生物医学工程学杂志最新文献

Musical emotion perception, as a key pathway to decoding the essence of human emotions, requires the analysis of its regulatory mechanisms for the development of precise neural modulation strategies. Although electroencephalography (EEG) signals can be used to capture the dynamic neural activities associated with music emotion processing, and virtual reality (VR) technology can offer immersive enhancement effects for emotion regulation, the interaction mechanism among the VR environment, music emotion and neural activity remains unclear. This study established a multimodal experimental paradigm of "VR environment-music stimulation-EEG response" and employed multi-band feature analysis to systematically elucidate the neural dynamics patterns of musical emotion perception during transitions between different virtual scenarios. The results demonstrated that the right temporal lobe exhibited significant electrophysiological changes when comparing real and virtual scenarios, while posterior brain regions were sensitive to differences in virtual environments. Furthermore, the environment exerted specific modulation on both low-frequency and high-frequency EEG activities, with the δ energy percentage demonstrating a context-dependent differentiation in music emotion perception. This study, through virtual scenario-modulated music emotion perception experiments, systematically reveals the frequency-band-specific modulation effects of environmental factors on music emotion, establishes the energy ratio of the δ band as a key biomarker for environment-emotion interaction, and provides an important theoretical basis and quantitative assessment methods for the development of immersive emotion regulation strategies and clinical psychological interventions.

Transcranial direct current stimulation (tDCS) is a non-invasive neuromodulation technique that can modulate cortical neuronal excitability through scalp electrodes, thereby potentially enhancing cognitive function. However, to date, no specific stimulation targets have been identified in studies on tDCS for improving cognitive function. Previous research has suggested that the left dorsolateral prefrontal cortex (DLPFC) and parietal-occipital regions (PO) of the human brain may be potential therapeutic targets. Based on this, the present study aims to compare the mechanisms of how tDCS affect working memory by modulating DLPFC and PO regions, providing empirical support for clinical application. According to different stimulation targets, the experiment was divided into DLPFC group, PO group and sham group in this study. A total of 20 participants were recruited to participate in the tDCS regulation trial. Each participant was randomly assigned to receive two types of stimuli, with a minimum interval of 3 days between each stimulus (a total of 40 stimuli). This study designed the "3-back " working memory task paradigm, calculated and analyzed the reaction time (RT) and accuracy (AC) of three groups of subjects in cognitive tasks before and after receiving tDCS regulation. This study collected resting state electroencephalogram (EEG) signals from three groups of subjects before and after regulation, and compared and analyzed the autocorrelation of each brain functional area, the cross-correlation between different brain functional regions, and the corresponding network topology characteristics. The results showed that after regulation, for subjects in the DLPFC group and PO group, the AC increased and RT decreased, with the DLPFC group demonstrating better effects. Additionally, DLPFC stimulation could enhance the autocorrelation and cross-brain connectivity of targets and related brain regions in the theta and beta frequency bands, and improve the clustering coefficient and local efficiency of brain regions in these frequency bands. However, PO stimulation and sham stimulation had no such effects. This study confirms that tDCS stimulation of DLPFC can improve cognitive function by enhancing the network connectivity of brain regions related to the theta and beta frequency bands, providing experimental evidence and theoretical support for the clinical rehabilitation of brain cognitive dysfunction using tDCS.

Aiming at the limitations of low accuracy and poor stability in the transit time method for estimating carotid local pulse wave velocity, this paper proposed a machine learning-based local pulse wave velocity estimation method, which integrated carotid pulse wave time-domain features, age, and cardiac function parameters. The research was based on a dataset of carotid pulse wave propagation from 4 374 virtual subjects. By combining the Pearson correlation coefficient method and the least absolute shrinkage and selection algorithm to select multi-position combinations of pulse wave time-domain features, and integrating age, heart rate, and other parameters as input features, five machine learning models including multiple linear regression, Bayesian ridge regression, k-nearest neighbor regression, support vector regression and convolutional neural network were used to construct the carotid local pulse wave velocity estimation model, respectively. The results demonstrated that all five machine learning models showed higher accuracy and stronger stability than the traditional methods, and the support vector regression model achieved the optimal performance, with a normalized root mean square error of less than 1.80% and a coefficient of determination exceeding 0.980. In conclusion, it is hoped that the research results presented in this paper can provide a theoretical basis and technical support for the early quantitative assessment of local vascular elasticity of the carotid artery in clinic.

