Embodied and disembodied allocentric simulation in high schizotypal subjects.

It is known that non-clinical subjects with high levels of schizotypal personality traits (High-S), as well as schizophrenic patients, have difficulties to judge how a scene would appear (so-called Appearance questions) from a point of view other than their own after having performed a disembodied perspective taking (D-PT, a mental self-rotation cued by an object like a chair). This inability has been defined allocentric simulation deficit. However, it is still unclear whether this inability might also regard an embodied transformation (E-PT), which is a self-rotation cued by another individual in the scene, and whether the observed deficit regards the pure mental transformation phase. In the present study, we took advantage of a virtual reality environment to explore both embodied and disembodied allocentric simulation in healthy volunteers with low and high levels of schizotypal personality traits, as assessed by the Schizotypal Personality Questionnaire. All subjects performed a pure self-rotation cued by a chair (D-PT) or by an avatar (E-PT), or a control array rotation. Each rotation was followed by classical Appearance and Item questions. Results revealed no between-groups differences in the mental transformation phase, while High-S subjects were significantly slower than Low-S subjects in the Appearance task after D-PT, but not after E-PT. Accordingly, higher schizotypy levels (cognitive-perceptual subscale) were positively correlated with slower reaction times in the Appearance task after D-PT. These data suggest the existence of a disembodied allocentric simulation deficit in non-clinical High-S, paving the way to future studies on clinical populations.

A post-stroke rehabilitation system integrating robotics, VR and high-resolution EEG imaging.

We propose a system for the neuro-motor rehabilitation of upper limbs in stroke survivors. The system is composed of a passive robotic device (Trackhold) for kinematic tracking and gravity compensation, five dedicated virtual reality (VR) applications for training of distinct movement patterns, and high-resolution EEG for synchronous monitoring of cortical activity. In contrast to active devices, the Trackhold omits actuators for increased patient safety and acceptance levels, and for reduced complexity and costs. VR applications present all relevant information for task execution as easy-to-understand graphics that do not need any written or verbal instructions. High-resolution electroencephalography (HR-EEG) is synchronized with kinematic data acquisition, allowing for the epoching of EEG signals on the basis of movement-related temporal events. Two healthy volunteers participated in a feasibility study and performed a protocol suggested for the rehabilitation of post-stroke patients. Kinematic data were analyzed by means of in-house code. Open source packages (EEGLAB, SPM, and GMAC) and in-house code were used to process the neurological data. Results from kinematic and EEG data analysis are in line with knowledge from currently available literature and theoretical predictions, and demonstrate the feasibility and potential usefulness of the proposed rehabilitation system to monitor neuro-motor recovery.

Early detection of coronary artery disease in patients studied with magnetocardiography: an automatic classification system based on signal entropy.

We propose an automatic system for the classification of coronary artery disease (CAD) based on entropy measures of MCG recordings. Ten patients with coronary artery narrowing ≥ or ≤ 50% were categorized by a multilayer perceptron (MLP) neural network based on Linear Discriminant Analysis (LDA). Best results were obtained with MCG at rest: 99% sensitivity, 97% specificity, 98% accuracy, 96% and 99% positive and negative predictive values for single heartbeats. At patient level, these results correspond to a correct classification of all patients. The classifier's suitability to detect CAD-induced changes on the MCG at rest was validated with surrogate data.

A virtual environment for egocentric and allocentric mental transformations: a study on a nonclinical population of adults with distinct levels of schizotypy.

We benefited from the flexibility provided by virtual reality to enhance a classical paradigm on array and self mental rotations and related questions on a set of items. We used this paradigm to investigate how the Level of Schizotypy in nonclinical subjects might influence their behavior in egocentric and allocentric mental transformations. Three elements of novelty were introduced: (i) we separated the phases of mental transformation (Imagined Rotation Phase) and task performance (Task Phase), (ii) we measured the time required for Imagined Rotation Phase and Task Phase separately, and (iii) we cued self-rotations with a virtual human being (self-avatar) or an inanimate object (self-chair). Twenty-four nonclinical participants were categorized in low- and high-schizotypal subjects (Low-S, High-S). A mixed-design analysis of variance showed that High-S were significantly faster than Low-S during the Imagined Rotation Phase (array and self-chair rotations) and during the Task Phase (self-chair). High-S were also faster in the self-chair than in the self-avatar rotation, supporting the existence of a dissociation between perspective changing and perspective taking in High-S. In line with the literature, we found that participant performances decreased with increasing angular difference between the initial and the imagined perspective.