Search Results - "Strother, S.C"

Bayesian Kernel Methods for Analysis of Functional Neuroimages by Lukic, A.S., Wernick, M.N., Tzikas, D.G., Xu Chen, Likas, A., Galatsanos, N.P., Yongyi Yang, Fuqiang Zhao, Strother, S.C.

“…We propose an approach to analyzing functional neuroimages in which (1) regions of neuronal activation are described by a superposition of spatial kernel…”
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Effect of Spatial Alignment Transformations in PCA and ICA of Functional Neuroimages by Lukic, A.S., Wernick, M.N., Yongyi Yang, Kai Hansen, L., Arfanakis, K., Strother, S.C.

“…It has been previously observed that independent component analysis (ICA), if applied to data pooled in a particular way, may lessen the need for spatial…”
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Evaluating fMRI preprocessing pipelines by Strother, S.C.

“…This article reviews the evaluation and optimization of the preprocessing steps for blood-oxygenation-level-dependent (BOLD) functional magnetic resonance…”
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P.263 The impact of different preprocessing strategies in positron emission tomography: a [11C]DASB Study by Nørgaard, M., Ganz, M., Svarer, C., Frokjaer, V.G., Greve, D.N., Strother, S.C., Knudsen, G.M.

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Comparing reconstruction algorithms using a multi-variate analysis by Jeih-San Liow, Anderson, J.R., Strother, S.C.

“…Proposes a voxel-based multi-variate analysis to evaluate the performance of tomographic image reconstruction. This technique allows simultaneous comparison of…”
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Group specific optimisation of fMRI processing steps for child and adult data by Evans, J.W., Todd, R.M., Taylor, M.J., Strother, S.C.

“…Motion is a major issue in functional magnetic resonance imaging (fMRI) dataseries and causes artifacts or increased overall noise obscuring signals of…”
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Optimizing processing choices for motion compensation in pediatric fMRI by Evans, J.W., Todd, R.M., Taylor, M.J., Strother, S.C.

“…Applying standard criterion for acceptable motion (e.g. <1 voxel) in an fMRI timeseries to pediatric data usually results in the loss of a large number of…”
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The Quantitative Evaluation of Functional Neuroimaging Experiments: Mutual Information Learning Curves by Kjems, U., Hansen, L.K., Anderson, J., Frutiger, S., Muley, S., Sidtis, J., Rottenberg, D., Strother, S.C.

“…Learning curves are presented as an unbiased means for evaluating the performance of models for neuroimaging data analysis. The learning curve measures the…”
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Enhancing the multivariate signal of [/sup 15/O] water PET studies with a new nonlinear neuroanatomical registration algorithm [MRI application] by Kjems, U., Strother, S.C., Anderson, J., Law, I., Hansen, L.K.

“…This paper addresses the problem of neuro-anatomical registration across individuals for functional [/sup 15/O] water PET activation studies. A new algorithm…”
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“Iyngby” a Matlab toolbox for fMRI analysis: Results of a user questionnaire by Hansen, L.K., Nielsen, F.Å., Liptrot, M., Strother, S.C., Lange, N., Gade, A., Rottenberg, D.A., Paulson, O.B.

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lyngby 2.0 — A modeler's matlab tool☐ for spatio-temporal analysis of functional neuroimages by Hansen, L.K., Nielsen, F.Å., Liptrot, M.G., Goutte, C., Strother, S.C., Lange, N., Gade, A., Rottenberg, D.A., Paulson, O.B.

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Telecommunications Cost Management by Strother, S. C

“…The telecommunication industry has been regulated since the turn of the century. By the 1990s it had been deregulated in many countries. S.C. Strothers…”
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A concordance correlation coefficient for reproducibility of spatial actibation patterns by Lange, N., Hansen, L.K., Pedersen, M.W., Savoy, R.L., Strother, S.C.

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Visual and quantitative comparisons across models, groups and modalities by Rehm, K., Schaper, K.A., Summers, D.W., Strother, S.C., Rottenberg, D.A.

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Comparing Univariate and Multivariate analyses of [15O]water PET and fMRI data sets by Svarer, C., Strother, S.C., Law, I., Savoy, R., Paulson, O.B.

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Delay analysis of fMRI time series by Goutte, C., Hansen, L.K., Savoy, R., Strother, S.C.

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Practical tradeoffs between noise, quantitation, and number of iterations for maximum likelihood-based reconstructions by Liow, J.-S., Strother, S.C.

“…Emission computerised tomography images reconstructed using a maximum likelihood-expectation maximization (ML)-based method with different reconstruction…”
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A reversible jump Markov chain Monte Carlo algorithm for analysis of functional neuroimages by Lukic, A.S., Wernick, M.N., Galatsanos, N.P., Yongyi Yang, Strother, S.C.

“…We propose a new signal-detection approach for detecting brain activations from PET or fMRI images in a two-state ("on-off") neuroimaging study. We model the…”
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An ICA algorithm for analyzing multiple data sets by Lukic, A.S., Wernick, M.N., Hansen, L.K., Strother, S.C.

“…We derive an independent-component analysis (ICA) method for analyzing two or more data sets simultaneously. Our model permits there to be components…”
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Predicting Performance From Functional Imaging Data by Sidtis, J.J., Anderson, JR, Daly, D., Kneer, M., Strother, S.C., Rottenberg, D.A.

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