The Experts below are selected from a list of 150 Experts worldwide ranked by ideXlab platform
Christian Barillot - One of the best experts on this subject based on the ideXlab platform.
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fast identification of optimal fascicle configurations from standard clinical diffusion mri using akaike information criterion
International Symposium on Biomedical Imaging, 2014Co-Authors: Aymeric Stamm, Olivier Commowick, Patrick Perez, Christian BarillotAbstract:Analytic multi-compartment Models have gained a tremen- dous popularity in the recent literature for studying the brain white matter microstructure from diffusion MRI. This class of Models require the number of compartments to be known in advance. In the white matter however, several non-collinear bundles of axons, termed fascicles, often coexist in a same voxel. Determining the optimal fascicle configuration is a Model selection problem. In this paper, we aim at proposing a novel approach to identify such a configuration from clinical diffusion MRI where only few diffusion images can be ac- quired and time is of the essence. Starting from a set of fitted Models with increasing number of fascicles, we use Akaike information criterion to estimate the probability of each can- didate Model to be the best Kullback-Leibler Model. These probabilities are then used to average the different candidate Models and output an MCM with optimal fascicle configura- tion. This strategy is fast and can be adapted to any multi- compartment Model. We illustrate its implementation with the Ball-and-Stick Model and show that we obtain better re- sults on single-shell low angular resolution diffusion MRI, compared to the state-of-the-art automatic relevance detection method, in a shorter processing time.
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ISBI - Fast Identification of Optimal Fascicle Configurations from Standard Clinical Diffusion MRI Using Akaike Information Criterion
2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), 2014Co-Authors: Aymeric Stamm, Olivier Commowick, Patrick Perez, Christian BarillotAbstract:Analytic multi-compartment Models have gained a tremen- dous popularity in the recent literature for studying the brain white matter microstructure from diffusion MRI. This class of Models require the number of compartments to be known in advance. In the white matter however, several non-collinear bundles of axons, termed fascicles, often coexist in a same voxel. Determining the optimal fascicle configuration is a Model selection problem. In this paper, we aim at proposing a novel approach to identify such a configuration from clinical diffusion MRI where only few diffusion images can be ac- quired and time is of the essence. Starting from a set of fitted Models with increasing number of fascicles, we use Akaike information criterion to estimate the probability of each can- didate Model to be the best Kullback-Leibler Model. These probabilities are then used to average the different candidate Models and output an MCM with optimal fascicle configura- tion. This strategy is fast and can be adapted to any multi- compartment Model. We illustrate its implementation with the Ball-and-Stick Model and show that we obtain better re- sults on single-shell low angular resolution diffusion MRI, compared to the state-of-the-art automatic relevance detection method, in a shorter processing time.
Alain Destexhe - One of the best experts on this subject based on the ideXlab platform.
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A modified cable formalism for Modeling neuronal membranes at high frequencies.
Biophysical journal, 2008Co-Authors: Claude Bédard, Alain DestexheAbstract:Intracellular recordings of cortical neurons in vivo display intense subthreshold membrane potential (V(m)) activity. The power spectral density of the V(m) displays a power-law structure at high frequencies (>50 Hz) with a slope of approximately -2.5. This type of frequency scaling cannot be accounted for by traditional Models, as either single-compartment Models or Models based on reconstructed cell morphologies display a frequency scaling with a slope close to -4. This slope is due to the fact that the membrane resistance is short-circuited by the capacitance for high frequencies, a situation which may not be realistic. Here, we integrate nonideal capacitors in cable equations to reflect the fact that the capacitance cannot be charged instantaneously. We show that the resulting nonideal cable Model can be solved analytically using Fourier transforms. Numerical simulations using a Ball-and-Stick Model yield membrane potential activity with similar frequency scaling as in the experiments. We also discuss the consequences of using nonideal capacitors on other cellular properties such as the transmission of high frequencies, which is boosted in nonideal cables, or voltage attenuation in dendrites. These results suggest that cable equations based on nonideal capacitors should be used to capture the behavior of neuronal membranes at high frequencies.
