Classifying spatial patterns of brain activity with machine learning methods: Application to lie detection

By Christos Davatzikos, Kosha Ruparel, Yong Fan, Dinggang Shen, Mausumi Acharyya, James Loughead, Ruben C. Gur, Daniel D. Langleben. NeuroImage, 2005

452 citations recorded by the source corpus.

Subfields: Clinical Psychology, Cognitive Neuroscience, Social Psychology

View Paper: https://www.semanticscholar.org/paper/71f729980d8cc1686666bf03712ceb2574039998

How researchers see this paper

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

In Clinical Psychology

Vinod Menon

In Cognitive Neuroscience

In Social Psychology

Citations in the corpus

Neuroimaging: Decoding mental states from brain activity in humans (2006, unknown): “Indeed, in the domain of lie detection, limited progress has been made in decoding such covert state”
Analytic estimation of statistical significance maps for support vector machine based multi-variate image analysis and classification (2013, unknown): “, 2008), which have been quite widely used in functional (Craddock et al., 2009; Davatzikos et al., 2005; De Martino et al., 2008; Mouro-Miranda et al., 2005; Wang et al., 2007) and structural (Cuingnet et al.”
Decoding subject-driven cognitive states with whole-brain connectivity patterns. (2012, methodology): “…standard method for demonstrating classification generalizability (Mitchell et al. 2003; Mourao-Miranda et al. 2005), it is prone to cohort effects as the classifier may be overfit to a data set that is not fully representative of the broader population (Davatzikos et al. 2005; Hastie et al. 2009).”
Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging. (2008, result): “This study adds to mounting vidence in the literature for the importance of pattern clasification methods in detecting subtle and complex structural nd functional patterns (Davatzikos et al., 2005a,b; Golland t al., 2002; Liu et al., 2004).”
Model sparsity and brain pattern interpretation of classification models in neuroimaging (2012, unknown): “[5,6,9,14,20–23].”
Machine Learning and Radiology (2012, methodology): “As a practical application of machine learning in spatial patterns of brain activity analysis, Davatzikos et al. proposed a new method for lie detection from fMR images (Davatzikos et al., 2005).”
Structural and functional biomarkers of prodromal Alzheimer's disease: A high-dimensional pattern classification study (2008, background): “…Golland et al., 2002; Lao et al., 2004; Liu et al., 2004; Timoner et al., 2002), and have shown great potential in a variety of neuroimaging studies (Davatzikos et al., 2008, 2005a,b; Fan et al., 2008a, 2005, 2006b, 2007b, Kawasaki et al., 2007; Mourao-Miranda et al., 2005; Yoon et al., 2007).”
Inverse retinotopy: Inferring the visual content of images from brain activation patterns (2006, background): “, 2003; Cox and Savoy, 2003), the orientation of visual stimuli (Haynes and Rees, 2005; Kamitani and Tong, 2005) and lie detection (Davatzikos et al., 2005).”
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).”
Alzheimer's disease diagnosis in individual subjects using structural MR images: Validation studies (2008, background): “…their attention toward multivariate analysis and machine learning-based algorithms for distinguishing patients from cognitively normal (CN) subjects (Alexander and Moeller, 1994; Csernansky et al., 2005; Davatzikos et al., 2005; Fan et al., 2005; Freeborough and Fox, 1998b; Lao et al., 2004).”
Automatic deception detection in Italian court cases (2013, result): “Undeutsch was interested in verbal behavior, but his theoretical framework is also suitable to study non-verbal communication, and is consistent with recent findings using neuro-imaging techniques (Davatzikos et al. 2005; Ganis et al. 2003; Merikangas 2008; Simpson 2008).”
Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline (2008, methodology): “This approach has been recently used in several neuroimaging studies and has shown great potential as a diagnostic tool on individuals (Lao et al., 2004; Davatzikos et al., 2005a,b; Fan et al., 2007; Davatzikos et al., in press).”