Complexity Analysis of Cortical Surface Detects Changes in Future Alzheimer’s Disease Converters

Alzheimer’s disease (AD) is a neurological disorder that creates neurodegenerative changes at several structural and functional levels in human brain tissue. The fractal dimension (FD) is a quantitative parameter that characterizes the morphometric variability of the human brain. In this study we investigate spherical harmonic-based FD (SHFD), thickness and local gyrification index (LGI) to assess whether they identify cortical surface abnormalities toward the conversion to AD. We study 33 AD patients, 122 mild cognitive impairment (MCI) patients (50 MCI-converters and 29 MCI-non converters) and 32 healthy controls (HC). SHFD, thickness and LGI methodology allowed us to perform not only global but also local level assessments in each cortical surface vertex. First, we found that global SHFD decreased in AD and future MCI-converters compared to HC, and in MCI-converters compared to MCI-non-converters. Second, we found that local white matter SHFD was reduced in AD compared to HC and MCI mainly in medial temporal lobe. Third, local white matter SHFD was significantly reduced in MCI-converters compared to MCI-non-converters in distributed areas, including the medial frontal lobe. Thickness and LGI metrics presented a reduction in AD compared to HC. Thickness was significantly reduced in MCI-converters compared to healthy controls in entorhinal cortex and lateral temporal. In summary, SHFD was the only surface measure showing differences between MCI individuals that will convert or remain stable in the next four years. We suggest that SHFD may be an optimal complement to thickness loss analysis in monitoring longitudinal changes in preclinical and clinical stages of AD

An analysis of MRI derived cortical complexity in premature-born adults : regional patterns, risk factors, and potential significance

Premature birth bears an increased risk for aberrant brain development concerning its structure and function. Cortical complexity (CC) expresses the fractal dimension of the brain surface and changes during neurodevelopment. We hypothesized that CC is altered after premature birth and associated with long-term cognitive development. One-hundred-and-one very premature-born adults (gestational age <32 weeks and/or birth weight <1500 ​g) and 111 term-born adults were assessed by structural MRI and cognitive testing at 26 years of age. CC was measured based on MRI by vertex-wise estimation of fractal dimension. Cognitive performance was measured based on Griffiths-Mental-Development-Scale (at 20 months) and Wechsler-Adult-Intelligence-Scales (at 26 years). In premature-born adults, CC was decreased bilaterally in large lateral temporal and medial parietal clusters. Decreased CC was associated with lower gestational age and birth weight. Furthermore, decreased CC in the medial parietal cortices was linked with reduced full-scale IQ of premature-born adults and mediated the association between cognitive development at 20 months and IQ in adulthood. Results demonstrate that CC is reduced in very premature-born adults in temporoparietal cortices, mediating the impact of prematurity on impaired cognitive development. These data indicate functionally relevant long-term alterations in the brain’s basic geometry of cortical organization in prematurity

Extraction of Structural Metrics from Crossing Fiber Models

Diffusion MRI (dMRI) measurements allow us to infer the microstructural properties of white matter and to reconstruct fiber pathways in-vivo. High angular diffusion imaging (HARDI) allows for the creation of more and more complex local models connecting the microstructure to the measured signal. One of the challenges is the derivation of meaningful metrics describing the underlying structure from the local models. The aim hereby is to increase the specificity of the widely used metric fractional anisotropy (FA) by using the additional information contained within the HARDI data. A local model which is connected directly to the underlying microstructure through the model of a single fiber population is spherical deconvolution. It produces a fiber orientation density function (fODF), which can often be interpreted as superposition of multiple peaks, each associated to one relatively coherent fiber population (bundle). Parameterizing these peaks one is able to disentangle and characterize these bundles. In this work, the fODF peaks are approximated by Bingham distributions, capturing first and second order statistics of the fiber orientations, from which metrics for the parametric quantification of fiber bundles are derived. Meaningful relationships between these measures and the underlying microstructural properties are proposed. The focus lies on metrics derived directly from properties of the Bingham distribution, such as peak length, peak direction, peak spread, integral over the peak, as well as a metric derived from the comparison of the largest peaks, which probes the complexity of the underlying microstructure. These metrics are compared to the conventionally used fractional anisotropy (FA) and it is shown how they may help to increase the specificity of the characterization of microstructural properties. Visualization of the micro-structural arrangement is another application of dMRI. This is done by using tractography to propagate the fiber layout, extracted from the local model, in each voxel. In practice most tractography algorithms use little of the additional information gained from HARDI based local models aside from the reconstructed fiber bundle directions. In this work an approach to tractography based on the Bingham parameterization of the fODF is introduced. For each of the fiber populations present in a voxel the diffusion signal and tensor are computed. Then tensor deflection tractography is performed. This allows incorporating the complete bundle information, performing local interpolation as well as using multiple directions per voxel for generating tracts. Another aspect of this work is the investigation of the spherical harmonic representation which is used most commonly for the fODF by means of the parameters derived from the Bingham distribution fit. Here a strong connection between the approximation errors in the spherical representation of the Dirac delta function and the distribution of crossing angles recovered from the fODF was discovered. The final aspect of this work is the application of the metrics derived from the Bingham fit to a number of fetal datasets for quantifying the brain’s development. This is done by introducing the Gini-coefficient as a metric describing the brain’s age

