A novel semi-immersive virtual reality visuo-motor task activates ventrolateral prefrontal cortex: a functional near-infrared spectroscopy study

Deep neural network to differentiate internet gaming disorder from healthy controls during stop-signal task: a multichannel near-infrared spectroscopy study

Internet Gaming Disorder (IGD), as one of worldwide mental health issues, leads to negative effects on physical and mental health and has attracted public attention. Most studies on IGD are based on screening scales and subjective judgments of doctors, without objective quantitative assessment. However, public understanding of internet gaming disorder lacks objectivity. Therefore, the researches on internet gaming disorder still have many limitations. In this paper, a stop-signal task (SST) was designed to assess inhibitory control in patients with IGD based on prefrontal functional near-infrared spectroscopy (fNIRS). According to the scale, the subjects were divided into health and gaming disorder. A total of 40 subjects (24 internet gaming disorders; 16 healthy controls) signals were used for deep learning-based classification. The seven algorithms used for classification and comparison were deep learning algorithms (DL) and machine learning algorithms (ML), with four and three algorithms in each category, respectively. After applying hold-out method, the performance of the model was verified by accuracy. DL models outperformed traditional ML algorithms. Furthermore, the classification accuracy of the two-dimensional convolution neural network (2D-CNN) was 87.5% among all models. This was the highest accuracy out of all models that were tested. The 2D-CNN was able to outperform the other models due to its ability to learn complex patterns in data. This makes it well-suited for image classification tasks. The findings suggested that a 2D-CNN model is an effective approach for predicting internet gaming disorder. The results show that this is a reliable method with high accuracy to identify patients with IGD and demonstrate that the use of fNIRS to facilitate the development of IGD diagnosis has great potential.

Immersive Virtual Reality in Post-Stroke Rehabilitation: A Systematic Review

In recent years, next to conventional rehabilitation's techniques, new technologies have been applied in stroke rehabilitation. In this context, fully immersive virtual reality (FIVR) has showed interesting results thanks to the level of immersion of the subject in the illusional world, with the feeling of being a real part of the virtual environment. This study aims to investigate the efficacy of FIVR in stroke rehabilitation. PubMed, Web of Science and Scopus were screened up to November 2022 to identify eligible randomized controlled trials (RCTs). Out of 4623, we included 12 RCTs involving post-acute and chronic stroke survivors, with a total of 350 patients (234 men and 115 women; mean age 58.36 years). High heterogeneity of the outcomes considered, the results showed that FIVR provides additional benefits, in comparison with standard rehabilitation. In particular, results showed an improvement in upper limb dexterity, gait performance and dynamic balance, influencing patient independence. Therefore, FIVR represents an adaptable, multi-faceted rehabilitation tool that can be considered in post-stroke rehabilitation, improving the compliance of the patients to the treatment and increasing the level of functioning and quality of life of stroke survivors.

Prefrontal cortical activation in Internet Gaming Disorder Scale high scorers during actual real-time internet gaming: A preliminary study using fNIRS

Background

Observation of real-time neural characteristics during gameplay would provide distinct evidence for discriminating the currently controversial diagnosis of internet gaming disorder (IGD), and elucidate neural mechanisms that may be involved in addiction. We aimed to provide preliminary findings on possible neural features of IGD during real-time internet gaming using functional near-infrared spectroscopy (fNIRS).

Methods

Prefrontal cortical activations accompanying positive and negative in-game events were investigated. Positive events: (1) participant's champion slays or assists in slaying an opponent without being slain. (2) the opposing team's nexus is destroyed. Negative events: (1) participant's champion is slain without slaying or assisting in slaying any opponent. (2) the team's nexus is destroyed. Collected data were compared between the IGD group and control group, each with 15 participants.

Results

The IGD group scored significantly higher than the CTRL group on the craving scale. Following positive events, the IGD group displayed significantly stronger activation in the DLPFC. Following negative events, the IGD group displayed significantly weaker activation in the lateral OFC.

Discussion and Conclusions

Individuals scoring high on the IGD scale may crave for more internet gaming after encountering desired events during the game. Such observations are supported by the correlation between the craving scale and DLPFC activation. The IGD group may also show diminished punishment sensitivity to negative in-game experiences rendering them to continue playing the game. The present study provides preliminary evidence that IGD may demonstrate neural characteristics observed in other addictive disorders and suggests the use of fNIRS in behavioral addiction studies.

