Attention-based convolutional neural network with multi-modal temporal information fusion for motor imagery EEG decoding.

Convolutional neural network (CNN) has been widely applied in motor imagery (MI)-based brain computer interface (BCI) to decode electroencephalography (EEG) signals. However, due to the limited perceptual field of convolutional kernel, CNN only extracts features from local region without considering long-term dependencies for EEG decoding. Apart from long-term dependencies, multi-modal temporal information is equally important for EEG decoding because it can offer a more comprehensive understanding of the temporal dynamics of neural processes. In this paper, we propose a novel deep learning network that combines CNN with self-attention mechanism to encapsulate multi-modal temporal information and global dependencies. The network first extracts multi-modal temporal information from two distinct perspectives: average and variance. A shared self-attention module is then designed to capture global dependencies along these two feature dimensions. We further design a convolutional encoder to explore the relationship between average-pooled and variance-pooled features and fuse them into more discriminative features. Moreover, a data augmentation method called signal segmentation and recombination is proposed to improve the generalization capability of the proposed network. The experimental results on the BCI Competition IV-2a (BCIC-IV-2a) and BCI Competition IV-2b (BCIC-IV-2b) datasets show that our proposed method outperforms the state-of-the-art methods and achieves 4-class average accuracy of 85.03% on the BCIC-IV-2a dataset. The proposed method implies the effectiveness of multi-modal temporal information fusion in attention-based deep learning networks and provides a new perspective for MI-EEG decoding. The code is available at https://github.com/Ma-Xinzhi/EEG-TransNet.

SKF82958, a dopamine D1 receptor agonist, disrupts prepulse inhibition in the medial prefrontal cortex and nucleus accumbens in C57BL/6J mice.

Prepulse inhibition (PPI) is a crucial indicator of sensorimotor gating that is often impaired in neuropsychiatric diseases. Although dopamine D1 receptor agonists have been found to disrupt PPI in mice, the underlying mechanisms are not fully understood. In this study, we aimed to identify the brain regions responsible for the PPI-disruptive effect of the D1 agonist in mice. Results demonstrated that intraperitoneal administration of the selective dopamine D1 receptor agonist SKF82958 dramatically inhibited PPI, while the dopamine D1 receptor antagonist SCH23390 enhanced PPI. Additionally, local infusion of SKF82958 into the nucleus accumbens and medial prefrontal cortex disrupted PPI, but not in the ventral hippocampus. Infusion of SCH23390 into these brain regions also failed to enhance PPI. Overall, the study suggests that the nucleus accumbens and medial prefrontal cortex are responsible for the PPI-disruptive effect of dopamine D1 receptor agonists. These findings provide essential insights into the cellular and neural circuit mechanisms underlying the disruptive effects of dopamine D1 receptor agonists on PPI and may contribute to the development of novel treatments for neuropsychiatric diseases.

Metabolic Regulation-Mediated Reversion of the Tumor Immunosuppressive Microenvironment for Potentiating Cooperative Metabolic Therapy and Immunotherapy.

Tumor cells exhibit heightened glucose (Glu) consumption and increased lactic acid (LA) production, resulting in the formation of an immunosuppressive tumor microenvironment (TME) that facilitates malignant proliferation and metastasis. In this study, we meticulously engineer an antitumor nanoplatform, denoted as ZLGCR, by incorporating glucose oxidase, LA oxidase, and CpG oligodeoxynucleotide into zeolitic imidazolate framework-8 that is camouflaged with a red blood cell membrane. Significantly, ZLGCR-mediated consumption of Glu and LA not only amplifies the effectiveness of metabolic therapy but also reverses the immunosuppressive TME, thereby enhancing the therapeutic outcomes of CpG-mediated antitumor immunotherapy. It is particularly important that the synergistic effect of metabolic therapy and immunotherapy is further augmented when combined with immune checkpoint blockade therapy. Consequently, this engineered antitumor nanoplatform will achieve a cooperative tumor-suppressive outcome through the modulation of metabolism and immune responses within the TME.

Modeling and optimization of a novel multi-DOF electromagnetic driven spherical motion generator.

