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Background: The objective of this study is to estimate the causal relationship between plasma proteins and myocardial infarction (MI) through Mendelian randomization (MR), predict potential target-mediated side effects associated with protein interventions, and ensure a comprehensive assessment of clinical safety. Methods: From 3 proteome genome-wide association studies (GWASs) involving 9775 European participants, 331 unique blood proteins were screened and chosed. The summary data related to MI were derived from a GWAS meta-analysis, incorporating approximately 61,000 cases and 577,000 controls. The assessment of associations between blood proteins and MI was conducted through MR analyses. A phenome-wide MR (Phe-MR) analysis was subsequently employed to determine the potential on-target side effects of protein interventions. Results: Causal mediators for MI were identified, encompassing cardiotrophin-1 (CT-1) (odds ratio [OR] per SD increase: 1.16; 95% confidence interval [CI]: 1.13-1.18; p = 1.29 × 10 - 31 ), Selenoprotein S (SELENOS) (OR: 1.16; 95% CI: 1.13-1.20; p = 4.73 × 10 - 24 ), killer cell immunoglobulin-like receptor 2DS2 (KIR2DS2) (OR: 0.93; 95% CI: 0.90-0.96; p = 1.08 × 10 - 5 ), vacuolar protein sorting-associated protein 29 (VPS29) (OR: 0.92; 95% CI: 0.90-0.94; p = 8.05 × 10 - 13 ), and histo-blood group ABO system transferase (NAGAT) (OR: 1.05; 95% CI: 1.03-1.07; p = 1.41 × 10 - 5 ). In the Phe-MR analysis, memory loss risk was mediated by CT-1, VPS29 exhibited favorable effects on the risk of 5 diseases, and KIR2DS2 showed no predicted detrimental side effects. Conclusions: Elevated genetic predictions of KIR2DS2 and VPS29 appear to be linked to a reduced risk of MI, whereas an increased risk is associated with CT-1, SELENOS, and NAGAT. The characterization of side effect profiles aids in the prioritization of drug targets. Notably, KIR2DS2 emerges as a potentially promising target for preventing and treating MI, devoid of predicted detrimental side effects.
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Noise exposure relates to various pathological disorders including liver damage, preventive measures of which are being demanded. Hyperbaric oxygen treatment (HBOT), as a non-invasive procedure, exerts convincing therapeutic potency on multiple liver diseases. The efficacy of HBOT in mitigating noise induced liver damage (NILD) and associated mechanisms would be elucidated here. Mice were subject to broad band noise (20-20k Hz, 90-110 dB) for 5 days by 3 hours/day. HBOT with 2.5 atmosphere absolute (ata) was employed before noise exposure. Morphology of liver tissue was examined by hematoxylin-eosin (HE) staining. Oil Red O (ORO), transferase-mediated dUTP nick end labelling (TUNEL) test and western blot were utilized to detect lipid accumulation, apoptotic cells and protein expression, respectively. Ceramide (Cer) level was assayed by immunohistochemistry (IHC) analysis. With noise exposure, conspicuous structural derangement and lipid deposition occurred in liver tissue of mice, which was alleviated significantly by the application of HBOT. Meanwhile, HBOT reduced the proportion of apoptotic hepatocytes, restraining the superoxide production in noise exposed mice. In view of underlying mechanisms, noise enhanced the acid sphingomyelinase (ASM) protein expression and the Cer generation in liver tissue of mice which was reversed substantially by HBOT. Altogether, HBOT ameliorates the structural and functional derangement of liver by neutralizing the ASM/Cer pathway in noise exposed mice.