In clinical diagnosis of brain tumors, accurate segmentation based on multimodal magnetic resonance imaging (MRI) is essential for determining tumor type, extent, and spatial boundaries. However, differences in imaging mechanisms, information emphasis, and feature distributions among multimodal MRI data have posed significant challenges for precise tumor modeling and fusion-based segmentation. In recent years, fusion neural networks have provided effective strategies for integrating multimodal information and have become a major research focus in multimodal brain tumor segmentation. This review systematically summarized relevant studies on fusion neural networks for multimodal brain tumor segmentation published since 2019. First, the fundamental concepts of multimodal data fusion and model fusion were introduced. Then, existing methods were categorized into three types according to fusion levels: prediction fusion models, feature fusion models, and stage fusion models, and their structural characteristics and segmentation performance were comparatively analyzed. Finally, current limitations were discussed, and potential development trends of fusion neural networks for multimodal MRI brain tumor segmentation were summarized. This review aims to provide references for the design and optimization of future multimodal brain tumor segmentation models.

This study aims to investigate the effects of curved and straight free edges on the hemodynamic performance and mechanical properties of polymeric heart valves. Two aortic valve models with different free-edge configurations were established, and valve motion throughout the entire cardiac cycle was simulated using a two-way fluid-structure interaction (FSI) method. Hemodynamic parameters and stress distribution characteristics were compared and analyzed. The results revealed that the curved free edge valve exhibited a significantly faster opening response than the straight free edge valve, with an approximately 13% increase in the effective orifice area (EOA) and an approximately 27% reduction in regurgitant volume. However, after valve closure, the curved free-edge model demonstrated higher stress levels across all critical regions. The free-edge configuration did not significantly alter the vortical structure within the aortic flow field; both models exhibited a flow pattern characterized by a combination of sinus vortices and wall-mounted spindle-shaped vortices. The findings indicate that a curved free edge can improve valve opening efficiency and regurgitation control, but may exacerbate stress concentration during closure, potentially increasing the risk of fatigue damage to the valve.

Transcranial alternating current stimulation (tACS) holds significant potential for improving motor function in stroke patients, but its underlying mechanisms remain unclear. In this study, 20 Hz tACS was applied to 15 stroke patients, and their motor imagery (MI) signals were collected before and after stimulation, which were for assessment by combining with the Fugl-Meyer Assessment for Upper Extremity (FMA-UE). Additionally, 11 subjects were recruited as a healthy control group. The study demonstrated that FMA-UE scores of stroke patients significantly increased after tACS intervention. The duration of EEG microstate C and F decreased significantly, while microstate D (coverage, duration, and occurrence probability) increased markedly, and microstate E decreased. The transition probabilities of C→D and D→B were positively correlated with FMA-UE scores. Based on these findings, this study concludes that 20 Hz tACS can enhance neuroplasticity and motor function in patients, and the transition probabilities (C→D/D→B) may serve as potential indicators for assessing motor function, providing experimental evidence for the clinical application of tACS and the development of rehabilitation brain-computer interfaces.