This paper introduces a new spherical motion generator and presents a method for modeling its magnetic field and analyzing its moments. The generator employs an electromagnetic drive of a spherical motor as its driving method and utilizes a spherical parallel manipulator to execute the spherical motion. The combination of these two technologies offers several advantages, including a large workspace and high motion accuracy. The equivalent magnetizing current method is used in the magnetic field modeling and the average air-gap flux density is optimized to achieve a better magnetic field distribution, and the accuracy of the analytical model is verified by finite element simulations and experiments.

Design and control of a novel variable stiffness actuator based on antagonistic variable radius principle.

Variable stiffness actuators (VSAs) are essential for ensuring safe human-robot interactions in robotic applications. This paper proposes a novel rotary VSA using an antagonistic Hoberman linkage mechanism (AHLM), which offers a large stiffness range and a compact structure. The VSA-AHLM consists of two sets of antagonistic-type quadratic springs based on spiral cams connected to the Hoberman linkage mechanism (HLM) through four cables. By simultaneously adjusting both the radius of the HLM and the spring preload, the stiffness of the VSA-AHLM can be varied within a large range. Furthermore, the position and stiffness of the VSA-AHLM can be controlled independently by two rotary motors. The geometric parameters of the spiral cam are determined to achieve the desired linear stiffness-elongation behavior of a quadratic spring, and detailed models of the actuator's stiffness, elastic energy, and torque are established. An actuator prototype is fabricated to demonstrate the proposed variable stiffness approach. Experiments show that the developed actuator can achieve significant stiffness changes and exhibits excellent positioning and trajectory tracking performance.

Boosting lower-limb motor imagery performance through an ensemble method for gait rehabilitation.

Lower-limb exoskeletons have been used extensively in many rehabilitation applications to assist disabled people with their therapies. Brain-machine interfaces (BMIs) further provide effective and natural control schemes. However, the limited performance of brain signal decoding from lower-limb kinematics restricts the broad growth of both BMI and rehabilitation industry. To address these challenges, we propose an ensemble method for lower-limb motor imagery (MI) classification. The proposed model employs multiple techniques to boost performance, including deep and shallow parts. Traditional wavelet transformation followed by filter-bank common spatial pattern (CSP) employs neurophysiologically reasonable patterns, while multi-head self-attention (MSA) followed by temporal convolutional network (TCN) extracts deeper encoded generalized patterns. Experimental results in a customized lower-limb exoskeleton on 8 subjects in 3 consecutive sessions showed that the proposed method achieved 60.27% and 64.20% for three (MI of left leg, MI of right leg, and rest) and two classes (lower-limb MI vs. rest), respectively. Besides, the proposed model achieves improvements of up to 4% and 2% accuracy for the subject-specific and subject-independent modes compared to the current state-of-the-art (SOTA) techniques, respectively. Finally, feature analysis was conducted to show discriminative brain patterns in each MI task and sessions with different feedback modalities. The proposed models integrated in the brain-actuated lower-limb exoskeleton established a potential BMI for gait training and neuroprosthesis.

Effects of Combined Use of Intermittent Theta Burst Stimulation and Cognitive Training on Poststroke Cognitive Impairment: A Single-Blind Randomized Controlled Trial.

OBJECTIVE: Poststroke cognitive impairment substantially affects patients' quality of life. This study explored the therapeutic efficacy of intermittent theta burst stimulation combined with cognitive training for poststroke cognitive impairment. DESIGN: The experimental group received intermittent theta burst stimulation and cognitive training, whereas the control group only received cognitive training, both for 6 wks. The outcome measures were the Loewenstein Occupational Therapy Cognitive Assessment, modified Barthel Index, transcranial Doppler ultrasonography, and functional near-infrared spectroscopy. RESULTS: After therapy, between-group comparisons revealed a substantial difference in the Loewenstein Occupational Therapy Cognitive Assessment scores ( P = 0.024). Improvements in visuomotor organization and thinking operations were more noticeable in the experimental group than in the other groups ( P = 0.017 and P = 0.044, respectively). After treatment, the resistance index of the experimental group differed from that of the control group; channels 29, 37, and 41 were activated ( P < 0.05). The active locations were the left dorsolateral prefrontal cortex, prefrontal polar cortex, and left Broca's region. CONCLUSIONS: Intermittent theta burst stimulation combined with cognitive training had a superior effect on improving cognitive function and everyday activities compared with cognitive training alone, notably in visuomotor organization and thinking operations. Intermittent theta burst stimulation may enhance cognitive performance by improving network connectivity.