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Apoptosis , Oxigenoterapia Hiperbárica , Hepatopatías/prevención & control , Hígado/patología , Ruido , Animales , Ceramidas/metabolismo , Modelos Animales de Enfermedad , Gotas Lipídicas/metabolismo , Gotas Lipídicas/patología , Hígado/metabolismo , Hepatopatías/etiología , Hepatopatías/metabolismo , Hepatopatías/patología , Masculino , Ratones , Mitocondrias Hepáticas/metabolismo , Mitocondrias Hepáticas/patología , Estrés Oxidativo , Transducción de Señal , Esfingomielina Fosfodiesterasa/metabolismo , Superóxidos/metabolismoRESUMEN
This study compares event-related potentials (ERPs) elicited by variations of sound location in free and reverberant fields. The virtual sound sources located at azimuths 0°-40° were synthesized with head-related transfer functions and binaural room impulse responses for free and reverberant fields, respectively. The sound stimulus at 0° was assigned as standard in the oddball paradigm. Results show that the P3 amplitude is larger in the free field and acoustical conditions have no significant effect on the amplitudes of N2 and mismatch negativity. Moreover, a linear relationship between sound angle and amplitude of ERP components is observed.
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Localización de Sonidos , Estimulación Acústica , Electroencefalografía , Potenciales Evocados , Potenciales Evocados Auditivos , SonidoRESUMEN
Noise-induced structural and functional disorder of the liver has been realized, but the underlying mechanism remains to be characterized, which has limited the introduction of precautious measures. Over-activation of acid sphingomyelinase (ASM)/ceramide (Cer) pathway takes centre stage in hepatocyte injury entailed by various stimulus. We aimed to investigate whether it mediated the noise elicited liver disorder on infrastructure, lipid metabolism, apoptosis, and oxidative stress. Mice were exposed to broad band noise (20-20k Hz, 90-110 dB) for 1, 3, 5 or 7 days by 3 hr/d. Doxepin hydrochloride (DOX), an ASM inhibitor was given by 5 mg/kg/d gavage. We showed that 5 or 7 days intense, broad band noise exposure caused significant infrastructure derangement and lipid droplets storage in hepatocytes. The content of cholesterol, free fatty acids or triglyceride was increased significantly in liver tissue upon noise stimulation. Moreover, the noise promoted apoptosis and superoxide generation in hepatocytes significantly, enhancing activity of aspartate aminotransferase (AST) or alanine amino transferase (ALT) in serum. Acid sphingomyelinase activity and Cer generation in liver tissue were elevated by noise exposure, which was normalized with DOX administrated. Accordingly, DOX alleviated steatosis, apoptosis, oxidative stress and enzymatic change in hepatocytes or serum of noise exposed mice substantially. In summary, our results suggest the ASM/Cer pathway contributes to the broad band noise elicited liver damage in mice.
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Hepatopatías/enzimología , Hepatopatías/etiología , Ruido/efectos adversos , Esfingomielina Fosfodiesterasa/metabolismo , Alanina Transaminasa/metabolismo , Animales , Apoptosis/efectos de los fármacos , Aspartato Aminotransferasas/metabolismo , Ceramidas/metabolismo , Doxepina/farmacología , Fibrosis , Hepatocitos/efectos de los fármacos , Hepatocitos/metabolismo , Hepatocitos/patología , Gotas Lipídicas/efectos de los fármacos , Gotas Lipídicas/metabolismo , Hepatopatías/metabolismo , Hepatopatías/patología , Masculino , Ratones , Estrés Oxidativo/efectos de los fármacos , Factores de TiempoRESUMEN
Endomorphin-1 (EM1) and endomorphin-2 (EM2) are two endogenous ligands that belong to the opioid peptide family and have the highest affinity and selectivity for the µ-opioid receptor (MOR). The neuroanatomical distribution, ultrastructural features and neural circuitry of EM-containing neuronal structures have been morphologically demonstrated. In addition, the modulation effects of the EMs in different areas reflect their potential endogenous roles in many major physiological processes, including their remarkable roles in the transmission and modulation of noxious information. The distinguished antinociceptive property of the EMs in acute and chronic pain, including neuropathic pain, cancer pain and inflammatory pain, has been revealed and investigated for therapeutic purposes. However, EMs exert adverse effects in the gastrointestinal, urinary, cardiovascular, and respiratory systems, which impede the development of EMs as new analgesics. Numerous studies have synthesized and investigated EM analogues and demonstrated that these EM derivatives had improved pharmacological properties, supporting their therapeutic perspectives. In the present review, the results of previous studies, particularly morphological and pharmacological studies, were summarized. Finally, EM modifications and their potential clinical implications were described. Applying this knowledge about EMs may provide information for further investigations in clinical application.