Data Processing in Functional Near-Infrared Spectroscopy (fNIRS) Motor Control Research

FNIRS pre-processing and processing methodologies are very important-how a researcher chooses to process their data can change the outcome of an experiment. The purpose of this review is to provide a guide on fNIRS pre-processing and processing techniques pertinent to the field of human motor control research. One hundred and twenty-three articles were selected from the motor control field and were examined on the basis of their fNIRS pre-processing and processing methodologies. Information was gathered about the most frequently used techniques in the field, which included frequency cutoff filters, wavelet filters, smoothing filters, and the general linear model (GLM). We discuss the methodologies of and considerations for these frequently used techniques, as well as those for some alternative techniques. Additionally, general considerations for processing are discussed.

Data integration by two-sensors in a LEAP-based Virtual Glove for human-system interaction

Virtual Glove (VG) is a low-cost computer vision system that utilizes two orthogonal LEAP motion sensors to provide detailed 4D hand tracking in real–time. VG can find many applications in the field of human-system interaction, such as remote control of machines or tele-rehabilitation. An innovative and efficient data-integration strategy, based on the velocity calculation, for selecting data from one of the LEAPs at each time, is proposed for VG. The position of each joint of the hand model, when obscured to a LEAP, is guessed and tends to flicker. Since VG uses two LEAP sensors, two spatial representations are available each moment for each joint: the method consists of the selection of the one with the lower velocity at each time instant. Choosing the smoother trajectory leads to VG stabilization and precision optimization, reduces occlusions (parts of the hand or handling objects obscuring other hand parts) and/or, when both sensors are seeing the same joint, reduces the number of outliers produced by hardware instabilities. The strategy is experimentally evaluated, in terms of reduction of outliers with respect to a previously used data selection strategy on VG, and results are reported and discussed. In the future, an objective test set has to be imagined, designed, and realized, also with the help of an external precise positioning equipment, to allow also quantitative and objective evaluation of the gain in precision and, maybe, of the intrinsic limitations of the proposed strategy. Moreover, advanced Artificial Intelligence-based (AI-based) real-time data integration strategies, specific for VG, will be designed and tested on the resulting dataset.

The Clinical Utility of Virtual Reality in Neurorehabilitation: A Systematic Review

Background:

Virtual reality (VR) experiences (through games and virtual environments) are increasingly being used in physical, cognitive, and psychological interventions. However, the impact of VR as an approach to rehabilitation is not fully understood, and its advantages over traditional rehabilitation techniques are yet to be established.

Method:

We present a systematic review which was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). During February and March of 2018, we conducted searches on PubMed (Medline), Virtual Health Library Search Portal databases (BVS), Web of Science (WOS), and Embase for all VR-related publications in the past 4 years (2015, 2016, 2017, and 2018). The keywords used in the search were “neurorehabilitation” AND “Virtual Reality” AND “devices.”

Results:

We summarize the literature which highlights that a range of effective VR approaches are available. Studies identified were conducted with poststroke patients, patients with cerebral palsy, spinal cord injuries, and other pathologies. Healthy populations have been used in the development and testing of VR approaches meant to be used in the future by people with neurological disorders. A range of benefits were associated with VR interventions, including improvement in motor functions, greater community participation, and improved psychological and cognitive function.

Conclusions:

The results from this review provide support for the use of VR as part of a neurorehabilitation program in maximizing recovery.

Using virtual reality for drug discovery: a promising new outlet for novel leads

Introduction: Virtual reality (VR) environments are increasingly being used by researchers in various fields in addition to being increasingly integrated into various areas of human life, ranging from videogames to different industrial uses. VR can be used to create interactive and multimodal sensory stimuli and thus offers unique advantages over other computer-based approaches for scientific research and molecular-level applications. Consequently, VR is starting to be used in novel drug development, such as in drug discovery, and rational drug design. Areas covered: In this review, the authors discuss the basic development of VR technology, including the available hardware and software. The latest advances of VR technology in novel drug development are then detailed, and the VR programs that can be applied in relevant studies are highlighted. Expert opinion: VR will lead to a revolution in pharmaceutical development. However, there are still obstacles to the successful and extensive application of VR to drug development, including the demand for further improvements to the available hardware and software and the various limitations described with regard to accuracy and precision. As technology continues to improve, the barriers to the widespread adoption of VR will diminish and VR technologies will play an increasingly important role in novel drug development.