Lower Limb Activity Recognition Based on sEMG Using Stacked Weighted Random Forest.

The existing surface electromyography-based pattern recognition system (sEMG-PRS) exhibits limited generalizability in practical applications. In this paper, we propose a stacked weighted random forest (SWRF) algorithm to enhance the long-term usability and user adaptability of sEMG-PRS. First, the weighted random forest (WRF) is proposed to address the issue of imbalanced performance in standard random forests (RF) caused by randomness in sampling and feature selection. Then, the stacking is employed to further enhance the generalizability of WRF. Specifically, RF is utilized as the base learner, while WRF serves as the meta-leaning layer algorithm. The SWRF is evaluated against classical classification algorithms in both online experiments and offline datasets. The offline experiments indicate that the SWRF achieves an average classification accuracy of 89.06%, outperforming RF, WRF, long short-term memory (LSTM), and support vector machine (SVM). The online experiments indicate that SWRF outperforms the aforementioned algorithms regarding long-term usability and user adaptability. We believe that our method has significant potential for practical application in sEMG-PRS.

The effect and optimal parameters of repetitive transcranial magnetic stimulation on lower extremity motor function in stroke patient: a systematic review and meta-analysis.

PURPOSE: This study aimed to evaluate the efficacy of repetitive transcranial magnetic stimulation (rTMS) in treating lower limb motor dysfunction after stroke and explore the optimal stimulation parameters. METHODS: PubMed, Embase, Cochrane Library, and other relevant databases were systematically queried for randomised controlled trials (RCTs) investigating the efficacy of rTMS in addressing lower limb motor dysfunction post-stroke. The search encompassed records from inception to July 2022. The assessed outcomes encompassed parameters such as the Fugl-Meyer motor function score for lower limbs, balance function, and Barthel index (BI). Three independent researchers were responsible for research selection, data extraction, and quality assessment. Study screening, data extraction, and bias evaluation were performed independently by two reviewers. Data synthesis was undertaken using Review Manager 5.3, while Stata version 14.0 software was employed for generating the funnel plot. RESULTS: A total of 13 studies and 428 patients were included. The meta-analysis indicated that rTMS had a positive effect on the BI (MD = 5.87, 95% CI [0.99, 10.76], p = 0.02, I2 = 86%, N of studies = 8, N of participants = 248). Subgroup analysis was performed on the stimulation frequency, treatment duration, and different stroke stages (stimulation frequency was low-frequency (LF)-rTMS (MD = 4.45, 95% CI [1.05, 7.85], p = 0.01, I2 = 0%, N of studies = 4, N of participants = 120); treatment time ≤ 15 d: (MD = 4.41, 95% CI [2.63, 6.18], p < 0.00001, I2 = 0%, N of studies = 4, N of participants = 124); post-stroke time ≤6 months: (MD = 4.37, 95% CI [2.42, 6.32], p < 0.0001, I2 = 0%, N of studies = 5, N of participants = 172). CONCLUSION: LF-rTMS had a significant improvement effect on BI score, while high-frequency (HF)-rTMS and iTBS had no significant effect. And stroke time ≤6 months in patients with treatment duration ≤15 d had the best treatment effect.

Stroke Lower limb dysfunction is a common complication after stroke, seriously affecting the daily life of patients.Lower limb motor function improved significantly within 6 months after low frequency treatment.To maximise motor gains in patients with lower limb motor dysfunction following stroke in the shortest possible time, it is recommended to initiate early rehabilitation therapy using low-frequency transcranial magnetic stimulation during the subacute phase of stroke.

Immunostimulant Hydrogel-Guided Tumor Microenvironment Reprogramming to Efficiently Potentiate Macrophage-Mediated Cellular Phagocytosis for Systemic Cancer Immunotherapy.

