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Daniel Rueckert - One of the best experts on this subject based on the ideXlab platform.
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a flexible graphical model for multi modal Parcellation of the cortex
NeuroImage, 2017Co-Authors: Sarah Parisot, Ben Glocker, Markus D Schirmer, Sofia Ira Ktena, Salim Arslan, Daniel RueckertAbstract:Abstract Advances in neuroimaging have provided a tremendous amount of in-vivo information on the brain's organisation. Its anatomy and cortical organisation can be investigated from the point of view of several imaging modalities, many of which have been studied for mapping functionally specialised cortical areas. There is strong evidence that a single modality is not sufficient to fully identify the brain's cortical organisation. Combining multiple modalities in the same Parcellation task has the potential to provide more accurate and robust subdivisions of the cortex. Nonetheless, existing brain Parcellation methods are typically developed and tested on single modalities using a specific type of information. In this paper, we propose Graph-based Multi-modal Parcellation (GraMPa), an iterative framework designed to handle the large variety of available input modalities to tackle the multi-modal Parcellation task. At each iteration, we compute a set of Parcellations from different modalities and fuse them based on their local reliabilities. The fused Parcellation is used to initialise the next iteration, forcing the Parcellations to converge towards a set of mutually informed modality specific Parcellations, where correspondences are established. We explore two different multi-modal configurations for group-wise Parcellation using resting-state fMRI, diffusion MRI tractography, myelin maps and task fMRI. Quantitative and qualitative results on the Human Connectome Project database show that integrating multi-modal information yields a stronger agreement with well established atlases and more robust connectivity networks that provide a better representation of the population.
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human brain mapping a systematic comparison of Parcellation methods for the human cerebral cortex
NeuroImage, 2017Co-Authors: Salim Arslan, Daniel Rueckert, Sofia Ira Ktena, Antonios Makropoulos, Emma C Robinson, Sarah ParisotAbstract:Abstract The macro-connectome elucidates the pathways through which brain regions are structurally connected or functionally coupled to perform a specific cognitive task. It embodies the notion of representing and understanding all connections within the brain as a network, while the subdivision of the brain into interacting functional units is inherent in its architecture. As a result, the definition of network nodes is one of the most critical steps in connectivity network analysis. Although brain atlases obtained from cytoarchitecture or anatomy have long been used for this task, connectivity-driven methods have arisen only recently, aiming to delineate more homogeneous and functionally coherent regions. This study provides a systematic comparison between anatomical, connectivity-driven and random Parcellation methods proposed in the thriving field of brain Parcellation. Using resting-state functional MRI data from the Human Connectome Project and a plethora of quantitative evaluation techniques investigated in the literature, we evaluate 10 subject-level and 24 groupwise Parcellation methods at different resolutions. We assess the accuracy of Parcellations from four different aspects: (1) reproducibility across different acquisitions and groups, (2) fidelity to the underlying connectivity data, (3) agreement with fMRI task activation, myelin maps, and cytoarchitectural areas, and (4) network analysis. This extensive evaluation of different Parcellations generated at the subject and group level highlights the strengths and shortcomings of the various methods and aims to provide a guideline for the choice of Parcellation technique and resolution according to the task at hand. The results obtained in this study suggest that there is no optimal method able to address all the challenges faced in this endeavour simultaneously.
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grampa graph based multi modal Parcellation of the cortex using fusion moves
Medical Image Computing and Computer-Assisted Intervention, 2016Co-Authors: Sarah Parisot, Ben Glocker, Markus D Schirmer, Daniel RueckertAbstract:Parcellating the brain into a set of distinct subregions is an essential step for building and studying brain connectivity networks. Connectivity driven Parcellation is a natural approach, but suffers from the lack of reliability of connectivity data. Combining modalities in the Parcellation task has the potential to yield more robust Parcellations, yet hasn’t been explored much. In this paper, we propose a graph-based multi-modal Parcellation method that iteratively computes a set of modality specific Parcellations and merges them using the concept of fusion moves. The merged Parcellation initialises the next iteration, forcing all modalities to converge towards a set of mutually informed Parcellations. Experiments on 50 subjects of the Human Connectome Project database show that the multi-modal setting yields parcels that are more reproducible and more representative of the underlying connectivity.
