The Experts below are selected from a list of 276 Experts worldwide ranked by ideXlab platform
Marco Lorenzi - One of the best experts on this subject based on the ideXlab platform.
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MLCN/DLF/iMIMIC@MICCAI - Alzheimer's Disease Modelling and Staging through Independent Gaussian Process Analysis of Spatio-Temporal Brain Changes
Understanding and Interpreting Machine Learning in Medical Image Computing Applications, 2018Co-Authors: Clement Abi Nader, Nicholas Ayache, Philippe Robert, Marco LorenziAbstract:Alzheimer’s disease (AD) is characterized by complex and largely unknown progression dynamics affecting the brain’s morphology. Although the disease evolution spans decades, to date we cannot rely on long-term data to model the pathological progression, since most of the available measures are on a short-term scale. It is therefore difficult to understand and quantify the temporal progression patterns affecting the brain regions across the AD evolution. In this work, we present a generative model based on probabilistic matrix factorization across temporal and spatial sources. The proposed method addresses the problem of disease progression modelling by introducing clinically-inspired statistical priors. To promote smoothness in time and model plausible pathological evolutions, the temporal sources are defined as monotonic and Independent Gaussian Processes. We also estimate an individual time-shift parameter for each patient to automatically position him/her along the sources time-axis. To encode the spatial continuity of the brain sub-structures, the spatial sources are modeled as Gaussian random fields. We test our algorithm on grey matter maps extracted from brain structural images. The experiments highlight differential temporal progression patterns mapping brain regions key to the AD pathology, and reveal a disease-specific time scale associated with the decline of volumetric biomarkers across clinical stages.
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Alzheimer's Disease Modelling and Staging through Independent Gaussian Process Analysis of Spatio-Temporal Brain Changes
2018Co-Authors: Clement Abi Nader, Nicholas Ayache, Philippe Robert, Marco LorenziAbstract:Alzheimer's disease (AD) is characterized by complex and largely unknown progression dynamics affecting the brain's morphology. Although the disease evolution spans decades, to date we cannot rely on long-term data to model the pathological progression, since most of the available measures are on a short-term scale. It is therefore difficult to understand and quantify the temporal progression patterns affecting the brain regions across the AD evolution. In this work, we present a generative model based on probabilistic matrix factorization across temporal and spatial sources. The proposed method addresses the problem of disease progression modelling by introducing clinically-inspired statistical priors. To promote smoothness in time and model plausible pathological evolutions, the temporal sources are defined as monotonic and Independent Gaussian Processes. We also estimate an individual time-shift parameter for each patient to automatically position him/her along the sources time-axis. To encode the spatial continuity of the brain sub-structures, the spatial sources are modeled as Gaussian random fields. We test our algorithm on grey matter maps extracted from brain structural images. The experiments highlight differential temporal progression patterns mapping brain regions key to the AD pathology, and reveal a disease-specific time scale associated with the decline of volumetric biomarkers across clinical stages.
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Alzheimer's Disease Modelling and Staging through Independent Gaussian Process Analysis of Spatio-Temporal Brain Changes
arXiv: Applications, 2018Co-Authors: Clement Abi Nader, Nicholas Ayache, Philippe Robert, Marco Lorenzi, Alzheimer’s Disease Neuroimaging InitiativeAbstract:Alzheimer's disease (AD) is characterized by complex and largely unknown progression dynamics affecting the brain's morphology. Although the disease evolution spans decades, to date we cannot rely on long-term data to model the pathological progression, since most of the available measures are on a short-term scale. It is therefore difficult to understand and quantify the temporal progression patterns affecting the brain regions across the AD evolution. In this work, we tackle this problem by presenting a generative model based on probabilistic matrix factorization across temporal and spatial sources. The proposed method addresses the problem of disease progression modelling by introducing clinically-inspired statistical priors. To promote smoothness in time and model plausible pathological evolutions, the temporal sources are defined as monotonic and Independent Gaussian Processes. We also estimate an individual time-shift parameter for each patient to automatically position him/her along the sources time-axis. To encode the spatial continuity of the brain sub-structures, the spatial sources are modeled as Gaussian random fields. We test our algorithm on grey matter maps extracted from brain structural images. The experiments highlight differential temporal progression patterns mapping brain regions key to the AD pathology, and reveal a disease-specific time scale associated with the decline of volumetric biomarkers across clinical stages.
