Structural covariance networks are coupled to expression of genes enriched in supragranular layers of the human cortex.

Complex network topology is characteristic of many biological systems, including anatomical and functional brain networks (connectomes). Here, we first constructed a structural covariance network from MRI measures of cortical thickness on 296 healthy volunteers, aged 14-24 years. Next, we designed a new algorithm for matching sample locations from the Allen Brain Atlas to the nodes of the SCN. Subsequently we used this to define, transcriptomic brain networks by estimating gene co-expression between pairs of cortical regions. Finally, we explored the hypothesis that transcriptional networks and structural MRI connectomes are coupled. A transcriptional brain network (TBN) and a structural covariance network (SCN) were correlated across connection weights and showed qualitatively similar complex topological properties: assortativity, small-worldness, modularity, and a rich-club. In both networks, the weight of an edge was inversely related to the anatomical (Euclidean) distance between regions. There were differences between networks in degree and distance distributions: the transcriptional network had a less fat-tailed degree distribution and a less positively skewed distance distribution than the SCN. However, cortical areas connected to each other within modules of the SCN had significantly higher levels of whole genome co-expression than expected by chance. Nodes connected in the SCN had especially high levels of expression and co-expression of a human supragranular enriched (HSE) gene set that has been specifically located to supragranular layers of human cerebral cortex and is known to be important for large-scale, long-distance cortico-cortical connectivity. This coupling of brain transcriptome and connectome topologies was largely but not entirely accounted for by the common constraint of physical distance on both networks

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

Multimodal image analysis of the human brain

Gedurende de laatste decennia heeft de snelle ontwikkeling van multi-modale en niet-invasieve hersenbeeldvorming technologieën een revolutie teweeg gebracht in de mogelijkheid om de structuur en functionaliteit van de hersens te bestuderen. Er is grote vooruitgang geboekt in het beoordelen van hersenschade door gebruik te maken van Magnetic Reconance Imaging (MRI), terwijl Elektroencefalografie (EEG) beschouwd wordt als de gouden standaard voor diagnose van neurologische afwijkingen. In deze thesis focussen we op de ontwikkeling van nieuwe technieken voor multi-modale beeldanalyse van het menselijke brein, waaronder MRI segmentatie en EEG bronlokalisatie. Hierdoor voegen we theorie en praktijk samen waarbij we focussen op twee medische applicaties: (1) automatische 3D MRI segmentatie van de volwassen hersens en (2) multi-modale EEG-MRI data analyse van de hersens van een pasgeborene met perinatale hersenschade. We besteden veel aandacht aan de verbetering en ontwikkeling van nieuwe methoden voor accurate en ruisrobuuste beeldsegmentatie, dewelke daarna succesvol gebruikt worden voor de segmentatie van hersens in MRI van zowel volwassen als pasgeborenen. Daarenboven ontwikkelden we een geïntegreerd multi-modaal methode voor de EEG bronlokalisatie in de hersenen van een pasgeborene. Deze lokalisatie wordt gebruikt voor de vergelijkende studie tussen een EEG aanval bij pasgeborenen en acute perinatale hersenletsels zichtbaar in MRI

The functional anatomy of white matter pathways for visual configuration learning

The role of the medial temporal lobes (MTL) in visuo-spatial learning has been extensively studied and documented in the neuroscientific literature. Numerous animal and human studies have demonstrated that the parahippocampal place area (PPA), which sits at the confluence of the parahippocampal and lingual gyri, is particularly important for learning the spatial configuration of objects in visually presented scenes. In current visuo-spatial processing models, the PPA sits downstream from the parietal lobes which are involved in multiple facets of spatial processing. Yet, direct input to the PPA from early visual cortex (EVC) is rarely discussed and poorly understood. This thesis adopted a multimodal neuroimaging analysis approach to study the functional anatomy of these connections. First, the pattern of structural connectivity between EVC and the MTL was explored by means of surface-based ‘connectomes’ constructed from diffusion MRI tractography in a cohort of 200 healthy young adults from the Human Connectome Project. Through this analysis, the PPA emerged as a primary recipient of EVC connections within the MTL. Second, a data-driven clustering analysis of the PPA’s connectivity to an extended cortical region (including EVC, retrosplenial cortex, and other areas) revealed multiple clusters with different connectivity profiles within the PPA. The two main clusters were located in the posterior and anterior portions of the PPA, with the posterior cluster preferentially connected to EVC. Motivated by this result, virtual tractography dissections were used to delineate the medial occipital longitudinal tract (MOLT), the white matter bundle connecting the PPA with EVC. The properties of this bundle and its relation to visual configuration learning were verified in a different, cross-sectional adult cohort of 90 subjects. Finally, the role of the MOLT in the visuo-spatial learning domain was further confirmed in the case of a stroke patient who, after bilateral occipital injury, exhibited deficits confined to this domain. The results presented in this work suggest that the MOLT should be included in current visuo-spatial processing models as it offers additional insight into how the MTL acquires and processes information for spatial learning

