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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, Worsley et al 1992) are based on linear models, for example ANCOVA, correlation coefficients and t tests. In the(More)
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 fMRI, but are discussed with particular reference to PET images which represent changes in cerebral blood flow elicited by a specific cognitive or sensorimotor task.(More)
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(More)
Current approaches to detecting significantly activated regions of cerebral tissue use statistical parametric maps, which are thresholded to render the probability of one or more activated regions of one voxel, or larger, suitably small (e. g., 0.05). We present an approximate analysis giving the probability that one or more activated regions of a specified(More)
In this paper we present an approach to the identification of nonlinear input-state-output systems. By using a bilinear approximation to the dynamics of interactions among states, the parameters of the implicit causal model reduce to three sets. These comprise (1) parameters that mediate the influence of extrinsic inputs on the states, (2) parameters that(More)
Voxel-based-morphometry (VBM) is a whole-brain, unbiased technique for characterizing regional cerebral volume and tissue concentration differences in structural magnetic resonance images. We describe an optimized method of VBM to examine the effects of age on grey and white matter and CSF in 465 normal adults. Global grey matter volume decreased linearly(More)
This paper concerns the spatial and intensity transformations that map one image onto another. We present a general technique that facilitates nonlinear spatial (stereotactic) normalization and image realignment. This technique minimizes the sum of squares between two images following nonlinear spatial deformations and transformations of the voxel(More)
In 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(More)
A probabilistic framework is presented that enables image registration, tissue classification, and bias correction to be combined within the same generative model. A derivation of a log-likelihood objective function for the unified model is provided. The model is based on a mixture of Gaussians and is extended to incorporate a smooth intensity variation and(More)
Within the framework of statistical mapping, there are up to now only two tests used to assess the regional significance in functional images. One is based on the magnitude of the foci and tends to detect high intensity signals, while the second is based on the spatial extent of regions defined by a simple thresholding of the statistical map, a test that is(More)