Macrophage-mediated cellular phagocytosis (MMCP) plays a critical role in conducting antitumor immunotherapy but is usually impaired by the intrinsic phagocytosis evading ability of tumor cells and the immunosuppressive tumor microenvironment (TME). Herein, a MMCP-boosting hydrogel (TCCaGM) was elaborately engineered by encapsulating granulocyte-macrophage colony-stimulating factor (GM-CSF) and a therapeutic nanoplatform (TCCaN) that preloaded with the tunicamycin (Tuni) and catalase (CAT) with the assistance of CaCO3 nanoparticles (NPs). Strikingly, the hypoxic/acidic TME was efficiently alleviated by the engineered hydrogel, "eat me" signal calreticulin (CRT) was upregulated, while the "don't eat me" signal CD47 was downregulated on tumor cells, and the infiltrated DCs were recruited and activated, all of which contributed to boosting the macrophage-mediated phagocytosis and initiating tumor-specific CD8+ T cells responses. Meanwhile, the remodeled TME was beneficial to accelerate the polarization of tumor-associated macrophages (TAMs) to the antitumoral M1-like phenotype, further heightening tumoricidal immunity. With the combination of PD-1 antibody (αPD-1), the designed hydrogel significantly heightened systemic antitumor immune responses and long-term immunological effects to control the development of primary and distant tumors as well as suppress tumor metastasis and recurrence, which established an optimal strategy for high-performance antitumor immunotherapy.

A Temporal Dependency Learning CNN With Attention Mechanism for MI-EEG Decoding.

Deep learning methods have been widely explored in motor imagery (MI)-based brain computer interface (BCI) systems to decode electroencephalography (EEG) signals. However, most studies fail to fully explore temporal dependencies among MI-related patterns generated in different stages during MI tasks, resulting in limited MI-EEG decoding performance. Apart from feature extraction, learning temporal dependencies is equally important to develop a subject-specific MI-based BCI because every subject has their own way of performing MI tasks. In this paper, a novel temporal dependency learning convolutional neural network (CNN) with attention mechanism is proposed to address MI-EEG decoding. The network first learns spatial and spectral information from multi-view EEG data via the spatial convolution block. Then, a series of non-overlapped time windows is employed to segment the output data, and the discriminative feature is further extracted from each time window to capture MI-related patterns generated in different stages. Furthermore, to explore temporal dependencies among discriminative features in different time windows, we design a temporal attention module that assigns different weights to features in various time windows and fuses them into more discriminative features. The experimental results on the BCI Competition IV-2a (BCIC-IV-2a) and OpenBMI datasets show that our proposed network outperforms the state-of-the-art algorithms and achieves the average accuracy of 79.48%, improved by 2.30% on the BCIC-IV-2a dataset. We demonstrate that learning temporal dependencies effectively improves MI-EEG decoding performance. The code is available at https://github.com/Ma-Xinzhi/LightConvNet.

Fused Cytomembrane-Camouflaged Nanoparticles for Tumor-Specific Immunotherapy.

Tumor immunotherapy is commonly hindered by inefficient delivery and presentation of tumor antigens as well as immunosuppressive tumor microenvironment. To overcome these barriers, a tumor-specific nanovaccine capable of delivering tumor antigens and adjuvants to antigen-presenting cells and modulating the immune microenvironment to elicit strong antitumor immunity is reported. This nanovaccine, named FCM@4RM, is designed by coating the nanocore (FCM) with a bioreconstituted cytomembrane (4RM). The 4RM, which is derived from fused cells of tumorous 4T1 cells and RAW264.7 macrophages, enables effective antigen presentation and stimulation of effector T cells. FCM is self-assembled from Fe(II), unmethylated cytosine-phosphate-guanine oligodeoxynucleotide (CpG), and metformin (MET). CpG, as the stimulator of toll-like receptor 9, induces the production of pro-inflammatory cytokine and the maturation of cytotoxic T lymphocytes (CTLs), thereby enhancing antitumor immunity. Meanwhile, MET functions as the programmed cell death ligand 1 inhibitor and can restore the immune responses of T cells against tumor cells. Therefore, FCM@4RM exhibits high targeting capabilities toward homologous tumors that develop from 4T1 cells. This work offers a paradigm for developing a nanovaccine that systematically regulates multiple immune-related processes to achieve optimal antitumor immunotherapy.

Three-Dimensional Gait Analysis and sEMG Measures for Robotic-Assisted Gait Training in Subacute Stroke: A Randomized Controlled Trial.

