Probabilistic independent component analysis for functional magnetic resonance imaging. Export

@article{citeulike:767722, abstract = {We present an integrated approach to probabilistic independent component analysis (ICA) for functional MRI (FMRI) data that allows for nonsquare mixing in the presence of Gaussian noise. In order to avoid overfitting, we employ objective estimation of the amount of Gaussian noise through Bayesian analysis of the true dimensionality of the data, i.e., the number of activation and non-Gaussian noise sources. This enables us to carry out probabilistic modeling and achieves an asymptotically unique decomposition of the data. It reduces problems of interpretation, as each final independent component is now much more likely to be due to only one physical or physiological process. We also describe other improvements to standard ICA, such as temporal prewhitening and variance normalization of timeseries, the latter being particularly useful in the context of dimensionality reduction when weak activation is present. We discuss the use of prior information about the spatiotemporal nature of the source processes, and an alternative-hypothesis testing approach for inference, using Gaussian mixture models. The performance of our approach is illustrated and evaluated on real and artificial FMRI data, and compared to the spatio-temporal accuracy of results obtained from classical ICA and GLM analyses.}, address = {Medical Vision Laboratory, Department of Engineering Science and the Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, University of Oxford, Oxford OX3 9DU, UK. beckmann@fmrib.ox.ac.uk}, author = {Beckmann, C. F. and Smith, S. M.}, citeulike-article-id = {767722}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/TMI.2003.822821}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/14964560}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=14964560}, doi = {10.1109/TMI.2003.822821}, issn = {0278-0062}, journal = {IEEE Trans Med Imaging}, keywords = {components, fmri, methods}, month = {February}, number = {2}, pages = {137--152}, posted-at = {2009-04-11 02:47:43}, priority = {2}, title = {Probabilistic independent component analysis for functional magnetic resonance imaging.}, url = {http://dx.doi.org/10.1109/TMI.2003.822821}, volume = {23}, year = {2004} }

TY - JOUR ID - citeulike:767722 L3 - citeulike-article-id:767722 N2 - We present an integrated approach to probabilistic independent component analysis (ICA) for functional MRI (FMRI) data that allows for nonsquare mixing in the presence of Gaussian noise. In order to avoid overfitting, we employ objective estimation of the amount of Gaussian noise through Bayesian analysis of the true dimensionality of the data, i.e., the number of activation and non-Gaussian noise sources. This enables us to carry out probabilistic modeling and achieves an asymptotically unique decomposition of the data. It reduces problems of interpretation, as each final independent component is now much more likely to be due to only one physical or physiological process. We also describe other improvements to standard ICA, such as temporal prewhitening and variance normalization of timeseries, the latter being particularly useful in the context of dimensionality reduction when weak activation is present. We discuss the use of prior information about the spatiotemporal nature of the source processes, and an alternative-hypothesis testing approach for inference, using Gaussian mixture models. The performance of our approach is illustrated and evaluated on real and artificial FMRI data, and compared to the spatio-temporal accuracy of results obtained from classical ICA and GLM analyses. IS - 2 JF - IEEE Trans Med Imaging SN - 0278-0062 AD - Medical Vision Laboratory, Department of Engineering Science and the Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, University of Oxford, Oxford OX3 9DU, UK. beckmann@fmrib.ox.ac.uk EP - 152 TI - Probabilistic independent component analysis for functional magnetic resonance imaging. VL - 23 SP - 137 KW - components KW - fmri KW - methods AU - Beckmann, CF AU - Smith, SM PY - 2004/02// UR - http://dx.doi.org/10.1109/TMI.2003.822821 ER -