Fast and robust hybrid framework for infant brain classification from structural MRI : a case study for early diagnosis of autism.

The ultimate goal of this work is to develop a computer-aided diagnosis (CAD) system for early autism diagnosis from infant structural magnetic resonance imaging (MRI). The vital step to achieve this goal is to get accurate segmentation of the different brain structures: whitematter, graymatter, and cerebrospinal fluid, which will be the main focus of this thesis. The proposed brain classification approach consists of two major steps. First, the brain is extracted based on the integration of a stochastic model that serves to learn the visual appearance of the brain texture, and a geometric model that preserves the brain geometry during the extraction process. Secondly, the brain tissues are segmented based on shape priors, built using a subset of co-aligned training images, that is adapted during the segmentation process using first- and second-order visual appearance features of infant MRIs. The accuracy of the presented segmentation approach has been tested on 300 infant subjects and evaluated blindly on 15 adult subjects. The experimental results have been evaluated by the MICCAI MR Brain Image Segmentation (MRBrainS13) challenge organizers using three metrics: Dice coefficient, 95-percentile Hausdorff distance, and absolute volume difference. The proposed method has been ranked the first in terms of performance and speed

MR-based pseudo-CT generation using water-fat decomposition and Gaussian mixture regression

Tese de mestrado integrado em Engenharia Biomédica e Biofísica, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2017O uso de tomografia computorizada (CT) é considerado como a prática clínica adequada para aplicações clínicas onde a simulação da atenuação de radiação pelos tecidos corporais é necessária, tais como a correcção de atenuação dos fotões em Tomografia de Emissão de Positrões (PET) e no cálculo da dosagem a ser administrada durante o planeamento de radioterapia (RTP). Imagens de ressonância magnética (MRI) têm vindo a substituir o uso de TC em algumas aplicações, sobretudo devido ao seu superior contraste entre tecidos moles e ao facto de não usar radiação ionizante. Desta forma, técnicas como PET-MRI e o planeamento de radioterapia apenas com recurso a imagens de ressonância magnética são alvo de uma crescente atenção. No entanto, estas técnicas estão limitadas pelo facto de imagens de ressonância magnética não fornecerem informação acerca da atenuação e absorção de radiação pelos tecidos. Normalmente, de forma a solucionar este problema, uma imagem de tomografia computorizada é adquirida de forma a realizar a correcção da atenuação dos fotões, assim como a dose a ser entregue em radioterapia. No entanto, esta prática introduz erros aquando do alinhamento entre as imagens de MRI e CT, que serão propagados durante todo o procedimento. Por outro lado, o uso de radiação ionizante e os custos adicionais e tempo de aquisição associado à obtenção de múltiplas modalidades de imagem limitam a aplicação clínica destas práticas. Assim, o seguimento natural prende-se com a completa substituição do uso de CT por MRI. Desta forma, o desenvolvimento de um método para a obtenção de uma imagem equivalente a CT usando MRI é necessário, sendo a imagem resultante designada de pseudo-CT. Vários métodos foram desenvolvidos de forma a construir pseudo-CT, usando métodos baseados na anatomia do paciente ou em métodos de regressão entre CT e MRI. No entanto, no primeiro caso, erros significativos são frequentes devido ao difícil alinhamento entre as imagens em casos em que a geometria do paciente é muito diferente da presente no atlas. No segundo caso, a ausência de sinal no osso cortical em MRI, torna-o indistinguível do ar. Sequências que usam um tempo de eco muito curto são normalmente utilizadas para distinguir osso cortical de ar. No entanto, para áreas com maior dimensão, como a área pélvica, dificuldades relacionadas com o equipamento e com o ruído limitam a sua aplicação nestas áreas. Por outro lado, estes métodos utilizam frequentemente diferentes imagens de MRI de forma a obter diferentes contrastes, aumentando assim o tempo de aquisição das imagens. Nesta