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852
A unified statistical approach for determining significant signals in images of cerebral activation
, 1996
"... Abstract: We present a unified statistical theory for assessing the significance of apparent signal observed in noisy difference images. The results are usable in a wide range of applications, including astrophysics, but are discussed with particular reference to images which represent changes in ce ..."
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Cited by 300 (38 self)
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Abstract: We present a unified statistical theory for assessing the significance of apparent signal observed in noisy difference images. The results are usable in a wide range of applications, including astrophysics, but are discussed with particular reference to images which represent changes in cerebral blood flow elicited by a specific cognitive or sensorimotor task. Our main result is an estimate of the pvalue for local maxima of Gaussian, t, χ 2 and F fields over search regions of any shape or size in any number of dimensions. This unifies the pvalues for large search areas in 2D (Friston et al. 1991), large search regions in 3D (Worsley et al. 1992), and the usual uncorrected pvalue at a single pixel or voxel.
Nonparametric Permutation Tests for Functional Neuroimaging: A Primer with Examples. Human Brain Mapping
, 2001
"... The statistical analyses of functional mapping experiments usually proceeds at the voxel level, involving the formation and assessment of a statistic image: at each voxel a statistic indicating evidence of the experimental effect of interest, at that voxel, is computed, giving an image of statistics ..."
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Cited by 266 (8 self)
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The statistical analyses of functional mapping experiments usually proceeds at the voxel level, involving the formation and assessment of a statistic image: at each voxel a statistic indicating evidence of the experimental effect of interest, at that voxel, is computed, giving an image of statistics, a statistic
HAMMER: hierarchical attribute matching mechanism for elastic registration
 IEEE Trans. on Medical Imaging
, 2002
"... A new approach is presented for elastic registration of medical images, and is applied to magnetic resonance images of the brain. Experimental results demonstrate remarkably high accuracy in superposition of images from different subjects, thus enabling very precise localization of morphological cha ..."
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Cited by 251 (90 self)
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A new approach is presented for elastic registration of medical images, and is applied to magnetic resonance images of the brain. Experimental results demonstrate remarkably high accuracy in superposition of images from different subjects, thus enabling very precise localization of morphological characteristics in population studies. There are two major novelties in the proposed algorithm. First, it uses an attribute vector, i.e. a set of geometric moment invariants that is defined on each voxel in an image, to reflect the underlying anatomy at different scales. The attribute vector, if rich enough, can distinguish between different parts of an image, which helps establish anatomical correspondences in the deformation procedure. This is a fundamental deviation of our method from other volumetric deformation methods, which are typically based on maximizing image similarity. Second, it employs a hierarchical deformation mechanism, which is initially influenced by parts of the anatomy that can be identified relatively more reliably than others. Moreover, the deformation mechanism involves a sequence of local smooth transformations, which do not update positions of individual voxels, but rather are based on evaluating a similarity of attribute vectors over a larger subvolume of a volumetric image. This renders this algorithm very robust to suboptimal solutions. A number of experiments in this paper have demonstrated excellent performance. 1.
Psychophysiological and Modulatory Interactions in Neuroimaging
, 1997
"... this paper we introduce the idea of explaining responses, in one cortical area, in terms of an interaction between the influence of another area and some experimental (sensory or taskrelated) parameter. We refer to these effects as psychophysiological interactions and relate them to interactions b ..."
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Cited by 223 (20 self)
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this paper we introduce the idea of explaining responses, in one cortical area, in terms of an interaction between the influence of another area and some experimental (sensory or taskrelated) parameter. We refer to these effects as psychophysiological interactions and relate them to interactions based solely on experimental factors (i.e., psychological interactions), in factorial designs, and interactions among neurophysiological measurements (i.e., physiological interactions) . We have framed psychophysiological interactions in terms of functional integration by noting that the degree to which the activity in one area can be predicted, on the basis of activity in another, corresponds to the contribution of the second to the first, where this contribution can be related to effective connectivity. A psychophysiological interaction means that the contribution of one area to another changes significantly with the experimental or psychological context.Alternatively these interactions can be thought of as a contributiondependent change in regional responses to an experimental or psychological factor. In other words the contribution can be thought of as modulating the responses elicited by a particular stimulus or psychological process. The potential importance of this approach lies in (i) conferring a degree of functional specificity on this aspect of effective connectivity and (ii) providing a model of modulation, where the contribution from a distal area can be considered to modulate responses to the psychological or stimulusspecific factor defining the interaction. Although distinct in neurobiological terms, these are equivalent perspectives on the same underlying interaction. We illustrate these points using a functional magnetic resonance imaging study of attention t...
