Heroin addiction represents a chronic neuropsychiatric disorder characterized by profound alterations in the brain's reward circuitry, particularly within the medial prefrontal cortex (mPFC), ventral tegmental area (VTA), and nucleus accumbens (NAc). Despite mounting evidence that addiction involves dysregulated neural oscillations, frequency domain analysis of reward processing in deeper brain structures remains critically understudied. We collected$\mathbf{6 4}$-channel EEG data from heroin addicts and healthy controls during a monetary incentive delay task with three conditions: positive (potential monetary gain), neutral (no gain or loss), and negative (potential monetary loss). Using advanced source localization techniques with fine realistic head models, we reconstructed brain source signals from key reward circuitry regions (mPFC, VTA, and NAc), analyzing neural responses across three temporal stages: reward anticipation, reward expectation, and reward outcome through event-related desynchronization/synchronization (ERD/ERS) analysis and time-frequency analysis. Heroin addicts exhibited significantly altered neural oscillatory patterns compared to healthy controls across delta, alpha, beta, and gamma frequency bands within the reward circuitry, with frequency-specific abnormalities observed in both cortical and subcortical reward-related regions during different stages of reward processing. This study provides the first comprehensive characterization of frequency-specific neural dysfunction spanning the entire reward circuitry in heroin addiction, offering novel insights into the oscillatory mechanisms underlying reward processing abnormalities and informing the development of frequency-targeted therapeutic interventions.

In this paper, we focus on both the communication and sensing security in the proposed multiple-subcarrier (MC) non-orthogonal multiple access (NOMA)-assisted integrated sensing and communication (ISAC) systems, where an active eaves-dropper is considered with imperfect channel state information (CSI). We consider a secure ISAC design by adopting the proposed secrecy rate and secrecy Cramér-Rao bound (S-CRB) metrics under the bounded CSI errors model. The joint design of artificial noise (AN) and dual-functional radar communications (DFRC) signals beamforming as well as subcarrier allocation is formulated to maximize the minimum achievable rate, while ensuring the hierarchical confidentiality requirements for users and satisfying the leakage CRB constraint for the target. To handle the non-convex problem, we devise a low-complexity successive convex approximation (SCA)-based suboptimal algorithm, and its ε-optimality is validated via our proposed branch and bound (B&B) algorithm in simulations. Numerical results reveal the effectiveness and superiority of our proposed scheme compared to the other baselines. Moreover, the inherent win-win relationship between the sum secrecy rate and target S-CRB is demonstrated via various simulation results, which provides several insights into secure MC-NOMA ISAC network deployment.

Depression detection via wearable Electroencephalogram (EEG) sensor-assisted diagnosis system demands computationally efficient models compatible with resource-constrained edge devices. Spiking Neural Networks (SNNs) offer inherent advantages for processing the spatio-temporal patterns of EEG through event-driven manner. In this study, we innovatively present LSNNet, a lightweight SNN model specifically designed for wearable EEG sensors. The model exhibits low computational complexity with 7.18 K parameters and 67.68 M Floating-Point Operations (FLOPs). It requires only 246.88 KB of Random Access Memory (RAM) and 57.33 KB of Read-Only Memory (ROM) for on-board execution, and has been validated on both the single-core STM32U535CET6 and the multi-core GAP8 microcontrollers. Despite its minimal computational and memory requirements, LSNNet achieves impressive performance metrics, with a classification accuracy of 89.2%, specificity of 92.4%, and sensitivity of 86.4% in independent tests conducted on EEG data collected from 73 depressed patients and 108 healthy controls using our three-lead EEG sensor. Especially, when running on the GAP8 microcontrollers, the LSNNet model has a low power consumption of 21.43 mW and a satisfactory inference time of 0.63 s while maintaining a classification accuracy of 87.5% (only with a reduction of 1.98%). These results underscore the potential of integrating wearable EEG sensors with the LSNNet model for depression detection in the Internet of Things (IoT) era.

Depressive affective dysfunction could be manifested by dynamic reorganization processes of functional brain networks under emotional tasks. However, such emotional task-specific reorganizations remain not fully understood in terms of spatiotemporal organization of oscillation dynamics. The insufficiency of approaches for quantifying such dynamic reorganization limits the effective extraction of dynamic neuromarkers of depressive affective dysfunction. To address this gap, this study presents a neural oscillation-dependent microstate approach to quantify the dynamic reorganization process of functional networks at high temporal resolution. The approach was tested by analyzing a 64-channel electroencephalography (EEG) dataset with 110 participants (55 depressed patients and 55 normal controls) collected during emotional tasks with four affective polarities (positive, neutral, negative, and resting state). Our analyses revealed oscillation-dependent microstates that reflected abnormalities in the networks associated with external information processing and interoception in depression. Our analyses further suggest that such abnormalities may be caused by dysfunction in a limited number of brain regions, which then dynamically affects functional brain networks. These findings may reflect increased self-focus and deficits in the perception of external information in depression. In summary, our study further explores source-level evidence related to abnormalities in microstate features of depression based on the generalization of depression microstate research to a multi-band framework. Our study provides a direction for expanding the application of dynamic reorganization analyses of functional networks with high temporal resolution, and provides new support and insights into the neural mechanisms of affective dysfunction in depression.