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Background Deception is present in all walks of life, from social interactions to matters of homeland security. Nevertheless, reliable indicators of deceptive behavior in real‐life scenarios remain elusive. Methods By integrating electrophysiological and communicative approaches, we demonstrate a new and objective detection approach to identify participant‐specific indicators of deceptive behavior in an interactive scenario of a two‐person deception task. We recorded participants' facial muscle activity using novel dry screen‐printed electrode arrays and applied machine‐learning algorithms to identify lies based on brief facial responses. Results With an average accuracy of 73%, we identified two groups of participants: Those who revealed their lies by activating their cheek muscles and those who activated their eyebrows. We found that the participants lied more often with time, with some switching their telltale muscle groups. Moreover, while the automated classifier, reported here, outperformed untrained human detectors, their performance was correlated, suggesting reliance on shared features. Conclusions Our findings demonstrate the feasibility of using wearable electrode arrays in detecting human lies in a social setting and set the stage for future research on individual differences in deception expression., What classifies lying from truth‐telling behavior? The current study adopts a physiological‐social approach, combining state‐of‐the‐art techniques from electrical and neuro‐engineering (nanotechnology‐based skin printed electrodes for measuring electromyography), computer science (machine‐learning algorithms), and cognitive psychology (deception task) to automatically and objectively characterize different deceptive behaviors based on “giveaway” signs of facial expressions.

Purpose To provide a biophysical basis to estimate the effect of cytoplasmic flow in neurons, and assess their contribution to the drop in the Apparent Diffusion Coefficient (ADC) in a nerve tissue following extreme conditions, such as brain injury and epileptic seizures. Methods Three mechanisms are treated using the relevant physics of hydrodynamics and electrostatics: cargo induced streaming, electroosmosis, and membrane swelling. Results We begin by discussing the lack of experimental evidence on the necessary velocities required to influence the Magnetic Resonance (MR) experiments. This is followed by demonstrating that cargo induced streaming, a widely known phenomenon in plant cells, has a minor effect on the ADC in neurons. Subsequently, we suggest and analyze two additional mechanisms that may induce fluid displacement in neurons, and are related to the electrical activity: electroosmosis and membrane swelling. Conclusion Although these mechanisms may induce interesting fluid displacements, these cannot explain the significant drop in the ADC. We conclude by outlining the criteria that any future mechanism should meet to have an influence on standard diffusion-MR measurements. Magn Reson Med, 2016. © 2016 International Society for Magnetic Resonance in Medicine