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Analgésicos Opioides/uso terapéutico , Péptidos Opioides/metabolismo , Dolor/tratamiento farmacológico , Analgésicos Opioides/farmacología , Animales , Desarrollo de Medicamentos , Humanos , Dolor/metabolismoRESUMEN
Estimating the locations and spatial extents of brain sources poses a long-standing challenge for electroencephalography and magnetoencephalography (E/MEG) source imaging. In the present work, a novel source imaging method, Bayesian Electromagnetic Spatio-Temporal Imaging of Extended Sources (BESTIES), which is built upon a Bayesian framework that determines the spatio-temporal smoothness of source activities in a fully data-driven fashion, is proposed to address this challenge. In particular, a Markov Random Field (MRF), which can precisely capture local cortical interactions, is employed to characterize the spatial smoothness of source activities, the temporal dynamics of which are modeled by a set of temporal basis functions (TBFs). Crucially, all of the unknowns in the MRF and TBF models are learned from the data. To accomplish model inference efficiently on high-resolution source spaces, a scalable algorithm is developed to approximate the posterior distribution of the source activities, which is based on the variational Bayesian inference and convex analysis. The performance of BESTIES is assessed using both simulated and actual human E/MEG data. Compared with L2-norm constrained methods, BESTIES is superior in reconstructing extended sources with less spatial diffusion and less localization error. By virtue of the MRF, BESTIES also overcomes the drawback of over-focal estimates in sparse constrained methods.
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Mapeo Encefálico/métodos , Corteza Cerebral/fisiología , Electroencefalografía , Fenómenos Electromagnéticos , Magnetoencefalografía , Modelos Neurológicos , Teorema de Bayes , Humanos , Procesamiento de Imagen Asistido por Computador , Cadenas de Markov , Procesamiento de Señales Asistido por ComputadorRESUMEN
Developing an electroencephalogram (EEG)-based motor imagery and motor execution (MI/ME) decoding system that is both highly accurate and calibration-free for cross-subject applications remains challenging due to domain shift problem inherent in such scenario. Recent research has increasingly embraced transfer learning strategies, especially domain adaptation techniques. However, domain adaptation becomes impractical when the target subject data is either difficult to obtain or unknown. To address this issue, we propose a supervised contrastive learning-based domain generalization network (SCLDGN) for cross-subject MI/ME decoding. Firstly, the feature encoder is purposefully designed to learn the EEG discriminative feature representations. Secondly, the domain alignment based on deep correlation alignment constrains the representations distance across various domains to learn domain-invariant features. In addition, the class regularization block is proposed, where the supervised contrastive learning with domain-agnostic mixup is established to learn the class-relevant features and achieve class-level alignment. Finally, in the latent space, clusters of domain-agnostic representations from the same class are mapped closer together. Consequently, SCLDGN is capable of learning domain-invariant and class-relevant discriminative representations, which are essential for effective cross-subject decoding. Extensive experiments conducted on six MI/ME datasets demonstrate the effectiveness of the proposed method in comparison with other state-of-the-art approaches. Furthermore, ablation study and visualization analyses explain the generalization mechanism of the proposed method and also show neurophysiologically meaningful patterns related to MI/ME.