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MICCAI (1) - GraMPa: Graph-based Multi-modal Parcellation of the Cortex using Fusion Moves
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016, 2016Co-Authors: Sarah Parisot, Ben Glocker, Markus D Schirmer, Daniel RueckertAbstract:Parcellating the brain into a set of distinct subregions is an essential step for building and studying brain connectivity networks. Connectivity driven Parcellation is a natural approach, but suffers from the lack of reliability of connectivity data. Combining modalities in the Parcellation task has the potential to yield more robust Parcellations, yet hasn’t been explored much. In this paper, we propose a graph-based multi-modal Parcellation method that iteratively computes a set of modality specific Parcellations and merges them using the concept of fusion moves. The merged Parcellation initialises the next iteration, forcing all modalities to converge towards a set of mutually informed Parcellations. Experiments on 50 subjects of the Human Connectome Project database show that the multi-modal setting yields parcels that are more reproducible and more representative of the underlying connectivity.
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group wise Parcellation of the cortex through multi scale spectral clustering
NeuroImage, 2016Co-Authors: Sarah Parisot, Salim Arslan, William M Wells, Jonathan Passeratpalmbach, Daniel RueckertAbstract:The delineation of functionally and structurally distinct regions as well as their connectivity can provide key knowledge towards understanding the brain's behaviour and function. Cytoarchitecture has long been the gold standard for such Parcellation tasks, but has poor scalability and cannot be mapped in vivo. Functional and diffusion magnetic resonance imaging allow in vivo mapping of brain's connectivity and the Parcellation of the brain based on local connectivity information. Several methods have been developed for single subject connectivity driven Parcellation, but very few have tackled the task of group-wise Parcellation, which is essential for uncovering group specific behaviours. In this paper, we propose a group-wise connectivity-driven Parcellation method based on spectral clustering that captures local connectivity information at multiple scales and directly enforces correspondences between subjects. The method is applied to diffusion Magnetic Resonance Imaging driven Parcellation on two independent groups of 50 subjects from the Human Connectome Project. Promising quantitative and qualitative results in terms of information loss, modality comparisons, group consistency and inter-group similarities demonstrate the potential of the method.
Sarah Parisot - One of the best experts on this subject based on the ideXlab platform.
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a flexible graphical model for multi modal Parcellation of the cortex
NeuroImage, 2017Co-Authors: Sarah Parisot, Ben Glocker, Markus D Schirmer, Sofia Ira Ktena, Salim Arslan, Daniel RueckertAbstract:Abstract Advances in neuroimaging have provided a tremendous amount of in-vivo information on the brain's organisation. Its anatomy and cortical organisation can be investigated from the point of view of several imaging modalities, many of which have been studied for mapping functionally specialised cortical areas. There is strong evidence that a single modality is not sufficient to fully identify the brain's cortical organisation. Combining multiple modalities in the same Parcellation task has the potential to provide more accurate and robust subdivisions of the cortex. Nonetheless, existing brain Parcellation methods are typically developed and tested on single modalities using a specific type of information. In this paper, we propose Graph-based Multi-modal Parcellation (GraMPa), an iterative framework designed to handle the large variety of available input modalities to tackle the multi-modal Parcellation task. At each iteration, we compute a set of Parcellations from different modalities and fuse them based on their local reliabilities. The fused Parcellation is used to initialise the next iteration, forcing the Parcellations to converge towards a set of mutually informed modality specific Parcellations, where correspondences are established. We explore two different multi-modal configurations for group-wise Parcellation using resting-state fMRI, diffusion MRI tractography, myelin maps and task fMRI. Quantitative and qualitative results on the Human Connectome Project database show that integrating multi-modal information yields a stronger agreement with well established atlases and more robust connectivity networks that provide a better representation of the population.
