Catálogo de publicaciones - libros

Functional Magnetic Resonance Imaging (FMRI) allows indirect observation of brain activity through changes in blood oxygenation, which are driven by neural activity. ICA has become a popular exploratory analysis approach due its advantages over regression methods in accounting for structured noise as well as signals of interest. However, standard ICA in FMRI ignores some of the spatial and temporal structure contained in such data. Using prior knowledge that the Blood Oxygenation Level Dependent (BOLD) response is spatially smooth and manifests itself on certain spatial scales, we estimate the unmixing matrix using only the coarse coefficients of a 3D Discrete Wavelet Transform (DWT). We utilise prior biophysical knowledge that the BOLD response manifests itself mainly at the spatial scales we use for unmixing. Tests on realistic synthetic FMRI data show improved accuracy, greater robustness to misspecification of underlying dimensionality, and an approximate fourfold speed increase; in addition the algorithm becomes parallelizable.

Functional Magnetic Resonance Imaging (FMRI) allows indirect observation of brain activity through changes in blood oxygenation, which are driven by neural activity. ICA has become a popular exploratory analysis approach due its advantages over regression methods in accounting for structured noise as well as signals of interest. However, standard ICA in FMRI ignores some of the spatial and temporal structure contained in such data. Using prior knowledge that the Blood Oxygenation Level Dependent (BOLD) response is spatially smooth and manifests itself on certain spatial scales, we estimate the unmixing matrix using only the coarse coefficients of a 3D Discrete Wavelet Transform (DWT). We utilise prior biophysical knowledge that the BOLD response manifests itself mainly at the spatial scales we use for unmixing. Tests on realistic synthetic FMRI data show improved accuracy, greater robustness to misspecification of underlying dimensionality, and an approximate fourfold speed increase; in addition the algorithm becomes parallelizable.

Functional Magnetic Resonance Imaging (FMRI) allows indirect observation of brain activity through changes in blood oxygenation, which are driven by neural activity. ICA has become a popular exploratory analysis approach due its advantages over regression methods in accounting for structured noise as well as signals of interest. However, standard ICA in FMRI ignores some of the spatial and temporal structure contained in such data. Using prior knowledge that the Blood Oxygenation Level Dependent (BOLD) response is spatially smooth and manifests itself on certain spatial scales, we estimate the unmixing matrix using only the coarse coefficients of a 3D Discrete Wavelet Transform (DWT). We utilise prior biophysical knowledge that the BOLD response manifests itself mainly at the spatial scales we use for unmixing. Tests on realistic synthetic FMRI data show improved accuracy, greater robustness to misspecification of underlying dimensionality, and an approximate fourfold speed increase; in addition the algorithm becomes parallelizable.

Functional Magnetic Resonance Imaging (FMRI) allows indirect observation of brain activity through changes in blood oxygenation, which are driven by neural activity. ICA has become a popular exploratory analysis approach due its advantages over regression methods in accounting for structured noise as well as signals of interest. However, standard ICA in FMRI ignores some of the spatial and temporal structure contained in such data. Using prior knowledge that the Blood Oxygenation Level Dependent (BOLD) response is spatially smooth and manifests itself on certain spatial scales, we estimate the unmixing matrix using only the coarse coefficients of a 3D Discrete Wavelet Transform (DWT). We utilise prior biophysical knowledge that the BOLD response manifests itself mainly at the spatial scales we use for unmixing. Tests on realistic synthetic FMRI data show improved accuracy, greater robustness to misspecification of underlying dimensionality, and an approximate fourfold speed increase; in addition the algorithm becomes parallelizable.

Functional Magnetic Resonance Imaging (FMRI) allows indirect observation of brain activity through changes in blood oxygenation, which are driven by neural activity. ICA has become a popular exploratory analysis approach due its advantages over regression methods in accounting for structured noise as well as signals of interest. However, standard ICA in FMRI ignores some of the spatial and temporal structure contained in such data. Using prior knowledge that the Blood Oxygenation Level Dependent (BOLD) response is spatially smooth and manifests itself on certain spatial scales, we estimate the unmixing matrix using only the coarse coefficients of a 3D Discrete Wavelet Transform (DWT). We utilise prior biophysical knowledge that the BOLD response manifests itself mainly at the spatial scales we use for unmixing. Tests on realistic synthetic FMRI data show improved accuracy, greater robustness to misspecification of underlying dimensionality, and an approximate fourfold speed increase; in addition the algorithm becomes parallelizable.

