Characterizing the response of PET and fMRI data using multivariate linear models.

By Keith J. Worsley, J B Poline, Karl J. Friston, Alan C. Evans. 1997

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Citations in the corpus

Visual Feature Attribution Using Wasserstein GANs (2017, methodology): “The most common approach is to use regression analysis or machine learning tools to generate population average maps, which highlights features that are salient across the population [3, 32, 61, 16, 39, 45, 14].”
Relevant Feature Set Estimation with a Knock-out Strategy and Random Forests (2015, unknown): “, 1995) and multivariate linear modeling (Worsley et al., 1997).”
Learning brain connectivity of Alzheimer’s disease by sparse inverse covariance estimation (2010, methodology): “…have been used, such as principle component analysis (PCA) (Friston, 1994), PCA-based scaled sub-pro ﬁ le model (Alexander and Moeller, 1994), independent component analysis (Calhoun et al., 2001; Calhoun et al., 2003), and partial least squares (McIntosh et al., 1996; Worsley et al., 1997).”
A Survey of Multi-View Representation Learning (2016, unknown): “To handle the influence of outliers, Archambeau et al. [67] introduce robust canonical correlation analysis by replacing Gaussian distributions with Student-t distributions, which constructs mixtures of robust CCA and can deal with missing data quite easily.”
Fair Kernel Learning (2017, methodology): “The solution resembles that of the standard orthonormalized partial least squares (OPLS) [28].”
Constructing Subject- and Disease-Specific Effect Maps: Application to Neurodegenerative Diseases (2016, background): “Inference-based models, whether univariate [1] or multivariate [7,8], detect condition-related diﬀerences in image-based measurements between two groups of subjects, one composed of patients and the other composed of controls.”
Increased sensitivity in neuroimaging analyses using robust regression (2005, background): “the time series data at each voxel (Worsley and Friston, 1995; Worsley et al., 1997).”
Support vector machine learning-based fMRI data group analysis (2007, methodology): “Treating each functional image as an independent observation without considering the acquisition time, the SVM-based ap- proaches have the disadvantage of ignoring the dynamic changes in the brain response, which can be explored using other multivariate methods such as ICA, CVA, or PLS.”
Global analytic solution of fully-observed variational Bayesian matrix factorization (2013, methodology): “, 2004) and partial least-squares (Worsley et al., 1997; Rosipal and Krämer, 2006) are also closely related to MF.”
Multivariate Model Specification for fMRI Data (2002, methodology): “Before applying the MLM-constructed model (or MoDef model) at the voxel level, we check that this model led to a significant global test across voxels using the test proposed in (Worsley et al., 1995, 1997).”
Theoretical Analysis of Bayesian Matrix Factorization (2011, methodology): “…and Hornik, 1995; Reinsel and Velu, 1998), canonical correlation analysis (Hotelling, 1936; Anderson, 1984), partial least-squares (Wold, 1966; Worsley et al., 1997; Rosipal and Krämer, 2006), and multi-task learning (Chapelle and Harchaoui, 2005; Yu et al., 2005) can be seen as special…”
Reconstructing subject‐specific effect maps (2017, unknown): “n used at the population level, where at each measurement, maps indicate statistical relationship between the condition and measurement across the entire population. Either a group analysis technique [2,22,40] or a machine learning based predictive model [1,14,26,31,13] is used to compare two cohort”