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human brain mapping a systematic comparison of Parcellation methods for the human cerebral cortex
NeuroImage, 2017Co-Authors: Salim Arslan, Daniel Rueckert, Sofia Ira Ktena, Antonios Makropoulos, Emma C Robinson, Sarah ParisotAbstract:Abstract The macro-connectome elucidates the pathways through which brain regions are structurally connected or functionally coupled to perform a specific cognitive task. It embodies the notion of representing and understanding all connections within the brain as a network, while the subdivision of the brain into interacting functional units is inherent in its architecture. As a result, the definition of network nodes is one of the most critical steps in connectivity network analysis. Although brain atlases obtained from cytoarchitecture or anatomy have long been used for this task, connectivity-driven methods have arisen only recently, aiming to delineate more homogeneous and functionally coherent regions. This study provides a systematic comparison between anatomical, connectivity-driven and random Parcellation methods proposed in the thriving field of brain Parcellation. Using resting-state functional MRI data from the Human Connectome Project and a plethora of quantitative evaluation techniques investigated in the literature, we evaluate 10 subject-level and 24 groupwise Parcellation methods at different resolutions. We assess the accuracy of Parcellations from four different aspects: (1) reproducibility across different acquisitions and groups, (2) fidelity to the underlying connectivity data, (3) agreement with fMRI task activation, myelin maps, and cytoarchitectural areas, and (4) network analysis. This extensive evaluation of different Parcellations generated at the subject and group level highlights the strengths and shortcomings of the various methods and aims to provide a guideline for the choice of Parcellation technique and resolution according to the task at hand. The results obtained in this study suggest that there is no optimal method able to address all the challenges faced in this endeavour simultaneously.
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grampa graph based multi modal Parcellation of the cortex using fusion moves
Medical Image Computing and Computer-Assisted Intervention, 2016Co-Authors: Sarah Parisot, Ben Glocker, Markus D Schirmer, Daniel RueckertAbstract:Parcellating the brain into a set of distinct subregions is an essential step for building and studying brain connectivity networks. Connectivity driven Parcellation is a natural approach, but suffers from the lack of reliability of connectivity data. Combining modalities in the Parcellation task has the potential to yield more robust Parcellations, yet hasn’t been explored much. In this paper, we propose a graph-based multi-modal Parcellation method that iteratively computes a set of modality specific Parcellations and merges them using the concept of fusion moves. The merged Parcellation initialises the next iteration, forcing all modalities to converge towards a set of mutually informed Parcellations. Experiments on 50 subjects of the Human Connectome Project database show that the multi-modal setting yields parcels that are more reproducible and more representative of the underlying connectivity.
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MICCAI (1) - GraMPa: Graph-based Multi-modal Parcellation of the Cortex using Fusion Moves
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016, 2016Co-Authors: Sarah Parisot, Ben Glocker, Markus D Schirmer, Daniel RueckertAbstract:Parcellating the brain into a set of distinct subregions is an essential step for building and studying brain connectivity networks. Connectivity driven Parcellation is a natural approach, but suffers from the lack of reliability of connectivity data. Combining modalities in the Parcellation task has the potential to yield more robust Parcellations, yet hasn’t been explored much. In this paper, we propose a graph-based multi-modal Parcellation method that iteratively computes a set of modality specific Parcellations and merges them using the concept of fusion moves. The merged Parcellation initialises the next iteration, forcing all modalities to converge towards a set of mutually informed Parcellations. Experiments on 50 subjects of the Human Connectome Project database show that the multi-modal setting yields parcels that are more reproducible and more representative of the underlying connectivity.
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group wise Parcellation of the cortex through multi scale spectral clustering
NeuroImage, 2016Co-Authors: Sarah Parisot, Salim Arslan, William M Wells, Jonathan Passeratpalmbach, Daniel RueckertAbstract:The delineation of functionally and structurally distinct regions as well as their connectivity can provide key knowledge towards understanding the brain's behaviour and function. Cytoarchitecture has long been the gold standard for such Parcellation tasks, but has poor scalability and cannot be mapped in vivo. Functional and diffusion magnetic resonance imaging allow in vivo mapping of brain's connectivity and the Parcellation of the brain based on local connectivity information. Several methods have been developed for single subject connectivity driven Parcellation, but very few have tackled the task of group-wise Parcellation, which is essential for uncovering group specific behaviours. In this paper, we propose a group-wise connectivity-driven Parcellation method based on spectral clustering that captures local connectivity information at multiple scales and directly enforces correspondences between subjects. The method is applied to diffusion Magnetic Resonance Imaging driven Parcellation on two independent groups of 50 subjects from the Human Connectome Project. Promising quantitative and qualitative results in terms of information loss, modality comparisons, group consistency and inter-group similarities demonstrate the potential of the method.
Dinggang Shen - One of the best experts on this subject based on the ideXlab platform.