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Brachial-ankle pulse wave velocity (baPWV) is a noninvasive index of vascular aging. However, the metabolic profile underlying vascular aging has not yet been fully elucidated. The current study aimed to identify circulating markers of vascular aging as assessed by baPWV and to elucidate its mechanism from a metabolomic perspective in older adults. A total of 60 and 61 Chinese male participants aged ≥80 years were recruited to the metabolome and validation cohorts, respectively. The baPWV of participants was measured using an automatic waveform analyzer. Plasma metabolic profile was investigated using ultra-performance liquid chromatography coupled with triple quadrupole linear ion trap tandem mass spectrometry. Orthogonal partial least squares (OPLS) regression modeling established the association between metabolic profile and baPWV to determine important metabolites predictive of vascular aging. Additionally, an enzyme-linked immunosorbent assay was employed to validate the metabolites in plasma and culture media of vascular smooth muscle cells in vitro. OPLS modeling identified 14 and 22 metabolites inversely and positively associated with baPWV, respectively. These 36 biomarkers were significantly enriched in seven metabolite sets, especially in cysteine and methionine metabolism (p <0.05). Notably, among metabolites involved in cysteine and methionine metabolism, S-adenosylmethionine (SAM) level was inversely related to baPWV, with a significant correlation coefficient in the OPLS model (p <0.05). Furthermore, the relationship between SAM and vascular aging was reconfirmed in an independent cohort and at the cellular level in vitro. SAM was independently associated with baPWV after adjustments for clinical covariates (ß = -0.448, p <0.001) in the validation cohort. In summary, plasma metabolomics identified an inverse correlation between SAM and baPWV in older males. SAM has the potential to be a novel biomarker and therapeutic target for vascular aging.
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Índice Tobillo Braquial , S-Adenosilmetionina , Humanos , Masculino , Anciano , Presión Sanguínea , Cisteína , Análisis de la Onda del Pulso , Envejecimiento , Biomarcadores , Factores de RiesgoRESUMEN
BACKGROUND: Ischemic cardio-cerebrovascular disease is the leading cause of mortality worldwide. However, studies focusing on elderly and very elderly patients are scarce. Hence, our study aimed to characterize and investigate the long-term prognostic implications of ischemic cardio-cerebrovascular diseases in elderly Chinese patients. METHODS: This retrospective cohort study included 1026 patients aged ≥ 65 years who were categorized into the mono ischemic cardio-cerebrovascular disease (MICCD) (either coronary artery disease or ischemic stroke/transient ischemic attack) (n = 912) and the comorbidity of ischemic cardio-cerebrovascular disease (CICCD) (diagnosed with both coronary artery disease and ischemic stroke/transient ischemic attack at admission) (n = 114). The primary outcome was all-cause death. The mortality risk was evaluated using the Cox proportional hazards risk model with multiple adjustments by conventional and propensity-score-based approaches. RESULTS: Of the 2494 consecutive elderly patients admitted to the hospital, 1026 (median age 83 years [interquartile range]: 76.5-86.4; 94.4% men) met the inclusion criteria. Patients with CICCD consisted mostly of very elderly (79.2% vs. 66.1%, P < 0.001) individuals with a higher burden of comorbidities. Over a median follow-up of 10.4 years, 398 (38.8%) all-cause deaths were identified. Compared with the MICCD group, the CICCD group exhibited a higher adjusted hazard ratio (HR) (95% confidential interval, CI) of 1.71 (1.32-2.39) for long-term mortality after adjusting for potential confounders. The sensitivity analysis results remained robust. After inverse probability of treatment weighting (IPTW) modeling, the CICCD group displayed an even worse mortality risk (IPTW-adjusted HR: 2.07; 95% CI 1.47-2.90). In addition, anemia (adjusted HR: 1.48; 95% CI 1.16-1.89) and malnutrition (adjusted HR: 1.43; 95% CI 1.15-1.78) are also independent risk factors for all-cause mortality among elderly and very elderly patients. CONCLUSIONS: Our results thus suggest that elderly patients with ischemic cardio-cerebrovascular disease and anemia or malnutrition may have higher mortality, which may be predicted upon admission. These findings, however, warrant further investigation.
