Multiclass motor imagery classification with Riemannian geometry and temporal-spectral selection.

Motor imagery (MI) based brain-computer interfaces (BCIs) decode the users' intentions from electroencephalography (EEG) to achieve information control and interaction between the brain and external devices. In this paper, firstly, we apply Riemannian geometry to the covariance matrix extracted by spatial filtering to obtain robust and distinct features. Then, a multiscale temporal-spectral segmentation scheme is developed to enrich the feature dimensionality. In order to determine the optimal feature configurations, we utilize a linear learning-based temporal window and spectral band (TWSB) selection method to evaluate the feature contributions, which efficiently reduces the redundant features and improves the decoding efficiency without excessive loss of accuracy. Finally, support vector machines are used to predict the classification labels based on the selected MI features. To evaluate the performance of our model, we test it on the publicly available BCI Competition IV dataset 2a and 2b. The results show that the method has an average accuracy of 79.1% and 83.1%, which outperforms other existing methods. Using TWSB feature selection instead of selecting all features improves the accuracy by up to about 6%. Moreover, the TWSB selection method can effectively reduce the computational burden. We believe that the framework reveals more interpretable feature information of motor imagery EEG signals, provides neural responses discriminative with high accuracy, and facilitates the performance of real-time MI-BCI.

STCGRU: A hybrid model based on CNN and BiGRU for mild cognitive impairment diagnosis.

BACKGROUND AND OBJECTIVE: Early diagnosis of mild cognitive impairment (MCI) is one of the essential measures to prevent its further development into Alzheimer's disease (AD). In this paper, we propose a hybrid deep learning model for early diagnosis of MCI, called spatio-temporal convolutional gated recurrent unit network (STCGRU). METHODS: The STCGRU comprises three bespoke convolutional neural network (CNN) modules and a bi-directional gated recurrent unit (BiGRU) module, which can effectively extract the spatial and temporal features of EEG and obtain excellent diagnostic results. We use a publicly available EEG dataset that has not undergone pre-processing to verify the robustness and accuracy of the model. Ablation experiments on STCGRU are conducted to showcase the individual performance improvement of each module. RESULTS: Compared with other state-of-the-art approaches using the same publicly available EEG dataset, the results show that STCGRU is more suitable for early diagnosis of MCI. After 10-fold cross-validation, the average classification accuracy of the hybrid model reached 99.95 %, while the average kappa value reached 0.9989. CONCLUSIONS: The experimental results show that the hybrid model proposed in this paper can directly extract compelling spatio-temporal features from the raw EEG data for classification. The STCGRU allows for accurate diagnosis of patients with MCI and has a high practical value.

Effect of GABAa-receptors on neuronal discharge and ion activity in focal seizures.

Focal seizures are a type of epileptic event that has plagued the medical community for a long time, and the existing drug treatment is mainly based on the modulation of ${GABA}_a$-receptors to affect GABAergic signaling to achieve the therapeutic purpose. The majority of research currently focuses on the impact of ${GABA}_a$-receptors on neuronal firing, failing to analyze the molecular and ionic mechanisms involved. Specifically, the research on deeper-level mechanisms on how ${GABA}_a$-receptors affect neuronal firing by altering ion activity has not been addressed. This research aimed to study the effects of different ${GABA}_a$-receptor structures on ion activity in focal seizures model by adjusting parameters of the ${GABA}_a$-receptors: the rise time constant (${tau}_1$) and decay time constant (${tau}_2$). The research indicates that as the values of ${tau}_1$ and ${tau}_2$ of the ${GABA}_a$-receptor change, the ion concentration will vary based on the change of the ${GABA}_a$-receptor potential. To a certain extent, the duration of epileptic activity will also be affected to a certain extent. In conclusion, the alteration of ${GABA}_a$-receptor structure will affect the inhibitory effect of interneurons on pyramidal neurons, and different parameters of the ${GABA}_a$-receptor will directly impact the therapeutic effect.

Functional connectivity key feature analysis of cognitive impairment patients based on microstate brain network.

