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396
Controlling the familywise error rate in functional neuroimaging: a comparative review
 Statistical Methods in Medical Research
, 2003
"... Functional neuroimaging data embodies a massive multiple testing problem, where 100 000 correlated test statistics must be assessed. The familywise error rate, the chance of any false positives is the standard measure of Type I errors in multiple testing. In this paper we review and evaluate three a ..."
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Cited by 173 (7 self)
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Functional neuroimaging data embodies a massive multiple testing problem, where 100 000 correlated test statistics must be assessed. The familywise error rate, the chance of any false positives is the standard measure of Type I errors in multiple testing. In this paper we review and evaluate three approaches to thresholding images of test statistics: Bonferroni, random �eld and the permutation test. Owing to recent developments, improved Bonferroni procedures, such as Hochberg’s methods, are now applicable to dependent data. Continuous random �eld methods use the smoothness of the image to adapt to the severity of the multiple testing problem. Also, increased computing power has made both permutation and bootstrap methods applicable to functional neuroimaging. We evaluate these approaches on t images using simulations and a collection of real datasets. We �nd that Bonferronirelated tests offer little improvement over Bonferroni, while the permutation method offers substantial improvement over the random �eld method for low smoothness and low degrees of freedom. We also show the limitations of trying to �nd an equivalent number of independent tests for an image of correlated test statistics. 1
Neural correlates of firstpersonperspective.
 Trends in Cognitive Science,
, 2003
"... Abstract & Taking the firstperson perspective (1PP) centered upon one's own body as opposed to the thirdperson perspective (3PP), which enables us to take the viewpoint of someone else, is constitutive for human selfconsciousness. At the underlying representational or cognitive level, t ..."
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Cited by 140 (5 self)
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Abstract & Taking the firstperson perspective (1PP) centered upon one's own body as opposed to the thirdperson perspective (3PP), which enables us to take the viewpoint of someone else, is constitutive for human selfconsciousness. At the underlying representational or cognitive level, these operations are processed in an egocentric reference frame, where locations are represented centered around another person's (3PP) or one's own perspective (1PP). To study 3PP and 1PP, both operating in egocentric frames, a virtual scene with an avatar and red balls in a room was presented from different camera viewpoints to normal volunteers (n = 11) in a functional magnetic resonance imaging experiment. The task for the subjects was to count the objects as seen either from the avatar's perspective (3PP) or one's own perspective (1PP). The scene was presented either from a ground view (GV ) or an aerial view (AV ) to investigate the effect of view on perspective taking. The factors perspective (3PP vs. 1PP) and view (GV vs. AV ) were arranged in a twofactorial way. Reaction times were increased and percent correctness scores were decreased in 3PP as opposed to 1PP. To detect the neural mechanisms associated with perspective taking, functional magnetic resonance imaging was employed. Data were analyzed using SPM'99 in each subject and nonparametric statistics on the group level. Activations common to 3PP and 1PP (relative to baseline) were observed in a network of occipital, parietal, and prefrontal areas. Deactivations common to 3PP and 1PP (relative to baseline) were observed predominantly in mesial (i.e., parasagittal) cortical and lateral superior temporal areas bilaterally. Differential increases of neural activity were found in mesial superior parietal and right premotor cortex during 3PP (relative to 1PP), whereas differential increases during 1PP (relative to 3PP) were found in mesial prefrontal cortex, posterior cingulate cortex, and superior temporal cortex bilaterally. The data suggest that in addition to joint neural mechanisms, for example, due to visuospatial processing and decision making, 3PP and 1PP rely on differential neural processes. Mesial cortical areas are involved in decisional processes when the spatial task is solved from one's own viewpoint, whereas egocentric operations from another person's perspective differentially draw upon cortical areas known to be involved in spatial cognition. &
Multilevel linear modelling for FMRI group analysis using Bayesian inference
 Neuroimage
, 2004
"... Functional magnetic resonance imaging studies often involve the acquisition of data from multiple sessions and/or multiple subjects. A hierarchical approach can be taken to modelling such data with a general linear model (GLM) at each level of the hierarchy introducing different random effects varia ..."
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Cited by 90 (6 self)
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Functional magnetic resonance imaging studies often involve the acquisition of data from multiple sessions and/or multiple subjects. A hierarchical approach can be taken to modelling such data with a general linear model (GLM) at each level of the hierarchy introducing different random effects variance components. Inferring on these models is nontrivial with frequentist solutions being unavailable. A solution is to use a Bayesian framework. One important ingredient in this is the choice of prior on the variance components and toplevel regression parameters. Due to the typically small numbers of sessions or subjects in neuroimaging, the choice of prior is critical. To alleviate this problem, we introduce to neuroimage modelling the approach of reference priors, which drives the choice of prior such that it is noninformative in an informationtheoretic sense. We propose two inference techniques at the top level for multilevel hierarchies (a fast approach and a slower more accurate approach). We also demonstrate that we can infer on the top level of multilevel hierarchies by inferring on the levels of the hierarchy separately and passing summary statistics of a noncentral multivariate t distribution between them.
Mapping cortical change in Alzheimer’s disease, brain development, and schizophrenia
, 2004
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An evaluation of thresholding techniques in fMRI analysis
, 2004
"... This paper reviews and compares individual voxelwise thresholding methods for identifying active voxels in singlesubject fMRI datasets. Different error rates are described which may be used to calibrate activation thresholds. We discuss methods which control each of the error rates at a prespecifi ..."