Structural brain complexity and cognitive decline in late life : A longitudinal study in the Aberdeen 1936 Birth Cohort

Copyright © 2014 Elsevier Inc. All rights reserved.Peer reviewedPostprin

Development of cortical shape in the human brain from 6 to 24months of age via a novel measure of shape complexity

The quantification of local surface morphology in the human cortex is important for examining population differences as well as developmental changes in neurodegenerative or neurodevelopmental disorders. We propose a novel cortical shape measure, referred to as the ‘shape complexity index’ (SCI), that represents localized shape complexity as the difference between the observed distributions of local surface topology, as quantified by the shape index (SI) measure, to its best fitting simple topological model within a given neighborhood. We apply a relatively small, adaptive geodesic kernel to calculate the SCI. Due to the small size of the kernel, the proposed SCI measure captures fine differences of cortical shape. With this novel cortical feature, we aim to capture comparatively small local surface changes that capture a) the widening versus deepening of sulcal and gyral regions, as well as b) the emergence and development of secondary and tertiary sulci. Current cortical shape measures, such as the gyrification index (GI) or intrinsic curvature measures, investigate the cortical surface at a different scale and are less well suited to capture these particular cortical surface changes. In our experiments, the proposed SCI demonstrates higher complexity in the gyral/sulcal wall regions, lower complexity in wider gyral ridges and lowest complexity in wider sulcal fundus regions. In early postnatal brain development, our experiments show that SCI reveals a pattern of increased cortical shape complexity with age, as well as sexual dimorphisms in the insula, middle cingulate, parieto-occipital sulcal and Broca's regions. Overall, sex differences were greatest at 6 months of age and were reduced at 24 months, with the difference pattern switching from higher complexity in males at 6 months to higher complexity in females at 24months. This is the first study of longitudinal, cortical complexity maturation and sex differences, in the early postnatal period from 6 to 24 months of age with fine scale, cortical shape measures. These results provide information that complement previous studies of gyrification index in early brain development