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Puntaje de Propensión , Humanos , Masculino , Femenino , Anciano , Anciano de 80 o más Años , Estudios Retrospectivos , China/epidemiología , Factores de Riesgo , Trastornos Cerebrovasculares/mortalidad , Accidente Cerebrovascular Isquémico/mortalidad , Accidente Cerebrovascular Isquémico/epidemiología , Enfermedad de la Arteria Coronaria/mortalidad , Enfermedad de la Arteria Coronaria/complicaciones , Causas de Muerte , Pronóstico , Comorbilidad , Pueblos del Este de AsiaRESUMEN
Atherosclerosis is the pathological cause of atherosclerotic cardiovascular disease (ASCVD), which rapidly progresses during the cellular senescence. Sodium-glucose cotransporter 2 inhibitors (SGLT2is) reduce major cardiovascular events in patients with ASCVD and have potential antisenescence effects. Here, we investigate the effects of the SGLT2 inhibitor dapagliflozin on cellular senescence in atherosclerotic mice. Compared with ApoE-/- control mice treated with normal saline, those in the ApoE-/- dapagliflozin group, receiving intragastric dapagliflozin (0.1 mg kg-1 d-1) for 14 weeks, exhibited the reduction in the total aortic plaque area (48.8%±6.6% vs. 74.6%±8.0%, P<0.05), the decrease in the lipid core area ((0.019±0.0037) mm2vs. (0.032±0.0062) mm2, P<0.05) and in the percentage of senescent cells within the plaques (16.4%±3.7% vs. 30.7%±2.0%, P<0.01), while the increase in the thickness of the fibrous cap ((21.6±2.1) µm vs. (14.6±1.5) µm, P<0.01). Transcriptome sequencing of the aortic arch in the mice revealed the involvement of the PPARα and the fatty acid metabolic signaling pathways in dapagliflozin's mechanism of ameliorating cellular aging and plaque progression. In vitro, dapagliflozin inhibited the expression of PPARα and its downstream signal FABP4, by which the accumulation of senescent cells in human aortic smooth muscle cells (HASMCs) was reduced under high-fat conditions. This effect was accompanied by a reduction in the intracellular lipid content and alleviation of oxidative stress. However, these beneficial effects of dapagliflozin could be reversed by the PPARα overexpression. Bioinformatics analysis and molecular docking simulations revealed that dapagliflozin might exert its effects by directly interacting with the RXRA protein, thereby influencing the expression of the PPARα signaling pathway. In conclusion, the cellular senescence of aortic smooth muscle cells is potentially altered by dapagliflozin through the suppression of the RXRA-PPARα-FABP4 signaling pathway, resulting in a deceleration of atherosclerotic progression.
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Accurate reconstruction of the brain activities from electroencephalography and magnetoencephalography (E/MEG) remains a long-standing challenge for the intrinsic ill-posedness in the inverse problem. In this study, to address this issue, we propose a novel data-driven source imaging framework based on sparse Bayesian learning and deep neural network (SI-SBLNN). Within this framework, the variational inference in conventional algorithm, which is built upon sparse Bayesian learning, is compressed via constructing a straightforward mapping from measurements to latent sparseness encoding parameters using deep neural network. The network is trained with synthesized data derived from the probabilistic graphical model embedded in the conventional algorithm. We achieved a realization of this framework with the algorithm, source imaging based on spatio-temporal basis function (SI-STBF), as backbone. In numerical simulations, the proposed algorithm validated its availability for different head models and robustness against distinct intensities of the noise. Meanwhile, it acquired superior performance compared to SI-STBF and several benchmarks in a variety of source configurations. Additionally, in real data experiments, it obtained the concordant results with the prior studies.
