Activation detection in functional MRI using subspace modeling and maximum likelihood estimation. Export

@article{citeulike:791583, abstract = {A statistical method for detecting activated pixels in functional MRI (fMIRI) data is presented. In this method, the fMRI time series measured at each pixel is modeled as the sum of a response signal which arises due to the experimentally controlled activation-baseline pattern, a nuisance component representing effects of no interest, and Gaussian white noise. For periodic activation-baseline patterns, the response signal is modeled by a truncated Fourier series with a known fundamental frequency but unknown Fourier coefficients. The nuisance subspace is assumed to be unknown. A maximum likelihood estimate is derived for the component of the nuisance subspace which is orthogonal to the response signal subspace. An estimate for the order of the nuisance subspace is obtained from an information theoretic criterion. A statistical test is derived and shown to be the uniformly most powerful (UMP) test invariant to a group of transformations which are natural to the hypothesis testing problem. The maximal invariant statistic used in this test has an F distribution. The theoretical F distribution under the null hypothesis strongly concurred with the experimental frequency distribution obtained by performing null experiments in which the subjects did not perform any activation task. Application of the theory to motor activation and visual stimulation fMRI studies is presented.}, address = {Department of Radiology and Nuclear Medicine, Research Institute for Brain and Blood Vessels, Akita, Japan.}, author = {Ardekani, B. A. and Kershaw, J. and Kashikura, K. and Kanno, I.}, citeulike-article-id = {791583}, citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/10232667}, citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=10232667}, comment = {looks like the GLM, but he also models the BOLD response via a subspace. Very mathematic.}, issn = {0278-0062}, journal = {IEEE Trans Med Imaging}, keywords = {drift, fmri, modelling}, month = {February}, number = {2}, pages = {101--114}, posted-at = {2006-08-09 23:39:51}, priority = {2}, title = {Activation detection in functional MRI using subspace modeling and maximum likelihood estimation.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/10232667}, volume = {18}, year = {1999} }

TY - JOUR ID - citeulike:791583 L3 - citeulike-article-id:791583 N2 - A statistical method for detecting activated pixels in functional MRI (fMIRI) data is presented. In this method, the fMRI time series measured at each pixel is modeled as the sum of a response signal which arises due to the experimentally controlled activation-baseline pattern, a nuisance component representing effects of no interest, and Gaussian white noise. For periodic activation-baseline patterns, the response signal is modeled by a truncated Fourier series with a known fundamental frequency but unknown Fourier coefficients. The nuisance subspace is assumed to be unknown. A maximum likelihood estimate is derived for the component of the nuisance subspace which is orthogonal to the response signal subspace. An estimate for the order of the nuisance subspace is obtained from an information theoretic criterion. A statistical test is derived and shown to be the uniformly most powerful (UMP) test invariant to a group of transformations which are natural to the hypothesis testing problem. The maximal invariant statistic used in this test has an F distribution. The theoretical F distribution under the null hypothesis strongly concurred with the experimental frequency distribution obtained by performing null experiments in which the subjects did not perform any activation task. Application of the theory to motor activation and visual stimulation fMRI studies is presented. IS - 2 JF - IEEE Trans Med Imaging SN - 0278-0062 AD - Department of Radiology and Nuclear Medicine, Research Institute for Brain and Blood Vessels, Akita, Japan. EP - 114 TI - Activation detection in functional MRI using subspace modeling and maximum likelihood estimation. VL - 18 SP - 101 KW - drift KW - fmri KW - modelling N1 - looks like the GLM, but he also models the BOLD response via a subspace. Very mathematic. AU - Ardekani, BA AU - Kershaw, J AU - Kashikura, K AU - Kanno, I PY - 1999/02// UR - http://view.ncbi.nlm.nih.gov/pubmed/10232667 ER -