Objective
To study the relationship between the expression ratio of induced nitric oxide synthase (iNOS) over glial fibrillary acidic protein (GFAP) and the time of injury after brain concussion in rat, in order to acquire a new visual angle for determining injury time of cerebral concussion.
Methods
Eighty-five healthy Sprague-Dawley rats were divided into three groups randomly: model group (n=25), experimental group (n=55), and control group (n=5). The rats in the model group were used to confirm the attack hight to make the model of brain concussion; according to the time of execution, rats in the experimental group were then subdivided into 11 groups with 5 rats in each subgroup, and their execution time was respectively hour 0.5, 1, 3, 6, 12, 24, 48, 96, 168, 240, and 336; the rats in the control group were executed after fed for 24 hours. After the model of cerebral concussion was established through freefalling dart method, hematoxylin-eosin staining and immunohistochemistry staining of iNOS and GFAP were conducted for the brain of the rats. All related experimental results were studied by using microscope with image analytical system and homologous statistics.
Results
The ratio of positive expression of iNOS over that of GFAP increased gradually during hour 0.5- 3 after injury in brain (from 5.03 to 10.47). At the same time, the positive expression of iNOS increased significantly (from 14.61% to 37.45%). However, the increase of the positive expression of GFAP was not obvious. Between hour 3 and 12, the ratio began to decline to 4.98, which was still at a high level, and during the same time period, the positive expressions of iNOS and GFAP also experienced the same change pattern. Later, the ratio began to decline between hour 12 and 336 after injury (from 4.98 to 0.95). All ratios at this time were lower than those between hour 0.5 and 12. The positive expression of iNOS and GFAP both increased to a climax before declining.
Conclusions
The ratio of positive expression of iNOS over GFAP and the respective change pattern of iNOS and GFAP can be used as the evidence of estimating the injury time of cerebral concussion. We can use the ratio of two or more markers to provide a new visual angle for concluding the concussion injury time.
This paper proposes a motor imagery recognition algorithm based on feature fusion and transfer adaptive boosting (TrAdaboost) to address the issue of low accuracy in motor imagery (MI) recognition across subjects, thereby increasing the reliability of MI-based brain-computer interfaces (BCI) for cross-individual use. Using the autoregressive model, power spectral density and discrete wavelet transform, time-frequency domain features of MI can be obtained, while the filter bank common spatial pattern is used to extract spatial domain features, and multi-scale dispersion entropy is employed to extract nonlinear features. The IV-2a dataset from the 4th International BCI Competition was used for the binary classification task, with the pattern recognition model constructed by combining the improved TrAdaboost integrated learning algorithm with support vector machine (SVM), k nearest neighbor (KNN), and mind evolutionary algorithm-based back propagation (MEA-BP) neural network. The results show that the SVM-based TrAdaboost integrated learning algorithm has the best performance when 30% of the target domain instance data is migrated, with an average classification accuracy of 86.17%, a Kappa value of 0.723 3, and an AUC value of 0.849 8. These results suggest that the algorithm can be used to recognize MI signals across individuals, providing a new way to improve the generalization capability of BCI recognition models.
Objective To investigate development and perspectives of brain death donation and transplantation. Methods The related literatures about the research of brain dead donors were reviewed. Results Brain death effects hemodynamic stability， hormonal changes， neuroimmunologic effects，and unleashes a cascade of inflammatory events， which may affect quality of graft， graft survival， and patient outcome. Moreover， the exact mechanism linked to brain death is incompletely understood. Conclusions The pathological physiology changes of brain dead donors has important impact on graft outcomes. However， subsequent work remains to be done.
In the field of brain-computer interfaces (BCIs) based on functional near-infrared spectroscopy (fNIRS), traditional subject-specific decoding methods suffer from the limitations of long calibration time and low cross-subject generalizability, which restricts the promotion and application of BCI systems in daily life and clinic. To address the above dilemma, this study proposes a novel deep transfer learning approach that combines the revised inception-residual network (rIRN) model and the model-based transfer learning (TL) strategy, referred to as TL-rIRN. This study performed cross-subject recognition experiments on mental arithmetic (MA) and mental singing (MS) tasks to validate the effectiveness and superiority of the TL-rIRN approach. The results show that the TL-rIRN significantly shortens the calibration time, reduces the training time of the target model and the consumption of computational resources, and dramatically enhances the cross-subject decoding performance compared to subject-specific decoding methods and other deep transfer learning methods. To sum up, this study provides a basis for the selection of cross-subject, cross-task, and real-time decoding algorithms for fNIRS-BCI systems, which has potential applications in constructing a convenient and universal BCI system.
ObjectiveTo investigate the feasibility and effectiveness of motor imagery based brain computer interface with wrist passive movement in chronic stroke patients with wrist extension impairment.MethodsFifteen chronic stroke patients with a mean age of (47.60±14.66) years were recruited from March 2017 to June 2018. At baseline, motor imagery ability was assessed first. Then motor imagery based brain computer interface with wrist passive movement was given as an intervention. Both range of motion of paretic wrist and Barthel index was assessed before and after the intervention.ResultsAmong the 15 chronic stroke patients admitted in the study, 12 finished the whole therapy, and 3 failed to pass the initial assessment. After the therapy, the 12 participants who completed the whole sessions of the treatment and follow up had improved ability of control electroencephalogram, in whom 9 regained the ability to actively extend the affected wrist, and the other 3 failed to actively extend their wrist (the rate of active extending wrist was 75%). The activity of daily life of all the participants did not change significantly before and after intervention, and no discomfort was found after daily treatment.ConclusionIn chronic stroke patients with wrist extension impairment, motor imagery based brain computer interface with wrist passive movement training is feasible and effective.
