Generalised coherent point drift for group-wise multi-dimensional analysis of diffusion brain MRI data

A probabilistic framework for registering generalised point sets comprising multiple voxel-wise data features such as positions, orientations and scalar-valued quantities, is proposed. It is employed for the analysis of magnetic resonance diffusion tensor image (DTI)-derived quantities, such as fractional anisotropy (FA) and fibre orientation, across multiple subjects. A hybrid Student’s t-Watson-Gaussian mixture model-based non-rigid registration framework is formulated for the joint registration and clustering of voxel-wise DTI-derived data, acquired from multiple subjects. The proposed approach jointly estimates the non-rigid transformations necessary to register an unbiased mean template (represented as a 7-dimensional hybrid point set comprising spatial positions, fibre orientations and FA values) to white matter regions of interest (ROIs), and approximates the joint distribution of voxel spatial positions, their associated principal diffusion axes, and FA. Specific white matter ROIs, namely, the corpus callosum and cingulum, are analysed across healthy control (HC) subjects (K = 20 samples) and patients diagnosed with mild cognitive impairment (MCI) (K = 20 samples) or Alzheimer’s disease (AD) (K = 20 samples) using the proposed framework, facilitating inter-group comparisons of FA and fibre orientations. Group-wise analyses of the latter is not afforded by conventional approaches such as tract-based spatial statistics (TBSS) and voxel-based morphometry (VBM)

Unsupervised Clustering of Neural Pathways

International audienceDiffusion-weighted Magnetic Resonance Imaging (dMRI) can unveil the microstructure of the brain white matter. The analysis of the anisotropy observed in the dMRI and highlighted by tractography methods can help to understand the pattern of connections between brain regions and characterize neurological diseases. Because of the amount of information produced by such analyses and the errors carried by the reconstruction step, it is necessary to simplify this output. Clustering algorithms can be used to group samples that are similar according to a given metric.In this work, we address important questions related to the use of clustering for brain fibers obtained by dMRI, namely: i) what is the most adequate metric to quantify the similarity between brain fiber tracts? ii) How to select the best clustering algorithm and parametrization among standard possibilities? While trying to solve these questions, we perform a new contribution: we show how to combine the well-known K-means clustering algorithm with various metrics while keeping an efficient procedure. We analyze the performance and usability of the ensuing algorithms on a dataset of ten subjects. We show that the association of K-means with Point Density Model, a recently proposed metric to analyze geometric structures, outperforms other state-of-the-art solutions

Manifold-driven Grouping of Skeletal Muscle Fibers

In this report, we present a manifold clustering method for the classification of fibers obtained from diffusion tensor images (DTI) of the human skeletal muscle. To this end, we propose the use of angular Hilbertian metrics between multivariate normal distributions to define a family of distances between tensors that we generalize to fibers. The obtained metrics between fiber tracts encompasses both diffusion and localization information. As far as clustering is concerned, we use two methods. The first approach is based on diffusion maps and k-means clustering in the spectral embedding space. The second approach uses a linear programming formulation of prototype-based clustering. This formulation allows for classification over manifolds without the necessity to embed the data in low dimensional spaces and determines automatically the number of clusters. The experimental validation of the proposed framework is done using a manually annotated significant dataset of DTI of the calf muscle for healthy and diseased subjects

Automatic atlas-based segmentation of brain white-matter in neonates at risk for neurodevelopmental disorders

Dissertação de Mestrado Integrado em Engenharia Biomédica apresentada à Faculdade de Ciências e Tecnologia da Universidade de Coimbra.Na Europa, mais de meio milhão de bebés nasce prematuramente por ano. Os recémnascidos com menos de 32 semanas de gestação estão especialmente em risco para desordens de desenvolvimento neuronal. Para estes bebés, os principais problemas de desenvolvimento surgem a nível cognitivo (40%). Reabilitação é possível, principalmente se for feita nos primeiros tempos de vida quando o cérebro é caracterizado pela sua enorme plasticidade. No entanto, não existem bio-marcadores que possibilitem prever quais os bebés prematuros que estão em risco. Este trabalho tem como objetivo analisar a maturação da matéria branca do cérebro em bebés prematuros e investigar a sua usabilidade como possível marcador para desordens de desenvolvimento neuronal. Um pipeline automático para segmentação atlas-based de matéria branca visualizada com tratografia de Diffusion Tensor Imaging (DTI) foi implementado. O atlas usado foi construído previamente com tratografias de bebés prematuros em term equivalente age (TEA). Principais contribuições correspondem à automatização do pipeline e desenvolvimento de algoritmos específicos para tratografias neonatais para: registo entre tratografias, skull-stripping e sampling. O algoritmo para registo entre tratografias foi inspirado no trabalho de O’Donnell (2012). Este tipo de registo utiliza a informação relativa à conectividade global de regiões de matéria branca no cérebro, característica dos dados de tratografia. Em comparação com métodos de segmentação manual, este método consome menos tempo e é menos dependente do utilizador. Os resultados são promissores, apenas 12% das segmentações contêm mais de 30% de fibras erroneamente segmentadas por estrutura anatómica. A performance da segmentação não foi influenciada pela presença de patologias da matéria branca nos pacientes. As estruturas anatómicas automaticamente segmentadas do corpus callosum foram também analisadas relativamente aos seus volumes e valores de anisotropia. Volume e difusão média são significamente correlacionados com a intensidade de patologia da matéria branca. Estes resultados estão de acordo com descobertas prévias sobre como patologia na matéria branca influencia os valores de anisotropia. Em conclusão, tratografia neonatal pode ser segmentada nas principais estruturas anatómicas de interesse para estudo de desordens do desenvolvimento neuronal