Visual Exploration And Information Analytics Of High-Dimensional Medical Images

Data visualization has transformed how we analyze increasingly large and complex data sets. Advanced visual tools logically represent data in a way that communicates the most important information inherent within it and culminate the analysis with an insightful conclusion. Automated analysis disciplines - such as data mining, machine learning, and statistics - have traditionally been the most dominant fields for data analysis. It has been complemented with a near-ubiquitous adoption of specialized hardware and software environments that handle the storage, retrieval, and pre- and postprocessing of digital data. The addition of interactive visualization tools allows an active human participant in the model creation process. The advantage is a data-driven approach where the constraints and assumptions of the model can be explored and chosen based on human insight and confirmed on demand by the analytic system. This translates to a better understanding of data and a more effective knowledge discovery. This trend has become very popular across various domains, not limited to machine learning, simulation, computer vision, genetics, stock market, data mining, and geography. In this dissertation, we highlight the role of visualization within the context of medical image analysis in the field of neuroimaging. The analysis of brain images has uncovered amazing traits about its underlying dynamics. Multiple image modalities capture qualitatively different internal brain mechanisms and abstract it within the information space of that modality. Computational studies based on these modalities help correlate the high-level brain function measurements with abnormal human behavior. These functional maps are easily projected in the physical space through accurate 3-D brain reconstructions and visualized in excellent detail from different anatomical vantage points. Statistical models built for comparative analysis across subject groups test for significant variance within the features and localize abnormal behaviors contextualizing the high-level brain activity. Currently, the task of identifying the features is based on empirical evidence, and preparing data for testing is time-consuming. Correlations among features are usually ignored due to lack of insight. With a multitude of features available and with new emerging modalities appearing, the process of identifying the salient features and their interdependencies becomes more difficult to perceive. This limits the analysis only to certain discernible features, thus limiting human judgments regarding the most important process that governs the symptom and hinders prediction. These shortcomings can be addressed using an analytical system that leverages data-driven techniques for guiding the user toward discovering relevant hypotheses. The research contributions within this dissertation encompass multidisciplinary fields of study not limited to geometry processing, computer vision, and 3-D visualization. However, the principal achievement of this research is the design and development of an interactive system for multimodality integration of medical images. The research proceeds in various stages, which are important to reach the desired goal. The different stages are briefly described as follows: First, we develop a rigorous geometry computation framework for brain surface matching. The brain is a highly convoluted structure of closed topology. Surface parameterization explicitly captures the non-Euclidean geometry of the cortical surface and helps derive a more accurate registration of brain surfaces. We describe a technique based on conformal parameterization that creates a bijective mapping to the canonical domain, where surface operations can be performed with improved efficiency and feasibility. Subdividing the brain into a finite set of anatomical elements provides the structural basis for a categorical division of anatomical view points and a spatial context for statistical analysis. We present statistically significant results of our analysis into functional and morphological features for a variety of brain disorders. Second, we design and develop an intelligent and interactive system for visual analysis of brain disorders by utilizing the complete feature space across all modalities. Each subdivided anatomical unit is specialized by a vector of features that overlap within that element. The analytical framework provides the necessary interactivity for exploration of salient features and discovering relevant hypotheses. It provides visualization tools for confirming model results and an easy-to-use interface for manipulating parameters for feature selection and filtering. It provides coordinated display views for visualizing multiple features across multiple subject groups, visual representations for highlighting interdependencies and correlations between features, and an efficient data-management solution for maintaining provenance and issuing formal data queries to the back end

Spherical harmonics to quantify cranial asymmetry in deformational plagiocephaly

[EN] Cranial deformation and deformational plagiocephaly (DP) in particular affect an important percentage of infants. The assessment and diagnosis of the deformation are commonly carried by manual measurements that provide low interuser accuracy. Another approach is the use of three-dimensional (3D) models. Nevertheless, in most cases, deformation measurements are carried out manually on the 3D model. It is necessary to develop methodologies for the detection of DP that are automatic, accurate and take profit on the high quantity of information of the 3D models. Spherical harmonics are proposed as a new methodology to identify DP from head 3D models. The ideal fitted ellipsoid for each head is computed and the orthogonal distances between head and ellipsoid are obtained. Finally, the distances are modelled using spherical harmonics. Spherical harmonic coefficients of degree 2 and order - 2 are identified as the correct ones to represent the asymmetry characteristic of DP. The obtained coefficient is compared to other anthropometric deformation indexes, such as Asymmetry Index, Oblique Cranial Length Ratio, Posterior Asymmetry Index and Anterior Asymmetry Index. The coefficient of degree 2 and order - 2 with a maximum degree of 4 is found to provide better results than the commonly computed anthropometric indexes in the detection of DP.This article was funded by Instituto de Salud Carlos III and European Regional Development Fund (FEDER) (Grant no. PI18/00881).Grieb, J.; Barbero-García, I.; Lerma, JL. (2022). Spherical harmonics to quantify cranial asymmetry in deformational plagiocephaly. Scientific Reports. 12(1):1-10. https://doi.org/10.1038/s41598-021-04181-z11012

Integration of multi-shell diffusion imaging derived metrics in tractography reconstructions of clinical data

