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

• Published in: NeuroImage (Orlando, Fla.) Vol. 28; no. 3; pp. 663 - 668
• Main Authors: Davatzikos, C., Ruparel, K., Fan, Y., Shen, D.G., Acharyya, M., Loughead, J.W., Gur, R.C., Langleben, D.D.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.11.2005; Elsevier Limited
• Subjects: Adult; Artificial Intelligence; Brain; Brain - physiology; Classification; Humans; Hypotheses; Image Processing, Computer-Assisted; Lie Detection - psychology; Magnetic Resonance Imaging - methods; Magnetic Resonance Imaging - statistics & numerical data; Male; Nonlinear Dynamics; Polygraphs; Reproducibility of Results
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2005.08.009

Patterns of brain activity during deception have recently been characterized with fMRI on the multi-subject average group level. The clinical value of fMRI in lie detection will be determined by the ability to detect deception in individual subjects, rather than group averages. High-dimensional non-linear pattern classification methods applied to functional magnetic resonance (fMRI) images were used to discriminate between the spatial patterns of brain activity associated with lie and truth. In 22 participants performing a forced-choice deception task, 99% of the true and false responses were discriminated correctly. Predictive accuracy, assessed by cross-validation in participants not included in training, was 88%. The results demonstrate the potential of non-linear machine learning techniques in lie detection and other possible clinical applications of fMRI in individual subjects, and indicate that accurate clinical tests could be based on measurements of brain function with fMRI.