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Introduction: During the past decades there has been an increasing interest in tracking brain network fluctuations in health and disease by means of resting state functional magnetic resonance imaging (rs-fMRI). Rs-fMRI however does not provide the ideal environmental setting, as participants are continuously exposed to noise generated by MRI coils during acquisition of Echo Planar Imaging (EPI). We investigated the effect of EPI noise on resting state activity and connectivity using magnetoencephalography (MEG), by reproducing the acoustic characteristics of rs-fMRI environment during the recordings. As compared to fMRI, MEG has little sensitivity to brain activity generated in deep brain structures, but has the advantage to capture both the dynamic of cortical magnetic oscillations with high temporal resolution and the slow magnetic fluctuations highly correlated with BOLD signal., Methods: Thirty healthy subjects were enrolled in a counterbalanced design study including three conditions: a) silent resting state (Silence), b) resting state upon EPI noise (fMRI), and c) resting state upon white noise (White). White noise was employed to test the specificity of fMRI noise effect. The amplitude envelope correlation (AEC) in alpha band measured the connectivity of seven Resting State Networks (RSN) of interest (default mode network, dorsal attention network, language, left and right auditory and left and right sensory-motor). Vigilance dynamic was estimated from power spectral activity., Results: fMRI and White acoustic noise consistently reduced connectivity of cortical networks. The effects were widespread, but noise and network specificities were also present. For fMRI noise, decreased connectivity was found in the right auditory and sensory-motor networks. Progressive increase of slow theta-delta activity related to drowsiness was found in all conditions, but was significantly higher for fMRI . Theta-delta significantly and positively correlated with variations of cortical connectivity., Discussion: rs-fMRI connectivity is biased by unavoidable environmental factors during scanning, which warrant more careful control and improved experimental designs. MEG is free from acoustic noise and allows a sensitive estimation of resting state connectivity in cortical areas. Although underutilized, MEG could overcome issues related to noise during fMRI, in particular when investigation of motor and auditory networks is needed., Competing Interests: Declaration of Competing Interest The authors declare that there is no conflict of interest regarding the publication of this paper., (Copyright © 2021. Published by Elsevier Inc.)

Action observation and motor imagery are valuable strategies for motor learning. Their simultaneous use (AOMI) increases neural activity, with related benefits for motor learning, compared to the two strategies alone. In this study, we explored how sonification influences AOMI. Twenty-five participants completed a practice block based on AOMI, motor imagery and physical execution of the same action. Participants were divided into two groups: An experimental group that practiced with sonification during AOMI (sAOMI), and a control group, which did not receive any extrinsic feedback. Corticospinal excitability at rest and during action observation and AOMI was assessed before and after practice, with and without sonification sound, to test the development of an audiomotor association. The practice block increased corticospinal excitability in all testing conditions, but sonification did not affect this. In addition, we found no differences in action observation and AOMI, irrespective of sonification. These results suggest that, at least for simple tasks, sonification of AOMI does not influence corticospinal excitability; In these conditions, sonification may have acted as a distractor. Future studies should further explore the relationship between task complexity, value of auditory information and action, to establish whether sAOMI is a valuable for motor learning., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Random number generation (RNG) is a procedurally-simple task related to specific executive functions, such as updating and monitoring of information and inhibition of automatic responses. The effect of practice on executive functions has been widely investigated, however little is known on the impact of practice on RNG. Transcranial direct current stimulation (tDCS) allows to modulate, non-invasively, brain activity and to enhance the effects of training on executive functions. Hence, this study aims to investigate the effect of practice on RNG and to explore the possibility to influence it by tDCS applied over dorsolateral prefrontal cortex. Twenty-six healthy volunteers have been evaluated within single session and between different sessions of RNG using several measures of randomness, which are informative of separable cognitive components servicing random behavior. We found that repetition measures significantly change within single session, seriation measures significantly change both within and between sessions, while cycling measures are not affected by practice. tDCS does not produce any additional effect, however a sub-analysis limited to the first session revealed an increasing trend in seriation measure after anodal compared to cathodal stimulation. Our findings support the hypothesis that practice selectively and consistently influences specific cognitive components related to random behavior, while tDCS transiently affects RNG performance., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Background: By implanting electrodes inside peripheral nerves, amputee's intentions are picked up and exploited to control novel dexterous sensorized hand prostheses. Under the pretext of presenting surgical technique and clinical outcomes of the implant of invasive peripheral neural interfaces in a human amputee, this article critically comments, from the point of view of the surgeon, strengths and weaknesses of the procedure., Materials and Methods: Four multielectrodes were implanted in the medial and ulnar nerves of a young volunteer, which, following a car-crash, had a left transradial amputation. Both nerves were approached with a single incision in the medial aspect of the upper arm. Four weeks later, the electrodes were removed., Results: Even if the trauma and the postamputation plastic processes altered the anatomy, electrodes were proficiently implanted with an overall success of 66%. Looking at the procedure from the surgeon's viewpoint unveils few still open issues. Electrodes weaknesses were related to the absence of stabilizing structures, the cable transit through the skin, the implant angle, and the unproven magnetic resonance imaging compatibility. Future investigations are needed to definitely address the better anesthesia, number and sites of incisions, the nerves to implant, and the convenience of performing epineural microdissection., Conclusions: Invasive neural interfaces developmental process almost completely relies on the efforts of bioengineers and neurophysiologists; however, the surgeon is responsible for intra and perioperative factors. Therefore, he deserves to play a major role also at the stage of specifying the requirements, to satisfy the requisites of a safe, stable, and long-lasting implant., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Insects exhibit remarkable navigation capabilities that current control architectures are still far from successfully mimic and reproduce. In this chapter, we present the results of a study on conceptualizing insect/machine hybrid controllers for improving autonomy of exploratory vehicles. First, the different principally possible levels of interfacing between insect and machine are examined followed by a review of current approaches towards hybridity and enabling technologies. Based on the insights of this activity, we propose a double hybrid control architecture which hinges around the concept of "insect-in-a-cockpit." It integrates both biological/artificial (insect/robot) modules and deliberative/reactive behavior. The basic assumption is that "low-level" tasks are managed by the robot, while the "insect intelligence" is exploited whenever high-level problem solving and decision making is required. Both neural and natural interfacing have been considered to achieve robustness and redundancy of exchanged information.