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The Peripersonal Space (PPS), the space immediately surrounding the body, is coded in a multisensory, body part-centered (e.g hand-centered, trunk-centered), modular fashion. This coding is ascribed to multisensory neurons that integrate tactile stimuli on a specific body part (e.g. hand, trunk) with visual/auditory information occurring near the same body part. A recent behavioral study, using an audiotactile psychophysical paradigm, evidenced that different body parts (hand and trunk) have distinct but not independent PPS representations. The hand-PPS exhibited properties different from the trunk-PPS when the hand was placed far from the trunk, while it assumed the same properties as the trunk-PPS when the hand was placed near the trunk. Here, we propose a neural network model to help unrevealing the underlying neurocomputational mechanisms. The model includes two subnetworks, devoted to PPS representations around the hand and around the trunk. Each subnetwork contains two areas of unisensory (tactile and auditory) neurons communicating, via feedforward and feedback synapses, with a pool of audiotactile multisensory neurons. The two subnetworks are characterized by different properties of the multisensory neurons. An interaction mechanism is postulated between the two subnetworks, controlled by proprioceptive neurons coding the hand position. Results show that the network is able to reproduce the behavioral data. Network mechanisms are commented and novel predictions provided.

We designed and conducted an experiment using a repetitive task to investigate associations between mental workload, performance, and Heart Rate Variability (HRV) features across repetitions. According to the literature, we define mental workload as the interaction between a person and a task that causes task demands to exceed the person’s capacity to deliver. Mental workload was triggered by the use of a highly-paced video game repeated over time. Before engaging with the task, each subject was assessed in controlled condition (i.e., relaxing period) for a short time. Short-term HRV features variations between the baseline (i.e., control situation) and each repetitive gaming session (i.e., mental task) were explored. The results show that HRV dynamics diminish with repetitions, while performance increases. Importantly, this suggests that HRV features can be well correlated with performance. Overall, this study advances the use of HRV analysis in the behavioral sciences at large, allowing the design of flexible neurophysiological lab-based experiments. Thus, it also opens the way to future autonomic behavioral neuroscience research.

During an MR procedure, most of the transmitted RF power is transformed into heat within the patient’s tissue and implants as a result of resistive losses which is referred to as the specific energy absorption rate (SAR) (1). The European committee for electrotechnical standardisation (CENELEC) has mandated that all scanners must measure the specific absorption rate of radiofrequency in patients and develop system safeguards to ensure that the limits set out IEC 60602-3-33 are not exceeded. Accurate estimation of SAR is critical to safeguard in unconscious/sedated patients, patients with compromised thermoregulation, implant patients, pregnant patients and neonates who require an MRI procedure. The increased static field strength and RF duty cycle capabilities in modern MRI scanners means that systems can easily exceed safe SAR levels for patients (2). Advisory protocols routine used to establish QA protocols do not have advise on the testing of SAR levels in MRI and this is not routinely measured in annual medical physics QA. There is increasing need to verify the manufacturers SAR estimations. peer-reviewed 2018-12-31

Mental stress is one of the first causes of cognitive dysfunctions, cardiovascular disorders and depression. In addition, it reduces performances, on the work place and in daily life. The diffusion of wearable sensors (embedded in smart-watches, phones, etc.) has opened up the potential to assess mental stress detection through ultra-short term Heart Rate Variability (HRV) analysis (i.e., less than 5 min). Although informative analyses of features coming from short HRV (i.e., 5 min) have already been performed, the reliability of ultra-short HRV remains unclear. This study aims to tackle this gap by departing from a systematic review of the existing literature and investigating, in healthy subjects, the associations between acute mental stress and short/ultra-short term HRV features in time, frequency, and non-linear domains. Building on these findings, three experiments were carried out to empirically assess the usefulness of HRV for mental stress detection using ultra-short term analysis and wearable devices. Experiment 1 detected mental stress in a real life situation by exploring to which extent HRV excerpts can be shortened without losing their ability to detect mental stress. This allowed us to advance a method to explore to what extent ultra-short HRV features can be considered as good surrogates of 5 min HRV features. Experiment 2 and 3 sought to develop automatic classifiers to detect mental stress through 2 min HRV excerpts, by using a Stroop Color Word Test (CWT) and a highly paced video game, which are two common laboratory-based stressors. Results from experiment 1 demonstrated that 7 ultra-short HRV features can be considered as good surrogates of short HRV features in detecting mental stress in real life. By leveraging these 7 features, experiment 2 and 3 offered an automatic classifier detecting mental stress with ultra-short features (2min), achieving sensitivity, specificity and accuracy rate above 60%. Mental stress is one of the first causes of cognitive dysfunctions, cardiovascular disorders and depression. In addition, it reduces performances, on the work place and in daily life. The diffusion of wearable sensors (embedded in smart-watches, phones, etc.) has opened up the potential to assess mental stress detection through ultra-short term Heart Rate Variability (HRV) analysis (i.e., less than 5 min). Although informative analyses of features coming from short HRV (i.e., 5 min) have already been performed, the reliability of ultra-short HRV remains unclear. This study aims to tackle this gap by departing from a systematic review of the existing literature and investigating, in healthy subjects, the associations between acute mental stress and short/ultra-short term HRV features in time, frequency, and non-linear domains. Building on these findings, three experiments were carried out to empirically assess the usefulness of HRV for mental stress detection using ultra-short term analysis and wearable devices. Experiment 1 detected mental stress in a real life situation by exploring to which extent HRV excerpts can be shortened without losing their ability to detect mental stress. This allowed us to advance a method to explore to what extent ultra-short HRV features can be considered as good surrogates of 5 min HRV features. Experiment 2 and 3 sought to develop automatic classifiers to detect mental stress through 2 min HRV excerpts, by using a Stroop Color Word Test (CWT) and a highly paced video game, which are two common laboratory-based stressors. Results from experiment 1 demonstrated that 7 ultra-short HRV features can be considered as good surrogates of short HRV features in detecting mental stress in real life. By leveraging these 7 features, experiment 2 and 3 offered an automatic classifier detecting mental stress with ultra-short features (2min), achieving sensitivity, specificity and accuracy rate above 60%.