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registration free infant cortical surface Parcellation using deep convolutional neural networks
Medical Image Computing and Computer-Assisted Intervention, 2018Co-Authors: Zhengwang Wu, Gang Li, Li Wang, John H Gilmore, Dinggang ShenAbstract:Automatic Parcellation of infant cortical surfaces into anatomical regions of interest (ROIs) is of great importance in brain structural and functional analysis. Conventional cortical surface Parcellation methods suffer from two main issues: (1) Cortical surface registration is needed for establishing the atlas-to-individual correspondences; (2) The mapping from cortical shape to the Parcellation labels requires designing of specific hand-crafted features. To address these issues, in this paper, we propose a novel cortical surface Parcellation method, which is free of surface registration and designing of hand-crafted features, based on deep convolutional neural network (DCNN). Our main idea is to formulate surface Parcellation as a patch-wise classification problem. Briefly, we use DCNN to train a classifier, whose inputs are the local cortical surface patches with multi-channel cortical shape descriptors such as mean curvature, sulcal depth, and average convexity; while the outputs are the Parcellation label probabilities of cortical vertices. To enable effective convolutional operation on the surface data, we project each spherical surface patch onto its intrinsic tangent plane by a geodesic-distance-preserving mapping. Then, after classification, we further adopt the graph cuts method to improve spatial consistency of the Parcellation. We have validated our method based on 90 neonatal cortical surfaces with manual Parcellations, showing superior accuracy and efficiency of our proposed method.
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MICCAI (1) - Developmental Patterns Based Individualized Parcellation of Infant Cortical Surface
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Inte, 2017Co-Authors: Gang Li, Li Wang, Dinggang ShenAbstract:The human cerebral cortex develops dynamically during the early postnatal stage, reflecting the underlying rapid changes of cortical microstructures and their connections, which jointly determine the functional principles of cortical regions. Hence, the dynamic cortical developmental patterns are ideal for defining the distinct cortical regions in microstructure and function for neurodevelopmental studies. Moreover, given the remarkable inter-subject variability in terms of cortical structure/function and their developmental patterns, the individualized cortical Parcellation based on each infant’s own developmental patterns is critical for precisely localizing personalized distinct cortical regions and also understanding inter-subject variability. To this end, we propose a novel method for individualized Parcellation of the infant cortical surface into distinct and meaningful regions based on each individual’s cortical developmental patterns. Specifically, to alleviate the effects of cortical measurement errors and also make the individualized cortical Parcellation comparable across subjects, we first create a population-based cortical Parcellation to capture the general developmental landscape of the cortex in an infant population. Then, this population-based Parcellation is leveraged to guide the individualized Parcellation based on each infant’s own cortical developmental patterns in an iterative manner. At each iteration, the individualized Parcellation is gradually updated based on (1) the prior information of the population-based Parcellation, (2) the individualized Parcellation at the previous iteration, and also (3) the developmental patterns of all vertices. Experiments on fifteen healthy infants, each with longitudinal MRI scans acquired at six time points (i.e., 1, 3, 6, 9, 12 and 18 months of age), show that our method generates a reliable and meaningful individualized cortical Parcellation based on each infant’s own developmental patterns.
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ISBI - Consistent sulcal Parcellation of longitudinal cortical surfaces
2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2011Co-Authors: Gang Li, Yang Li, Yaping Wang, Dinggang ShenAbstract:Automatic consistent sulcal Parcellation of longitudinal cortical surfaces is of great importance in studying morphological and functional changes of human brains. This paper proposes a novel energy-function based method for consist sulcal Parcellation of longitudinal cortical surfaces. Specifically, both spatial and temporal smoothness are imposed in the energy function to obtain consistent longitudinal sulcal Parcellation results. The proposed method has been successfully applied to sulcal Parcellation of longitudinal inner cortical surfaces of 10 normal subjects, each with 4 scans acquired within 2 years. Both quantitative and qualitative evaluation results demonstrate the validity of the proposed method.
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Consistent Sulcal Parcellation of Longitudinal Cortical Surfaces
NeuroImage, 2011Co-Authors: Gang Li, Dinggang ShenAbstract:Abstract Automated accurate and consistent sulcal Parcellation of longitudinal cortical surfaces is of great importance in studying longitudinal morphological and functional changes of human brains, since longitudinal cortical changes are normally very subtle, especially in aging brains. However, applying the existing methods (which were typically developed for cortical sulcal Parcellation of a single cortical surface) independently to longitudinal cortical surfaces might generate longitudinally-inconsistent results. To overcome this limitation, this paper presents a novel energy function based method for accurate and consistent sulcal Parcellation of longitudinal cortical surfaces. Specifically, both spatial and temporal smoothness are imposed in the energy function to obtain consistent longitudinal sulcal Parcellation results. The energy function is efficiently minimized by a graph cut method. The proposed method has been successfully applied to sulcal Parcellation of both real and simulated longitudinal inner cortical surfaces of human brain MR images. Both qualitative and quantitative evaluation results demonstrate the validity of the proposed method.
