Characterizing dynamic brain responses with fMRI: a multivariate approach.

By Karl J. Friston, ChrisD . Frith, Richard S. J. Frackowiak, Robert Turner. 1995

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Citations in the corpus

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging (2015, positive): “Pioneering work that applied multivariate analysis to functional time series data was presented in Friston et al. (1995) Friston et al. (1993).”
Reconstructing subject‐specific effect maps (2017, unknown): “At this point, we should also point out that multivariate methods lack the possibility to perform localized interpretations (Friston et al., 1995) contrary to univariate models.”
Spatial pattern analysis of functional brain images using partial least squares. (1996, methodology): “The PLS analysis is quite different in principle from the analysis of eigenimages recently suggested by Friston et al. (1995).”
The Global Signal in fMRI: Nuisance or Information? (2017, unknown): “Common choices for the regressor terms are the low-order Legendre polynomials (e.g linear and quadratic terms) and low-frequency sine and cosine terms (Friston et al., 1995a; Cox, 1996).”
A Primer on Pattern-Based Approaches to fMRI: Principles, Pitfalls, and Perspectives. (2015, methodology): “…fMRI signal patterns, a multivariate (as opposed to mass-univariate) framework is required (Allefeld and Haynes, 2015; Cox and Savoy, 2003; Friston et al., 1995a; Haynes and Rees, 2006; Haxby et al., 2001; Haxby et al., 2014; Kamitani and Tong, 2005; Kriegeskorte et al., 2006; Mitchell et…”
Deconstructing multivariate decoding for the study of brain function (2017, background): “It is worth noting that multivariate encoding approaches with similar potential to multivariate decoding have been suggested previously, such as MANCOVA (Friston et al., 1995), canonical correlation analysis (Friman et al.”
Characterizing the response of PET and fMRI data using multivariate linear models. (1997, methodology): “-CVA (Preliminary SVD, then CVA) Friston et al. (1995b, 1996) overcome the problem of more voxels than scans by reducing the number of voxels in a preliminary SVD step: Y Ϸ U*Ј⌳*V*, replacing Y by the reduced set of variables Y* ϭ YV*Ј, then finding canonical variates between X and Y*: (XЈX) Ϫ1/2…”
Relevant Feature Set Estimation with a Knock-out Strategy and Random Forests (2015, unknown): “, 2011), canonical variant analysis (Friston, 1997), (Friston et al., 1995) and multivariate linear modeling (Worsley et al.”
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.”
Shared networks for auditory and motor processing in professional pianists: Evidence from fMRI conjunction (2006, methodology): “Active voxels were searched for using the General Linear Model approach for the time-series data (Friston et al., 1995a).”
The neural correlates of theory of mind within interpersonal interactions (2004, methodology): “A separate general linear model (GLM) was defined for each participant (Friston et al., 1995) in both games that compared the neural response to human, computer, and control trials.”
Dissociable Neural Responses in Human Reward Systems (2000, methodology): “Two levels of statistical inference were used, in accordance with the established SPM conventions discussed in detail elsewhere (Friston et al., 1995b,c,e).”
Traditional Chinese Acupuncture and Placebo (Sham) Acupuncture Are Differentiated by Their Effects on μ-Opioid Receptors (MORs) (2009, methodology): “05 after correction by multiple comparisons using family wise error approach (Friston et al., 1995a,b).”
Neocortical modulation of the amygdala response to fearful stimuli. (2003, methodology): “Whole-brain image analysis was completed using the general linear model in SPM99 (Friston et al 1995).”
SubCMap: Subject and Condition Specific Effect Maps (2017, methodology): “However, it is a univariate method by design to allow for localised interpretations, which is not trivial with multivariate approaches [28], and to avoid issues related to effects of regularization.”
Parcellation‐dependent small‐world brain functional networks: A resting‐state fMRI study (2009, methodology): “First, all functional images were corrected for the acquisition time delay between slices of each volume using the sinc interpolation and for the geometrical displacement due to head movement using a six-parameter (rigid body) spatial transformation (Friston et al., 1995).”
Reward, motivation, and emotion systems associated with early-stage intense romantic love. (2005, methodology): “The fMRI data analyses were performed using Statistical Parametric Mapping software (SPM 99 Wellcome Department of Imaging Neuroscience, London, UK; Friston et al. 1995).”
Activations related to "mirror" and "canonical" neurones in the human brain: an fMRI study (2003, methodology): “The normalized images of 2 2 2 mm3 were spatially smoothed by a Gaussian kernel of FWHM 6 mm (Ashburner and Friston, 1997; Friston et al., 1995).”
Decreased activation of lateral orbitofrontal cortex during risky choices under uncertainty is associated with disadvantageous decision-making and suicidal behavior (2010, unknown): “The contrast images were then entered into one-sample t-tests, to instantiate random-effects group analyses (Friston et al., 1995).”