Mild cognitive impairment (MCI) is the initial phase of Alzheimer's disease (AD). The cognitive decline is linked to abnormal connectivity between different regions of the brain. Most brain network studies fail to consider the changes in brain patterns and do not reflect the dynamic pathological characteristics of patients. Therefore, this paper proposes a method for constructing brain networks based on microstate sequences. It also analyzes the microstate temporal parameters and introduces a new feature, the brain homeostasis coefficient (Bhc), to quantify the stability of patient brain connections. The results showed that microstate class B parameters were higher in the MCI than in the HC group. Additionally, the Bhc values in most channels of the MCI and AD groups were lower than those of the HC group, with the most significant differences observed in the right frontal lobe. These differences were statistically significant (P < 0.05). The findings indicate that connectivity in the right frontal lobe may be most severely disrupted in patients with cognitive impairment. Furthermore, the Montreal Cognitive Assessment score showed a strong positive correlation with Bhc. This suggests that Bhc could be a novel biomarker for evaluating cognitive function in patients with cognitive impairment.

[A study on the application of cross-frequency coupling characteristics of neural oscillation in the diagnosis of mild cognitive impairment].

In order to fully explore the neural oscillatory coupling characteristics of patients with mild cognitive impairment (MCI), this paper analyzed and compared the strength of the coupling characteristics for 28 MCI patients and 21 normal subjects under six different-frequency combinations. The results showed that the difference in the global phase synchronization index of cross-frequency coupling under δ-θ rhythm combination was statistically significant in the MCI group compared with the normal control group ( P = 0.025, d = 0.398). To further validate this coupling feature, this paper proposed an optimized convolutional neural network model that incorporated a time-frequency data enhancement module and batch normalization layers to prevent overfitting while enhancing the robustness of the model. Based on this optimized model, with the phase locking value matrix of δ-θ rhythm combination as the single input feature, the diagnostic accuracy of MCI patients was (95.49 ± 4.15)%, sensitivity and specificity were (93.71 ± 7.21)% and (97.50 ± 5.34)%, respectively. The results showed that the characteristics of the phase locking value matrix under the combination of δ-θ rhythms can adequately reflect the cognitive status of MCI patients, which is helpful to assist the diagnosis of MCI.

[Research on mild cognitive impairment diagnosis based on Bayesian optimized long-short-term neural network model].

The recurrent neural network architecture improves the processing ability of time-series data. However, issues such as exploding gradients and poor feature extraction limit its application in the automatic diagnosis of mild cognitive impairment (MCI). This paper proposed a research approach for building an MCI diagnostic model using a Bayesian-optimized bidirectional long short-term memory network (BO-BiLSTM) to address this problem. The diagnostic model was based on a Bayesian algorithm and combined prior distribution and posterior probability results to optimize the BO-BiLSTM network hyperparameters. It also used multiple feature quantities that fully reflected the cognitive state of the MCI brain, such as power spectral density, fuzzy entropy, and multifractal spectrum, as the input of the diagnostic model to achieve automatic MCI diagnosis. The results showed that the feature-fused Bayesian-optimized BiLSTM network model achieved an MCI diagnostic accuracy of 98.64% and effectively completed the diagnostic assessment of MCI. In conclusion, based on this optimization, the long short-term neural network model has achieved automatic diagnostic assessment of MCI, providing a new diagnostic model for intelligent diagnosis of MCI.

Serum sulfatide as a biomarker of carotid atherosclerosis in patients with rheumatoid arthritis.

PURPOSE: Rheumatoid arthritis (RA) patients have accelerated atherosclerosis (AS) leading to excess cardiovascular morbidity and mortality, but traditional risk factors for cardiovascular disease (CVD) are invalid to explain the problem. Sulfatides, as major components of serum lipoproteins, are synthesized in the liver. These molecules are reported to play an important role in the development of AS, thrombogenesis, and inflammation. However, it is unclear whether sulfatides are responsible for such issue. To elucidate the possible association between serum sulfatide and the accelerated progress of AS, evaluated by carotid intima-media thickness (CIMT), and ascertain the related mechanism underlying the correlation in RA cases. METHODS: We performed an observational study of 144 patients with RA and 120 sex and age-matched controls. Meanwhile, 107 patients (of the 144 RA patients enrolled at baseline) were invited to undergo a second measurement after 12 months. Serum sulfatide levels of all the enrolled subjects were quantified by mass spectrometry after they were converted into lysosulfatides (LS), and then calculated as the sum of the levels of seven LS molecular species. Serum oxidative stress marker, malondialdehyde (MDA) was detected by ELISA. We subsequently statistically analyzed the causalities between carotid AS and clinical parameters, and the association of serum sulfatide with other variables. Multivariable logistic regression analysis was finally employed by taking all factors to identify independent determinant for carotid atherosclerotic plaque and serum sulfatide level. RESULTS: A gradual declined trend in serum sulfatide levels was observed in control subjects, non-plaque group, and the plaque group (8.56 ± 1.37 nmol/mL, 5.63 ± 1.57 nmol/mL, 3.18 ± 1.32 nmol/mL, respectively, p < 0.01), along with an increased value of CIMT (0.63 ± 0.07 mm, 0.92 ± 0.14 mm, 1.43 ± 0.22 mm, respectively, p < 0.01). Meanwhile, a negative linear correlation between CIMT and serum sulfatide was further confirmed by Spearman's analysis (r = -0.622, p < 0.01). Eventually, multivariate logistic regression analysis identified serum MDA as the only independent factor for the abnormal level of serum sulfatide, and serum sulfatide was detected as a significant protective factor for the occurrence of carotid plaques in RA cases (p < 0.01), which was confirmed repeatedly by our cross-sectional and longitudinal studies. CONCLUSION: Excessive abnormal levels of oxidative stress decreased serum sulfatide levels, followed by a high occurrence of AS in RA patients. Serum sulfatide level might be useful as a predictor (biomarker) for the progression of AS in RA cases.