Repeated transcranial magnetic stimulation (rTMS) is one of the commonly used brain stimulation techniques. In order to investigate the effects of rTMS on the excitability of different types of neurons, this study is conducted to investigate the effects of rTMS on the cognitive function of mice and the excitability of hippocampal glutaminergic neurons and gamma-aminobutyric neurons from the perspective of electrophysiology. In this study, mice were randomly divided into glutaminergic control group, glutaminergic magnetic stimulation group, gamma-aminobutyric acid energy control group, and gamma-aminobutyric acid magnetic stimulation group. The four groups of mice were injected with adeno-associated virus to label two types of neurons and were implanted optical fiber. The stimulation groups received 14 days of stimulation and the control groups received 14 days of pseudo-stimulation. The fluorescence intensity of calcium ions in mice was recorded by optical fiber system. Behavioral experiments were conducted to explore the changes of cognitive function in mice. The patch-clamp system was used to detect the changes of neuronal action potential characteristics. The results showed that rTMS significantly improved the cognitive function of mice, increased the amplitude of calcium fluorescence of glutamergic neurons and gamma-aminobutyric neurons in the hippocampus, and enhanced the action potential related indexes of glutamergic neurons and gamma-aminobutyric neurons. The results suggest that rTMS can improve the cognitive ability of mice by enhancing the excitability of hippocampal glutaminergic neurons and gamma-aminobutyric neurons.
Motor imagery (MI) is a mental process that can be recognized by electroencephalography (EEG) without actual movement. It has significant research value and application potential in the field of brain-computer interface (BCI) technology. To address the challenges posed by the non-stationary nature and low signal-to-noise ratio of MI-EEG signals, this study proposed a Riemannian spatial filtering and domain adaptation (RSFDA) method for improving the accuracy and efficiency of cross-session MI-BCI classification tasks. The approach addressed the issue of inconsistent data distribution between source and target domains through a multi-module collaborative framework, which enhanced the generalization capability of cross-session MI-EEG classification models. Comparative experiments were conducted on three public datasets to evaluate RSFDA against eight existing methods in terms of classification accuracy and computational efficiency. The experimental results demonstrated that RSFDA achieved an average classification accuracy of 79.37%, outperforming the state-of-the-art deep learning method Tensor-CSPNet (76.46%) by 2.91% (P < 0.01). Furthermore, the proposed method showed significantly lower computational costs, requiring only approximately 3 minutes of average training time compared to Tensor-CSPNet’s 25 minutes, representing a reduction of 22 minutes. These findings indicate that the RSFDA method demonstrates superior performance in cross-session MI-EEG classification tasks by effectively balancing accuracy and efficiency. However, its applicability in complex transfer learning scenarios remains to be further investigated.
Objective To discuss the feasibility of treating the brain ischemic stroke by the co-transplantation of the neural stem cells(NSCs) and the endothelial progenitor cells(EPCs). Methods The original biomedical articles concerned with the treatment of the brain ischemic therapy by the use of the NSCs and the EPCs were extensively reviewed as well as retrieved and analyzed. Results The review revealed that the NSCs and the EPCs could migrate to the injured area due to brain ischemic stroke, the environment of the local microcirculation could induce the neurogenesis and the vasculogenesis to repair the injury, and the neurogenesis and vasculogenesis could promote each other. Conclusion The co-transplantation of the NSCs and the EPCscan represent a new promising strategy formore effectively solving the two difficult problems of the neural cell loss andthe vascular obstruction caused by the brain ischemic stroke.
ObjectiveTo evaluate the monitoring value of brain injury biomarkers in the patients during extracorporeal membrane oxygenation (ECMO).
MethodsWe searched PubMed, EMbase, the Cochrane Library, CNKI, and CBM from inception of each database to May 2015 to identify randomized controlled trials, or case-control trials, or cohort trials of brain injury biomarkers predict brain injury during ECMO. Data were extracted independently by two reviewers. Meta-analysis was conducted using STATA 12.0 software.
ResultsFour retrospective trials were included. The results showed that compared with patients without brain injury, the patients with brain injury had a higher level of S100B protein (P < 0.05). The incidence of major neurological events was higher for high neuron-specific enolase level patients than mild-to-moderate neuron-specific enolase level patients (85% vs. 29%, P=0.01). The incidence of brain injury was higher for normal glial fibrillary acidic protein level than patients with glial fibrillary acidic protein > 0.436 ng/ml (OR=11.5, 95%CI 1.3-98.3).
ConclusionsBrain injury biomarkers may be used as an indicator for earlier diagnosis of brain injury in patients during ECMO.
Brain-computer interface (BCI) can establish a direct communications pathway between the human brain and the external devices, which is independent of peripheral nerves and muscles. Compared with invasive BCI, non-invasive BCI has the advantages of low cost, low risk, and ease of operation. In recent years, using non-invasive BCI technology to control devices has gradually evolved into a new type of human-computer interaction manner. Moreover, the control strategy for BCI is an essential component of this manner. First, this study introduced how the brain control techniques were developed and classified. Second, the basic characteristics of direct and shared control strategies were thoroughly explained. And then the benefits and drawbacks of these two strategies were compared and further analyzed. Finally, the development direction and application prospects for non-invasive brain control strategies were suggested.
