connections in humans. We use a data-driven approach to segment the amygdala into two subregions using k-means clustering. The identified subregions are spatially contiguous and their location corresponds to deep and superficial nuclear groups. Quantification of the connection strength between these amygdala clusters and individual target regions corresponds to qualitative histological findings in non-human primates, indicating such findings can be extrapolated to humans. We propose that connectivity profiles provide a potentially powerful approach for in vivo amygdala parcellation and can serve as a guide in studies that exploit functional and anatomical neuroimaging.The Wellcome Trust, a Max Planck Research Award and Swiss National Science Foundation

Optimization of the diffusion-weighted MRI processing pipeline for the longitudinal assessment of the brain microstructure in a rat model of Alzheimer’s disease

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Radiações em Diagnóstico e Terapia) Universidade de Lisboa, Faculdade de Ciências, 2019The mechanism that triggers Alzheimer’s disease (AD) is not well-established, with amyloid plaques, neurofibrillary tangles of tau protein, microgliosis and glucose hypometabolism all likely involved in the early cascade. One main advantage of animal models is the possibility to tease out the impact of each insult on the neurodegeneration. Following an intracerebroventricular (icv) injection of streptozotocin (STZ), rats and monkeys develop impaired brain glucose metabolism, i.e. “diabetes of the brain”. Nu-merous studies have reported AD-like features in icv-STZ animals, but this model has never been char-acterized in terms of Magnetic Resonance Imaging (MRI)-derived biomarkers beyond structural brain atrophy. White matter degeneration has been proposed as a promising biomarker for AD that well pre-cedes cortical atrophy and correlates strongly with disease severity. Therefore, this project proposes a longitudinal study of white matter degeneration in icv-STZ rats using diffusion MRI. An existing image processing pipeline was primarily used to obtain preliminary results and propose an optimization strat-egy to improve it in terms of data quality and reliability. These strategies were tested and implemented in the pipeline when confirmed to be valuable, in order to achieve results as reproducible as possible and find the spatio-temporal pattern of brain degeneration in this animal model. All experiments were approved by the local Service for Veterinary Affairs. Male Wistar rats (N=18) (236±11 g) underwent a bilateral icv-injection of either streptozotocin (3 mg/kg, STZ group, N=10) or buffer (control group, CTL, N=8). Rats were scanned at four timepoints following surgery on a 14 T Varian system. Diffusion data were acquired using a semi-adiabatic SE-EPI PGSE sequence as follows: 4 (b=0 ms/μm2), 12 (b=0.8 ms/μm2), 16 (b=1.3 ms/μm2) and 30 (b=2 ms/μm2) directions; TE/TR=48/2500 ms, 9 coronal 1 mm slices, δ/Δ=4/27 ms, FOV=23x17 mm2, matrix=128x64 and 4 shots. The existing image processing pipeline included image denoising and eddy-correction. Moreover, diffusion and kurtosis tensors were calculated for each voxel, producing parametric maps of fractional anisotropy (FA), mean, axial and radial diffusivity (MD, AxD and RD) and mean, axial and radial kur-tosis (MK, AK and RK). Additionally, the two-compartment WMTI-Watson model was further esti-mated to provide specificity to the microstructure assessment. The following metrics were derived from the model: volume water fraction , parallel intra-axonal diffusivity , parallel ,║ and perpendicular extra-axonal diffusivities ,ꓕ and dispersion of fiber orientations 2. Since the model allows for two mathematical solutions, the >,║ solution was retained based on recent evidence. Considering pre-vious findings, the corpus callosum, cingulum, fornix and fimbria were chosen as white matter regions of interest (ROIs) and automatically segmented using anatomical atlas-based registration. Mean diffu-sion metrics were calculated in each ROI for each dataset. CTL and STZ groups were compared using two-sided t-tests at each timepoint. Within-group longitudinal changes were assessed using one-way ANOVA. Because of the small cohort, statistical analysis excluded the last time point. In the course of this project, strategies to optimize the existing pipeline were developed and tested. The existing brain atlas template was supplemented with white matter labels, rat brain extraction was semi-automated, and bias field correction of anatomical data was added before registration. Ventricle enlargement is typically reported in icv-STZ animals and normally constitutes an issue of misalignment in registration. In order to better match the label ROIs with the respective underlying tissue, several registration procedures were tested with different FA and color-coded FA template images. Color-coded FA-based