Learning to Decode Cognitive States from Brain Images

By Tom Michael Mitchell, Rebecca A. Hutchinson, Radu Stefan Niculescu, Francisco Pereira, Xuerui Wang, Marcel Adam Just, Sharlene D. Newman. 2004

763 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing

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Citations in the corpus

Causal and anti-causal learning in pattern recognition for neuroimaging (2015, background): “A decoding model p(Y |X) represents how different experimental conditions can be inferred from different brain states [9].”
Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns (2008, neutral): “Thus, the application of SLR could prove as useful as SVM, which has been gaining popularity in several recent fMRI studies (Cox and Savoy, 2003; Mitchell et al., 2004; LaConte et al., 2005; Kamitani and Tong, 2005, 2006; Haynes et al., 2007 and so on).”
A supervised clustering approach for fMRI-based inference of brain states (2011, neutral): “Keywords: fMRI, brain reading, prediction, hierarchical clustering, dimension reduction, multi-scale analysis, feature agglomeration”
Comparison of multivariate classifiers and response normalizations for pattern-information fMRI (2010, positive): “For a given classifier, dimensionality reduction of the response patterns can further reduce overfitting and improve classification performance (Mitchell et al., 2004; Guyon and Elisseeff, 2003; De Martino et al., 2008).”
Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns (2008, positive): “Statistical assessment of discriminative maps obtained by MVPA, however, is not simple and it requires assumption or estimation of a null hypothesis distribution for the voxels' discriminative weights.”
Machine Learning and Radiology (2012, background): “In early work, T. Mitchell et al., tried to decode cognitive states from brain images (Mitchell et al., 2004).”
Training fMRI Classifiers to Detect Cognitive States across Multiple Human Subjects (2003, background): “In recent work [6,7,9], Mitchell et al. have demonstrated the feasibility of training such classifiers (e.g., to distinguish whether the word a person is reading is a noun or a verb).”
Encoding and decoding in fMRI (2010, methodology): “…technique is the linear classifier (see De Martino et al., 2008; Formisano et al., 2008a; Hansen, 2007; Haynes and Rees, 2006; Haynes, 2009; Mitchell et al., 2004; Norman et al., 2006; O'Toole et al., 2007; Pereira et al., 2009 for reviews; see Kippenhan et al., 1992 and Lautrup et al.,…”
Searchlight analysis: Promise, pitfalls, and potential (2013, methodology): “…has developed new methods for addressing fMRI datasets and questions, as seen in the proliferation of relevant articles (e.g. Cuingnet et al., 2011; Mitchell et al., 2004; Van De Ville and Lee, 2012) and Searchlight analysis (also called information mapping) is an MVPA method introduced as a…”
Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data (2005, methodology): “The first group applied classifiers after preprocessing using a feature selection methods based on prior hypotheses (Mitchell et al., 2004;Wang et al., 2003; Cox and Savoy, 2003; Ford et al., 2003).”
How concepts are encoded in the human brain: A modality independent, category-based cortical organization of semantic knowledge (2016, methodology): “In addition, a cluster correction (nearest-neighbor, minimum cluster size of 100 μL) was applied to each map in order to remove small isolated clusters, yielding to a stable location of all the above threshold regions across the probability maps of the participants.”
Support vector machine learning-based fMRI data group analysis (2007, methodology): “…a data classification algorithm, that has been increasingly employed as a multivariate method in BOLD fMRI for brain response prediction and classification (Cox and Savoy, 2003; Wang et al., 2003a; Mitchell et al., 2004; Davatzikos et al., 2005; LaConte et al., 2005; Mourão-Miranda et al., 2005).”
Decoding brain states from fMRI connectivity graphs (2011, background): “Another interesting approach is to use spatiotemporal observations as an input to the classi ﬁ er (Mitchell et al., 2004; Mourao-Miranda et al., 2007).”