Tese de mestrado integrado Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica), Universidade de Lisboa, Faculdade de Ciências, 2019Nos últimos anos, com o rápido avanço das técnicas imagiológicas, a oportunidade de mapear o cérebro humano in vivo com uma resolução sem precedentes tornou-se realidade, permanecendo ainda hoje como uma das áreas de maior interesse da neurociência. Sabendo que o movimento natural das moléculas de água nos tecidos biológicos é altamente influenciado pelo ambiente microestrutural envolvente, e que a anisotropia que este processo aleatório assume na matéria branca pode ser explorada com o intuito de inferir características importantes associadas ao tecido neuronal, a ressonância magnética ponderada por difusão (dMRI, do inglês “Diffusion-Weighted Magnetic Resonance Imaging") afirmou-se como a técnica de imagem mais amplamente utilizada para a investigação in vivo e não invasiva da conectividade cerebral. A primeira técnica padrão de dMRI foi a imagiologia por tensor de difusão (DTI, do inglês "Diffusion Tensor Imaging"). Implementada com a capacidade de fornecer sensibilidade à microestrutura do tecido, esta técnica permite extrair informação acerca da tridimensionalidade da distribuição da difusão de moléculas de água através da aplicação de seis gradientes de difusão não colineares entre si. Além da difusividade média (MD, do inglês "Mean Diffusivity"), é também possível extrair outros índices microestruturais, como a anisotropia fraccional (FA, do inglês "Fractional Anisotropy"), que fornece informação acerca da percentagem de difusão anisotrópica num determinado voxel. Ambas as métricas são amplamente utilizadas como medidas de alterações microestruturais, todavia, apesar da sua sensibilidade, estes marcadores não são específicos quanto às características individuais da microestrutura tecidual. Regiões com reduzida FA podem camuflar regiões de conformação de cruzamento de fibras, ou fibras muito anguladas, que a DTI não consegue resolver. A razão para esta limitação reside no número reduzido de diferentes direções de difusão que são exploradas, assim como no pressuposto de que a distribuição das moléculas de água é gaussiana, o que não é necessariamente verdade. De forma alternativa e com o intuito de tais limitações serem ultrapassadas, é possível implementar uma representação matemática do sinal adquirido de forma a explorar o propagador de difusão, da qual a imagiologia por ressonância magnética do propagador aparente médio (MAP-MRI, do inglês “Mean Apparent Propagator Magnetic Resonance Imaging”) é exemplo. Esta técnica analítica caracteriza-se pelo cálculo da função de densidade de probabilidade associada ao deslocamento de spin, o que permite descrever o caráter não-gaussiano do processo de difusão tridimensional e quantificar índices escalares inerentes ao processo de difusão, os quais sublinham as características complexas intrínsecas à microestrutura do tecido. Estes parâmetros incluem o deslocamento médio quadrático (MSD, em inglês “mean square displacement”), a probabilidade de retorno à origem (RTOP, do inglês “return-to-the origin probability”) e suas variantes de difusão em uma e duas dimensões – a probabilidade de retorno ao plano (RTPP, do inglês “return-to-the plane probability”) e a probabilidade de retorno ao eixo (RTAP, do inglês “return-to-the axis probability”), respetivamente. Em resposta às limitações da DTI associadas à falta de especificidade para distinguir características microestruturais dos tecidos, surgiu ainda o modelo de Dispersão de Orientação de Neurite e Imagem de Densidade (NODDI, do inglês “Neurite Orientation Dispersion and Density Imaging”), o qual utiliza o processo de difusão para estimar a morfologia das neurites. Tendo como premissa subjacente que o sinal de difusão pode ser definido pela soma da contribuição dos sinais de diferentes compartimentos, este modelo biofísico diferencia o espaço intra e extracelular o que, por sua vez, permite quantificar a dispersão e densidade das neurites. Deste modo, dois parâmetros intrínsecos à microestrutura envolvente podem ser calculados: a densidade neurítica e o índice de dispersão da orientação das neurites. No entanto, de forma a garantir a viabilidade clínica do modelo, este pode ser aplicado por meio do método AMICO (do inglês “Accelerated Microstructure Imaging via Convex Optimization”) através do seu ajuste linear, o que permite o cálculo do índice de dispersão da orientação das neurites (ODI, do inglês “Orientation Dispersion Index”), da fração de volume intracelular (ICVF do inglês, “Intracellular Volume Fraction”), e da fração de volume isotrópico (ISOVF, do inglês “Isotropic Volume Fraction”). O estudo da configuração arquitetural das estruturas cerebrais