50 research outputs found

    Quantification of 18F-FDG PET kinetic parameters using an image-derived input function and multimodal integration with resting-state fMRI metrics

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    Metabolic demand associated with resting-state brain activity is one of the main focus of neuroscience research. Task-free brain activation has been found to exhibit coherent spatial patterns, and the associated glucose consumption is predominant if compared to task activation. However, a complete characterization of the link between energy and function in the brain is still missing. The aim of this thesis project was to explore novel strategies for the integration between metabolic measures coming from Positron Emission Tomography based on fluorodeoxyglucose ([18F]FDG PET) and functional information extracted from resting-state Functional Magnetic Resonance Imaging (rsfMRI) measures. This was done adopting two different perspectives. On one hand, it was verified how metabolic and functional networks, inferred from time-series correlation across brain regions, relate to each other. On the other hand, across-subject similarity between sets of metabolic parameters and functional features was assessed. The analysis was performed on a dataset provided by Washington University in St.Louis, consisting of non-simultaneous PET and MR acquisitions on a large cohort of subjects. A first part of the work focused on [18F]FDG data. An Image-derived input function (IDIF) was extracted from the internal carotid arteries. This was later used for microparameter estimation with Variational Bayesian approach. Across-subjects correlation matrices were obtained for subjects series of K1 and k3 values. Moreover, average metabolic connectivity matrix was extracted from [18F]FDG parcel-level TACs. Similarly, from fMRI data, average functional connectivity matrix was extracted. Regional Homogeneity (ReHo) and Global Functional Connectivity (GFC) were estimated and across-subjects connectivity matrices were obtained for both parameters. Time-series connectivity matrices coming from both PET and fMRI images were used to assess similarity between metabolic and functional networks, whereas across-subject connectivity matrices were used to compare metabolic and functional parameters. To agevolate comparison, embedding was used on both timeseries and across-subjects connectivity: this was based on application of a gaussian kernel, followed by calculation of the Laplacian Eigenmaps, a nonlinear dimensionality reduction techinque. Resulting manifolds are called gradients in neuroscience, and are commonly used to study functional architecture in the brain. From a network perspective, metabolic and functional gradients exhibited significant correlation, and the regions in which they overlapped the most belong to visual and sensorimotor networks. Similar results were found between all combinations of [18F]FDG microparameters and fMRI features gradients, implying that both local and global functional relationship in the brain may be associated with specific metabolic fingerprints.Metabolic demand associated with resting-state brain activity is one of the main focus of neuroscience research. Task-free brain activation has been found to exhibit coherent spatial patterns, and the associated glucose consumption is predominant if compared to task activation. However, a complete characterization of the link between energy and function in the brain is still missing. The aim of this thesis project was to explore novel strategies for the integration between metabolic measures coming from Positron Emission Tomography based on fluorodeoxyglucose ([18F]FDG PET) and functional information extracted from resting-state Functional Magnetic Resonance Imaging (rsfMRI) measures. This was done adopting two different perspectives. On one hand, it was verified how metabolic and functional networks, inferred from time-series correlation across brain regions, relate to each other. On the other hand, across-subject similarity between sets of metabolic parameters and functional features was assessed. The analysis was performed on a dataset provided by Washington University in St.Louis, consisting of non-simultaneous PET and MR acquisitions on a large cohort of subjects. A first part of the work focused on [18F]FDG data. An Image-derived input function (IDIF) was extracted from the internal carotid arteries. This was later used for microparameter estimation with Variational Bayesian approach. Across-subjects correlation matrices were obtained for subjects series of K1 and k3 values. Moreover, average metabolic connectivity matrix was extracted from [18F]FDG parcel-level TACs. Similarly, from fMRI data, average functional connectivity matrix was extracted. Regional Homogeneity (ReHo) and Global Functional Connectivity (GFC) were estimated and across-subjects connectivity matrices were obtained for both parameters. Time-series connectivity matrices coming from both PET and fMRI images were used to assess similarity between metabolic and functional networks, whereas across-subject connectivity matrices were used to compare metabolic and functional parameters. To agevolate comparison, embedding was used on both timeseries and across-subjects connectivity: this was based on application of a gaussian kernel, followed by calculation of the Laplacian Eigenmaps, a nonlinear dimensionality reduction techinque. Resulting manifolds are called gradients in neuroscience, and are commonly used to study functional architecture in the brain. From a network perspective, metabolic and functional gradients exhibited significant correlation, and the regions in which they overlapped the most belong to visual and sensorimotor networks. Similar results were found between all combinations of [18F]FDG microparameters and fMRI features gradients, implying that both local and global functional relationship in the brain may be associated with specific metabolic fingerprints

    Improving the Tractography Pipeline: on Evaluation, Segmentation, and Visualization