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Consistent sulcal Parcellation of longitudinal cortical surfaces
2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2011Co-Authors: Gang Li, Yang Li, Yaping Wang, Dinggang ShenAbstract:Automatic consistent sulcal Parcellation of longitudinal cortical surfaces is of great importance in studying morphological and functional changes of human brains. This paper proposes a novel energy-function based method for consist sulcal Parcellation of longitudinal cortical surfaces. Specifically, both spatial and temporal smoothness are imposed in the energy function to obtain consistent longitudinal sulcal Parcellation results. The proposed method has been successfully applied to sulcal Parcellation of longitudinal inner cortical surfaces of 10 normal subjects, each with 4 scans acquired within 2 years. Both quantitative and qualitative evaluation results demonstrate the validity of the proposed method.
Salim Arslan - One of the best experts on this subject based on the ideXlab platform.
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a flexible graphical model for multi modal Parcellation of the cortex
NeuroImage, 2017Co-Authors: Sarah Parisot, Ben Glocker, Markus D Schirmer, Sofia Ira Ktena, Salim Arslan, Daniel RueckertAbstract:Abstract Advances in neuroimaging have provided a tremendous amount of in-vivo information on the brain's organisation. Its anatomy and cortical organisation can be investigated from the point of view of several imaging modalities, many of which have been studied for mapping functionally specialised cortical areas. There is strong evidence that a single modality is not sufficient to fully identify the brain's cortical organisation. Combining multiple modalities in the same Parcellation task has the potential to provide more accurate and robust subdivisions of the cortex. Nonetheless, existing brain Parcellation methods are typically developed and tested on single modalities using a specific type of information. In this paper, we propose Graph-based Multi-modal Parcellation (GraMPa), an iterative framework designed to handle the large variety of available input modalities to tackle the multi-modal Parcellation task. At each iteration, we compute a set of Parcellations from different modalities and fuse them based on their local reliabilities. The fused Parcellation is used to initialise the next iteration, forcing the Parcellations to converge towards a set of mutually informed modality specific Parcellations, where correspondences are established. We explore two different multi-modal configurations for group-wise Parcellation using resting-state fMRI, diffusion MRI tractography, myelin maps and task fMRI. Quantitative and qualitative results on the Human Connectome Project database show that integrating multi-modal information yields a stronger agreement with well established atlases and more robust connectivity networks that provide a better representation of the population.
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Connectivity-driven Parcellation methods for the human cerebral cortex
arXiv: Neurons and Cognition, 2017Co-Authors: Salim ArslanAbstract:In this thesis, we present robust and fully-automated methods for the subdivision of the entire human cerebral cortex based on connectivity information. Our contributions are four-fold: First, we propose a clustering approach to delineate a cortical Parcellation that provides a reliable abstraction of the brain's functional organisation. Second, we cast the Parcellation problem as a feature reduction problem and make use of manifold learning and image segmentation techniques to identify cortical regions with distinct structural connectivity patterns. Third, we present a multi-layer graphical model that combines within- and between-subject connectivity, which is then decomposed into a cortical Parcellation that can represent the whole population, while accounting for the variability across subjects. Finally, we conduct a large-scale, systematic comparison of existing Parcellation methods, with a focus on providing some insight into the reliability of brain Parcellations in terms of reflecting the underlying connectivity, as well as, revealing their impact on network analysis. We evaluate the proposed Parcellation methods on publicly available data from the Human Connectome Project and a plethora of quantitative and qualitative evaluation techniques investigated in the literature. Experiments across multiple resolutions demonstrate the accuracy of the presented methods at both subject and group levels with regards to reproducibility and fidelity to the data. The neuro-biological interpretation of the proposed Parcellations is also investigated by comparing parcel boundaries with well-structured properties of the cerebral cortex. Results show the advantage of connectivity-driven Parcellations over traditional approaches in terms of better fitting the underlying connectivity.