The rehabilitation of motor dysfunction following stroke remains a major clinical challenge, underscoring the urgent need to develop novel therapeutic strategies to improve functional recovery in patients. Brain-computer interface (BCI) technology has emerged as a cutting-edge approach in neurorehabilitation, demonstrating significant potential for motor function restoration. Transcranial electrical stimulation (tES), a non-invasive neuromodulation technique, can promote neuroplasticity by regulating cortical excitability. In recent years, studies have begun to explore the combination of BCI with tES to synergistically enhance neural remodeling within the central nervous system. This integrated multi-technology strategy is increasingly becoming a key focus in the field of neurorehabilitation. This review systematically summarized recent advances in tES-BCI integrated systems for neurorehabilitation, with a particular emphasis on widely adopted BCI paradigms and tES parameter configurations and stimulation modalities. Based on a comprehensive synthesis of existing evidence, this review summarizes the efficacy of this combined intervention strategy in rehabilitating upper and lower limb motor functions following stroke, highlights the methodological limitations and clinical translation challenges present in current research, and aims to provide insights for mechanistic exploration, system optimization, and clinical translation of integrated BCI-tES technology.

Motor imagery electroencephalogram (MI-EEG) decoding algorithms face multiple challenges. These include incomplete feature extraction, susceptibility of attention mechanisms to distraction under low signal-to-noise ratios, and limited capture of long-range temporal dependencies. To address these issues, this paper proposes a multi-branch differential attention temporal network (MDAT-Net). First, the method constructed a multi-branch feature fusion module to extract and fuse diverse spatio-temporal features from different scales. Next, to suppress noise and stabilize attention, a novel multi-head differential attention mechanism was introduced to enhance key signal dynamics by calculating the difference between attention maps. Finally, an adaptive residual separable temporal convolutional network was designed to efficiently capture long-range dependencies within the feature sequence for precise classification. Experimental results showed that the proposed method achieved average classification accuracies of 85.73%, 90.04%, and 96.30% on the public datasets BCI-IV-2a, BCI-IV-2b, and HGD, respectively, significantly outperforming several baseline models. This research provides an effective new solution for developing high-precision motor imagery brain-computer interface systems.

Although transcranial magnetic stimulation (TMS) is widely used in neuromodulation, conventional TMS struggles to achieve both depth and focal specificity. Temporal interference TMS (TI-TMS) offers a promising approach to enhance stimulation depth while reducing the focal area; however, current research remains largely simulation-based, with limited studies on system implementation and experimental validation in rodent deep brain regions. To address this, we developed a TI-TMS system based on a realistic mouse head model using finite element simulation. Electrophysiological recordings of local field potentials (LFPs) in the ventral hippocampal (vHPC) formation were performed to evaluate changes in θ rhythm power spectral density (PSD) and θ-γ phase-amplitude coupling (PAC) following stimulation. The results demonstrated that TI-TMS enhanced θ rhythm power and strengthened θ-γ PAC, indicating effective modulation of deep brain regions. This study establishes a functional TI-TMS system capable of effectively stimulating deep vHPC, providing an experimental basis for its application in precise neuromodulation of subcortical brain areas.

40 Hz light flicker stimulation is deemed to hold considerable promise in the treatment of Alzheimer's disease (AD). However, whether its long-term effect can improve working memory and its related mechanisms remains to be further explored. In this study, 21 adult Wistar rats were randomly divided into the AD light-stimulation group, the AD group and the control group. AD models were established in the first two of these groups, with the light-stimulation group receiving long-term 40 Hz light flicker stimulation. Working memory performance across groups was subsequently evaluated using the T-maze task. To investigate the potential neural mechanisms underlying the effects of 40 Hz light stimulation on working memory, we examined changes in neuronal excitability within the hippocampus (HPC) and medial prefrontal cortex (mPFC), as well as alterations in inter-regional synchronization of neural activity. The findings demonstrated that prolonged 40 Hz light stimulation significantly improved working memory performance in AD model rats. Furthermore, the intervention enhanced the synchronization of neural activity between the hippocampus (HPC) and medial prefrontal cortex (mPFC), as well as the efficiency of information transfer, primarily mediated by theta and low-frequency gamma oscillations. This study provides theoretical support for exploring the mechanisms of 40 Hz light flicker stimulation and its further clinical application in the prevention and treatment of Alzheimer's disease.