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    Recent advances in tractography allow for connectomes to be constructed in vivo. These have applications for example in brain tumor surgery and understanding of brain development and diseases. The large size of the data produced by these methods lead to a variety problems, including how to evaluate tractography outputs, development of faster processing algorithms for tractography and clustering, and the development of advanced visualization methods for verification and exploration. This thesis presents several advances in these fields. First, an evaluation is presented for the robustness to noise of multiple commonly used tractography algorithms. It employs a Monte–Carlo simulation of measurement noise on a constructed ground truth dataset. As a result of this evaluation, evidence for obustness of global tractography is found, and algorithmic sources of uncertainty are identified. The second contribution is a fast clustering algorithm for tractography data based on k–means and vector fields for representing the flow of each cluster. It is demonstrated that this algorithm can handle large tractography datasets due to its linear time and memory complexity, and that it can effectively integrate interrupted fibers that would be rejected as outliers by other algorithms. Furthermore, a visualization for the exploration of structural connectomes is presented. It uses illustrative rendering techniques for efficient presentation of connecting fiber bundles in context in anatomical space. Visual hints are employed to improve the perception of spatial relations. Finally, a visualization method with application to exploration and verification of probabilistic tractography is presented, which improves on the previously presented Fiber Stippling technique. It is demonstrated that the method is able to show multiple overlapping tracts in context, and correctly present crossing fiber configurations

    Cerebral white matter analysis using diffusion imaging

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    Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2006.Includes bibliographical references (p. 183-198).In this thesis we address the whole-brain tractography segmentation problem. Diffusion magnetic resonance imaging can be used to create a representation of white matter tracts in the brain via a process called tractography. Whole brain tractography outputs thousands of trajectories that each approximate a white matter fiber pathway. Our method performs automatic organization, or segmention, of these trajectories into anatomical regions and gives automatic region correspondence across subjects. Our method enables both the automatic group comparison of white matter anatomy and of its regional diffusion properties, and the creation of consistent white matter visualizations across subjects. We learn a model of common white matter structures by analyzing many registered tractography datasets simultaneously. Each trajectory is represented as a point in a high-dimensional spectral embedding space, and common structures are found by clustering in this space. By annotating the clusters with anatomical labels, we create a model that we call a high-dimensional white matter atlas.(cont.) Our atlas creation method discovers structures corresponding to expected white matter anatomy, such as the corpus callosum, uncinate fasciculus, cingulum bundles, arcuate fasciculus, etc. We show how to extend the spectral clustering solution, stored in the atlas, using the Nystrom method to perform automatic segmentation of tractography from novel subjects. This automatic tractography segmentation gives an automatic region correspondence across subjects when all subjects are labeled using the atlas. We show the resulting automatic region correspondences, demonstrate that our clustering method is reproducible, and show that the automatically segmented regions can be used for robust measurement of fractional anisotropy.by Lauren Jean O'Donnell.Ph.D

    Decoding Task-Based fMRI Data Using Graph Neural Networks, Considering Individual Differences

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    Functional magnetic resonance imaging (fMRI) is a non-invasive technology that provides high spatial resolution in determining the human brain\u27s responses and measures regional brain activity through metabolic changes in blood oxygen consumption associated with neural activity. Task fMRI provides an opportunity to analyze the working mechanisms of the human brain during specific task performance. Over the past several years, a variety of computational methods have been proposed to decode task fMRI data that can identify brain regions associated with different task stimulations. Despite the advances made by these methods, several limitations exist due to graph representations and graph embeddings transferred from task fMRI signals. In the present study, we proposed an end-to-end graph convolutional network by combining the convolutional neural network with graph representation, with three convolutional layers to classify task fMRI data from the Human Connectome Project (302 participants, 22–35 years of age). One goal of this dissertation was to improve classification performance. We applied four of the most widely used node embedding algorithms—NetMF, RandNE, Node2Vec, and Walklets—to automatically extract the structural properties of the nodes in the brain functional graph, then evaluated the performance of the classification model. The empirical results indicated that the proposed GCN framework accurately identified the brain\u27s state in task fMRI data and achieved comparable macro F1 scores of 0.978 and 0.976 with the NetMF and RandNE embedding methods, respectively. Another goal of the dissertation was to assess the effects of individual differences (i.e., gender and fluid intelligence) on classification performance. We tested the proposed GCN framework on sub-datasets divided according to gender and fluid intelligence. Experimental results indicated significant differences in the classification predictions of gender, but not high/low fluid intelligence fMRI data. Our experiments yielded promising results and demonstrated the superior ability of our GCN in modeling task fMRI data

    Graph embedding and geometric deep learning relevance to network biology and structural chemistry

