Xizi Song

Transcranial acoustoelectric brain imaging (tABI) is a potential brain activity imaging technique with high spatiotemporal resolution. Precise focus of transcranial ultrasound is critical for realizing millimeter-level spatial resolution in tABI. In this study, a precise focusing simulation platform is proposed by constructing a high-precision 3D skull model from Bama pig computed tomography data and a mathematical model equation by considering the skull’s heterogeneous properties. Then, the delay parameters derived from the simulation platform improve the precision of transcranial ultrasound focusing, enabling precise localization of brain activation sources with tABI. Phantom experimental results demonstrate that the transcranial ultrasound field is precisely focused at the target with a 0.20 mm deviation when delay parameters are obtained from the proposed simulation platform, whereas it exhibits divergence when using delay parameters derived from pure water or homogeneous skull models. Furthermore, using the proposed simulation platform, tABI can accurately identify intracranial electrical signals of distinct frequencies and precisely locate the corresponding activation sources with a spatial deviation of 0.50 mm. These results demonstrate that the proposed simulation platform is a powerful tool for the precise focusing of tABI.

Sudden cardiac death (SCD) remains one of the leading causes of mortality worldwide, with coronary artery disease (CAD) as its predominant underlying condition. However, noninvasive and accessible screening approaches for CAD are still limited. This study aims to develop and evaluate a photoplethysmography (PPG)‐based method for CAD detection using a two‐dimensional Gramian angular field (GAF) transformation combined with deep learning. We enrolled 89 patients with CAD and 70 healthy controls and converted their PPG signals into two GAF representations—Gramian angular summation field (GASF) and Gramian angular difference field. The GASF representation, which preserves both magnitude and phase relationships within the PPG waveform, was found to provide superior discriminative capability. Using GASF as input, the proposed SE‐ResNet model achieved an accuracy of 92.43% (95% CI: 91.51–93.36), outperforming prior work that reported 83.8% accuracy (95% CI: 82.2–85.3). These results demonstrate that the GAF transformation enhances CAD detection by encoding the temporal–phase dynamics of PPG signals, which are often overlooked in conventional one‐dimensional analyses. The proposed GASF‐SE‐ResNet framework therefore shows strong potential as a noninvasive low‐cost tool for CAD screening and SCD risk reduction.

Adolescent idiopathic scoliosis (AIS) is a common three‐dimensional spinal deformity and one of the most prevalent spinal disorders in children. In addition to the cosmetic deformity, its secondary effects may lead to cardiopulmonary dysfunction, a decrease in physical ability, and a reduction in quality of life, making it the third most common health threat to children and adolescents after obesity and myopia. Therefore, effective intervention for AIS is of great significance for the physical and mental health of adolescents. In recent years, Physiotherapeutic Scoliosis‐Specific Exercises (PSSE), as one of the mainstream conservative treatments, have been widely applied in clinical practice with positive feedback. However, its effectiveness is influenced by various factors. To this end, this study systematically searched relevant literature published in the past decade in PubMed, CNKI, and Web of Science databases on topics such as AIS, scoliosis‐specific exercises, exercise interventions, and randomized controlled trials. A review analysis of the factors influencing PSSE in the treatment of AIS was conducted, aiming to provide a theoretical basis for optimizing conservative treatment strategies and improving effectiveness.

Transcranial acoustoelectric brain imaging (tABI) is a potential neuroimaging technique with high spatial (~mm) and temporal (~ms) resolution. Decoding precision is critical for decoded signal quality and imaging performance. However, traditional method is based on the fundamental component of acoustoelectric (AE) signal, overlooking the valuable information of its harmonics. Here we present a signal decoding framework combining fundamental and harmonica components. Adopting filter bank analysis to efficiently integrate the fundamental and harmonic components, a weighted filter bank Hilbert transform (wFBHT) decoding method is implemented. With steady-state visual stimulation, both brain electric signal (steady-state visual evoked potential ， SSVEP) and AE signal of live rats were recorded using tABI. First, the signal-to-noises (SNRs) characteristics of harmonic was analyzed. The decoded AE (decAE) signals of 2nd-6th harmonic exhibited highly consistent spectral response with SSVEP, which provided theoretical basis for the framework. Then, using the grid search and nonlinear fitting, the impacts of harmonic number N and weight w on the change rate of correlation coefficient (CRCC) between decAE signal and SSVEP were further explored. Results demonstrated that CRCC increased with harmonic number N significantly superior to traditional method (p=0.0004). With equal weight of 1st-4th harmonics, the highest correlation coefficient increased by 47%. What’s more, the feasibility and performance were validated in live rats. Compared with that of traditional method, the SNR of decAE signal with wFBHT was 20.20dB and increased by 72.1%.

Peripheral nerve injuries often lead to motor, sensory and autonomic dysfunction in patients, which seriously affects their quality of life. Diagnosis and treatment of these injuries require precise neurological function assessment techniques. Conventional neurophysiologic assays are limited in specific aspects and new assays are urgently needed to provide higher temporal and spatial resolution. In this study, we investigated the peripheral nerve detection based on the acoustoelectric(AE) effect for the first time, aiming to introduce a novel peripheral nerve detection method characterized by enhanced adaptability and superior spatiotemporal resolution. This study analyzed in detail the time domain and frequency domain characteristics of low-frequency(LF) neuroelectric stimulation signal and demonstrated the feasibility of peripheral nerve detection based on the AE effect in accurately reflecting neuroelectric activity.

Brain imaging technologies can reveal the neural basis behind thoughts, emotions, and behaviors. Acousto-electric brain imaging is an emerging neuroimaging technique with high spatiotemporal resolution. This study designed and implemented multi-frequency steady-state visual evoked stimuli in rats to explore the classification performance of acousto-electric imaging. Canonical correlation analysis was used to classify the signals, and results were compared with traditional scalp electroencephalography (EEG) methods. The findings indicate that acousto-electric brain imaging is less affected by electrode placement (n.s.) compared to traditional EEG(*). However, the classification performance of ABI signals still lags behind traditional EEG when averaging trials and placing electrode optimally(****).

Acoustoelectric brain imaging (ABI) is a novel functional imaging technique based on the interaction between pressure waves and the electrical conductivity of biological tissues, which overcomes the limitations of traditional bioelectrical signal imaging and allows for the mapping of bioelectrical brain signal activity images with high spatial and temporal resolution. However, the spatial resolution of ABI is affected by the focused ultrasound focal spot and ultrasound scanning step sizes. In this study, we analyzed and compared the reconstructed graphs of activation signals at different scanning step sizes in acoustoelectric imaging based on phantom experiments. Under the condition that the focal spot size of the focused ultrasound beam is constant, the spatial resolution can be improved by choosing a smaller scanning step size.