Partial least squares analysis of neuroimaging data: applications and advances.

By Anthony Randal McIntosh, Nancy J. Lobaugh. NeuroImage, 2004

1,126 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Food Science

View Paper: https://www.semanticscholar.org/paper/3c5cb8fec4eebb4b06219f043ad8eb269baf1e7a

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Citations in the corpus

Clinical applications of the functional connectome (2013, neutral): “Once extracted, there is no clear statistical theory for thresholding the weights to determine which have statistically significant involvement, although nonparametric methods such as bootstrapping can be used (McIntosh and Lobaugh, 2004).”
The prediction error in CLS and PLS: the importance of feature selection prior to multivariate calibration (2005, methodology): “Therefore, the use of PLS as a dimensional reduction tool in spectroscopy, as well as its recent use in other areas with a large number of variables and a small number of samples, such as functional MRI [ 46 ] and gene expression data [47], should be carefully re-examined.”
Power in Voxel-based Lesion-Symptom Mapping (2007, methodology): “Such techniques include machine learning techniques such as Support Vector Machines (Mourao-Miranda, Bokde, Born, Hampel, & Stetter, 2005) and MVPA (Norman, Polyn, Detre, & Haxby, 2006), Canonical Correlation Analysis (Nandy & Cordes, 2003), and Partial Least Squares ( McIntosh & Lobaugh, 2004 ).”
Partial least squares regression and projection on latent structure regression (PLS Regression) (2010, background): “Ŷ = TBCT = XBPLS with BPLS = (PT+)BCT (8)”
Network-Level Structure-Function Relationships in Human Neocortex (2016, methodology): “In the present analysis, this corresponds to a weighted combination of structural connections (interpreted as a structural network) and aweighted combination of functional connections (interpreted as a functional network).”
Partial Least Square Regression PLS-Regression (2007, methodology): “But PLS regression is also becoming a tool of choice in the social sciences as a multivariate technique for nonexperimental and experimental data alike (e.g., neuroimaging, see Mcintosh & Lobaugh, 2004; Worsley, 1997).”
Author ' s personal copy Partial Least Squares ( PLS ) methods for neuroimaging : A tutorial and review (2011, positive): “PLSC canbe subdivided into four variants: (1) behavior PLSC, (2) task PLSC, (3) seed PLSC, and (4) multi-table or multi-block PLSC (McIntosh and Lobaugh, 2004; McIntosh et al., 1998).”
The co-occurrence of multisensory facilitation and cross-modal conflict in the human brain. (2011, neutral): “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).”
Machine learning classifiers and fMRI: a tutorial overview (2008, background): “…this is that it will generally be possible to find a function that can classify the examples in the training set well, without this necessarily meaning that it will do well in the test set (for a more detailed explanation of the reason why see Mitchell, 1997): this phenomenon is called overfitting.”
Increased Brain Signal Variability Accompanies Lower Behavioral Variability in Development (2008, methodology): “Statistical assessment of maturational trends in MSE and PCA was performed using partial least squares (PLS) for EEG data [24].”
Models of the Aging Brain Structure and Individual Decline (2012, background): “Finally, the obtained pairs of brain regional patterns and predictor profiles in a sample can be generalized to population level by the use of permutation tests (McIntosh and Lobaugh, 2004).”
Analyzing Neuroimaging Data Through Recurrent Deep Learning Models (2018, background): “Over the recent years, the whole-brain lasso, as well as closely related decoding approaches (e.g., Gramfort et al., 2013; McIntosh and Lobaugh, 2004; Ryali et al., 2010), have found widespread application throughout the neuroscience literature (e.”