dissertação, é proposto um método para a obtenção de um pseudo-CT baseado na combinação de um algoritmo de decomposição de água e gordura e um modelo de regressão de mistura gaussiana para a região pélvica através da aquisição de sequências de MRI convencionais. Desta forma, a aquisição de diferentes contrastes é obtida por pós-processamento das imagens originais. Desta forma, uma imagem ponderada em T1 foi adquirida com 3 tempos de eco. Um algoritmo de decomposição do sinal de ressonância magnética em sinal proveniente de água e gordura foi utilizado, permitindo a obtenção de duas imagens, cada uma representando apenas o sinal da água e gordura, respectivamente. Usando estas duas imagens, uma imagem da fracção de gordura em cada voxel foi também calculada. Por outro lado, usando o primeiro e o terceiro eco foi possível calcular o decaimento de sinal devido a efeitos relacionados com o decaimento T2*. O método para gerar o pseudo-CT baseia- se num modelo de regressão duplo entre as variáveis relacionadas com MRI e CT. Assim, o primeiro modelo aplica-se aos tecidos moles, enquanto que o segundo modelo se aplica aos tecidos ósseos. A segmentação entre estes tecidos foi realizada através da delineação manual dos tecidos ósseos. No caso do modelo de regressão para os tecidos moles, o modelo consiste numa regressão polinomial entre as imagens da fracção de gordura e os valores de CT. A ordem do polinómio usada foi obtida pela minimização do erro absoluto médio. No caso do modelo de regressão para os tecidos ósseos, um modelo de regressão de mistura gaussiana foi aplicado usando as imagens de gordura, água, de fracção de gordura e de R2*. Estas variáveis foram selecionadas, uma vez que estudos prévios correlacionam esta com a densidade mineral óssea, que por sua vez está relacionada com as intensidades em CT. A influência de incluir no modelo de regressão informação acerca da vizinhança foi estudada através da inclusão de imagens do desvio padrão nos 27 voxéis na vizinhança das variáveis previamente incluídas no modelo. O número de componentes a usar no modelo de regressão de mistura gaussiana foi obtido através da minimização do critério de Akaike. O pseudo-CT final foi obtido pela sobreposição das imagens obtidas através do duplo modelo de regressão, seguido da aplicação de um filtro gaussiano com desvio padrão de 0.5 de forma a mitigar os erros na segmentação dos tecidos ósseos. Este método foi validado usando imagens da zona pélvica de 6 pacientes usando um procedimento leave-one-out-cross-validation (LOOCV). Durante este procedimento, o modelo foi estimado através das variáveis de 5 pacientes (imagens de treino) e aplicado às variáveis relacionadas com MRI do paciente restante (imagem de validação), de forma a gerar o pseudo-CT. Este procedimento foi repetido para todas as seis combinações de imagens de treino e de validação e os pseudo-CT obtidos foram comparados com a imagem TC correspondente. No caso do modelo para os tecidos moles, verificou-se que a utilização de um polinómio de segundo grau permitia a obtenção de melhores resultados. Da mesma forma, verificou-se que a inclusão de informação acerca da vizinhança permitia uma melhor estimativa dos valores de pseudo-CT no caso dos tecidos ósseos. A segmentação dos tecidos ósseos foi considerada adequada uma vez que o valor médio do coeficiente de Dice entre estes tecidos e o osso em CT foi de 0.91 ±0.02. O valor médio do erro absoluto entre o pseudo-CT e a correspondente CT para todos os pacientes foi de 37.76±3.11 HU, enquanto que no caso dos tecidos ósseos o valor foi de 96.61±10.49 HU. Um erro médio de -2.68 ± 6.32 HU foi obtido, denotando a presença de bias no processo. Por outro lado, valores médios de peak-to-signal-noise-ratio (PSNR) e strucutre similarity índex (SSIM) de 23.92±1.62 dB e 0.91±0.01 foram obtidos, respectivamente. Os maiores erros foram encontrados no recto, uma vez que o ar não foi considerado neste método, nas interfaces entre diferentes tecidos, devido a erros no alinhamento das imagens, e nos tecidos ósseos. Desta forma, o método de obtenção de um pseudo-CT proposto nesta dissertação demonstrou ter potencial para permitir uma correcta estimativa da intensidade em CT. Os resultados obtidos demonstram uma melhoria significativa quando