Support vector machines for temporal classification of block design fMRI data

By Stephen LaConte, Stephen C. Strother, Vladimir Cherkassky, Jon R. Anderson, Xiaoping P. Hu. NeuroImage, 2005

428 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/0074ffe2bafcd97096b39d832e839d3cbdafeaed

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Citations in the corpus

Beyond mind-reading: multi-voxel pattern analysis of fMRI data. (2006, methodology): “of voxel i to detecting category j by looking at the weight between voxel i and category j [23,29, 32 ].”
PRoNTo: Pattern Recognition for Neuroimaging Toolbox (2013, methodology): “To the extent of our knowledge, the five freely available packages for machine learning modeling of neuroimaging data are the 3dsvm plugin for AFNI 1 (LaConte et al. 2005), the MATLAB MVPA toolbox, 2 PROBID, 3 PyMVPA (Hanke et al. 2009a, b) and Sci-kit Learn 4 (Pedregosa et al. 2011).”
PyMVPA: a Python Toolbox for Multivariate Pattern Analysis of fMRI Data (2009, methodology): “, shell scripting for AFNI and FSL command line tools , or Matlab - scripting of SPM functions ) .”
Decoding the Representation of Code in the Brain: An fMRI Study of Code Review and Expertise (2017, background): “This can be solved using a simple linear map (a kernel function) that reduces the dimensionality of the feature space [43], [64], [68].”
Representational Similarity Analysis – Connecting the Branches of Systems Neuroscience (2008, neutral): “…Hanson et al., 2004; Haxby et al., 2001; Haynes and Rees, 2005a,b; Haynes et al., 2007; Kamitani and Tong, 2005, 2006; Kriegeskorte et al., 2006; LaConte et al., 2005; Mitchell et al., 2004; Mourao-Miranda et al., 2005; Pessoa and Padmala, 2006; Polyn et al., 2005; Serences and Boynton, 2007;…”
Classifying spatial patterns of brain activity with machine learning methods: Application to lie detection (2005, positive): “We presented an approach to classification of patterns of brain activity obtained during lying and truth-telling, during a forced choice deception experiment (LaConte et al., 2005, Lykken, 1991, OTA, 1983 and Rosenfeld et al., 1988).”
Deconvolving BOLD activation in event-related designs for multivoxel pattern classification analyses (2011, methodology): “…used in the classification analysis to improve the classification accuracy (Chen et al., 2006; DeMartino et al., 2008;Mourao-Miranda et al., 2006) as well as the performance of different classification models (Carlson et al., 2003; Cox and Savoy, 2003; LaConte et al., 2005; Misaki et al., 2010).”
Model sparsity and brain pattern interpretation of classification models in neuroimaging (2012, methodology): “Introduction In recent years there has been increasing interest in applying discriminative multivariate analysis techniques in the analysis of functional neu3 roimaging data [1, 2, 3, 4, 5, 6, 7, 8, 9, 10].”
Generative-Discriminative Basis Learning for Medical Imaging (2012, positive): “In order to overcome these limitations, high-dimensional pattern classification methods have been proposed in recent literature for morphological analysis [13]–[16] and fMRI [9], [17], [18], which aim to capture multivariate nonlinear relationships in the data and seek to achieve high classification”