Background: The efficacy of robotic-assisted gait training (RAGT) should be considered versatilely; among which, gait assessment is one of the most important measures; observational gait assessment is the most commonly used method in clinical practice, but it has certain limitations due to the deviation of subjectivity; instrumental assessments such as three-dimensional gait analysis (3DGA) and surface electromyography (sEMG) can be used to obtain gait data and muscle activation during walking in stroke patients with hemiplegia, so as to better evaluate the rehabilitation effect of RAGT. Objective: This single-blind randomized controlled trial is aimed at analyzing the impact of RAGT on the 3DGA parameters and muscle activation in patients with subacute stroke and evaluating the clinical effect of improving walking function of RAGT. Methods: This randomized controlled trial evaluated the improvement of 4-week RAGT on patients with subacute stroke by 3DGA and surface electromyography (sEMG), combined with clinical scales: experimental group (n = 18, 20 sessions of RAGT) or control group (n = 16, 20 sessions of conventional gait training). Gait performance was evaluated by the 3DGA, and clinical evaluations based on Fugl-Meyer assessment for lower extremity (FMA-LE), functional ambulation category (FAC), and 6-minute walk test (6MWT) were used. Of these patients, 30 patients underwent sEMG measurement synchronized with 3DGA; the cocontraction index in swing phase of the knee and ankle of the affected side was calculated. Results: After 4 weeks of intervention, intragroup comparison showed that walking speed, temporal symmetry, bilateral stride length, range of motion (ROM) of the bilateral hip, flexion angle of the affected knee, ROM of the affected ankle, FMA-LE, FAC, and 6MWT in the experimental group were significantly improved (p < 0.05), and in the control group, significant improvements were observed in walking speed, temporal symmetry, stride length of the affected side, ROM of the affected hip, FMA-LE, FAC, and 6MWT (p < 0.05). Intergroup comparison showed that the experimental group significantly outperformed the control group in walking speed, temporal symmetry of the spatiotemporal parameters, ROM of the affected hip and peak flexion of the knee in the kinematic parameters, and the FMA-LE and FAC in the clinical scale (p < 0.05). In patients evaluated by sEMG, the experimental group showed a noticeable improvement in the cocontraction index of the knee (p = 0.042), while no significant improvement was observed in the control group (p = 0.196), and the experimental group was better than the control group (p = 0.020). No noticeable changes were observed in the cocontraction index of the ankle in both groups (p > 0.05). Conclusions: Compared with conventional gait training, RAGT successfully improved part of the spatiotemporal parameters of patients and optimized the motion of the affected lower limb joints and muscle activation patterns during walking, which is crucial for further rehabilitation of walking ability in patients with subacute stroke. This trial is registered with ChiCTR2200066402.

Specific activation of cGAS-STING pathway by nanotherapeutics-mediated ferroptosis evoked endogenous signaling for boosting systemic tumor immunotherapy.

Activation of the cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS-STING) pathway could effectively initiate antitumor immunity, but specific activation of STING pathway is still an enormous challenge. Herein, a ferroptosis-induced mitochondrial DNA (mtDNA)-guided tumor immunotherapy nanoplatform (designated as HBMn-FA) was elaborately developed for activating and boosting STING-based immunotherapy. On the one hand, the high-levels of reactive oxygen species (ROS) in tumor cells induced by HBMn-FA-mediated ferroptosis elicited mitochondrial stress to cause the release of endogenous signaling mtDNA, which specifically initiate cGAS-STING pathway with the cooperation of Mn2+. On the other hand, the tumor-derived cytosolic double-stranded DNA (dsDNA) from debris of death cells caused by HBMn-FA further activated the cGAS-STING pathway in antigen-presenting cells (e.g., DCs). This bridging of ferroptosis and cGAS-STING pathway could expeditiously prime systemic antitumor immunity and enhance the therapeutic efficacy of checkpoint blockade to suppress tumor growth in both localized and metastatic tumor models. The designed nanotherapeutic platform paves the way for novel tumor immunotherapy strategies that are based on specific activation of STING pathway.

TCB-2, a 5-hydroxytryptamine 2A receptor agonist, disrupts prepulse inhibition in the ventral pallidum and nucleus accumbens.