comparados com outros métodos encontrados na literatura que se baseiam num método relacionado com a intensidade, enquanto que se encontram na mesma ordem de magnitude de métodos baseados na anatomia do paciente. Para além disso, quando comparados com os primeiros, este método tem a vantagem de apenas uma sequência MRI ser utilizada, levando a uma redução no tempo de aquisição e nos custos associados. Por outro lado, a principal limitação deste método prende-se com a segmentação manual dos tecidos ósseos, o que dificulta a sua implementação clínica. Desta forma, o desenvolvimento de técnicas de segmentação automáticas dos tecidos ósseos torna-se necessária, sendo exemplos destas técnicas a criação de um shape model ou através da segmentação baseada num atlas. A combinação destes métodos com o método descrito nesta dissertação pode permitir a obtenção de uma alternativa às imagens de CT para o cálculo das doses em radioterapia e correcção de atenuação em PET-MRI.Purpose: Methods for deriving computed tomography (CT) equivalent information from MRI are needed for attenuation correction in PET-MRI applications, as well as for dose planning in MRI based radiation therapy workflows, due to the lack of correlation between the MR signal and the electron density of different tissues. This dissertation presents a method to generate a pseudo-CT from MR images acquired with a conventional MR pulse sequence. Methods: A T1-weighted Fast Field Echo sequence with 3 echo times was used. A 3-point water-fat decomposition algorithm was applied to the original MR images to obtain water and fat-only images as well as a quantitative fat fraction image. A R2* image was calculated using a mono-exponential fit between the first and third echo of the original MR images. The method for generating the pseudo-CT includes a dual-model regression between the MR features and a matched CT image. The first model was applied to soft tissues, while the second-model was applied to the bone anatomy that were previously segmented. The soft-tissue regression model consists of a second-order polynomial regression between the fat fraction values in soft tissue and the HU values in the CT image, while the bone regression model consists of a Gaussian mixture regression including the water, fat, fat fraction and R2* values in bone tissues. Neighbourhood information was also included in the bone regression model by calculating an image of the standard deviation of 27-neighbourhood of each voxel in each MR related feature. The final pseudo-CT was generated by combining the pseudo-CTs from both models followed by the application of a Gaussian filter for additional smoothing. This method was validated using datasets covering the pelvic area of six patients and applying a leave-one-out-cross-validation (LOOCV) procedure. During LOOCV, the model was estimated from the MR related features and the CT data of 5 patients (training set) and applied to the MR features of the remaining patient (validation set) to generate a pseudo-CT image. This procedure was repeated for the all six training and validation data combinations and the pseudo-CTs were compared to the corresponding CT image. Results: The average mean absolute error for the HU values in the body for all patients was 37.76±3.11 HU, while the average mean absolute error in the bone anatomy was 96.61±10.49 HU. No large differences in method accuracy were noted for the different patients, except for the air in the rectum which was classified as soft tissue. The largest errors were found in the rectum and in the interfaces between different tissue types. Conclusions: The pseudo-CT generation method here proposed has the potential to provide an accurate estimation of HU values. The results here reported are substantially better than other voxel-based methods proposed. However, they are in the same range as the results presented in anatomy-based methods. Further investigation in automatic MRI bone segmentation methods is necessary to allow the automatic application of this method into clinical practice. The combination of these automatic bone segmentation methods with the model here reported is expected to provide an alternative to CT images for dose planning in radiotherapy and attenuation correction in PET-MRI