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Disentangling spatio-temporal patterns of brain changes in large-scale brain imaging databases through Independent Gaussian Process Analysis
2018Co-Authors: Clement Abi Nader, Nicholas Ayache, Valeria Manera, Philippe Robert, Marco LorenziAbstract:The morphological changes affecting the brain over time are related to several biological processes, governed either by healthy aging or by pathological factors. Since these processes are to date largely unknown , we need statistical approaches to automatically identify these latent morphological evolutions through the analysis of structural brain Magnetic Resonance Images (MRIs). Such approaches require to scale to high-dimensional volumetric observations (around 10e6 voxels) with application to the analysis of large scale biomedical databases such as UK BIOBANK (thousands of individuals across time). We present a novel spatio-temporal analysis method, aiming at automatically estimating latent spatio-temporal patterns of brain changes from collections of brain MRIs over time. This approach extends standard methods (such as ICA) to encode priors on spatial and temporal properties of the signal measured in brain images. The application to currently available large-scale biomedical datasets is addressed by focusing on scalable and distributed learning methods. Our method considers the observed data as a matrix factorization of both temporal and spatial sources. The temporal sources are treated as Independent Gaussian Processes to promote smoothness in time and model a plausible aging evolution. The spatial sources are modeled as Gaussian random fields to encode the spatial continuity of the brain sub-structures. This particular structure allows to factorize the spatial covariance matrix as a Kronecker product over the three spatial dimensions, which greatly simplifies computations and reduces the dimensions of the matrices we work on. The overall model is efficiently optimized through stochastic variational inference. We tested our model on synthetic and real data. We generated statistically Independent temporal sources and spatial sources as smooth heatmaps. Then we trained our model so that it disentangles the observed data in two matrices that best fit the generated mixed observations. The figure below shows on plot A) the original temporal sources (in red) and their approximation by the Independent Gaussian Processes (in blue). Likewise, plot B) shows on top the heatmaps manually generated and under them the maps generated by the algorithm. We can observe that the method is able to capture these raw sources from the noisy observations given as target.
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Disentangling spatio-temporal patterns of brain changes in large-scale brain imaging databases through Independent Gaussian Process Analysis
Revue d'Épidémiologie et de Santé Publique, 2018Co-Authors: C. Abi Nader, Nicholas Ayache, Valeria Manera, Philippe Robert, Marco LorenziAbstract:Introduction The morphological changes affecting the brain over time are related to several biological processes, governed either by healthy aging or by pathological factors. Since these processes are to date largely unknown, we need statistical approaches to automatically identify these latent morphological evolutions through the analysis of structural brain magnetic resonance images (MRIs). Such approaches require to scale to high-dimensional volumetric observations with application to the analysis of large-scale biomedical databases such as UK Biobank. We present a novel spatio-temporal analysis method, aiming at automatically estimating latent spatio-temporal patterns of brain changes from collections of brain MRIs over time. This approach extends standard methods (such as ICA) to encode priors on spatial and temporal properties of the signal measured in brain images. Methods Our method considers the observed data as a matrix factorization of both temporal and spatial sources. The temporal sources are treated as Independent Gaussian processes to promote smoothness in time and model a plausible aging evolution. The spatial sources are modeled as Gaussian random fields to encode the spatial continuity of the brain sub-structures. This particular structure allows to factorize the spatial covariance matrix as a Kronecker product over the three spatial dimensions, which greatly simplifies computations and reduces the dimensions of the matrices we work on. The overall model is efficiently optimized through stochastic variational inference. Results We tested our model on synthetic data. We generated statistically Independent temporal sources and spatial sources as smooth heatmaps. Then we trained our model so that it disentangles the observed data in two matrices that best fit the generated mixed observations. Fig. 1 below shows on plot A the original temporal sources (in red) and their approximation by the Independent Gaussian processes (in blue). Likewise, plot B shows on top the heatmaps manually generated and under them the maps generated by the algorithm. We can observe that the method is able to capture these raw sources from the noisy observations given as target. Conclusion Eventually this method may provide an ideal exploratory tool for analyzing large-scale medical imaging datasets such as the UK Biobank. Indeed, it allows to efficiently scale to both high-dimensional data and large sample sizes, and also identifies hidden spatio-temporal processes in a completely unsupervised manner.