Functional Magnetic Resonance Imaging (FMRI) allows indirect observation of brain activity through changes in blood oxygenation, which are driven by neural activity. ICA has become a popular exploratory analysis approach due its advantages over regression methods in accounting for structured noise as well as signals of interest. However, standard ICA in FMRI ignores some of the spatial and temporal structure contained in such data. Using prior knowledge that the Blood Oxygenation Level Dependent (BOLD) response is spatially smooth and manifests itself on certain spatial scales, we estimate the unmixing matrix using only the coarse coefficients of a 3D Discrete Wavelet Transform (DWT). We utilise prior biophysical knowledge that the BOLD response manifests itself mainly at the spatial scales we use for unmixing. Tests on realistic synthetic FMRI data show improved accuracy, greater robustness to misspecification of underlying dimensionality, and an approximate fourfold speed increase; in addition the algorithm becomes parallelizable.

Functional Magnetic Resonance Imaging (FMRI) allows indirect observation of brain activity through changes in blood oxygenation, which are driven by neural activity. ICA has become a popular exploratory analysis approach due its advantages over regression methods in accounting for structured noise as well as signals of interest. However, standard ICA in FMRI ignores some of the spatial and temporal structure contained in such data. Using prior knowledge that the Blood Oxygenation Level Dependent (BOLD) response is spatially smooth and manifests itself on certain spatial scales, we estimate the unmixing matrix using only the coarse coefficients of a 3D Discrete Wavelet Transform (DWT). We utilise prior biophysical knowledge that the BOLD response manifests itself mainly at the spatial scales we use for unmixing. Tests on realistic synthetic FMRI data show improved accuracy, greater robustness to misspecification of underlying dimensionality, and an approximate fourfold speed increase; in addition the algorithm becomes parallelizable.

Functional Magnetic Resonance Imaging (FMRI) allows indirect observation of brain activity through changes in blood oxygenation, which are driven by neural activity. ICA has become a popular exploratory analysis approach due its advantages over regression methods in accounting for structured noise as well as signals of interest. However, standard ICA in FMRI ignores some of the spatial and temporal structure contained in such data. Using prior knowledge that the Blood Oxygenation Level Dependent (BOLD) response is spatially smooth and manifests itself on certain spatial scales, we estimate the unmixing matrix using only the coarse coefficients of a 3D Discrete Wavelet Transform (DWT). We utilise prior biophysical knowledge that the BOLD response manifests itself mainly at the spatial scales we use for unmixing. Tests on realistic synthetic FMRI data show improved accuracy, greater robustness to misspecification of underlying dimensionality, and an approximate fourfold speed increase; in addition the algorithm becomes parallelizable.

Functional Magnetic Resonance Imaging (FMRI) allows indirect observation of brain activity through changes in blood oxygenation, which are driven by neural activity. ICA has become a popular exploratory analysis approach due its advantages over regression methods in accounting for structured noise as well as signals of interest. However, standard ICA in FMRI ignores some of the spatial and temporal structure contained in such data. Using prior knowledge that the Blood Oxygenation Level Dependent (BOLD) response is spatially smooth and manifests itself on certain spatial scales, we estimate the unmixing matrix using only the coarse coefficients of a 3D Discrete Wavelet Transform (DWT). We utilise prior biophysical knowledge that the BOLD response manifests itself mainly at the spatial scales we use for unmixing. Tests on realistic synthetic FMRI data show improved accuracy, greater robustness to misspecification of underlying dimensionality, and an approximate fourfold speed increase; in addition the algorithm becomes parallelizable.

Functional Magnetic Resonance Imaging (FMRI) allows indirect observation of brain activity through changes in blood oxygenation, which are driven by neural activity. ICA has become a popular exploratory analysis approach due its advantages over regression methods in accounting for structured noise as well as signals of interest. However, standard ICA in FMRI ignores some of the spatial and temporal structure contained in such data. Using prior knowledge that the Blood Oxygenation Level Dependent (BOLD) response is spatially smooth and manifests itself on certain spatial scales, we estimate the unmixing matrix using only the coarse coefficients of a 3D Discrete Wavelet Transform (DWT). We utilise prior biophysical knowledge that the BOLD response manifests itself mainly at the spatial scales we use for unmixing. Tests on realistic synthetic FMRI data show improved accuracy, greater robustness to misspecification of underlying dimensionality, and an approximate fourfold speed increase; in addition the algorithm becomes parallelizable.