Functional Connectivity: The Principal-Component Analysis of Large (PET) Data Sets

By Karl J. Friston, ChrisD . Frith, Peter F. Liddle, Richard S. J. Frackowiak. 1993

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Citations in the corpus

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging (2015, positive): “For instance, voxel based morphometry Ashburner and Friston (2000) Friston et al. (1993) Chung (2012) for population wide neuroimaging analysis still remains one of the most informative and useful tools in the neuroimaging community.”
Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer's Disease (2008, background): “Functional connectivity is defined as temporal correlations between spatially distinct brain regions [7].”
Detection of functional connectivity using temporal correlations in MR images (2002, methodology): “Finally, there are global variance-based analyses that can be used to identify distributed networks of brain areas that behave in similar ways over time or across tasks, such as principle-component analysis [Friston et al., 1993] and partial least squares [McIntosh et al.”
Support vector machine learning-based fMRI data group analysis (2007, methodology): “Several multivariate methods have been used in fMRI data analysis over the last decade, including principal component analysis (PCA) (Friston et al., 1993; Bullmore et al., 1996), partial least square (PLS) (McIntosha et al., 1996), canonical variate analysis (CVA) (Friston et al., 1995a; Worsley…”
Disease state prediction from resting state functional connectivity (2009, background): “Correlations between these fluctuations in spatially remote brain regions meet the definition of functional connectivity (4).”
Measuring interregional functional connectivity using coherence and partial coherence analyses of fMRI data (2004, background): “Functional connectivity, defined by Friston et al. (1993) as the ''temporal correlation between spatially remote neurophysiological events'', is one way to characterize such interactions.”
Brain spontaneous functional connectivity and intelligence (2008, unknown): “These temporal correlations suggest direct or indirect interactions between brain regions (Friston et al., 1993).”
Hierarchical Mixture of Classification Experts Uncovers Interactions between Brain Regions (2009, methodology): “Model-driven methods usually test a prior hypothesis by correlating the time courses of a seed voxel and a target voxel [12].”
Neurophysiological architecture of functional magnetic resonance images of human brain. (2005, unknown): “Keywords: connectivity, multivariate, network, scaling, symmetry, systems”
An Investigation of Functional and Anatomical Connectivity Using Magnetic Resonance Imaging (2002, background): “This phenomenon is an example of functional connectivity as defined by Friston et al. (1993).”
Two systems of resting state connectivity between the insula and cingulate cortex (2009, background): “Functional connectivity has been operationally defined to refer to temporal correlations across cortical regions and can represent an index of brain function (Friston et al., 1993; Horwitz, 2003).”
Independent component analysis of fMRI group studies by self-organizing clustering (2005, background): “The multivariate statistical nature of ICA allows one to transform threedimensional fMRI data sets into brain activity patterns starting from the spatial or temporal covariance of the measured signals and reveals multiple spatiotemporal bmodesQ of signal variability (Friston et al., 1993).”
Review of methods for functional brain connectivity detection using fMRI (2008, methodology): “…connections between two brain sites [12] and effective connectivity which characterizes the influence that a neural system may exert over another [10], functional connectivity examines regional interactions in the brain at a macro level, using datasets from electroencephalographic (EEG),…”