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Mapeo Encefálico , Magnetoencefalografía , Humanos , Mapeo Encefálico/métodos , Teorema de Bayes , Magnetoencefalografía/métodos , Electroencefalografía/métodos , Redes Neurales de la Computación , Algoritmos , Fenómenos Electromagnéticos , EncéfaloRESUMEN
Table recognition (TR) is one of the research hotspots in pattern recognition, which aims to extract information from tables in an image. Common table recognition tasks include table detection (TD), table structure recognition (TSR) and table content recognition (TCR). TD is to locate tables in the image, TCR recognizes text content, and TSR recognizes spatial & ontology (logical) structure. Currently, the end-to-end TR in real scenarios, accomplishing the three sub-tasks simultaneously, is yet an unexplored research area. One major factor that inhibits researchers is the lack of a benchmark dataset. To this end, we propose a new large-scale dataset named Table Recognition Set (TabRecSet) with diverse table forms sourcing from multiple scenarios in the wild, providing complete annotation dedicated to end-to-end TR research. It is the largest and first bi-lingual dataset for end-to-end TR, with 38.1 K tables in which 20.4 K are in English and 17.7 K are in Chinese. The samples have diverse forms, such as the border-complete and -incomplete table, regular and irregular table (rotated, distorted, etc.). The scenarios are multiple in the wild, varying from scanned to camera-taken images, documents to Excel tables, educational test papers to financial invoices. The annotations are complete, consisting of the table body spatial annotation, cell spatial & logical annotation and text content for TD, TSR and TCR, respectively. The spatial annotation utilizes the polygon instead of the bounding box or quadrilateral adopted by most datasets. The polygon spatial annotation is more suitable for irregular tables that are common in wild scenarios. Additionally, we propose a visualized and interactive annotation tool named TableMe to improve the efficiency and quality of table annotation.
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Motor imagery (MI) decoding plays a crucial role in the advancement of electroencephalography (EEG)-based brain-computer interface (BCI) technology. Currently, most researches focus on complex deep learning structures for MI decoding. The growing complexity of networks may result in overfitting and lead to inaccurate decoding outcomes due to the redundant information. To address this limitation and make full use of the multi-domain EEG features, a multi-domain temporal-spatial-frequency convolutional neural network (TSFCNet) is proposed for MI decoding. The proposed network provides a novel mechanism that utilize the spatial and temporal EEG features combined with frequency and time-frequency characteristics. This network enables powerful feature extraction without complicated network structure. Specifically, the TSFCNet first employs the MixConv-Residual block to extract multiscale temporal features from multi-band filtered EEG data. Next, the temporal-spatial-frequency convolution block implements three shallow, parallel and independent convolutional operations in spatial, frequency and time-frequency domain, and captures high discriminative representations from these domains respectively. Finally, these features are effectively aggregated by average pooling layers and variance layers, and the network is trained with the joint supervision of the cross-entropy and the center loss. Our experimental results show that the TSFCNet outperforms the state-of-the-art models with superior classification accuracy and kappa values (82.72% and 0.7695 for dataset BCI competition IV 2a, 86.39% and 0.7324 for dataset BCI competition IV 2b). These competitive results demonstrate that the proposed network is promising for enhancing the decoding performance of MI BCIs.
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Interfaces Cerebro-Computador , Electroencefalografía , Humanos , Entropía , Redes Neurales de la Computación , ImaginaciónRESUMEN
P300 potential is important to cognitive neuroscience research, and has also been widely applied in brain-computer interfaces (BCIs). To detect P300, many neural network models, including convolutional neural networks (CNNs), have achieved outstanding results. However, EEG signals are usually high-dimensional. Moreover, since collecting EEG signals is time-consuming and expensive, EEG datasets are typically small. Therefore, data-sparse regions usually exist within EEG dataset. However, most existing models compute predictions based on point-estimate. They cannot evaluate prediction uncertainty and tend to make overconfident decisions on samples located in data-sparse regions. Hence, their predictions are unreliable. To solve this problem, we propose a Bayesian convolutional neural network (BCNN) for P300 detection. The network places probability distributions over weights to capture model uncertainty. In prediction phase, a set of neural networks can be obtained by Monte Carlo sampling. Integrating the predictions of these networks implies ensembling. Therefore, the reliability of prediction can be improved. Experimental results demonstrate that BCNN can achieve better P300 detection performance than point-estimate networks. In addition, placing a prior distribution over the weight acts as a regularization technique. Experimental results show that it improves the robustness of BCNN to overfitting on small dataset. More importantly, with BCNN, both weight uncertainty and prediction uncertainty can be obtained. The weight uncertainty is then used to optimize the network through pruning, and the prediction uncertainty is applied to reject unreliable decisions so as to reduce detection error. Therefore, uncertainty modeling provides important information to further improve BCI systems.
