Statistical parametric maps in functional imaging: A general linear approach

Human Brain Mapping - Tập 2 Số 4 - Trang 189-210 - 1994
Karl Friston1, Andrew P. Holmes2, Keith J. Worsley3, J.‐P. Poline1, Chris Frith1, R. S. J. Frackowiak1
1The Wellcome Department of Cognitive Neurology, Institute of Neurology, Queen Square WC1N 3BG and the MRC Cyclotron Unit, Hammersmith Hospital, United Kingdom
2Department of Statistics, Glasgow University, Glasgow, United Kingdom
3Department of Mathematics and Statistics, McGill University, Montreal, H3A 2K6, Canada

Tóm tắt

AbstractStatistical 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.

Từ khóa


Tài liệu tham khảo

Adler RJ, 1981, The geometry of random fields

Aertsen A, 1991, Non Linear Dynamics and Neuronal Networks, 281

10.1017/S003329170003806X

10.1007/978-1-4899-3184-9_10

Fox PT, 1989, Non‐invasive functional brain mapping by change distribution analysis of averaged PET images of H15O2 tissue activity, J Nucl Med, 30, 141

10.1038/jcbfm.1988.111

10.1038/jcbfm.1990.88

10.1038/jcbfm.1991.122

10.1093/brain/115.2.367

10.1098/rspb.1992.0065

10.1016/0304-3940(92)90345-8

Friston KJ, 1993, Functional connectivity: The principal component analysis of large (PET) data sets, J Cereb Blood Flow Metab, 13, 5, 10.1038/jcbfm.1993.4

10.1002/hbm.460010306

FristonKJ AshburnerJ PolineJB FrithCD HeatherJD FrackowiakRSJ(in press):Spatial registration and normalization of images. Hum Brain Mapp.

10.1098/rspb.1991.0077

10.1523/JNEUROSCI.12-07-02542.1992

10.1007/BF02245127

10.2307/1427002

HolmesAP BlairRC WatsonJDG FordI(in press):Non‐parametric analysis of statistical images from functional mapping experiments. J Cereb Blood Flow Metab.

10.1038/340386a0

10.1038/jcbfm.1991.121

Nosko VP, 1969, Local structure of Gaussian random fields in the vicinity of high level shines, Soviet Mathematics Doklady, 10, 1481

Nosko VP, 1970, On shines of Gaussian random fields (in Russian), Vestnik Moscov. Univ Ser I Mat Meh, 1970, 18

10.1016/0304-3940(92)90072-F

Scheffe H, 1959, The Analysis of Variance

10.1088/0031-9155/37/8/002

Talairach J, 1988, A co‐planar stereotaxic atlas of a human brain

10.1109/42.108584

Winer BJ, 1971, Statistical Principles in Experimental Design

10.1080/01621459.1991.10475004

10.1038/jcbfm.1992.127

Worsley KJ, 1993, Qualification of Brain Function: Tracer Kinetics and Image Analysis in Brain PET, 535

10.1038/jcbfm.1993.135

10.2307/1427576

Thông tin
Thông tin xuất bản

Human Brain Mapping

Tập 2 Số 4

189-210

Thông tin tác giả