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A Modified Cable Formalism for Modeling Neuronal Membranes at High Frequencies
Biophysical Journal, 2007Co-Authors: Claude Bédard, Alain DestexheAbstract:Intracellular recordings of cortical neurons in vivo display intense subthreshold membrane potential (Vm) activity. The power spectral density of the Vm displays a power-law structure at high frequencies (.50 Hz) with a slope of ;� 2.5. This type of frequency scaling cannot be accounted for by traditional Models, as either single-compartment Models or Models based on reconstructed cell morphologies display a frequency scaling with a slope close to � 4. This slope is due to the fact that the membrane resistance is short-circuited by the capacitance for high frequencies, a situation which may not be realistic. Here, we integrate nonideal capacitors in cable equations to reflect the fact that the capacitance cannot be charged instantaneously. We show that the resulting nonideal cable Model can be solved analytically using Fourier transforms. Numerical simulations using a Ball-and-Stick Model yield membrane potential activity with similar frequency scaling as in the experiments. We also discuss the consequences of using nonideal capacitors on other cellular properties such as the transmission of high frequencies, which is boosted in nonideal cables, or voltage attenuation in dendrites. These results suggest that cable equations based on nonideal capacitors should be used to capture the behavior of neuronal membranes at high frequencies.
Aymeric Stamm - One of the best experts on this subject based on the ideXlab platform.
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fast identification of optimal fascicle configurations from standard clinical diffusion mri using akaike information criterion
International Symposium on Biomedical Imaging, 2014Co-Authors: Aymeric Stamm, Olivier Commowick, Patrick Perez, Christian BarillotAbstract:Analytic multi-compartment Models have gained a tremen- dous popularity in the recent literature for studying the brain white matter microstructure from diffusion MRI. This class of Models require the number of compartments to be known in advance. In the white matter however, several non-collinear bundles of axons, termed fascicles, often coexist in a same voxel. Determining the optimal fascicle configuration is a Model selection problem. In this paper, we aim at proposing a novel approach to identify such a configuration from clinical diffusion MRI where only few diffusion images can be ac- quired and time is of the essence. Starting from a set of fitted Models with increasing number of fascicles, we use Akaike information criterion to estimate the probability of each can- didate Model to be the best Kullback-Leibler Model. These probabilities are then used to average the different candidate Models and output an MCM with optimal fascicle configura- tion. This strategy is fast and can be adapted to any multi- compartment Model. We illustrate its implementation with the Ball-and-Stick Model and show that we obtain better re- sults on single-shell low angular resolution diffusion MRI, compared to the state-of-the-art automatic relevance detection method, in a shorter processing time.
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ISBI - Fast Identification of Optimal Fascicle Configurations from Standard Clinical Diffusion MRI Using Akaike Information Criterion
2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), 2014Co-Authors: Aymeric Stamm, Olivier Commowick, Patrick Perez, Christian BarillotAbstract:Analytic multi-compartment Models have gained a tremen- dous popularity in the recent literature for studying the brain white matter microstructure from diffusion MRI. This class of Models require the number of compartments to be known in advance. In the white matter however, several non-collinear bundles of axons, termed fascicles, often coexist in a same voxel. Determining the optimal fascicle configuration is a Model selection problem. In this paper, we aim at proposing a novel approach to identify such a configuration from clinical diffusion MRI where only few diffusion images can be ac- quired and time is of the essence. Starting from a set of fitted Models with increasing number of fascicles, we use Akaike information criterion to estimate the probability of each can- didate Model to be the best Kullback-Leibler Model. These probabilities are then used to average the different candidate Models and output an MCM with optimal fascicle configura- tion. This strategy is fast and can be adapted to any multi- compartment Model. We illustrate its implementation with the Ball-and-Stick Model and show that we obtain better re- sults on single-shell low angular resolution diffusion MRI, compared to the state-of-the-art automatic relevance detection method, in a shorter processing time.
Olivier Commowick - One of the best experts on this subject based on the ideXlab platform.
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fast identification of optimal fascicle configurations from standard clinical diffusion mri using akaike information criterion
International Symposium on Biomedical Imaging, 2014Co-Authors: Aymeric Stamm, Olivier Commowick, Patrick Perez, Christian BarillotAbstract:Analytic multi-compartment Models have gained a tremen- dous popularity in the recent literature for studying the brain white matter microstructure from diffusion MRI. This class of Models require the number of compartments to be known in advance. In the white matter however, several non-collinear bundles of axons, termed fascicles, often coexist in a same voxel. Determining the optimal fascicle configuration is a Model selection problem. In this paper, we aim at proposing a novel approach to identify such a configuration from clinical diffusion MRI where only few diffusion images can be ac- quired and time is of the essence. Starting from a set of fitted Models with increasing number of fascicles, we use Akaike information criterion to estimate the probability of each can- didate Model to be the best Kullback-Leibler Model. These probabilities are then used to average the different candidate Models and output an MCM with optimal fascicle configura- tion. This strategy is fast and can be adapted to any multi- compartment Model. We illustrate its implementation with the Ball-and-Stick Model and show that we obtain better re- sults on single-shell low angular resolution diffusion MRI, compared to the state-of-the-art automatic relevance detection method, in a shorter processing time.