The 5-hydroxytryptamine 2A (5-HT2A) receptor plays an important role in schizophrenia. The 5-HT2A receptor is also involved in the regulation of prepulse inhibition (PPI) in rodents. The aim of this study was to determine whether selective 5-HT2A receptor agonizts or antagonists may alter PPI in rats and to identify the critical brain regions in which the activity of 5-HT2A receptors regulates PPI. The results showed that infusion of the 5-HT2A receptor agonist TCB-2 into the lateral ventricle disrupted PPI, but the 5-HT2A receptor antagonist M100907 had no such effect. In addition, local infusion of TCB-2 into the nucleus accumbens and ventral pallidum disrupted PPI, whereas the same manipulation in the medial prefrontal cortex, ventral hippocampus, and ventral tegmental area did not disrupt PPI. In conclusion, agonism of 5-HT2A receptors in the ventral pallidum and nucleus accumbens can disrupt PPI. The ventral pallidum and nucleus accumbens are critical brain regions responsible for the regulation of PPI by serotonin. These findings contribute to the extensive exploration of the molecular and neural mechanisms underlying the regulatory effect of 5-HT2A receptor activity on PPI, especially the neural circuits modulated by 5-HT2A receptor activity.

Synchronous analyses between electroencephalogram and surface electromyogram based on motor imagery and motor execution.

The functional coupling of the cerebral cortex and muscle contraction indicates that electroencephalogram (EEG) and surface electromyogram (sEMG) signals are coherent. The objective of this study is to clearly describe the coupling relationship between EEG and sEMG through a variety of analysis methods. We collected the EEG and sEMG data of left- or right-hand motor imagery and motor execution from six healthy subjects and six stroke patients. To enhance the coherence coefficient between EEG and sEMG signals, the algorithm of EEG modification based on the peak position of sEMG signals is proposed. Through analyzing a variety of signal synchronization analysis methods, the most suitable coherence analysis algorithm is selected. In addition, the wavelet coherence analysis method based on time spectrum estimation was used to study the linear correlation characteristics of the frequency domain components of EEG and sEMG signals, which verified that wavelet coherence analysis can effectively describe the temporal variation characteristics of EEG-sEMG coherence. In the task of motor imagery, the significant EEG-sEMG coherence is mainly in the imagination process with the frequency distribution of the alpha and beta frequency bands; in the task of motor execution, the significant EEG-sEMG coherence mainly concentrates before and during the task with the frequency distribution of the alpha, beta, and gamma frequency bands. The results of this study may provide a theoretical basis for the cooperative working mode of neurorehabilitation training and introduce a new method for evaluating the functional state of neural rehabilitation movement.

Interference of Glucose Bioavailability of Tumor by Engineered Biohybrids for Potentiating Targeting and Uptake of Antitumor Nanodrugs.

The chemotherapy efficacy of nanodrugs is restricted by poor tumor targeting and uptake. Here, an engineered biohybrid living material (designated as EcN@HPB) is constructed by integrating paclitaxel and BAY-876 bound human serum albumin nanodrugs (HPB) with Escherichia coli Nissle 1917 (EcN). Due to the inherent tumor tropism of EcN, EcN@HPB could actively target the tumor site and competitively deprive glucose through bacterial respiration. Thus, albumin would be used as an alternative nutrient source for tumor metabolism, which significantly promotes the internalization of HPB by tumor cells. Subsequently, BAY-876 internalized along with HPB nanodrugs would further depress glucose uptake of tumor cells via inhibiting glucose transporter 1 (GLUT1). Together, the decline of glucose bioavailability of tumor cells would activate and promote the macropinocytosis in an AMP-activated protein kinase (AMPK)-dependent manner, resulting in more uptake of HPB by tumor cells and boosting the therapeutic outcome of paclitaxel.

Deep Convolutional Neural Network for EEG-Based Motor Decoding.

Brain-machine interfaces (BMIs) have been applied as a pattern recognition system for neuromodulation and neurorehabilitation. Decoding brain signals (e.g., EEG) with high accuracy is a prerequisite to building a reliable and practical BMI. This study presents a deep convolutional neural network (CNN) for EEG-based motor decoding. Both upper-limb and lower-limb motor imagery were detected from this end-to-end learning with four datasets. An average classification accuracy of 93.36 ± 1.68% was yielded on the four datasets. We compared the proposed approach with two other models, i.e., multilayer perceptron and the state-of-the-art framework with common spatial patterns and support vector machine. We observed that the performance of the CNN-based framework was significantly better than the other two models. Feature visualization was further conducted to evaluate the discriminative channels employed for the decoding. We showed the feasibility of the proposed architecture to decode motor imagery from raw EEG data without manually designed features. With the advances in the fields of computer vision and speech recognition, deep learning can not only boost the EEG decoding performance but also help us gain more insight from the data, which may further broaden the knowledge of neuroscience for brain mapping.

