Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data

By Janaina Mourão Miranda, Arun L. W. Bokde, Christine Born, Harald Hampel, Martin Stetter. 2005

749 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Visualizing Deep Neural Network Decisions: Prediction Difference Analysis (2017, methodology): “, 2010), or the p-values of these weights (determined by permutation testing) (Mourao-Miranda et al., 2005; Wang et al., 2007).”
Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques (2021, methodology): “Our paper highlights some of the notable research attempts that deployed Bagging and Boosting ensemble techniques as well as proposed some hybrid frameworks, however, none of those research attempts closely resembled our introduced approaches as a base classifier except DT [8] and kNN [7].”
Decoding brain states from fMRI connectivity graphs (2011, positive): “The principle is also similar to the idea of extracting discriminative volumes from classi ﬁ er parameters using voxels' intensity as features (Mourao-Miranda et al., 2005; Sato et al., 2009).”
Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome (2011, methodology): “To the best of our knowledge, other statistical analyses of the OMH (Mourão-Miranda et al., 2005; Wang et al., 2007; Sato et al., 2009) did not take the margin into account.”
Sparse logistic regression for whole brain classification of fMRI data (2010, methodology): “Multivariate pattern recognition (MPR) methods are rapidly becoming a popular tool for analyzing fMRI data (Cox and Savoy, 2003; De Martino et al., 2008; Haynes et al., 2007; Kriegeskorte et al., 2006; Mourao-Miranda et al., 2005; Pereira et al., 2009).”
Prioritizing spatial accuracy in high-resolution fMRI data using multivariate feature weight mapping (2014, methodology): “As an alternative to the searchlight approach, we present a global multivariate method adapted from previous work (Mourão-Miranda et al., 2005), which we combine with a nonparametric solution for the multiple comparison problem (Stelzer et al., 2013).”
Representational Similarity Analysis – Connecting the Branches of Systems Neuroscience (2008, neutral): “At a more general level, activity-pattern similarity is related to activity-pattern information as targeted in a number of recent studies in human fMRI (Carlson et al., 2003; Cox and Savoy, 2003; Davatzikos et al., 2005; Friston et al., 2008; Hanson et al., 2004; Haxby et al., 2001; Haynes and Rees,”
A Novel Transfer Learning Approach to Enhance Deep Neural Network Classification of Brain Functional Connectomes (2018, background): “Integration of resting-state functional magnetic resonance imaging (rs-fMRI) techniques and machine learning algorithms is showing great promise in unveiling hidden pathological functional connectome (FC) patterns to assist early diagnosis and prediction of brain disorders (Cox and Savoy, 2003; Mour”
Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline (2008, methodology): “classification using support vector machines, and it has been shown to possess great diagnostic value in neurodegenerative and neuropsychiatric disorders, and likely beyond (Zhang et al., 2002; Davatzikos et al., 2005a,b; LaConte et al., 2005; Mourao-Miranda et al., 2005).”
Automatic classification of MR scans in Alzheimer’s disease (2008, methodology): “The feasibility of such an approach using MR scans has recently been shown by automatic gender-based classification (Lao et al ., 2004) and by detection of a variety of diseases (Fan et al ., 2005; Kawasaki et al ., 2007).”
Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's disease (2010, methodology): “…(PCA) (Friston et al., 1996), independent component analysis (McKeown et al., 1998), structural equation modeling (McIntosh et al., 1994), and support vector machine (Mourao-Miranda et al., 2005, 2006) are potential candidates but have mostly been applied to functional neuroimaging data so far.”
Visualization of Nonlinear Classification Models in Neuroimaging - Signed Sensitivity Maps (2012, methodology): “Widely used classification schemes include kernel methods such as support vector machines (SVMs) (Cox and Savoy, 2003; Davatzikos et al., 2005; LaConte et al., 2005; Mourão Miranda et al., 2005).”
The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features (2018, positive): “In a linear prediction model, the absolute value of the weight/ regression coefficient represents the importance of corresponding feature in a prediction (Erus et al., 2015; Mourao-Miranda et al., 2005; Siegel et al., 2016).”
From brain maps to cognitive ontologies: informatics and the search for mental structure (2015, background): “…on decoding of specific stimulus or task features within a single individual, but subsequent work has shown that one can decode large-scale psychological functions from fMRI data in a way that generalizes across individuals (Mourão-Miranda et al. 2005, Shinkareva et al. 2008, Wager et al. 2013).”