Beyond mind-reading: multi-voxel pattern analysis of fMRI data.

By Kenneth A. Norman, Sean M. Polyn, Greg Detre, James V. Haxby. Trends in Cognitive Sciences, 2006

2,417 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/9e7c6355f3a50cf5aa3c2a1e4d5db56d289b14a4

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In Cognitive Neuroscience

Citations in the corpus

Multivariate pattern analysis of fMRI: The early beginnings (2012, background): “…of review papers began to appear that presented the principles of MVPA in a clear and accessible manner, further demystifying the basis of this new analytic approach (Haynes and Rees, 2006; Mur et al., 2009; Norman et al., 2006; O'Toole et al., 2007; Pereira et al., 2009; Tong and Pratte, 2012).”
PyMVPA: a Python Toolbox for Multivariate Pattern Analysis of fMRI Data (2009, methodology): “…- based analyses , harnessing machine - learning techniques based on statistical learning theory to extract information about the functional properties of the brain previously thought to be below the signal - to - noise ratio of fMRI data ( for reviews see Haynes and Rees , 2006 ; Norman et al .”
Mapping, Learning, Visualization, Classification, and Understanding of fMRI Data in the NeuCube Evolving Spatiotemporal Data Machine of Spiking Neural Networks (2017, methodology): “Many of these tasks have not been solved efficiently with the use of classical machine learning techniques, such as support vector machine (SVM), multilayer perceptron (MLP), and regression techniques [9], [11], the reason being that traditional machine learning techniques are”
Classification Accuracy as a Proxy for Two Sample Testing (2016, background): “(2005); Norman et al. (2006); Pereira et al. (2009)—to assess whether there is a significant difference between the two sets of data collected by learning a classifier”
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).”
Assessing and tuning brain decoders: Cross‐validation, caveats, and guidelines (2016, methodology): “predicting behavior or phenotypes from brain images or signals, has become a central tool in neuroimage data processing (Haynes and Rees, 2006; Haynes, 2015; Kamitani and Tong, 2005; Norman et al., 2006; Varoquaux and Thirion, 2014; Yarkoni and Westfall, 2016).”
Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns (2008, neutral): “For comprehensive reviews, see Haynes and Rees (2006) and Norman et al. (2006).”
Trial-by-trial data analysis using computational models (2011, methodology): “In some areas of neuroscience — notably multivoxel fMRI pattern analyses (Norman et al., 2006) — this “cross validation” procedure is the predominant method to assess and compare model ﬁt.”
Representational models: A common framework for understanding encoding, pattern-component, and representational-similarity analysis (2017, methodology): “74 This definition of explicit representation has motivated researchers to use linear decoding 75 methods to study neural representations [7-9].”
Moderate Levels of Activation Lead to Forgetting In the Think/No-Think Paradigm (2013, positive): “Other models (e.g., Norman et al., 2006) predict that, for very high levels of activation, the plasticity curve will go back down to zero.”
A Review of Feature Reduction Techniques in Neuroimaging (2013, neutral): “SVM or RVM) to neuroimaging data (Bray et al. 2009; Cheng et al. 2012; De Martino et al. 2008; Duchesnay et al. 2007; Duchesnay et al. 2004; Duff et al. 2011; Franke et al. 2010; Liu et al. 2012; Lohmann et al. 2007; Mitchell et al. 2004; Mwangi et al. 2012a; Norman et al. 2006; Pereira et al. 2009;”
Brains and brands: Developing mutually informative research in neuroscience and marketing (2008, background): “…2004) all the way Copyright # 2008 John Wiley & Sons, Ltd. Jour to distributed activation patterns among MRI voxels (Haynes and Rees, 2006; Norman et al., 2006) or the waveforms of electroencephalograms (EEGs), both of which represent the collective responses of hundreds of thousands of…”
Frontal Midline Theta Oscillations during Working Memory Maintenance and Episodic Encoding and Retrieval (2013, methodology): “Another account of FMT is that theta oscillations might have systematic effects on strong and weak representations in a neural network (Norman et al., 2005, 2006a, 2007).”