Philippe Robert - One of the best experts on this subject based on the ideXlab platform.
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MLCN/DLF/iMIMIC@MICCAI - Alzheimer's Disease Modelling and Staging through Independent Gaussian Process Analysis of Spatio-Temporal Brain Changes
Understanding and Interpreting Machine Learning in Medical Image Computing Applications, 2018Co-Authors: Clement Abi Nader, Nicholas Ayache, Philippe Robert, Marco LorenziAbstract:Alzheimer’s disease (AD) is characterized by complex and largely unknown progression dynamics affecting the brain’s morphology. Although the disease evolution spans decades, to date we cannot rely on long-term data to model the pathological progression, since most of the available measures are on a short-term scale. It is therefore difficult to understand and quantify the temporal progression patterns affecting the brain regions across the AD evolution. In this work, we present a generative model based on probabilistic matrix factorization across temporal and spatial sources. The proposed method addresses the problem of disease progression modelling by introducing clinically-inspired statistical priors. To promote smoothness in time and model plausible pathological evolutions, the temporal sources are defined as monotonic and Independent Gaussian Processes. We also estimate an individual time-shift parameter for each patient to automatically position him/her along the sources time-axis. To encode the spatial continuity of the brain sub-structures, the spatial sources are modeled as Gaussian random fields. We test our algorithm on grey matter maps extracted from brain structural images. The experiments highlight differential temporal progression patterns mapping brain regions key to the AD pathology, and reveal a disease-specific time scale associated with the decline of volumetric biomarkers across clinical stages.
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Alzheimer's Disease Modelling and Staging through Independent Gaussian Process Analysis of Spatio-Temporal Brain Changes
2018Co-Authors: Clement Abi Nader, Nicholas Ayache, Philippe Robert, Marco LorenziAbstract:Alzheimer's disease (AD) is characterized by complex and largely unknown progression dynamics affecting the brain's morphology. Although the disease evolution spans decades, to date we cannot rely on long-term data to model the pathological progression, since most of the available measures are on a short-term scale. It is therefore difficult to understand and quantify the temporal progression patterns affecting the brain regions across the AD evolution. In this work, we present a generative model based on probabilistic matrix factorization across temporal and spatial sources. The proposed method addresses the problem of disease progression modelling by introducing clinically-inspired statistical priors. To promote smoothness in time and model plausible pathological evolutions, the temporal sources are defined as monotonic and Independent Gaussian Processes. We also estimate an individual time-shift parameter for each patient to automatically position him/her along the sources time-axis. To encode the spatial continuity of the brain sub-structures, the spatial sources are modeled as Gaussian random fields. We test our algorithm on grey matter maps extracted from brain structural images. The experiments highlight differential temporal progression patterns mapping brain regions key to the AD pathology, and reveal a disease-specific time scale associated with the decline of volumetric biomarkers across clinical stages.
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Alzheimer's Disease Modelling and Staging through Independent Gaussian Process Analysis of Spatio-Temporal Brain Changes
arXiv: Applications, 2018Co-Authors: Clement Abi Nader, Nicholas Ayache, Philippe Robert, Marco Lorenzi, Alzheimer’s Disease Neuroimaging InitiativeAbstract:Alzheimer's disease (AD) is characterized by complex and largely unknown progression dynamics affecting the brain's morphology. Although the disease evolution spans decades, to date we cannot rely on long-term data to model the pathological progression, since most of the available measures are on a short-term scale. It is therefore difficult to understand and quantify the temporal progression patterns affecting the brain regions across the AD evolution. In this work, we tackle this problem by presenting a generative model based on probabilistic matrix factorization across temporal and spatial sources. The proposed method addresses the problem of disease progression modelling by introducing clinically-inspired statistical priors. To promote smoothness in time and model plausible pathological evolutions, the temporal sources are defined as monotonic and Independent Gaussian Processes. We also estimate an individual time-shift parameter for each patient to automatically position him/her along the sources time-axis. To encode the spatial continuity of the brain sub-structures, the spatial sources are modeled as Gaussian random fields. We test our algorithm on grey matter maps extracted from brain structural images. The experiments highlight differential temporal progression patterns mapping brain regions key to the AD pathology, and reveal a disease-specific time scale associated with the decline of volumetric biomarkers across clinical stages.