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    Graphs are used as a model of complex relationships among data in biological science since the advent of systems biology in the early 2000. In particular, graph data analysis and graph data mining play an important role in biology interaction networks, where recent techniques of artificial intelligence, usually employed in other type of networks (e.g., social, citations, and trademark networks) aim to implement various data mining tasks including classification, clustering, recommendation, anomaly detection, and link prediction. The commitment and efforts of artificial intelligence research in network biology are motivated by the fact that machine learning techniques are often prohibitively computational demanding, low parallelizable, and ultimately inapplicable, since biological network of realistic size is a large system, which is characterised by a high density of interactions and often with a non-linear dynamics and a non-Euclidean latent geometry. Currently, graph embedding emerges as the new learning paradigm that shifts the tasks of building complex models for classification, clustering, and link prediction to learning an informative representation of the graph data in a vector space so that many graph mining and learning tasks can be more easily performed by employing efficient non-iterative traditional models (e.g., a linear support vector machine for the classification task). The great potential of graph embedding is the main reason of the flourishing of studies in this area and, in particular, the artificial intelligence learning techniques. In this mini review, we give a comprehensive summary of the main graph embedding algorithms in light of the recent burgeoning interest in geometric deep learning

    A survey on classification algorithms of brain images in Alzheimer’s disease based on feature extraction techniques

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    Abstract: Alzheimer’s disease (AD) is one of the most serious neurological disorders for elderly people. AD affected patient experiences severe memory loss. One of the main reasons for memory loss in AD patients is atrophy in the hippocampus, amygdala, etc. Due to the enormous growth of AD patients and the paucity of proper diagnostic tools, detection and classification of AD are considered as a challenging research area. Before a Cognitively normal (CN) person develops symptoms of AD, he may pass through an intermediate stage, commonly known as Mild Cognitive Impairment (MCI). MCI is having two stages, namely StableMCI (SMCI) and Progressive MCI (PMCI). In SMCI, a patient remains stable, whereas, in the case of PMCI, a person gradually develops few symptoms of AD. Several research works are in progress on the detection and classification of AD based on changes in the brain. In this paper, we have analyzed few existing state-of-art works for AD detection and classification, based on different feature extraction approaches. We have summarized the existing research articles with detailed observations. We have also compared the performance and research issues in each of the feature extraction mechanisms and observed that the AD classification using the wavelet transform-based feature extraction approaches might achieve convincing results

    Layers Of Maturation In Cortical Hierarchies

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    Hierarchies form critical scaffolds for top-down processing but are often multiplex. In the brain, multiple layers of complex hierarchies intersect, dissociate, and re-converge over the lifespan. Although aspects of local hierarchical organizations are well-mapped for sensory systems, the fashion by which hierarchical organization extends globally is unknown. Human neuroimaging provides a means by which to observe both the developmental emergence and functions of global neurohierarchical organization. Here, we leveraged these advances to distill multiple layers of hierarchical formation across diverse brain-tissue quantifications. We demonstrate that these layers form common and dissociable biomarkers of the developmental emergence of complex cognition. Our results indicate that multiplex neurocognitive development both processes across a normative hierarchical pattern and contributes to engraining the pattern into cortical function. Further, our results suggest that neurocognitive development is largely contemporaneous with neurocognitive aging in an integrated, flexible lifespan sequence

    Automatic MRI segmentation of the developing neonatal brain

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    Detailed morphometric analysis of the neonatal brain is required to characterise normal brain development and investigate the neuroanatomical correlates of cognitive impairments. The segmentation of the brain in Magnetic Resonance Imaging (MRI) is a prerequisite to obtain quantitative measurements of regional brain structures. These measurements obtained at term-equivalent or early preterm age may lead to improved understanding of brain growth and may help evaluate long-term neurodevelopmental performance at an early stage. This thesis focuses on the development of an accurate segmentation algorithm for the neonatal brain MR images and its application in large cohorts of subjects. Neonatal brain segmentation is challenging due to the large anatomical variability as a result of the rapid brain development in the neonatal period. The lack of training data in the neonatal period, encoded in brain atlases, further hinders the development of automatic segmentation tools. A novel algorithm for the tissue segmentation of the neonatal brain is proposed. The algorithm is extended for the regional brain segmentation. This is the first segmentation method for the parcellation of the developing neonatal brain into multiple structures. A novel method is further proposed for the group-wise segmentation of the data that utilizes unlabelled data to complement the labelling information of brain atlases. Previous studies in the literature tended to overestimate the extent of the cortical region. A method based on the morphology of the cortex is introduced to correct for this over-segmentation. The segmentation method is applied on an extensive database of neonatal MR images. Regional volumetric, surface and diffusion tensor imaging measurements are derived from the early preterm period to term-equivalent age. These measurements allow characterisation of the regional brain development and the investigation of correlations with clinical factors. Finally, a spatio-temporal structural atlas is constructed for multiple regions of the neonatal brain.Open Acces
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