K
Karl J. Friston
Researcher at University College London
Publications - 1361
Citations - 243060
Karl J. Friston is an academic researcher from University College London. The author has contributed to research in topics: Inference & Bayesian inference. The author has an hindex of 217, co-authored 1267 publications receiving 217169 citations. Previous affiliations of Karl J. Friston include Queen's University & Wellcome Trust Centre for Neuroimaging.
Papers
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Journal ArticleDOI
Statistical parametric maps in functional imaging: A general linear approach
Karl J. Friston,Andrew P. Holmes,Keith J. Worsley,J-B. Poline,Chris D. Frith,Richard S. J. Frackowiak +5 more
TL;DR: In this paper, the authors present 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).
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Voxel-Based Morphometry—The Methods
John Ashburner,Karl J. Friston +1 more
TL;DR: In this paper, the authors describe the steps involved in VBM, with particular emphasis on segmenting gray matter from MR images with non-uniformity artifact and 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.
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The free-energy principle: a unified brain theory?
TL;DR: This Review looks at some key brain theories in the biological and physical sciences from the free-energy perspective, suggesting that several global brain theories might be unified within a free- energy framework.
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A voxel-based morphometric study of ageing in 465 normal adult human brains.
Catriona D. Good,Ingrid S. Johnsrude,John Ashburner,Richard N. Henson,Richard N. Henson,Karl J. Friston,Richard S. J. Frackowiak +6 more
TL;DR: Global grey matter volume decreased linearly with age, with a significantly steeper decline in males, and local areas of accelerated loss were observed bilaterally in the insula, superior parietal gyri, central sulci, and cingulate sulci.
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Dynamic causal modelling.
TL;DR: As with previous analyses of effective connectivity, the focus is on experimentally induced changes in coupling, but unlike previous approaches in neuroimaging, the causal model ascribes responses to designed deterministic inputs, as opposed to treating inputs as unknown and stochastic.