Efficacy of Intermittent Theta-Burst Stimulation and Transcranial Direct Current Stimulation in Treatment of Post-Stroke Cognitive Impairment.

BACKGROUND: The efficacy of intermittent theta-burst stimulation (iTBS) and transcranial direct current stimulation (tDCS) combined with cognitive training in the treatment of post-stroke cognitive impairment (PSCI) requires further investigation. METHODS: We randomly assigned 60 patients with PSCI to receive iTBS (n = 21), tDCS (n = 19), or cognitive training alone (n = 20). Cognitive function was evaluated by the Loewenstein Occupational Therapy Cognitive Assessment (LOTCA), and the performance of activities of daily living (ADL) was assessed with the modified Barthel Index (MBI). Of these patients, 14 participated in the functional near-infrared spectroscopy (fNIRS) measurement. RESULTS: After six weeks of treatment, cognitive function improved in all three groups of PSCI patients. Compared with patients receiving only cognitive training, the cognitive function of patients in the iTBS combined with cognitive training (p = 0.003) and tDCS combined with cognitive training groups (p = 0.006) showed greater improvement. The cognitive improvement from tDCS was related to the activation of the frontopolar cortex (FPC), while the improvement of cognition by iTBS was based on the activation of the stimulation site (the dorsolateral prefrontal cortex) and some distant regions. CONCLUSIONS: Both iTBS and tDCS in addition to cognitive training appear to improve cognitive function and quality of life of patients with PSCI, compared to cognitive training alone. tDCS improved cognitive function by improving the patient's valuation, motivation, and decision-making substructures, while iTBS improved patients' assessment and decision-making abilities, improving cognitive control and, ultimately, overall cognitive function.

Essential thrombocythemia with non-ST-segment elevation myocardial infarction as the first manifestation: A case report.

BACKGROUND: We report a case of essential thrombocythemia (ET) in a 44-year-old male who exhibited non-ST-segment-elevation myocardial infarction (NSTEMI) as the first manifestation without known cardiovascular risk factors (CVRFs). For the first time, we reported a left main trifurcation lesion in NSTEMI caused by ET, including continuous stenosis lesions from the left main to the ostial left anterior descending (LAD) artery and an obvious thrombotic lesion in the ostial and proximal left circumflex (LCX) artery. There was 60% diffuse stenosis in the left main (LM) that extended to the ostial LAD, thrombosis of the ostial LAD and proximal LCX, and 90% stenosis in the proximal LCX. During the operation, thrombus aspiration was performed, but no obvious thrombus was aspirated. Performing the kissing balloon technique (KBT) in the LCX and LM unexpectedly increased the narrowness of the LAD. Then, the single-stent crossover technique, final kissing balloon technique and proximal optimization technique (POT) were performed. On the second day after percutaneous coronary intervention (PCI), the number of platelets (PLTs) still increased significantly to as high as 696 × 109/L. The bone marrow biopsy done later, together with JAK2 (exon 14) V617F mutation, confirms the diagnosis of ET. Hydroxyurea was administered to inhibit bone marrow proliferation to control the number of PLTs. CASE SUMMARY: A 44-year-old male patient went to a local hospital for treatment for intermittent chest pain occurring over 8 h. The examination at the local hospital revealed elevated cTnI and significantly elevated platelet. Then, he was diagnosed with acute myocardial infarction and transferred to our hospital for emergency interventional treatment by ambulance. During the operation, thrombus aspiration, the single-stent crossover technique, final kissing balloon technique and POT were performed. Dual antiplatelet therapy comprising aspirin and ticagrelor was used after PCI. Evidence of mutated JAK2 V617F and bone marrow biopsy shown the onset of ET. Together with JAK2 (exon 14) V617F mutation, ET was diagnosed according to the World Health Organization (WHO) diagnostic criteria, and the patient was placed on hydroxyurea. During the one-year postoperative period, repeated examinations showed a slight increase in PLTs, but the patient no longer had chest tightness, chest pain or bleeding or developed new thromboembolisms. CONCLUSION: Routine physical examinations and screenings are conducive to the early detection of ET, and the risk for thrombosis should be assessed. Then, active antiplatelet therapy and myelosuppression therapy should be used for high-risk ET patients.