Neuronal Abnormalities Induced by an Intelligent Virtual Reality System for Methamphetamine Use Disorder

Abstract

In Extended Reality (XR) applications, high data rates and low latency are crucial for immersive experiences. Uplink transmission in XR is challenging due to the limited antennas and power of lightweight XR devices. To improve data transmission rates, we investigate a relay-assisted carrier aggregation (RACA) system. The XR device simultaneously transmits data to an access point (AP) and a relay in proximity over low-frequency and high-frequency bands, respectively. Then, the relay down-converts and amplifies the signals to the AP, effectively acting as an additional transmit antenna for the XR device. In this paper, we propose two algorithms to maximize the data rate of the XR device in their respective protocols. In the centralized protocol, the rate maximization problem is equivalently transformed as a weighted mean square error minimization (WMMSE) problem which can be solved iteratively by alternative optimization. In the distributed protocol, the rate maximization problem is decomposed into two independent subproblems where the rate of the direct link and the rate of the relay link are maximized by singular value decomposition (SVD)based methods with water-filling (WF). Simulation results show that the rate of the RACA system is improved by 32% compared to that of the conventional carrier aggregation scheme.

Impaired Brain-Heart Relation in Patients With Methamphetamine Use Disorder During VR Induction of Drug Cue Reactivity

“…Previous studies have shown that relatively higher α oscillations were associated with high impulsivity and trait anxiety [39] or with affective contents of the stimuli that have been memorized [40]. Additionally, the β activity was reported to be engaged in active attention or/and motor preparation [27] and negatively associated with inhibiting the behavior that would lead to punishment [17]. In this study, α and β PCRs in patients with MUD were significantly enhanced in the fourth stage, indicating a stronger level of intrinsic impulsivity and/or a higher affective association with the drug-related stimuli.…”

“…For instance, Ө oscillations were reported to be related with impulse control and action monitoring, α power involved in suppression of task-irrelevant responses, β activities were engaged in active concentration and motor preparation, and γ activities were associated with perceptual processing, attention and memory [27]. Therefore, it was suggested that investigation of the above four frequency bands should provide insights for the functional changes after abuse of METH [17], as patients with MUD exhibit impairments in working memory, error monitoring and attention. Tan et al employed drug-related stimuli in a VR environment to elicit brain drug cue reactivity in patients with MUD and found that the γ (30-48 Hz) activity in the frontal areas decreased during VR induction of drug craving when compared to neutral cues [15].…”

Challenges and future trends in wearable closed-loop neuromodulation to efficiently treat methamphetamine addiction

“…A support vector machine (SVM) algorithm has been implemented to stratify METH-user and healthy groups using FC network features of EEG signals (115). Concerning the relative frequency-specific power change ratio of EEG signals, SVM, logistic regression (LR), decision tree (DT), random forest (RF), multilayer perceptron, radial basis function networks, AdaBoost and gradian boost are implemented to compare the accuracy of classifying the METH and healthy groups (149). A convolutional neural network (CNN) model has been applied to EEG-fNIRS signals to classify METH addiction into light, moderate, and severe (93).…”

Neuronal Correlates of Task Irrelevant Distractions Enhance the Detection of Attention Deficit/Hyperactivity Disorder

“…The steps for EEG data analysis have been described in our previous studies in detail [33,34]. Basically, EEG data were bandpass filtered (2-56 Hz) offline using SPM12 (Wellcome Trust Centre for Neuroimaging, http://www.fil.ion.ucl.ac.uk/spm/) and then divided into GO and NOGO trials with distraction (D) and without distraction (ND) conditions, resulting in four subsets for each group for analysis: GO-D, GO-ND, NOGO-D, and NOGO-ND.…”