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Disentangling spatio-temporal patterns of brain changes in large-scale brain imaging databases through Independent Gaussian Process Analysis
2018Co-Authors: Clement Abi Nader, Nicholas Ayache, Valeria Manera, Philippe Robert, Marco LorenziAbstract:The morphological changes affecting the brain over time are related to several biological processes, governed either by healthy aging or by pathological factors. Since these processes are to date largely unknown , we need statistical approaches to automatically identify these latent morphological evolutions through the analysis of structural brain Magnetic Resonance Images (MRIs). Such approaches require to scale to high-dimensional volumetric observations (around 10e6 voxels) with application to the analysis of large scale biomedical databases such as UK BIOBANK (thousands of individuals across time). We present a novel spatio-temporal analysis method, aiming at automatically estimating latent spatio-temporal patterns of brain changes from collections of brain MRIs over time. This approach extends standard methods (such as ICA) to encode priors on spatial and temporal properties of the signal measured in brain images. The application to currently available large-scale biomedical datasets is addressed by focusing on scalable and distributed learning methods. Our method considers the observed data as a matrix factorization of both temporal and spatial sources. The temporal sources are treated as Independent Gaussian Processes to promote smoothness in time and model a plausible aging evolution. The spatial sources are modeled as Gaussian random fields to encode the spatial continuity of the brain sub-structures. This particular structure allows to factorize the spatial covariance matrix as a Kronecker product over the three spatial dimensions, which greatly simplifies computations and reduces the dimensions of the matrices we work on. The overall model is efficiently optimized through stochastic variational inference. We tested our model on synthetic and real data. We generated statistically Independent temporal sources and spatial sources as smooth heatmaps. Then we trained our model so that it disentangles the observed data in two matrices that best fit the generated mixed observations. The figure below shows on plot A) the original temporal sources (in red) and their approximation by the Independent Gaussian Processes (in blue). Likewise, plot B) shows on top the heatmaps manually generated and under them the maps generated by the algorithm. We can observe that the method is able to capture these raw sources from the noisy observations given as target.
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Disentangling spatio-temporal patterns of brain changes in large-scale brain imaging databases through Independent Gaussian Process Analysis
Revue d'Épidémiologie et de Santé Publique, 2018Co-Authors: C. Abi Nader, Nicholas Ayache, Valeria Manera, Philippe Robert, Marco LorenziAbstract:Introduction The morphological changes affecting the brain over time are related to several biological processes, governed either by healthy aging or by pathological factors. Since these processes are to date largely unknown, we need statistical approaches to automatically identify these latent morphological evolutions through the analysis of structural brain magnetic resonance images (MRIs). Such approaches require to scale to high-dimensional volumetric observations with application to the analysis of large-scale biomedical databases such as UK Biobank. We present a novel spatio-temporal analysis method, aiming at automatically estimating latent spatio-temporal patterns of brain changes from collections of brain MRIs over time. This approach extends standard methods (such as ICA) to encode priors on spatial and temporal properties of the signal measured in brain images. Methods Our method considers the observed data as a matrix factorization of both temporal and spatial sources. The temporal sources are treated as Independent Gaussian processes to promote smoothness in time and model a plausible aging evolution. The spatial sources are modeled as Gaussian random fields to encode the spatial continuity of the brain sub-structures. This particular structure allows to factorize the spatial covariance matrix as a Kronecker product over the three spatial dimensions, which greatly simplifies computations and reduces the dimensions of the matrices we work on. The overall model is efficiently optimized through stochastic variational inference. Results We tested our model on synthetic data. We generated statistically Independent temporal sources and spatial sources as smooth heatmaps. Then we trained our model so that it disentangles the observed data in two matrices that best fit the generated mixed observations. Fig. 1 below shows on plot A the original temporal sources (in red) and their approximation by the Independent Gaussian processes (in blue). Likewise, plot B shows on top the heatmaps manually generated and under them the maps generated by the algorithm. We can observe that the method is able to capture these raw sources from the noisy observations given as target. Conclusion Eventually this method may provide an ideal exploratory tool for analyzing large-scale medical imaging datasets such as the UK Biobank. Indeed, it allows to efficiently scale to both high-dimensional data and large sample sizes, and also identifies hidden spatio-temporal processes in a completely unsupervised manner.