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Interfaces Cerebro-Computador , Humanos , Electroencefalografía/métodos , Teorema de Bayes , Incertidumbre , Reproducibilidad de los Resultados , AlgoritmosRESUMEN
A brain-computer interface (BCI) measures and analyzes brain activity and converts it into computer commands to control external devices. Traditional BCIs usually require full calibration, which is time-consuming and makes BCI systems inconvenient to use. In this study, we propose an online P300 BCI spelling system with zero or shortened calibration based on a convolutional neural network (CNN) and big electroencephalography (EEG) data. Specifically, three methods are proposed to train CNNs for the online detection of P300 potentials: (i) training a subject-independent CNN with data collected from 150 subjects; (ii) adapting the CNN online via a semisupervised learning/self-training method based on unlabeled data collected during the user's online operation; and (iii) fine-tuning the CNN with a transfer learning method based on a small quantity of labeled data collected before the user's online operation. Note that the calibration process is eliminated in the first two methods and dramatically shortened in the third method. Based on these methods, an online P300 spelling system is developed. Twenty subjects participated in our online experiments. Average accuracies of 89.38%, 94.00% and 93.50% were obtained by the subject-independent CNN, the self-training-based CNN and the transfer learning-based CNN, respectively. These results demonstrate the effectiveness of our methods, and thus, the convenience of the online P300-based BCI system is substantially improved.
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Interfaces Cerebro-Computador , Humanos , Algoritmos , Calibración , Potenciales Relacionados con Evento P300 , Redes Neurales de la Computación , Electroencefalografía/métodosRESUMEN
Accurate reconstruction of cortical activation from electroencephalography and magnetoencephalography (E/MEG) is a long-standing challenge because of the inherently ill-posed inverse problem. In this paper, a novel algorithm under the empirical Bayesian framework, source imaging with smoothness in spatial and temporal domains (SI-SST), is proposed to address this issue. In SI-SST, current sources are decomposed into the product of spatial smoothing kernel, sparseness encoding coefficients, and temporal basis functions (TBFs). Further smoothness is integrated in the temporal domain with the employment of an underlying autoregressive model. Because sparseness encoding coefficients are constructed depending on overlapped clusters over cortex in this model, we derived a novel update rule based on fixed-point criterion instead of the convexity based approach which becomes invalid in this scenario. Entire variables and hyper parameters are updated alternatively in the variational inference procedure. SI-SST was assessed by multiple metrics with both simulated and experimental datasets. In practice, SI-SST had the superior reconstruction performance in both spatial extents and temporal profiles compared to the benchmarks.
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Mapeo Encefálico , Magnetoencefalografía , Algoritmos , Teorema de Bayes , Encéfalo/fisiología , Mapeo Encefálico/métodos , Electroencefalografía/métodos , Fenómenos Electromagnéticos , Humanos , Magnetoencefalografía/métodosRESUMEN
Recently, motor imagery brain-computer interfaces (MI-BCIs) with stimulation systems have been developed in the field of motor function assistance and rehabilitation engineering. An efficient stimulation paradigm and Electroencephalogram (EEG) decoding method have been designed to enhance the performance of MI-BCI systems. Therefore, in this study, a multimodal dual-level stimulation paradigm is designed for lower-limb rehabilitation training, whereby visual and auditory stimulations act on the sensory organ while proprioceptive and functional electrical stimulations are provided to the lower limb. In addition, upper triangle filter bank sparse spatial pattern (UTFB-SSP) is proposed to automatically select the optimal frequency sub-bands related to desynchronization rhythm during enhanced imaginary movement to improve the decoding performance. The effectiveness of the proposed MI-BCI system is demonstrated on an the in-house experimental dataset and the BCI competition IV IIa dataset. The experimental results show that the proposed system can effectively enhance the MI performance by inducing the α, ß and γ rhythms in lower-limb movement imagery tasks.