Voxelbased morphometry—The methods
 Neuroimage
, 2000
"... At its simplest, voxelbased morphometry (VBM) involves a voxelwise comparison of the local concentration of gray matter between two groups of subjects. The procedure is relatively straightforward and involves spatially normalizing highresolution images from all the subjects in the study into the ..."
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Cited by 183 (3 self)
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At its simplest, voxelbased morphometry (VBM) involves a voxelwise comparison of the local concentration of gray matter between two groups of subjects. The procedure is relatively straightforward and involves spatially normalizing highresolution images from all the subjects in the study into the same stereotactic space. This is followed by segmenting the gray matter from the spatially normalized images and smoothing the graymatter segments. Voxelwise parametric statistical tests which compare the smoothed graymatter images from the two groups are performed. Corrections for multiple comparisons are made using the theory of Gaussian random fields. This paper describes the steps involved in VBM, with particular emphasis on segmenting gray matter from MR images with nonuniformity artifact. We provide evaluations of the assumptions that underpin the method, including the accuracy of the segmentation and the assumptions made about the statistical distribution of the data. © 2000 Academic Press
Human Brain Function
, 1997
"... Dynamic representations and generative models of ..."
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Cited by 158 (14 self)
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Dynamic representations and generative models of
Spatial Transformation and Registration of Brain Images Using Elastically Deformable Models
, 1997
"... The development of algorithms for the spatial transformation and registration of tomographic brain images is a key issue in several clinical and basic science medical applications, including computer aided neurosurgery, functional image analysis, and morphometrics. This paper describes a technique f ..."
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Cited by 155 (29 self)
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The development of algorithms for the spatial transformation and registration of tomographic brain images is a key issue in several clinical and basic science medical applications, including computer aided neurosurgery, functional image analysis, and morphometrics. This paper describes a technique for the spatial transformation of brain images, which is based on elastically deformable models. A deformable surface algorithm is used to find a parametric representation of the outer cortical surface and then and then to define a map between corresponding cortical regions in two brain images. Based on the resulting map, a threedimensional elastic warping transformation is then determined, which brings two images into register. This transformation models images as inhomogeneous elastic objects which are deformed into registration with each other by external force fields. The elastic properties of the images can vary from one region to the other, allowing more variable brain regions, such as...
Classical and Bayesian inference in neuroimaging: Theory
 NeuroImage
, 2002
"... This paper reviews hierarchical observation models, used in functional neuroimaging, in a Bayesian light. It emphasizes the common ground shared by classical and Bayesian methods to show that conventional analyses of neuroimaging data can be usefully extended within an empirical Bayesian framework. ..."
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Cited by 146 (39 self)
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This paper reviews hierarchical observation models, used in functional neuroimaging, in a Bayesian light. It emphasizes the common ground shared by classical and Bayesian methods to show that conventional analyses of neuroimaging data can be usefully extended within an empirical Bayesian framework. In particular we formulate the procedures used in conventional data analysis in terms of hierarchical linear models and establish a connection between classical inference and parametric empirical Bayes (PEB) through covariance component estimation. This estimation is based on an expectation maximization or EM algorithm. The key point is that hierarchical models not only provide for appropriate inference at the highest level but that one can revisit lower levels suitably
probabilistic atlas and reference system for the human brain: International consortium for brain mapping (ICBM
 P, MACDONALD D, IACOBONI M, SCHORMANN T, AMUNTS K, PALOMEROGALLAGHER N, GEYER S, PARSONS L, NARR K, KABANI N, LE GOUALHER G, BOOMSMA D, CANNON T, KAWASHIMA R and MAZOYER B. A
, 2001
"... Motivated by the vast amount of information that is rapidly accumulating about the human brain in digital ..."
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Cited by 141 (32 self)
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Motivated by the vast amount of information that is rapidly accumulating about the human brain in digital