Nicholas Ayache - One of the best experts on this subject based on the ideXlab platform.
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MLCN/DLF/iMIMIC@MICCAI - Alzheimer's Disease Modelling and Staging through Independent Gaussian Process Analysis of Spatio-Temporal Brain Changes
Understanding and Interpreting Machine Learning in Medical Image Computing Applications, 2018Co-Authors: Clement Abi Nader, Nicholas Ayache, Philippe Robert, Marco LorenziAbstract:Alzheimer’s disease (AD) is characterized by complex and largely unknown progression dynamics affecting the brain’s morphology. Although the disease evolution spans decades, to date we cannot rely on long-term data to model the pathological progression, since most of the available measures are on a short-term scale. It is therefore difficult to understand and quantify the temporal progression patterns affecting the brain regions across the AD evolution. In this work, we present a generative model based on probabilistic matrix factorization across temporal and spatial sources. The proposed method addresses the problem of disease progression modelling by introducing clinically-inspired statistical priors. To promote smoothness in time and model plausible pathological evolutions, the temporal sources are defined as monotonic and Independent Gaussian Processes. We also estimate an individual time-shift parameter for each patient to automatically position him/her along the sources time-axis. To encode the spatial continuity of the brain sub-structures, the spatial sources are modeled as Gaussian random fields. We test our algorithm on grey matter maps extracted from brain structural images. The experiments highlight differential temporal progression patterns mapping brain regions key to the AD pathology, and reveal a disease-specific time scale associated with the decline of volumetric biomarkers across clinical stages.
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Alzheimer's Disease Modelling and Staging through Independent Gaussian Process Analysis of Spatio-Temporal Brain Changes
2018Co-Authors: Clement Abi Nader, Nicholas Ayache, Philippe Robert, Marco LorenziAbstract:Alzheimer's disease (AD) is characterized by complex and largely unknown progression dynamics affecting the brain's morphology. Although the disease evolution spans decades, to date we cannot rely on long-term data to model the pathological progression, since most of the available measures are on a short-term scale. It is therefore difficult to understand and quantify the temporal progression patterns affecting the brain regions across the AD evolution. In this work, we present a generative model based on probabilistic matrix factorization across temporal and spatial sources. The proposed method addresses the problem of disease progression modelling by introducing clinically-inspired statistical priors. To promote smoothness in time and model plausible pathological evolutions, the temporal sources are defined as monotonic and Independent Gaussian Processes. We also estimate an individual time-shift parameter for each patient to automatically position him/her along the sources time-axis. To encode the spatial continuity of the brain sub-structures, the spatial sources are modeled as Gaussian random fields. We test our algorithm on grey matter maps extracted from brain structural images. The experiments highlight differential temporal progression patterns mapping brain regions key to the AD pathology, and reveal a disease-specific time scale associated with the decline of volumetric biomarkers across clinical stages.
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Alzheimer's Disease Modelling and Staging through Independent Gaussian Process Analysis of Spatio-Temporal Brain Changes
arXiv: Applications, 2018Co-Authors: Clement Abi Nader, Nicholas Ayache, Philippe Robert, Marco Lorenzi, Alzheimer’s Disease Neuroimaging InitiativeAbstract:Alzheimer's disease (AD) is characterized by complex and largely unknown progression dynamics affecting the brain's morphology. Although the disease evolution spans decades, to date we cannot rely on long-term data to model the pathological progression, since most of the available measures are on a short-term scale. It is therefore difficult to understand and quantify the temporal progression patterns affecting the brain regions across the AD evolution. In this work, we tackle this problem by presenting a generative model based on probabilistic matrix factorization across temporal and spatial sources. The proposed method addresses the problem of disease progression modelling by introducing clinically-inspired statistical priors. To promote smoothness in time and model plausible pathological evolutions, the temporal sources are defined as monotonic and Independent Gaussian Processes. We also estimate an individual time-shift parameter for each patient to automatically position him/her along the sources time-axis. To encode the spatial continuity of the brain sub-structures, the spatial sources are modeled as Gaussian random fields. We test our algorithm on grey matter maps extracted from brain structural images. The experiments highlight differential temporal progression patterns mapping brain regions key to the AD pathology, and reveal a disease-specific time scale associated with the decline of volumetric biomarkers across clinical stages.