registration dramatically improved the segmentation of the corpus callosum and the fimbria and reliability of diffusion metrics extracted from these regions. Moreover, additional fiber metrics were extracted from a newly developed tractography pipeline to compare with tensors metrics and finally, tensors metrics were evaluated in the gray matter for a more comprehensive spatio-temporal character-ization of brain degeneration. Results from statistical analysis were obtained after implementing the successful optimization strat-egies into the pipeline. There were few significant differences within groups over time. However, be-tween-group differences at each time point were more pronounced. White matter microstructure altera-tions were consistent with previous studies of histology and cognitive performance of the icv-STZ model. Changes in tensors metrics indicate early axonal injury in the fimbria and fornix at 2 weeks after injection, a period of potential recovery at 6 weeks after injection and late axonal injury at 13 weeks in all ROIs. The WMTI-Watson biophysical model provided specificity to the underlying microstructure, by showing intra-axonal damage in the fimbria and corpus callosum as early as 2 weeks, followed by a recover period and definite axonal loss at 13 weeks after injection. Results from tensors metrics and the WMTI-Watson model are not only complementary, they are consistent with each other and with previously-established trends for structural thickness, memory per-formance, amyloid deposition and inflammation. The icv-STZ model displays white matter changes in tracts reportedly affected by AD, while the degeneration is induced primarily by impaired brain glucose metabolism. The icv-STZ constitutes an excellent model to reproduce sporadic AD and should allow to further explore the hypothesis of AD being “type III diabetes”. The combination of diffusion information extracted from tensor imaging and biophysical modelling is a promising set of tools to assess white matter in the AD brain and might be the upcoming strategy to assess the human brain. Regarding future work, it will focus on estimating the correlation between microstructural alterations and functional con-nectivity (from resting-state functional MRI), glucose hypometabolism (from FDG-PET), and patholog-ical features (from histological stainings) – all currently under processing at CIBM. Tractography is a cutting-edge methodology to assess brain connectivity and the pipeline created could be further devel-oped to improve understanding and support diffusion metrics. The relationship between white and gray matter will also improve the understanding of spatio-temporal degeneration and the progression nature of the disease.O mecanismo que desencadeia a doença de Alzheimer (DA) não é bem conhecido, contudo sabe-se que a presença de placas amilóides e de emaranhados neurofibrilares da proteína tau, microgliose e ainda hipometabolismo de glucose estão envolvidos na fase inicial da cascata de desenvolvimento da doença. A principal vantagem dos modelos animais é justamente a possibilidade de estudar individualmente o impacto de cada um destes mecanismos no processo de neurodegeneração. Após uma injeção intracere-broventricular (icv) de estreptozotocina (STZ), várias espécies de animais mostraram um metabolismo anormal de glucose no cérebro, processo que foi referido como “diabetes do cérebro”. Vários estudos demonstraram que animais icv-STZ são portadores de características típicas de DA, mas este modelo animal nunca foi estudado em termos de biomarcadores derivados de técnicas de imagem por ressonân-cia magnética (IRM), exceto atrofia estrutural do cérebro. Um biomarcador promissor de DA que se acredita preceder a atrofia do córtex cerebral é a degeneração da matéria branca do cérebro, uma vez que foi fortemente correlacionado com a progressão e gravidade da doença. Logo, este projeto propõe um estudo longitudinal da degeneração da matéria branca em ratazanas icv-STZ utilizando IRM de di-fusão. O plano de processamento de imagem existente foi utilizado primeiramente para obter resultados preliminares e viabilizar a proposta de estratégias de otimização da mesma, em termos de melhoramento da qualidade de imagem e credibilidade das variáveis extraídas das imagens resultantes. Estas estratégias foram testadas e implementadas no plano de processamento quando a sua performance confirmou ser de valor, para que os resultados fossem o mais reproduzíveis possível em caracterizar a distribuição espácio-temporal da degeneração do cérebro neste modelo animal. Todos os procedimentos aqui descritos foram aprovados pelo serviço local dos assuntos veterinários. Ratazanas macho Wistar (N=18, 236±11 g) foram submetidas a uma injeção icv de STZ (3 mg/kg) no caso do grupo infetado (N=10) ou de um buffer no caso do grupo de controlo (N=8). As ratazanas foram examinadas no scanner de IRM do tipo Varian de 14 T em quatro momentos no