Graph-based analysis of brain structural MRI data in Multiple System Atrophy

Il lavoro che ho sviluppato presso l’unità di RM funzionale del Policlinico S.Orsola-Malpighi, DIBINEM, è incentrato sull’analisi dei dati strutturali di risonanza magnetica mediante l’utilizzo della graph theory, con lo scopo di valutare eventuali differenze tra un campione di pazienti affetti da Atrofia Multi Sistemica (MSA) e uno di controlli sani (HC). L’MSA è una patologia neurodegenerativa sporadica e progressiva. Essa si divide in due sottotipi: MSA-P ed MSA-C. Circa un terzo delle persone affette da MSA sperimentano una particolare apnea respiratoria, chiamata Stridor. Nello studio sono stati confrontati tra loro tre coppie di gruppi: HC vs MSA, No-stridor vs Stridor, e MSA-C vs MSA-P. I grafi sono strutture matematiche definite da nodi e links, in campo neurologico, la graph theory è usata con lo scopo di comprendere il funzionamento del cervello visto come network. L'approccio qui utilizzato è bastato sulla correlazione volumetriche tra le diverse regioni del cervello. Per costruire un grafo per ogni gruppo il primo step è stato ottenere la parcellizzazione delle immagini cerebrali, in seguito sono stati valutati i volumi delle regioni cerebrali, e in fine le correlazioni tra esse. Una volta costruiti i grafi è stato possibile calcolare i parametri topologici che ne caratterizzano struttura ed organizzazione. Nei vari confronti fatti non sono state riscontrate differenze nelle proprietà globali del network. L’analisi regionale invece ha evidenziato un'alterazione tra MSA e HC relativa a regioni che appartengono al network centrale autonomico, particolarmente colpito dalla malattia. Sono state inoltre riscontrate alterazioni nella organizzazione modulare dei gruppi presi in esame. Questa analisi ha mostrato la possibilità di indagare la funzionalità dei network cerebrali e della loro architettura modulare con misure strutturali quali la covarianza dei volumi delle varie regioni cerebrali in gruppi di soggetti

Virtual histology of multi-modal magnetic resonance imaging of cerebral cortex in young men

Neurobiology underlying inter-regional variations - across the human cerebral cortex - in measures derived with multi-modal magnetic resonance imaging (MRI) is poorly understood. Here, we characterize inter-regional variations in a large number of such measures, including T1 and T2 relaxation times, myelin water fraction (MWF), T1w/T2w ratio, mean diffusivity (MD), fractional anisotropy (FA), magnetization transfer ratio (MTR) and cortical thickness. We then employ a virtual-histology approach and relate these inter-regional profiles to those in cell-specific gene expression. Virtual histology revealed that most MRI-derived measures, including T1, T2 relaxation time, MWF, T1w/T2w ratio, MTR, FA and cortical thickness, are associated with expression profiles of genes specific to CA1 pyramidal cells; these genes are enriched in biological processes related to dendritic arborisation. In addition, T2 relaxation time, MWF and T1w/T2w ratio are associated with oligodendrocyte-specific gene-expression profiles, supporting their use as measures sensitive to intra-cortical myelin. MWF contributes more variance than T1w/T2w ratio to the mean oligodendrocyte expression profile, suggesting greater sensitivity to myelin. These cell-specific MRI associations may help provide a framework for determining which MRI sequences to acquire in studies with specific neurobiological hypotheses

Combining global and local information for the segmentation of MR images of the brain

Magnetic resonance imaging can provide high resolution volumetric images of the brain with exceptional soft tissue contrast. These factors allow the complex structure of the brain to be clearly visualised. This has lead to the development of quantitative methods to analyse neuroanatomical structures. In turn, this has promoted the use of computational methods to automate and improve these techniques. This thesis investigates methods to accurately segment MRI images of the brain. The use of global and local image information is considered, where global information includes image intensity distributions, means and variances and local information is based on the relationship between spatially neighbouring voxels. Methods are explored that aim to improve the classification and segmentation of MR images of the brain by combining these elements. Some common artefacts exist in MR brain images that can be seriously detrimental to image analysis methods. Methods to correct for these artifacts are assessed by exploring their effect, first with some well established classification methods and then with methods that combine global information with local information in the form of a Markov random field model. Another characteristic of MR images is the partial volume effect that occurs where signals from different tissues become mixed over the finite volume of a voxel. This effect is demonstrated and quantified using a simulation. Analysis methods that address these issues are tested on simulated and real MR images. They are also applied to study the structure of the temporal lobes in a group of patients with temporal lobe epilepsy. The results emphasise the benefits and limitations of applying these methods to a problem of this nature. The work in this thesis demonstrates the advantages of using global and local information together in the segmentation of MR brain images and proposes a generalised framework that allows this information to be combined in a flexible way