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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 400 (39 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 p-value 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 p-values for large search areas in 2-D (Friston et al. 1991), large search regions in 3-D (Worsley et al. 1992), and the usual uncorrected p-value 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 396 (9 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
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 task-related) parameter. We refer to these effects as psychophysiological interactions and relate them to interactions b ..."
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Cited by 376 (21 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 task-related) 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 contribution-dependent 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...
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 278 (95 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.
Voxel-based morphometry—The methods
- Neuroimage
, 2000
"... At its simplest, voxel-based morphometry (VBM) involves a voxel-wise comparison of the local concentration of gray matter between two groups of subjects. The procedure is relatively straightforward and involves spatially normalizing high-resolution images from all the subjects in the study into the ..."
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Cited by 273 (4 self)
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At its simplest, voxel-based morphometry (VBM) involves a voxel-wise comparison of the local concentration of gray matter between two groups of subjects. The procedure is relatively straightforward and involves spatially normalizing high-resolution 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 gray-matter segments. Voxel-wise parametric statistical tests which compare the smoothed gray-matter 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
probabilistic atlas and reference system for the human brain: International consortium for brain mapping (ICBM
- P, MACDONALD D, IACOBONI M, SCHORMANN T, AMUNTS K, PALOMERO-GALLAGHER 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 208 (35 self)
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Motivated by the vast amount of information that is rapidly accumulating about the human brain in digital
Finding the Self? An Event-Related fMRI Study
"... & Researchers have long debated whether knowledge about the self is unique in terms of its functional anatomic representation within the human brain. In the context of memory function, knowledge about the self is typically remembered better than other types of semantic information. But why does ..."
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Cited by 195 (16 self)
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& Researchers have long debated whether knowledge about the self is unique in terms of its functional anatomic representation within the human brain. In the context of memory function, knowledge about the self is typically remembered better than other types of semantic information. But why does this memorial effect emerge? Extending previous research on this topic (see Craik et al., 1999), the present study used event-related functional magnetic resonance imaging to investigate potential neural substrates of self-referential processing. Participants were imaged while making judgments about trait adjectives under three experimental conditions (self-relevance, other-relevance, or case judgment). Relevance judgments, when compared to case judgments, were accompanied by activation of the left inferior frontal cortex and the anterior cingulate. A separate region of the medial prefrontal cortex was selectively engaged during self-referential processing. Collectively, these findings suggest that self-referential processing is functionally dissociable from other forms of semantic processing within the human brain. &
Human Brain Function
, 1997
"... Dynamic representations and generative models of ..."
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Cited by 192 (15 self)
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Dynamic representations and generative models of
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 173 (42 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