in vivo, por meio da dMRI associada aos métodos de tractografia, permitiu a reconstrução não invasiva das fibras neuronais e a exploração da informação direcional inerente às mesmas, sendo que o seu estudo tem revelado uma enorme expansão por meio do estabelecimento de marcadores biológicos perante a presença de diversas condições patológicas. O objetivo principal desta dissertação prende-se com existência de uma variação proeminentenas métricas de difusão ao longo dos tratos de matéria branca no cérebro humano. Atualmente, a maioriados estudos de tractografia tem por base uma abordagem que se resume à análise do valor escalar médio da métrica de difusão para a estrutura cerebral em estudo, pelo que se tem verificado um crescente interesse na utilização de métodos que considerem a extensão da variabilidade nas métricas de difusão ao longo dos tratos de modo a providenciarem um maior nível de detalhe ao nível do processo de difusão, evitando interpretações erróneas dos parâmetros microestruturais. Desta forma, em primeiro lugar, foi desenvolvido uma análise ao longo dos tratos de matéria branca, tendo por base a variação dos valores assumidos pelos parâmetros microestruturais acima mencionados. No presente estudo foi possível demonstrar a eficácia de tal abordagem ao longo de três tratos de matéria de branca (fascículo arqueado, trato corticoespinhal, e corpo caloso), para além de permitir, através da variância assumida pelos diversos parâmetros microestruturais, o estudo detalhado de regiões anatómicas que assumem uma distribuição complexa de múltiplos conjuntos populacionais de fibras, como é o caso do centro semioval, o qual constitui uma região de cruzamento de fibras provenientes dos três tratos de matéria branca em estudo. De seguida, esta técnica foi utilizada com sucesso na identificação de diferenças microestruturais por meio do estudo dos diversos parâmetros de difusão em pacientes com diagnóstico prévio de epilepsia no lobo temporal (TLE, do inglês “Temporal Lobe Epilepsy”), com foco epiléptico localizado no hemisfério esquerdo, e controlos. O estudo do ambiente microestrutural por meio dos múltiplos mapas escalares permitiu averiguar a alteração do processo de difusão e/ou anisotropia, associadas ao efeito fisiopatológico da TLE na organização da matéria branca. Os resultados revelaram diferenças localizadas, as quais se traduziram num aumento da difusividade e redução da anisotropia do processo de difusão ao longo dos tratos em estudo dos pacientes com TLE, sugerindo deste modo uma perda na organização das diversas estruturas anatómicas e a expansão do espaço extracelular face aos controlos. Verificou-se ainda que pacientes com esta condição neurológica sofrem de alterações microestruturais que afetam redes cerebrais em grande escala, envolvendo regiões temporais e extratemporais de ambos os hemisférios. Adicionalmente, aplicada como técnica capaz de investigar padrões de mudança na matéria branca, procedeu-se à realização de um estudo assente na estatística espacial baseada no trato (TBSS, do inglês “Tract-Based Spatial Statistics”). Após a exploração das diversas métricas de difusão, os pacientes com TLE (com lateralização à esquerda) demonstraram alterações no processo de difusão, ilustradas pelos diversos padrões de mudança microestrutural de cada métrica em estudo, concordantes com os resultados anteriormente aferidos pela análise ao longo do trato. Por fim, uma análise baseada em fixel (FBA, do inglês “Fixel-Based Analysis”) foi realizada, a qual permitiu uma análise estatística abrangente de medidas quantitativas da matéria branca, com o intuito de detetar alterações no volume intra-axonal por variação na densidade intra-voxel e/ou reorganização da morfologia macroscópica. Para identificar tais diferenças entre pacientes e controlos, três parâmetros foram considerados: densidade das fibras (FD, do inglês “Fibre Density”), seção transversal do feixe de fibras (FC, do inglês “Fibre-bundle Cross-section”), e densidade de fibras e seção transversal (FDC, do inglês “Fibre Density and Cross-section). Reduções na FD, FC e FDC foram identificadas em pacientes com TLE (com lateralização à esquerda) em comparação com os controlos, o que está de acordo com as mudanças microestruturais que resultam do processo de degeneração que afeta as estruturas de matéria branca com a perda de axónios na presença de uma condição neuropatológica como a TLE. Apesar do resultado final positivo, tendo em conta a meta previamente estabelecida, está aberto o caminho para o seu aperfeiçoamento, tendo em vista as direções futuras que emergem naturalmente desta dissertação. Como exemplo disso, poder-se-á recorrer ao estudo pormenorizado das metodologias técnicas associadas à abordagem apresentada