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Disentangling spatio-temporal patterns of brain changes in large-scale brain imaging databases through Independent Gaussian Process Analysis
2018Co-Authors: Clement Abi Nader, Nicholas Ayache, Valeria Manera, Philippe Robert, Marco LorenziAbstract:The morphological changes affecting the brain over time are related to several biological processes, governed either by healthy aging or by pathological factors. Since these processes are to date largely unknown , we need statistical approaches to automatically identify these latent morphological evolutions through the analysis of structural brain Magnetic Resonance Images (MRIs). Such approaches require to scale to high-dimensional volumetric observations (around 10e6 voxels) with application to the analysis of large scale biomedical databases such as UK BIOBANK (thousands of individuals across time). We present a novel spatio-temporal analysis method, aiming at automatically estimating latent spatio-temporal patterns of brain changes from collections of brain MRIs over time. This approach extends standard methods (such as ICA) to encode priors on spatial and temporal properties of the signal measured in brain images. The application to currently available large-scale biomedical datasets is addressed by focusing on scalable and distributed learning methods. Our method considers the observed data as a matrix factorization of both temporal and spatial sources. The temporal sources are treated as Independent Gaussian Processes to promote smoothness in time and model a plausible aging evolution. The spatial sources are modeled as Gaussian random fields to encode the spatial continuity of the brain sub-structures. This particular structure allows to factorize the spatial covariance matrix as a Kronecker product over the three spatial dimensions, which greatly simplifies computations and reduces the dimensions of the matrices we work on. The overall model is efficiently optimized through stochastic variational inference. We tested our model on synthetic and real data. We generated statistically Independent temporal sources and spatial sources as smooth heatmaps. Then we trained our model so that it disentangles the observed data in two matrices that best fit the generated mixed observations. The figure below shows on plot A) the original temporal sources (in red) and their approximation by the Independent Gaussian Processes (in blue). Likewise, plot B) shows on top the heatmaps manually generated and under them the maps generated by the algorithm. We can observe that the method is able to capture these raw sources from the noisy observations given as target.
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Disentangling spatio-temporal patterns of brain changes in large-scale brain imaging databases through Independent Gaussian Process Analysis
Revue d'Épidémiologie et de Santé Publique, 2018Co-Authors: C. Abi Nader, Nicholas Ayache, Valeria Manera, Philippe Robert, Marco LorenziAbstract:Introduction The morphological changes affecting the brain over time are related to several biological processes, governed either by healthy aging or by pathological factors. Since these processes are to date largely unknown, we need statistical approaches to automatically identify these latent morphological evolutions through the analysis of structural brain magnetic resonance images (MRIs). Such approaches require to scale to high-dimensional volumetric observations with application to the analysis of large-scale biomedical databases such as UK Biobank. We present a novel spatio-temporal analysis method, aiming at automatically estimating latent spatio-temporal patterns of brain changes from collections of brain MRIs over time. This approach extends standard methods (such as ICA) to encode priors on spatial and temporal properties of the signal measured in brain images. Methods Our method considers the observed data as a matrix factorization of both temporal and spatial sources. The temporal sources are treated as Independent Gaussian processes to promote smoothness in time and model a plausible aging evolution. The spatial sources are modeled as Gaussian random fields to encode the spatial continuity of the brain sub-structures. This particular structure allows to factorize the spatial covariance matrix as a Kronecker product over the three spatial dimensions, which greatly simplifies computations and reduces the dimensions of the matrices we work on. The overall model is efficiently optimized through stochastic variational inference. Results We tested our model on synthetic data. We generated statistically Independent temporal sources and spatial sources as smooth heatmaps. Then we trained our model so that it disentangles the observed data in two matrices that best fit the generated mixed observations. Fig. 1 below shows on plot A the original temporal sources (in red) and their approximation by the Independent Gaussian processes (in blue). Likewise, plot B shows on top the heatmaps manually generated and under them the maps generated by the algorithm. We can observe that the method is able to capture these raw sources from the noisy observations given as target. Conclusion Eventually this method may provide an ideal exploratory tool for analyzing large-scale medical imaging datasets such as the UK Biobank. Indeed, it allows to efficiently scale to both high-dimensional data and large sample sizes, and also identifies hidden spatio-temporal processes in a completely unsupervised manner.