Age, Pulse, Urea, and Albumin Score: A Tool for Predicting the Short-Term and Long-Term Outcomes of Community-Acquired Pneumonia Patients With Diabetes.

Objective: The predictive performances of CURB-65 and pneumonia severity index (PSI) were poor in patients with diabetes. This study aimed to develop a tool for predicting the short-term and long-term outcomes of CAP in patients with diabetes. Methods: A retrospective study was conducted on 531 CAP patients with type 2 diabetes. The short-term outcome was in-hospital mortality. The long-term outcome was 24-month all-cause death. The APUA score was calculated according to the levels of Age (0-2 points), Pulse (0-2 points), Urea (0-2 points), and Albumin (0-4 points). The area under curves (AUCs) were used to evaluate the abilities of the APUA score for predicting short-term outcomes. Cox regression models were used for modeling relationships between the APUA score and 24-month mortality. Results: The AUC of the APUA score for predicting in-hospital mortality was 0.807 in patients with type 2 diabetes (P<0.001). The AUC of the APUA score was higher than the AUCs of CURB-65 and PSI class (P<0.05). The long-term mortality increased with the risk stratification of the APUA score (low-risk group (0-1 points) 11.5%, intermediate risk group (2-4 points) 16.9%, high risk group (≥5 points) 28.8%, P<0.05). Compared with patients in the low-risk group, patients in the high-risk group had significantly increased risk of long-term death, HR (95%CI) was 2.093 (1.041~4.208, P=0.038). Conclusion: The APUA score is a simple and accurate tool for predicting short-term and long-term outcomes of CAP patients with diabetes.

Measuring Synchronization between Spikes and Local Field Potential Based on the Kullback-Leibler Divergence.

Neurophysiological studies have shown that there is a close relationship between spikes and local field potential (LFP), which reflects crucial neural coding information. In this paper, we used a new method to evaluate the synchronization between spikes and LFP. All possible phases of LFP from -π to π were first binned into a freely chosen number of bins; then, the probability of spikes falling in each bin was calculated, and the deviation degree from the uniform distribution based on the Kullback-Leibler divergence was calculated to define the synchronization between spikes and LFP. The simulation results demonstrate that the method is rapid, basically unaffected by the total number of spikes, and can adequately resist the noise of spike trains. We applied this method to the experimental data of patients with intractable epilepsy, and we observed the synchronization between spikes and LFP in the formation of memory. These results show that our proposed method is a powerful tool that can quantitatively measure the synchronization between spikes and LFP.

Estimating Phase Amplitude Coupling between Neural Oscillations Based on Permutation and Entropy.

Cross-frequency phase-amplitude coupling (PAC) plays an important role in neuronal oscillations network, reflecting the interaction between the phase of low-frequency oscillation (LFO) and amplitude of the high-frequency oscillations (HFO). Thus, we applied four methods based on permutation analysis to measure PAC, including multiscale permutation mutual information (MPMI), permutation conditional mutual information (PCMI), symbolic joint entropy (SJE), and weighted-permutation mutual information (WPMI). To verify the ability of these four algorithms, a performance test including the effects of coupling strength, signal-to-noise ratios (SNRs), and data length was evaluated by using simulation data. It was shown that the performance of SJE was similar to that of other approaches when measuring PAC strength, but the computational efficiency of SJE was the highest among all these four methods. Moreover, SJE can also accurately identify the PAC frequency range under the interference of spike noise. All in all, the results demonstrate that SJE is better for evaluating PAC between neural oscillations.