que tem por base a análise das métricas de difusão ao longo dos tratos de matéria branca, uma vez que o desvio padrão associado a cada valor atribuído pelas diversas métricas foi significativo, o que de alguma forma poderá ter influenciado os resultados e, consequentemente, as conclusões deles extraídas, tendo em vista a sua viabilidade enquanto aplicação clínica. Como nota final, gostaria apenas de salientar que a imagiologia por difusão e, em particular, a tractografia têm ainda muito espaço para progredir. A veracidade desta afirmação traduz-se pela existência de uma grande variedade de modelos e algoritmos implementados, bem como de técnicas e metodologias de análise à informação microestrutural retida tendo por base o perfil de difusão que carateriza cada trato em estudo, sem que no entanto, exista consenso na comunidade científica acerca da melhor abordagem a seguir.Diffusion-weighted magnetic resonance imaging (dMRI) is a non-invasive imaging method which has been successfully applied to study white matter (WM) in order to determine physiological information and infer tissue microstructure. The human body is filled with barriers affecting the mobility of molecules and preventing it from being constant in different directions (anisotropic diffusion). In the brain, the sources for this anisotropy arise from dense packing axons and from the myelin sheath that surrounds them. Diffusion Tensor Imaging (DTI) is widely used to extract fibre directions from diffusion data, but it fails in regions containing multiple fibre orientations. The constrained spherical deconvolution technique had been proposed to address this limitation. It provides an estimate of the fibre orientation distribution that is robust to noise whilst preserving angular resolution. As a noninvasive technique that generates a three-dimensional reconstruction of neuronal fibres, tractography is able to map in vivo the human WM based on the reconstruct of the fibre orientations from the diffusion profile. Most of the tractography studies use a “tract-averaged” approach to analysis, however it is well known that there is a prominent variation in diffusion metrics within WM tracts. In this study we address the challenge of defining a microstructural signature taking into account the potentially rich anatomical variation in diffusion metrics along the tracts. Therefore, a workflow to conduct along-tract analysis of WM tracts (namely, arcuate fasciculus, corticospinal and corpus callosum) and integrate not only DTI derived measures, but also more advanced parameters from Mean Apparent Propagator-Magnetic Resonance Imaging (MAP-MRI) and Neurite Orientation Dispersion and Density Imaging (NODDI) model, was developed across healthy controls and patients with Temporal Lobe Epilepsy (TLE). Beyond the true biological variation in diffusion properties along tracts, this technique was applied to show that it allows a more detailed analysis of small regions-of-interest extracted from the tract in order to avoid fibres from WM pathways in the neighbourhood, which might lead to equivocal biological interpretations of the microstructural parameters. Consequently, the along-tract streamline distribution from the centrum semiovale, which is known to be a complex fibre geometry with multiple fibres populations from arcuate fasciculus, corticospinal and corpus callosum, was investigated. Finally, to validate our approach and highlight the strength of this extensible framework, two other methods were implemented in order to support the conclusions derived from the along-tract analysis computed between-groups. Firstly, a tract-based spatial statistics (TBSS) analysis was performed to study the WM change patterns across the whole brain in patients with TLE, and explore the alteration of multiple diffusion metrics. This voxel-based technique provides a powerful and objective method to perform multi-subject comparison, based on voxel-wise statistics of diffusion metrics but simultaneous aiming to minimize the effects of misalignment using a conventional voxel-based analysis method. With this in mind, the results showed increased diffusivity and reduced diffusion anisotropy, suggesting a loss of structural organization and expansion of the extracellular space in the presence of neuropathological condition as TLE. Secondly, the fixel-based analysis (FBA) was performed allowing a comprehensive statistical analysis of WM quantitative measures in order to have access to changes that may result within WM tracts in the presence of TLE. The microstructural/macrostructural changes in WM tracts of TLE patients were observed in temporal and extratemporal regions of both hemispheres, which agrees with the concept that epilepsy is a network disorder