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human brain mapping a systematic comparison of Parcellation methods for the human cerebral cortex
NeuroImage, 2017Co-Authors: Salim Arslan, Daniel Rueckert, Sofia Ira Ktena, Antonios Makropoulos, Emma C Robinson, Sarah ParisotAbstract:Abstract The macro-connectome elucidates the pathways through which brain regions are structurally connected or functionally coupled to perform a specific cognitive task. It embodies the notion of representing and understanding all connections within the brain as a network, while the subdivision of the brain into interacting functional units is inherent in its architecture. As a result, the definition of network nodes is one of the most critical steps in connectivity network analysis. Although brain atlases obtained from cytoarchitecture or anatomy have long been used for this task, connectivity-driven methods have arisen only recently, aiming to delineate more homogeneous and functionally coherent regions. This study provides a systematic comparison between anatomical, connectivity-driven and random Parcellation methods proposed in the thriving field of brain Parcellation. Using resting-state functional MRI data from the Human Connectome Project and a plethora of quantitative evaluation techniques investigated in the literature, we evaluate 10 subject-level and 24 groupwise Parcellation methods at different resolutions. We assess the accuracy of Parcellations from four different aspects: (1) reproducibility across different acquisitions and groups, (2) fidelity to the underlying connectivity data, (3) agreement with fMRI task activation, myelin maps, and cytoarchitectural areas, and (4) network analysis. This extensive evaluation of different Parcellations generated at the subject and group level highlights the strengths and shortcomings of the various methods and aims to provide a guideline for the choice of Parcellation technique and resolution according to the task at hand. The results obtained in this study suggest that there is no optimal method able to address all the challenges faced in this endeavour simultaneously.
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group wise Parcellation of the cortex through multi scale spectral clustering
NeuroImage, 2016Co-Authors: Sarah Parisot, Salim Arslan, William M Wells, Jonathan Passeratpalmbach, Daniel RueckertAbstract:The delineation of functionally and structurally distinct regions as well as their connectivity can provide key knowledge towards understanding the brain's behaviour and function. Cytoarchitecture has long been the gold standard for such Parcellation tasks, but has poor scalability and cannot be mapped in vivo. Functional and diffusion magnetic resonance imaging allow in vivo mapping of brain's connectivity and the Parcellation of the brain based on local connectivity information. Several methods have been developed for single subject connectivity driven Parcellation, but very few have tackled the task of group-wise Parcellation, which is essential for uncovering group specific behaviours. In this paper, we propose a group-wise connectivity-driven Parcellation method based on spectral clustering that captures local connectivity information at multiple scales and directly enforces correspondences between subjects. The method is applied to diffusion Magnetic Resonance Imaging driven Parcellation on two independent groups of 50 subjects from the Human Connectome Project. Promising quantitative and qualitative results in terms of information loss, modality comparisons, group consistency and inter-group similarities demonstrate the potential of the method.
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MICCAI (3) - Multi-Level Parcellation of the Cerebral Cortex Using Resting-State fMRI
Lecture Notes in Computer Science, 2015Co-Authors: Salim Arslan, Daniel RueckertAbstract:Cortical Parcellation is one of the core steps for identifying the functional architecture of the human brain. Despite the increasing number of attempts at developing Parcellation algorithms using resting-state fMRI, there still remain challenges to be overcome, such as generating reproducible Parcellations at both single-subject and group levels, while sub-dividing the cortex into functionally homogeneous parcels. To address these challenges, we propose a three-layer Parcellation framework which deploys a different clustering strategy at each layer. Initially, the cortical vertices are clustered into a relatively large number of supervertices, which constitutes a high-level abstraction of the rs-fMRI data. These supervertices are combined into a tree of hierarchical clusters to generate individual subject Parcellations, which are, in turn, used to compute a groupwise Parcellation in order to represent the whole population. Using data collected as part of the Human Connectome Project from 100 healthy subjects, we show that our algorithm segregates the cortex into distinctive parcels at different resolutions with high reproducibility and functional homogeneity at both single-subject and group levels, therefore can be reliably used for network analysis.
Demian Wassermann - One of the best experts on this subject based on the ideXlab platform.