tempo: 2, 6, 13 e 21 semanas após a injeção. As imagens por difusão foram adquiridas com uma sequência semi-adiabática spin-echo EPI PGSE com os seguintes parâmetros: 4 (b=0), 12 (b=0.8 ms/μm2), 16 (b=1.3 ms/μm2) and 30 (b=2 ms/μm2) direções; TE/TR=48/2500 ms, 9 secções coronais de 1 mm, δ/Δ=4/27 ms, FOV=23x17 mm2, matriz=128x64 e 4 shots. O plano existente de processamento de imagem incluía a correção das imagens ao nível de ruído e correntes-eddy. Posteriormente, os tensores de difusão e curtose foram estimados para cada voxel e os mapas paramétricos de anisotropia fracional (FA), difusão média, axial e radial (MD, AD e RD) e cur-tose média, axial e radial (MK, AK e RK) foram calculados. Adicionalmente, um modelo de difusão de água nas fibras da matéria branca foi utilizado para providenciar maior especificidade ao estudo da microestrutura do cérebro. Como tal, o modelo de dois compartimentos denominado WMTI-Watson foi também estimado e as seguintes variáveis foram derivadas do mesmo: a fração do volume de água , a difusividade paralela intra-axonal , as difusividades paralela ,║ e perpendicular ,ꓕ extra-axonais e, finalmente, a orientação da dispersão axonal 2. Este modelo matemático tem duas soluções possíveis dada a sua natureza quadrática, pelo que a solução >,║ foi imposta com base em evidências re-centes. Considerando estudos anteriores, as regiões de interesse (RDIs) da matéria branca escolhidas para analisar a microestrutura cerebral foram o corpo caloso, o cíngulo, a fimbria e a fórnix. Estes foram automaticamente segmentados através de registo de imagem de um atlas das regiões do cérebro da rata-zana e as médias das medidas extraídas dos tensores de difusão e curtose e ainda do modelo biofísico neuronal foram calculadas em cada RDI para cada conjunto de imagens obtidas. Os dois grupos de teste e controlo foram comparados usando testes t de Student bilaterais em cada momento do tempo, e a comparação das alterações longitudinais em cada grupo foi feita usando uma ANOVA. Devido ao baixo número de amostras, o último momento no tempo às 21 semanas foi excluído da análise. No decorrer deste projeto, várias estratégias para otimizar o processamento de imagem ou comple-mentar a análise da informação disponível foram testadas. Nomeadamente, o atlas cerebral da ratazana foi aperfeiçoado relativamente às regiões de matéria branca, a segmentação do cérebro foi testada com algoritmos automáticos e a correção do bias field em imagens estruturais de IRM foi adicionada ao plano antes do registo de imagem. O aumento dos ventrículos cerebrais é uma característica frequente em animais icv-STZ, constituindo um problema de alinhamento nos métodos de registo de imagem. No sentido de otimizar a correspondência entre as regiões do atlas e as respetivas regiões na imagem estru-tural e por difusão, vários procedimentos de registo de imagem foram testados. O co-registo de imagem convencional utiliza imagens estruturais para normalizar o espaço das imagens por difusão, no entanto os mapas paramétricos de FA têm vindo a substituir este conceito dado o excelente contraste que provi-denciam entre a matéria branca e cinzenta do cérebro. Mapas de FA com diferentes direções predomi-nantes mostraram uma melhoria significante da segmentação do corpo caloso e da fimbria e também do poder estatístico das variáveis extraídas destas RDIs. Adicionalmente, um novo plano de processamento de tratografia foi construído de raiz no âmbito deste projeto para extrair variáveis adicionais das fibras de interesse e compará-las com as variáveis de difusão obtidas por análise voxel-a-voxel. Por último, as variáveis calculadas através dos tensores de difusão e curtose foram avaliadas na matéria cinzenta do cérebro para uma caracterização espácio-temporal da degeneração cerebral na DA. Os resultados da análise estatística foram obtidos após integrar no plano de processamento as estra-tégias que mostraram valorizar o projeto em termos de qualidade de imagem ou credibilidade das vari-áveis. Houve poucas diferenças significativas ao longo do tempo em cada grupo, no entanto as diferen-ças entre grupos foram bastante acentuadas. As alterações ao nível da microestrutura da matéria branca foram consistentes com estudos prévios em animais icv-STZ usando métodos histológicos e avaliações das suas capacidades cognitivas. Alterações nas variáveis extraídas dos tensores indicaram deficiência axonal inicial na fimbria e no fórnix 2 semanas após injeção no grupo de teste, um potencial período de recuperação às 6 semanas e novamente deficiência axonal às 13 semanas, sendo que neste período tardio todas as RDIs foram afetadas. O modelo biofísico WMTI-Watson confirmou aumentar especificidade ao estudo da microestrutura, visto que demostrou danos intra-axonais na fimbria e no corpo caloso 2 semanas após injeção, seguidos de um período de recuperação e de perda de estrutura axonal definitiva às 13 semanas em todas as RDIs. Não só estes