Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain

Advances in functional and structural MR image analysis and implementation as FSL.

Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

https://www.fil.ion.ucl.ac.uk/~karl/Voxel-Based%20Morphometry.pdf

Voxel-Based Morphometry—The Methods

https://www.sfs.uni-tuebingen.de/~hbaayen/publications/baayenDavidsonBates.pdf

Mixed-effects modeling with crossed random effects for subjects and items

+ 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. 119921: J Cereb Blood Flow Metab 12:YOO-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 accommodates 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.

/pdf/statistical-parametric-maps-in-functional-imaging-a-general-3e3ee6im7g.pdf

Statistical parametric maps in functional imaging: A general linear approach

Requiring only minimal assumptions for validity, nonparametric permutation testing provides a flexible and intuitive methodology for the statistical analysis of data from functional neuroimaging experiments, at some computational expense. Introduced into the functional neuroimaging literature by Holmes et al. ([1996]: J Cereb Blood Flow Metab 16:7-22), the permutation approach readily accounts for the multiple comparisons problem implicit in the standard voxel-by-voxel hypothesis testing framework. When the appropriate assumptions hold, the nonparametric permutation approach gives results similar to those obtained from a comparable Statistical Parametric Mapping approach using a general linear model with multiple comparisons corrections derived from random field theory. For analyses with low degrees of freedom, such as single subject PET/SPECT experiments or multi-subject PET/SPECT or fMRI designs assessed for population effects, the nonparametric approach employing a locally pooled (smoothed) variance estimate can outperform the comparable Statistical Parametric Mapping approach. Thus, these nonparametric techniques can be used to verify the validity of less computationally expensive parametric approaches. Although the theory and relative advantages of permutation approaches have been discussed by various authors, there has been no accessible explication of the method, and no freely distributed software implementing it. Consequently, there have been few practical applications of the technique. This article, and the accompanying MATLAB software, attempts to address these issues. The standard nonparametric randomization and permutation testing ideas are developed at an accessible level, using practical examples from functional neuroimaging, and the extensions for multiple comparisons described. Three worked examples from PET and fMRI are presented, with discussion, and comparisons with standard parametric approaches made where appropriate. Practical considerations are given throughout, and relevant statistical concepts are expounded in appendices.

/pdf/nonparametric-permutation-tests-for-functional-neuroimaging-2tnaunteli.pdf

Nonparametric permutation tests for functional neuroimaging: A primer with examples

Analysis of fMRI Time-Series Revisited

http://www.fil.ion.ucl.ac.uk/~karl/Event-Related%20fMRI%20Characterizing%20Differential%20Responses.pdf

Event-Related fMRI: Characterizing Differential Responses

http://www.fil.ion.ucl.ac.uk/~karl/Detecting%20Activations%20in%20PET%20and%20fMRI.pdf

Andrew P. Holmes

Papers

Statistical parametric maps in functional imaging: A general linear approach

Nonparametric permutation tests for functional neuroimaging: A primer with examples

Analysis of fMRI Time-Series Revisited

Event-Related fMRI: Characterizing Differential Responses

Detecting Activations in PET and fMRI: Levels of Inference and Power