Real-time monitoring prefrontal activities during online video game playing by functional near-infrared spectroscopy

A growing body of literature has suggested that video game playing can induce functional and structural plasticity of the brain. The underlying mechanisms, however, remain poorly understood. In this study, functional near-infrared spectroscopy (fNIRS) was used to record prefrontal activities in 24 experienced game players when they played a massively multiplayer online battle arena video game, League of Legends (LOL), under naturalistic conditions. It was observed that game onset was associated with significant activations in the ventrolateral prefrontal cortex (VLPFC) and concomitant deactivations in the dorsolateral prefrontal cortex (DLPFC) and frontal pole area (FPA). Game events, such as slaying an enemy and being slain by an enemy evoked region-specific time-locked hemodynamic/oxygenation responses in the prefrontal cortex (PFC). It was proposed that the VLPFC activities during LOL playing are likely responses to visuo-motor task load of the game, while the DLPFC/FPA activities may be involved in the constant shifts of attentional states and allocation of cognitive resources required by game playing. The present study demonstrated that it is feasible to use fNIRS to monitor real-time prefrontal activity during online video game playing.

Detecting Pilot's Engagement Using fNIRS Connectivity Features in an Automated vs. Manual Landing Scenario

Monitoring pilot's mental states is a relevant approach to mitigate human error and enhance human machine interaction. A promising brain imaging technique to perform such a continuous measure of human mental state under ecological settings is Functional Near-InfraRed Spectroscopy (fNIRS). However, to our knowledge no study has yet assessed the potential of fNIRS connectivity metrics as long as passive Brain Computer Interfaces (BCI) are concerned. Therefore, we designed an experimental scenario in a realistic simulator in which 12 pilots had to perform landings under two contrasted levels of engagement (manual vs. automated). The collected data were used to benchmark the performance of classical oxygenation features (i.e., Average, Peak, Variance, Skewness, Kurtosis, Area Under the Curve, and Slope) and connectivity features (i.e., Covariance, Pearson's, and Spearman's Correlation, Spectral Coherence, and Wavelet Coherence) to discriminate these two landing conditions. Classification performance was obtained by using a shrinkage Linear Discriminant Analysis (sLDA) and a stratified cross validation using each feature alone or by combining them. Our findings disclosed that the connectivity features performed significantly better than the classical concentration metrics with a higher accuracy for the wavelet coherence (average: 65.3/59.9 %, min: 45.3/45.0, max: 80.5/74.7 computed for HbO/HbR signals respectively). A maximum classification performance was obtained by combining the area under the curve with the wavelet coherence (average: 66.9/61.6 %, min: 57.3/44.8, max: 80.0/81.3 computed for HbO/HbR signals respectively). In a general manner all connectivity measures allowed an efficient classification when computed over HbO signals. Those promising results provide methodological cues for further implementation of fNIRS-based passive BCIs.

Eye Movement Analysis and Cognitive Assessment. The Use of Comparative Visual Search Tasks in a Non-immersive VR Application

BACKGROUND

An adequate behavioral response depends on attentional and mnesic processes. When these basic cognitive functions are impaired, the use of non-immersive Virtual Reality Applications (VRAs) can be a reliable technique for assessing the level of impairment. However, most non-immersive VRAs use indirect measures to make inferences about visual attention and mnesic processes (e.g., time to task completion, error rate).

OBJECTIVES

To examine whether the eye movement analysis through eye tracking (ET) can be a reliable method to probe more effectively where and how attention is deployed and how it is linked with visual working memory during comparative visual search tasks (CVSTs) in non-immersive VRAs.

METHODS

The eye movements of 50 healthy participants were continuously recorded while CVSTs, selected from a set of cognitive tasks in the Systemic Lisbon Battery (SLB). Then a VRA designed to assess of cognitive impairments were randomly presented.

RESULTS

The total fixation duration, the number of visits in the areas of interest and in the interstimulus space, along with the total execution time was significantly different as a function of the Mini Mental State Examination (MMSE) scores.

CONCLUSIONS

The present study demonstrates that CVSTs in SLB, when combined with ET, can be a reliable and unobtrusive method for assessing cognitive abilities in healthy individuals, opening it to potential use in clinical samples.