Spatial pattern analysis of functional brain images using partial least squares.

By Anthony Randal McIntosh, Fred L. Bookstein, James V. Haxby, C. L. Grady. 1996

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Citations in the corpus

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging (2015, positive): “Early work in McIntosh et al. (1996) described the extraction of multivariate spatial patterns using partial least squares regression.”
The co-occurrence of multisensory facilitation and cross-modal conflict in the human brain. (2011, unknown): “Importantly, with the use of bootstrap estimation of standard errors, no correction for multiple comparisons was necessary, because the source saliences were calculated in a single mathematical step on the whole brain at once (McIntosh et al. 1996; McIntosh and Lobaugh 2004).”
Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data (2005, methodology): “McIntosh et al. (1996) introduced partial least squares (PLS) as a multivariate method.”
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.”
Network-Level Structure-Function Relationships in Human Neocortex (2016, methodology): “Here, we use amultivariate statistical technique (partial least squares; PLS;McIntosh et al. 1996;McIntosh and Lobaugh 2004) to investigate the relationship between structural and functional networks, derived from diffusion-weighted imaging (DWI) and functional magnetic resonance imaging (fMRI).”
Corpus Callosum Shape and Neuropsychological Deficits in Adult Males with Heavy Fetal Alcohol Exposure (2002, methodology): “The statistical technique of partial least squares (PLS) is useful for representing relationships between highdimensional measurement blocks (Bookstein et al., 1996; McIntosh et al., 1996).”
Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest (2013, methodology): “This approach is similar to how significant latent dimensions are estimated in PLS (Krishnan et al., 2011; McIntosh et al., 1996) and in other fields studying time series (e.”
Effect of bilingualism on cognitive control in the Simon task: evidence from MEG (2005, methodology): “The statistical significance of each LV as a whole was assessed using a permutation test (Edgington, 1980; McIntosh et al., 1996 ).”
Limbic-frontal circuitry in major depression: a path modeling metanalysis (2004, methodology): “…to be tested, and to refine the voxel selection for each region to be used in the path analysis, all 119 patients were combined and compared to all controls (n = 42) in a partial least squares (PLS) analysis (see McIntosh et al., 1996 for description of PLS), run in Matlab (Mathworks Inc., MA).”
Morphological classification of brains via high-dimensional shape transformations and machine learning methods (2004, unknown): “Although linear multivariate methods have been previously proposed in the literature (McIntosh et al., 1996), we have found that nonlinear methods result into much better characterization of population differences.”
Disease state prediction from resting state functional connectivity (2009, methodology): “This is an adaptation of a previously described approach for determining a threshold for multivariate patterns derived from partial least squares (29).”
Health, USA Reviewed by: (2010, unknown): “subject- or experimental-variables associated with them (McIntosh et al., 1996; McIntosh and Lobaugh, 2004) as well”
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., 1996].”