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Cited by 53 (21 self)
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This paper reviews and compares individual voxelwise thresholding methods for identifying active voxels in singlesubject fMRI datasets. Different error rates are described which may be used to calibrate activation thresholds. We discuss methods which control each of the error rates at a prespecified level a, including simple procedures which ignore spatial correlation among the test statistics as well as more elaborate ones which incorporate this correlation information. The operating characteristics of the methods are shown through a simulation study, indicating that the error rate used has an important impact on the sensitivity of the thresholding method, but that accounting for correlation has little impact. Therefore, the simple procedures described work well for thresholding most singlesubject fMRI experiments and are recommended. The methods are illustrated with a real bilateral finger tapping experiment
Permutation Tests for Classification: Towards Statistical Significance in ImageBased Studies
, 2003
"... Estimating statistical significance of detected differences between two groups of medical scans is a challenging problem due to the high dimensionality of the data and the relatively small number of training examples. In this paper, we demonstrate a nonparametric technique for estimation of statis ..."
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Cited by 45 (0 self)
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Estimating statistical significance of detected differences between two groups of medical scans is a challenging problem due to the high dimensionality of the data and the relatively small number of training examples. In this paper, we demonstrate a nonparametric technique for estimation of statistical significance in the context of discriminative analysis (i.e., training a classifier function to label new examples into one of two groups). Our approach adopts permutation tests, first developed in classical statistics for hypothesis testing, to estimate how likely we are to obtain the observed classification performance, as measured by testing on a holdout set or crossvalidation, by chance. We demonstrate the method on examples of both structural and functional neuroimaging studies.
Generalized TensorBased Morphometry of HIV/AIDS Using Multivariate Statistics on Deformation Tensors
"... Abstract—This paper investigates the performance of a new multivariate method for tensorbased morphometry (TBM). Statistics on Riemannian manifolds are developed that exploit the full information in deformation tensor fields. In TBM, multiple brain images are warped to a common neuroanatomical temp ..."
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Cited by 44 (10 self)
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Abstract—This paper investigates the performance of a new multivariate method for tensorbased morphometry (TBM). Statistics on Riemannian manifolds are developed that exploit the full information in deformation tensor fields. In TBM, multiple brain images are warped to a common neuroanatomical template via 3D nonlinear registration; the resulting deformation fields are analyzed statistically to identify group differences in anatomy. Rather than study the Jacobian determinant (volume expansion factor) of these deformations, as is common, we retain the full deformation tensors and apply a manifold version of Hotelling’s 2 test to them, in a LogEuclidean domain. In 2D and 3D magnetic resonance imaging (MRI) data from 26 HIV/AIDS patients and 14 matched healthy subjects, we compared multivariate tensor analysis versus univariate tests of simpler tensorderived indices: the Jacobian determinant, the trace, geodesic anisotropy, and eigenvalues of the deformation tensor, and the angle of rotation of its eigenvectors. We detected consistent, but more extensive patterns of structural abnormalities, with multivariate tests on the full tensor manifold. Their improved power was established by analyzing cumulativevalue plots using false discovery rate (FDR) methods, appropriately controlling for false positives. This increased detection sensitivity may empower drug trials and largescale studies of disease that use tensorbased morphometry. Index Terms—Brain, image analysis, Lie groups, magnetic resonance imaging (MRI), statistics. I.
Waveletgeneralized least squares: a new BLU estimator of linear regression models with 1/f errors
 NeuroImage
, 2002
"... Longmemory noise is common to many areas of signal processing and can seriously confound estimation of linear regression model parameters and their standard errors. Classical autoregressive moving average (ARMA) methods can adequately address the problem of linear time invariant, shortmemory error ..."
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Cited by 42 (0 self)
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Longmemory noise is common to many areas of signal processing and can seriously confound estimation of linear regression model parameters and their standard errors. Classical autoregressive moving average (ARMA) methods can adequately address the problem of linear time invariant, shortmemory errors but may be inefficient and/or insufficient to secure type 1 error control in the context of fractal or scale invariant noise with a more slowly decaying autocorrelation function. Here we introduce a novel method, called waveletgeneralized least squares (WLS), which is (to a good approximation) the best linear unbiased (BLU) estimator of regression model parameters in the context of longmemory errors. The method also provides maximum likelihood (ML) estimates of the Hurst exponent
Mapping anatomical correlations across cerebral cortex (MACACC) using cortical thickness from MRI. Neuroimage. 31:9931003
, 2006
"... (MACACC) using cortical thickness from MRI ..."
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A comparison of random field theory and permutation methods for the statistical analysis of MEG data. NeuroImage
 Neuroimage
, 2005
"... We describe the use of random field and permutation methods to detect activation in cortically constrained maps of current density computed from MEG data. The methods are applicable to any inverse imaging method that maps eventrelated MEG to a coregistered cortical surface. These approaches also ex ..."
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Cited by 37 (7 self)
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We describe the use of random field and permutation methods to detect activation in cortically constrained maps of current density computed from MEG data. The methods are applicable to any inverse imaging method that maps eventrelated MEG to a coregistered cortical surface. These approaches also extend directly to images computed from eventrelated EEG data. We determine statistical thresholds that control the familywise error rate (FWER) across space or across both space and time. Both random field and permutation methods use the distribution of the maximum statistic under the null hypothesis to find FWER thresholds. The former methods make assumptions on the distribution and smoothness of the data and use approximate analytical solutions, the latter resample the data and rely on empirical distributions. Both methods account for spatial and temporal correlation in the cortical maps. Unlike previous nonparametric work in neuroimaging, we address the problem of nonuniform specificity that can arise without a Gaussianity assumption. We compare and evaluate the methods on simulated data and experimental data from a somatosensoryevoked response study. We find that the random field methods are conservative with or without smoothing, though with a 5 vertex FWHM smoothness, they are close to exact. Our permutation methods demonstrated exact specificity in simulation studies. In real data, the permutation method was not as sensitive as the RF method, although this could be due to violations of the random field theory assumptions.