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ISBI - Fast Identification of Optimal Fascicle Configurations from Standard Clinical Diffusion MRI Using Akaike Information Criterion
2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), 2014Co-Authors: Aymeric Stamm, Olivier Commowick, Patrick Perez, Christian BarillotAbstract:Analytic multi-compartment Models have gained a tremen- dous popularity in the recent literature for studying the brain white matter microstructure from diffusion MRI. This class of Models require the number of compartments to be known in advance. In the white matter however, several non-collinear bundles of axons, termed fascicles, often coexist in a same voxel. Determining the optimal fascicle configuration is a Model selection problem. In this paper, we aim at proposing a novel approach to identify such a configuration from clinical diffusion MRI where only few diffusion images can be ac- quired and time is of the essence. Starting from a set of fitted Models with increasing number of fascicles, we use Akaike information criterion to estimate the probability of each can- didate Model to be the best Kullback-Leibler Model. These probabilities are then used to average the different candidate Models and output an MCM with optimal fascicle configura- tion. This strategy is fast and can be adapted to any multi- compartment Model. We illustrate its implementation with the Ball-and-Stick Model and show that we obtain better re- sults on single-shell low angular resolution diffusion MRI, compared to the state-of-the-art automatic relevance detection method, in a shorter processing time.
Patrick Perez - One of the best experts on this subject based on the ideXlab platform.
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fast identification of optimal fascicle configurations from standard clinical diffusion mri using akaike information criterion
International Symposium on Biomedical Imaging, 2014Co-Authors: Aymeric Stamm, Olivier Commowick, Patrick Perez, Christian BarillotAbstract:Analytic multi-compartment Models have gained a tremen- dous popularity in the recent literature for studying the brain white matter microstructure from diffusion MRI. This class of Models require the number of compartments to be known in advance. In the white matter however, several non-collinear bundles of axons, termed fascicles, often coexist in a same voxel. Determining the optimal fascicle configuration is a Model selection problem. In this paper, we aim at proposing a novel approach to identify such a configuration from clinical diffusion MRI where only few diffusion images can be ac- quired and time is of the essence. Starting from a set of fitted Models with increasing number of fascicles, we use Akaike information criterion to estimate the probability of each can- didate Model to be the best Kullback-Leibler Model. These probabilities are then used to average the different candidate Models and output an MCM with optimal fascicle configura- tion. This strategy is fast and can be adapted to any multi- compartment Model. We illustrate its implementation with the Ball-and-Stick Model and show that we obtain better re- sults on single-shell low angular resolution diffusion MRI, compared to the state-of-the-art automatic relevance detection method, in a shorter processing time.
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ISBI - Fast Identification of Optimal Fascicle Configurations from Standard Clinical Diffusion MRI Using Akaike Information Criterion
2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), 2014Co-Authors: Aymeric Stamm, Olivier Commowick, Patrick Perez, Christian BarillotAbstract:Analytic multi-compartment Models have gained a tremen- dous popularity in the recent literature for studying the brain white matter microstructure from diffusion MRI. This class of Models require the number of compartments to be known in advance. In the white matter however, several non-collinear bundles of axons, termed fascicles, often coexist in a same voxel. Determining the optimal fascicle configuration is a Model selection problem. In this paper, we aim at proposing a novel approach to identify such a configuration from clinical diffusion MRI where only few diffusion images can be ac- quired and time is of the essence. Starting from a set of fitted Models with increasing number of fascicles, we use Akaike information criterion to estimate the probability of each can- didate Model to be the best Kullback-Leibler Model. These probabilities are then used to average the different candidate Models and output an MCM with optimal fascicle configura- tion. This strategy is fast and can be adapted to any multi- compartment Model. We illustrate its implementation with the Ball-and-Stick Model and show that we obtain better re- sults on single-shell low angular resolution diffusion MRI, compared to the state-of-the-art automatic relevance detection method, in a shorter processing time.
