Statistical parametric maps in functional imaging: A general linear approach

@article{friston1995, abstract = {Statistical parametric maps are spatially extended statistical processes that are used to test hypotheses about regionally specific effects in neuroimaging data. The most established sorts of statistical parametric maps (e.g., Friston et al. [1991]: J Cereb Blood Flow Metab 11:690-699; Worsley et al. [1992]: J Cereb Blood Flow Metab 12:900-918) are based on linear models, for example ANCOVA, correlation coefficients and t tests. In the sense that these examples are all special cases of the general linear model it should be possible to implement them (and many others) within a unified framework. We present here a general approach that accomodates most forms of experimental layout and ensuing analysis (designed experiments with fixed effects for factors, covariates and interaction of factors). This approach brings together two well established bodies of theory (the general linear model and the theory of Gaussian fields) to provide a complete and simple framework for the analysis of imaging data.The importance of this framework is twofold: (i) Conceptual and mathematical simplicity, in that the same small number of operational equations is used irrespective of the complexity of the experiment or nature of the statistical model and (ii) the generality of the framework provides for great latitude in experimental design and analysis. {\copyright} 1995 Wiley-Liss, Inc.}, address = {The Wellcome Department of Cognitive Neurology, Institute of Neurology, Queen Square WC1N 3BG and the MRC Cyclotron Unit, Hammersmith Hospital, United Kingdom; Department of Statistics, Glasgow University, Glasgow, United Kingdom; Department of Mathematics and Statistics, McGill University, Montreal H3A 2K6 Canada}, author = {Friston, K. J. and Holmes, A. P. and Worsley, K. J. and Poline, J. P. and Frith, C. D. and Frackowiak, R. S. J. }, citeulike-article-id = {819069}, doi = {10.1002/hbm.460020402}, journal = {Human Brain Mapping}, keywords = {fmri, methods, statistics}, number = {4}, pages = {189--210}, posted-at = {2008-07-03 20:26:34}, priority = {2}, title = {Statistical parametric maps in functional imaging: A general linear approach}, url = {http://dx.doi.org/10.1002/hbm.460020402}, volume = {2}, year = {1994} }

TY - JOUR ID - friston1995 L3 - citeulike-article-id:819069 TI - Statistical parametric maps in functional imaging: A general linear approach JF - Human Brain Mapping VL - 2 IS - 4 SP - 189 EP - 210 AD - The Wellcome Department of Cognitive Neurology, Institute of Neurology, Queen Square WC1N 3BG and the MRC Cyclotron Unit, Hammersmith Hospital, United Kingdom; Department of Statistics, Glasgow University, Glasgow, United Kingdom; Department of Mathematics and Statistics, McGill University, Montreal H3A 2K6 Canada N2 - Statistical parametric maps are spatially extended statistical processes that are used to test hypotheses about regionally specific effects in neuroimaging data. The most established sorts of statistical parametric maps (e.g., Friston et al. [1991]: J Cereb Blood Flow Metab 11:690-699; Worsley et al. [1992]: J Cereb Blood Flow Metab 12:900-918) are based on linear models, for example ANCOVA, correlation coefficients and t tests. In the sense that these examples are all special cases of the general linear model it should be possible to implement them (and many others) within a unified framework. We present here a general approach that accomodates most forms of experimental layout and ensuing analysis (designed experiments with fixed effects for factors, covariates and interaction of factors). This approach brings together two well established bodies of theory (the general linear model and the theory of Gaussian fields) to provide a complete and simple framework for the analysis of imaging data.The importance of this framework is twofold: (i) Conceptual and mathematical simplicity, in that the same small number of operational equations is used irrespective of the complexity of the experiment or nature of the statistical model and (ii) the generality of the framework provides for great latitude in experimental design and analysis. © 1995 Wiley-Liss, Inc. KW - fmri KW - methods KW - statistics AU - Friston, KJ AU - Holmes, AP AU - Worsley, KJ AU - Poline, JP AU - Frith, CD AU - Frackowiak, RSJ PY - 1994/// UR - http://dx.doi.org/10.1002/hbm.460020402 ER -