dois métodos de análise de IRM de difusão se complementam, como são também con-sistentes entre eles e com as tendências de alterações ao longo do tempo descritas noutros estudos. Além disso, o animal icv-STZ mostrou alterações características da DA, mesmo tendo a degeneração cerebral sido induzida pela disrupção do metabolismo de glucose no cérebro. Como tal, este modelo animal é excelente para reproduzir a doença e deverá continuar a ser avaliado nas diferentes áreas multidiscipli-nares para explorar a hipótese de a DA ser desencadeada pela falha do sistema insulina/glucose. A com-binação da informação de difusão obtida dos tensores e da modelação da difusão neuronal provou ser uma ferramenta promissora no estudo das fibras da matéria branca do cérebro e poderá vir a ser o desafio futuro no que toca a investigação clínica da DA. Este estudo focar-se-á em correlacionar as alterações microestruturais aqui descritas com dados de conectividade funcional (obtida por IRM funcional em repouso), hipometabolismo de glucose (por FDG-PET) e outras características patológicas (por colora-ção histológica) – todos já em curso no CIBM. Tratografia é a metodologia topo de gama para aceder à conetividade cerebral e o plano de processamento gerado neste projeto poderá continuar a ser desenvol-vido no futuro para informação adicional, assim como a relação entre a matéria branca e cinzenta poderá suplementar a compreensão da progressão da doença no espaço e no tempo

Influence of Analytic Techniques on Comparing Dti-Derived Measurements in Early Stage Parkinson\u27s Disease

Diffusion tensor imaging (DTI) studies in early Parkinson\u27s disease (PD) to understand pathologic changes in white matter (WM) organization are variable in their findings. Evaluation of different analytic techniques frequently employed to understand the DTI-derived change in WM organization in a multisite, well-characterized, early stage PD cohort should aid the identification of the most robust analytic techniques to be used to investigate WM pathology in this disease, an important unmet need in the field. Thus, region of interest (ROI)-based analysis, voxel-based morphometry (VBM) analysis with varying spatial smoothing, and the two most widely used skeletonwise approaches (tract-based spatial statistics, TBSS, and tensor-based registration, DTI-TK) were evaluated in a DTI dataset of early PD and Healthy Controls (HC) from the Parkinson\u27s Progression Markers Initiative (PPMI) cohort. Statistical tests on the DTI-derived metrics were conducted using a nonparametric approach from this cohort of early PD, after rigorously controlling for motion and signal artifacts during DTI scan which are frequent confounds in this disease population. Both TBSS and DTI-TK revealed a significantly negative correlation of fractional anisotropy (FA) with disease duration. However, only DTI-TK revealed radial diffusivity (RD) to be driving this FA correlation with disease duration. HC had a significantly positive correlation of MD with cumulative DaT score in the right middle-frontal cortex after a minimum smoothing level (at least 13mm) was attained. The present study found that scalar DTI-derived measures such as FA, MD, and RD should be used as imaging biomarkers with caution in early PD as the conclusions derived from them are heavily dependent on the choice of the analysis used. This study further demonstrated DTI-TK may be used to understand changes in DTI-derived measures with disease progression as it was found to be more accurate than TBSS. In addition, no singular region was identified that could explain both disease duration and severity in early PD. The results of this study should help standardize the utilization of DTI-derived measures in PD in an effort to improve comparability across studies and time, and to minimize variability in reported results due to variation in techniques

A hitchhiker's guide to diffusion tensor imaging

Diffusion Tensor Imaging (DTI) studies are increasingly popular among clinicians and researchers as they provide unique insights into brain network connectivity. However, in order to optimize the use of DTI, several technical and methodological aspects must be factored in. These include decisions on: acquisition protocol, artifact handling, data quality control, reconstruction algorithm, and visualization approaches, and quantitative analysis methodology. Furthermore, the researcher and/or clinician also needs to take into account and decide on the most suited software tool(s) for each stage of the DTI analysis pipeline. Herein, we provide a straightforward hitchhiker's guide, covering all of the workflow's major stages. Ultimately, this guide will help newcomers navigate the most critical roadblocks in the analysis and further encourage the use of DTI.The work was supported by SwitchBox-FP7-HEALTH-2010-grant 259772-2. The authors acknowledge Nadine Santos for her help in editing the manuscript