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groupwise structural Parcellation of the whole cortex a logistic random effects model based approach
NeuroImage, 2017Co-Authors: Guillermo Gallardo, William M Wells, Rachid Deriche, Demian WassermannAbstract:Current theories hold that brain function is highly related to long-range physical connections through axonal bundles, namely extrinsic connectivity. However, obtaining a groupwise cortical Parcellation based on extrinsic connectivity remains challenging. Current Parcellation methods are computationally expensive; need tuning of several parameters or rely on ad-hoc constraints. Furthermore, none of these methods present a model for the cortical extrinsic connectivity of the cortex. To tackle these problems, we propose a parsimonious model for the extrinsic connectivity and an efficient parceling technique based on clustering of tractograms. Our technique allows the creation of single subject and groupwise Parcellations of the whole cortex. The Parcellations obtained with our technique are in agreement with structural and functional Parcellations in the literature. In particular, the motor and sensory cortex are subdivided in agreement with the human homunculus of Penfield. We illustrate this by comparing our resulting parcels with the motor strip mapping included in the Human Connectome Project data.
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Groupwise Structural Parcellation of the Cortex: A Sound Approach Based on Logistic Models
arXiv: Applications, 2017Co-Authors: Guillermo Gallardo, William M Wells, Rachid Deriche, Demian WassermannAbstract:Current theories hold that brain function is highly related to long-range physical connections through axonal bundles, namely extrinsic connectiv-ity. However, obtaining a groupwise cortical Parcellation based on extrinsic connectivity remains challenging. Current Parcellation methods are compu-tationally expensive; need tuning of several parameters or rely on ad-hoc constraints. Furthermore, none of these methods present a model for the cortical extrinsic connectivity of the cortex. To tackle these problems, we propose a parsimonious model for the extrinsic connectivity and an efficient parceling technique based on clustering of tractograms. Our technique allows the creation of single subject and groupwise Parcellations of the whole cortex. The Parcellations obtained with our technique are in agreement with structural and functional Parcellations in the literature. In particular, the motor and sensory cortex are subdivided in agreement with the human ho-munculus of Penfield. We illustrate this by comparing our resulting parcels with the motor strip mapping included in the Human Connectome Project data.
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groupwise structural Parcellation of the cortex a sound approach based on logistic models
Medical Image Computing and Computer-Assisted Intervention, 2016Co-Authors: Guillermo Gallardo, Rutger Fick, William M Wells, Rachid Deriche, Demian WassermannAbstract:Current theories hold that brain function is highly related with long-range physical connections through axonal bundles, namely extrinsic connectivity. However, obtaining a groupwise cortical Parcellation based on extrinsic connectivity remains challenging. Current Parcellation methods are computationally expensive; need tuning of several parameters or rely on ad-hoc constraints. Furthermore, none of these methods present a model for the cortical extrinsic connectivity. To tackle these problems, we propose a parsimonious model for the extrinsic connectivity and an efficient Parcellation technique based on clustering of tractograms. Our technique allows the creation of single subject and groupwise Parcellations of the whole cortex. The Parcellations obtained with our technique are in agreement with anatomical and functional Parcellations in the literature. In particular, the motor and sensory cortex are subdivided in agreement with the human homunculus of Penfield. We illustrate this by comparing our resulting parcels with an anatomical atlas and the motor strip mapping included in the Human Connectome Project data.
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Efficient Population-Representative Whole-Cortex Parcellation Based on Tractography
2016Co-Authors: Guillermo Gallardo, Rachid Deriche, Demian WassermannAbstract:The human brain is arranged in areas based on criteria such as cytoarchitecture or extrinsic connectivity. Current hypotheses attribute specialized functions to several areas of this patchwork. Hence, parcellating the cortex into such areas and characterizing their interaction is key to understanding brain function. Diffusion MRI enables the exploration of physical connections through axonal bundles, namely extrinsic connectivity. Current theories hold that brain function is determined by extrinsic connectivity. However, obtaining a population-representative Parcellation based on extrinsic connectivity remains challenging (Jbabdi and Behrens, 2013). Particularly, whole-cortex Parcellation methods (Moreno-Dominguez et al., 2014; Parisot et al., 2015) are computationally expensive and need tuning of several parameters. Our main contribution is an effcient technique to create single-subject and population-representative Parcellations based on tractography. Our method creates a dendrogram using only one parameter: the minimum size of each parcel. Then, by choosing cutting criteria, we can explore different Parcellation granularities without recomputing the dendrogram. Experiments show that our extrinsic based Parcellations are consistent within subjects with anatomical