Clement Abi Nader - One of the best experts on this subject based on the ideXlab platform.
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Alzheimer's Disease Modelling and Staging through Independent Gaussian Process Analysis of Spatio-Temporal Brain Changes
2018Co-Authors: Clement Abi Nader, Nicholas Ayache, Philippe Robert, Marco LorenziAbstract:Alzheimer's disease (AD) is characterized by complex and largely unknown progression dynamics affecting the brain's morphology. Although the disease evolution spans decades, to date we cannot rely on long-term data to model the pathological progression, since most of the available measures are on a short-term scale. It is therefore difficult to understand and quantify the temporal progression patterns affecting the brain regions across the AD evolution. In this work, we present a generative model based on probabilistic matrix factorization across temporal and spatial sources. The proposed method addresses the problem of disease progression modelling by introducing clinically-inspired statistical priors. To promote smoothness in time and model plausible pathological evolutions, the temporal sources are defined as monotonic and Independent Gaussian Processes. We also estimate an individual time-shift parameter for each patient to automatically position him/her along the sources time-axis. To encode the spatial continuity of the brain sub-structures, the spatial sources are modeled as Gaussian random fields. We test our algorithm on grey matter maps extracted from brain structural images. The experiments highlight differential temporal progression patterns mapping brain regions key to the AD pathology, and reveal a disease-specific time scale associated with the decline of volumetric biomarkers across clinical stages.
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Disentangling spatio-temporal patterns of brain changes in large-scale brain imaging databases through Independent Gaussian Process Analysis
2018Co-Authors: Clement Abi Nader, Nicholas Ayache, Valeria Manera, Philippe Robert, Marco LorenziAbstract:The morphological changes affecting the brain over time are related to several biological processes, governed either by healthy aging or by pathological factors. Since these processes are to date largely unknown , we need statistical approaches to automatically identify these latent morphological evolutions through the analysis of structural brain Magnetic Resonance Images (MRIs). Such approaches require to scale to high-dimensional volumetric observations (around 10e6 voxels) with application to the analysis of large scale biomedical databases such as UK BIOBANK (thousands of individuals across time). We present a novel spatio-temporal analysis method, aiming at automatically estimating latent spatio-temporal patterns of brain changes from collections of brain MRIs over time. This approach extends standard methods (such as ICA) to encode priors on spatial and temporal properties of the signal measured in brain images. The application to currently available large-scale biomedical datasets is addressed by focusing on scalable and distributed learning methods. Our method considers the observed data as a matrix factorization of both temporal and spatial sources. The temporal sources are treated as Independent Gaussian Processes to promote smoothness in time and model a plausible aging evolution. The spatial sources are modeled as Gaussian random fields to encode the spatial continuity of the brain sub-structures. This particular structure allows to factorize the spatial covariance matrix as a Kronecker product over the three spatial dimensions, which greatly simplifies computations and reduces the dimensions of the matrices we work on. The overall model is efficiently optimized through stochastic variational inference. We tested our model on synthetic and real data. We generated statistically Independent temporal sources and spatial sources as smooth heatmaps. Then we trained our model so that it disentangles the observed data in two matrices that best fit the generated mixed observations. The figure below shows on plot A) the original temporal sources (in red) and their approximation by the Independent Gaussian Processes (in blue). Likewise, plot B) shows on top the heatmaps manually generated and under them the maps generated by the algorithm. We can observe that the method is able to capture these raw sources from the noisy observations given as target.
Radim Filip - One of the best experts on this subject based on the ideXlab platform.
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Estimation of nonclassical Independent Gaussian processes by classical interferometry
Scientific reports, 2017Co-Authors: László Ruppert, Radim FilipAbstract:We propose classical interferometry with low-intensity thermal radiation for the estimation of nonclassical Independent Gaussian processes in material samples. We generally determine the mean square error of the phase-Independent parameters of an unknown Gaussian process, considering a noisy source of radiation the phase of which is not locked to the pump of the process. We verify the sufficiency of passive optical elements in the interferometer, active optical elements do not improve the quality of the estimation. We also prove the robustness of the method against the noise and loss in both interferometric channels and the sample. The proposed method is suitable even for the case when a source of radiation sufficient for homodyne detection is not available.