Evaluation of kernel low-rank compressed sensing in preclinical diffusion magnetic resonance imaging

Compressed sensing (CS) is widely used to accelerate clinical diffusion MRI acquisitions, but it is not widely used in preclinical settings yet. In this study, we optimized and compared several CS reconstruction methods for diffusion imaging. Different undersampling patterns and two reconstruction approaches were evaluated: conventional CS, based on Berkeley Advanced Reconstruction Toolbox (BART-CS) toolbox, and a new kernel low-rank (KLR)-CS, based on kernel principal component analysis and low-resolution-phase (LRP) maps. 3D CS acquisitions were performed at 9.4T using a 4-element cryocoil on mice (wild type and a MAP6 knockout). Comparison metrics were error and structural similarity index measure (SSIM) on fractional anisotropy (FA) and mean diffusivity (MD), as well as reconstructions of the anterior commissure and fornix. Acceleration factors (AF) up to 6 were considered. In the case of retrospective undersampling, the proposed KLR-CS outperformed BART-CS up to AF = 6 for FA and MD maps and tractography. For instance, for AF = 4, the maximum errors were, respectively, 8.0% for BART-CS and 4.9% for KLR-CS, considering both FA and MD in the corpus callosum. Regarding undersampled acquisitions, these maximum errors became, respectively, 10.5% for BART-CS and 7.0% for KLR-CS. This difference between simulations and acquisitions arose mainly from repetition noise, but also from differences in resonance frequency drift, signal-to-noise ratio, and in reconstruction noise. Despite this increased error, fully sampled and AF = 2 yielded comparable results for FA, MD and tractography, and AF = 4 showed minor faults. Altogether, KLR-CS based on LRP maps seems a robust approach to accelerate preclinical diffusion MRI and thereby limit the effect of the frequency drift

Manifold-driven Grouping of Skeletal Muscle Fibers

In this report, we present a manifold clustering method for the classification of fibers obtained from diffusion tensor images (DTI) of the human skeletal muscle. To this end, we propose the use of angular Hilbertian metrics between multivariate normal distributions to define a family of distances between tensors that we generalize to fibers. The obtained metrics between fiber tracts encompasses both diffusion and localization information. As far as clustering is concerned, we use two methods. The first approach is based on diffusion maps and k-means clustering in the spectral embedding space. The second approach uses a linear programming formulation of prototype-based clustering. This formulation allows for classification over manifolds without the necessity to embed the data in low dimensional spaces and determines automatically the number of clusters. The experimental validation of the proposed framework is done using a manually annotated significant dataset of DTI of the calf muscle for healthy and diseased subjects

Joint impact on attention, alertness and inhibition of lesions at a frontal white matter crossroad.

In everyday life, information from different cognitive domains - such as visuospatial attention, alertness, and inhibition - needs to be integrated between different brain regions. Early models suggested that completely segregated brain networks control these three cognitive domains. However, more recent accounts, mainly based on neuroimaging data in healthy participants, indicate that different tasks lead to specific patterns of activation within the same, higher-order and "multiple-demand" network. If so, then a lesion to critical substrates of this common network should determine a concomitant impairment in all three cognitive domains. The aim of the present study was to critically investigate this hypothesis, i.e., to identify focal stroke lesions within the network that can concomitantly impact visuospatial attention, alertness and inhibition. We studied an unselected sample of 60 first-ever right-hemispheric, subacute stroke patients using a data-driven, bottom-up approach. Patients performed 12 standardized neuropsychological and oculomotor tests, four per cognitive domain. Principal component analyses revealed a strong relationship between all three cognitive domains: 10 of 12 tests loaded on a first, Common Component. Analysis of the neuroanatomical lesion correlates using different approaches (i.e., Voxel-Based and Tractwise Lesion-Symptom Mapping, Disconnectome maps) provided convergent evidence on the association between severe impairment of this Common Component and lesions at the intersection of Superior Longitudinal Fasciculus II and III, Frontal Aslant Tract and, to a lesser extent, the Putamen and Inferior Fronto-Occipital Fasciculus. Moreover, patients with a lesion involving this region were significantly more impaired in daily living cognition, which provides an ecological validation of our results. A probabilistic functional atlas of the multiple-demand network was performed to confirm the potential relationship between patients' lesion substrates and observed cognitive impairments as a function of the MD-network connectivity disruption. These findings show, for the first time, that a lesion to a specific white matter crossroad can determine a concurrent breakdown in all three considered cognitive domains. Our results support the multiple-demand network model, proposing that different cognitive operations depend on specific collaborators and their interaction, within the same underlying neural network. Our findings also extend this hypothesis by showing (1) the contribution of SLF and FAT to the multiple-demand network, and (2) a critical neuroanatomical intersection, crossed by a vast amount of long-range white matter tracts, many of which interconnect cortical areas of the multiple-demand network. The vulnerability of this crossroad to stroke has specific cognitive and clinical consequences; this has the potential to influence future rehabilitative approaches

A comparison of methods for the registration of tractographic fibre images

Diffusion tensor imaging (DTI) and tractography have opened up new avenues in neuroscience and are allowing previously unexplored areas of neuroanatomy and function to be researched