Information-based functional brain mapping.

By Nikolaus Kriegeskorte, Rainer Goebel, Peter A. Bandettini. Proceedings of the National Academy of Sciences of the United States of America, 2006

2,200 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/985cceaca8606443f9129616a26bbbbf952f2d7f

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In Cognitive Neuroscience

Citations in the corpus

Prioritizing spatial accuracy in high-resolution fMRI data using multivariate feature weight mapping (2014, methodology): “For this, “information mapping” methods, such as the “searchlight” approach are often employed (Kriegeskorte et al., 2006).”
Inferring brain-computational mechanisms with models of activity measurements (2016, background): “B 371: 20160278. http://dx.doi.org/10.1098/rstb.2016.0278 Accepted: 19 July 2016 One contribution of 15 to a Theo Murphy meeting issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’.”
A spatio-temporal support vector machine searchlight for fMRI analysis (2011, background): “Furthermore, our results show that the regions of high SVM classification accuracy are not noisy, and do not have a salt-and-pepper appearance, which is one of the processing outcomes to avoid [2].”
Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns (2008, positive): “…the voxels represent the decoded task parameter or some other correlated variable (Kamitani and Tong, 2005), comparisons of decoding performance for different brain areas can provide a powerful method for mapping the information available in local regions (see also Kriegeskorte et al., 2006).”
Matching Categorical Object Representations in Inferior Temporal Cortex of Man and Monkey (2008, background): “(4) The analysis targets the information in distributed response patterns (Haxby et al., 2001; Cox and Savoy, 2003; Carlson et al., 2003; Kamitani and Tong, 2005; Kriegeskorte et al., 2006).”
Assessing and tuning brain decoders: Cross‐validation, caveats, and guidelines (2016, background): “To study cognition, successful prediction is seen as evidence of a link between observed behavior and a brain region [19] or a small fraction of the image [28].”
An a contrario approach for the detection of patient-specific brain perfusion abnormalities with arterial spin labelling (2016, positive): “Another alternative, lying in between the massively univariate and the fully multivariate methods, is the searchlight approach (Kriegeskorte et al., 2006).”
A neural basis for real-world visual search in human occipitotemporal cortex (2011, methodology): “We used a spherical searchlight approach (15) to test for each voxel in the brain, the degree to which multivoxel patterns in a 10-mm sphere around this voxel could discriminate the scene types based on the category-specific patterns from the Fig.”
Machine learning classifiers and fMRI: a tutorial overview (2008, background): “Hence there is little justification to use it and one should resort either to the exact interval (Langford, 2005 provides a good explanation of how to compute it numerically) or one of several closed-form approximations proposed in Brown et al., (1999).”
A Toolbox for Representational Similarity Analysis (2014, methodology): “Searchlight analysis [5] provides a method of continuously mapping pattern information throughout the entire measured volume.”
Interpretable LSTMs For Whole-Brain Neuroimaging Analyses (2018, methodology): “We will also demonstrate that the performance of DLight is superior to the conventional searchlight analysis [4] in decoding the cognitive states from data.”
Decoding brain states from fMRI connectivity graphs (2011, background): “…classi ﬁ cation to a so-called “ searchlight ” that slides over the whole-brain data, such that the classi ﬁ cation success for each position of the spotlight can then be mapped to show brain regions that carry discriminative information between different conditions (Krieges-eskorte et al., 2006).”
Real-time fMRI and its application to neurofeedback (2012, methodology): “The advent of multivariate statistical analysis and classification will facilitate the feedback of hemodynamic response patterns (for reviews, see Kriegeskorte et al., 2006; Haynes and Rees, 2006; LaConte, 2011).”
PyMVPA: a Python Toolbox for Multivariate Pattern Analysis of fMRI Data (2009, methodology): “Alternatively , Kriegeskorte et al .”