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Behavioral assessment of sound localization in the Coma Recovery Scale-Revised (CRS-R) poses a significant challenge due to motor disability in patients with disorders of consciousness (DOC). Brain-computer interfaces (BCIs), which can directly detect brain activities related to external stimuli, may thus provide an approach to assess DOC patients without the need for any physical behavior. In this study, a novel audiovisual BCI system was developed to simulate sound localization evaluation in CRS-R. Specifically, there were two alternatively flashed buttons on the left and right sides of the graphical user interface, one of which was randomly chosen as the target. The auditory stimuli of bell sounds were simultaneously presented by the ipsilateral loudspeaker during the flashing of the target button, which prompted patients to selectively attend to the target button. The recorded electroencephalography data were analyzed in real time to detect event-related potentials evoked by the target and further to determine whether the target was attended to or not. A significant BCI accuracy for a patient implied that he/she had sound localization. Among eighteen patients, eleven and four showed sound localization in the BCI and CRS-R, respectively. Furthermore, all patients showing sound localization in the CRS-R were among those detected by our BCI. The other seven patients who had no sound localization behavior in CRS-R were identified by the BCI assessment, and three of them showed improvements in the second CRS-R assessment after the BCI experiment. Thus, the proposed BCI system is promising for assisting the assessment of sound localization and improving the clinical diagnosis of DOC patients.
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Interfaces Cerebro-Computador , Personas con Discapacidad , Trastornos Motores , Localización de Sonidos , Coma/diagnóstico , Estado de Conciencia , Trastornos de la Conciencia/diagnóstico , Electroencefalografía , Femenino , HumanosRESUMEN
Event-related potential (ERP) is bioelectrical activity that occurs in the brain in response to specific events or stimuli, reflecting the electrophysiological changes in the brain during cognitive processes. ERP is important in cognitive neuroscience and has been applied to brain-computer interfaces (BCIs). However, because ERP signals collected on the scalp are weak, mixed with spontaneous electroencephalogram (EEG) signals, and their temporal and spatial features are complex, accurate ERP detection is challenging. Compared to traditional neural networks, the capsule network (CapsNet) replaces scalar-output neurons with vector-output capsules, allowing the various input information to be well preserved in the capsules. In this study, we expect to utilize CapsNet to extract the discriminative spatial-temporal features of ERP and encode them in capsules to reduce the loss of valuable information, thereby improving the ERP detection performance for BCI. Therefore, we propose ERP-CapsNet to perform ERP detection in a BCI speller application. The experimental results on BCI Competition datasets and the Akimpech dataset show that ERP-CapsNet achieves better classification performances than do the state-of-the-art techniques. We also use a decoder to investigate the attributes of ERPs encoded in capsules. The results show that ERP-CapsNet relies on the P300 and P100 components to detect ERP. Therefore, ERP-CapsNet not only acts as an outstanding method for ERP detection, but also provides useful insights into the ERP detection mechanism.
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Interfaces Cerebro-Computador , Encéfalo , Electroencefalografía , Potenciales Evocados , Humanos , Redes Neurales de la ComputaciónRESUMEN
Stereoelectroencephalography (SEEG) signals can be obtained by implanting deep intracranial electrodes. SEEG depth electrodes can record brain activity from the shallow cortical layer and deep brain structures, which is not achievable through other recording techniques. Moreover, SEEG has the advantage of a high signal-to-noise ratio (SNR). Therefore, it provides a potential way to establish a highly efficient brain-computer interface (BCI) and aid in understanding human brain activity. In this study, we implemented a P300-based BCI using SEEG signals. A single-character oddball paradigm was applied to elicit P300. To predict target characters, we fed the feature vectors extracted from the signals collected by five SEEG contacts into a Bayesian linear discriminant analysis (BLDA) classifier. Thirteen epileptic patients implanted with SEEG electrodes participated in the experiment and achieved an average online spelling accuracy of 93.85%. Moreover, through single-contact decoding analysis and simulated online analysis, we found that the SEEG-based BCI system achieved a high performance even when using a single signal channel. Furthermore, contacts with high decoding accuracies were mainly distributed in the visual ventral pathway, especially the fusiform gyrus (FG) and lingual gyrus (LG), which played an important role in building P300-based SEEG BCIs. These results might provide new insights into P300 mechanistic studies and the corresponding BCIs.