A Primer on Pattern-Based Approaches to fMRI: Principles, Pitfalls, and Perspectives.

By John-Dylan Haynes. Neuron, 2015

414 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/437a3fdeaab605a83f2ef9415a29d2b718903ef1

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In Cognitive Neuroscience

Citations in the corpus

Assessing and tuning brain decoders: Cross‐validation, caveats, and guidelines (2016, background): “Decoding, ie predicting behavior or phenotypes from brain images or signals, has become a central tool in neuroimage data processing [20, 21, 24, 42, 58, 61].”
Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data (2016, methodology): “The most popular applications of MVPA are decoding (for recent reviews on fMRI decoding, see e.g., Haynes, 2015; Pereira et al., 2009) and, more recently, representational similarity analysis (RSA: Kriegeskorte & Kievit, 2013).”
Strong Evidence for Pattern Separation in Human Dentate Gyrus (2016, methodology): “Second, we used multivariate fMRI methods to investigate whether the DG performs pattern separation, an analytic technique that is particularly appropriate given that this computation is defined at the level of the underlying representations (Treves and Rolls, 1994; McClelland et al., 1995; Norman a”
The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features (2018, positive): “…Accepted 1 Jun . multivariate pattern analysis for exploring the relationship between behavior/cognition and complex patterns of multiple neuroimaging features, which is complementary to conventional brain-behavior correlational analysis (Dosenbach et al., 2010; Haynes, 2015; Norman et al., 2006).”
Cognitive computational neuroscience (2018, methodology): “The literature goes far beyond these examples and has been repeatedly reviewed.(70,80,78,73) A particularly impressive form of decoding is stimulus reconstruction.”
Deconstructing multivariate decoding for the study of brain function (2017, methodology): “…Rees, 2006; Norman et al., 2006, but see Allefeld et al., 2016) and ii) the increased specificity in being able to access widely distributed population codes by the joint analysis of multiple voxels that would not be available by assessing each voxel separately (Haynes, 2015; Kriegeskorte, 2011)11.”