Predicting Grating Orientations With Cross-Frequency Coupling and Least Absolute Shrinkage and Selection Operator in V1 and V4 of Rhesus Monkeys.

Orientation selectivity, as an emergent property of neurons in the visual cortex, is of critical importance in the processing of visual information. Characterizing the orientation selectivity based on neuronal firing activities or local field potentials (LFPs) is a hot topic of current research. In this paper, we used cross-frequency coupling and least absolute shrinkage and selection operator (LASSO) to predict the grating orientations in V1 and V4 of two rhesus monkeys. The experimental data were recorded by utilizing two chronically implanted multi-electrode arrays, which were placed, respectively, in V1 and V4 of two rhesus monkeys performing a selective visual attention task. The phase-amplitude coupling (PAC) and amplitude-amplitude coupling (AAC) were employed to characterize the cross-frequency coupling of LFPs under sinusoidal grating stimuli with different orientations. Then, a LASSO logistic regression model was constructed to predict the grating orientation based on the strength of PAC and AAC. Moreover, the cross-validation method was used to evaluate the performance of the model. It was found that the average accuracy of the prediction based on the combination of PAC and AAC was 73.9%, which was higher than the predicting accuracy with PAC or AAC separately. In conclusion, a LASSO logistic regression model was introduced in this study, which can predict the grating orientations with relatively high accuracy by using PAC and AAC together. Our results suggest that the principle behind the LASSO model is probably an alternative direction to explore the mechanism for generating orientation selectivity.

IDO1 impairs NK cell cytotoxicity by decreasing NKG2D/NKG2DLs via promoting miR-18a.

BACKGROUND: Indoleamine-2,3-dioxygenase 1 (IDO1) is an important enzyme for altering the tumour microenvironment and assisting tumour cells to escape the immune system. RESULTS: In this study, a significant reduction in NK cell cytotoxicity that was associated with a high expression of IDO1 in a reconstructed tumour microenvironment was observed. In a co-culture system of tumour cell culture supernatant (TSN) and murine NK cell, IDO1 was substantially increased, while NKG2D was markedly downregulated in NK cells. Based on computational predictions, miR-18a, which has two definite binding sites consisting of the 3'UTR of NKG2D and the 3'UTR of NKG2D ligand (Mult-1), was suspected to be a negative regulator of which its conjoined. As expected, the IDO1 could promote the expression of miR-18a and promote the downregulation effect of miR-18a on NKG2D and NKG2DL, and INCB024360 (INCB) could reverse the result. For digging the mechanism deeper, we authenticated IDO1 promoted the combination of miR-18a and AGO2 after argonaute 2 (AGO2) co-immunoprecipitation, which then degraded Mult-1 mRNA and inhibited the translation of it, further destructing NK cell cytotoxicity. CONCLUSION: Our findings revealed a new regulatory axis, IDO1/miR-18a/NKG2D/NKG2DL, in the regulation of NK cell function. This may provide insight into the mechanism of the priming effect of IDO1 inhibitors and miR-18a interference, then elicit possible new methods of cancer treatment.

Downregulation of MCT4 for lactate exchange promotes the cytotoxicity of NK cells in breast carcinoma.

Monocarboxylate transporter-4 (MCT4), a monocarboxylic acid transporter, demonstrates significantly increased expression in the majority of malignancies. We performed an experiment using BALB/C mice, and our results showed that ShMCT4 transfection or the pharmaceutic inhibition of MCT4 with 7acc1 strengthens the activity of NK cells. The results of a calcein assay revealed that the cytotoxicity of NK cells was strengthened via inhibition of MCT4. In addition, ELISA testing showed that the content of perforin and CD107a was increased, and PCR amplification and immunoblotting revealed that the expression of NKG2D and H60 was upregulated after the inhibition of MCT4. Further, we observed an elevated pH value, decreased extracellular lactate flow, and attenuated tumor growth. Therefore, we concluded that the inhibition of MCT4 enhanced the cytotoxicity of NK cells by blocking lactate flux and reversing the acidified tumor microenvironment. In addition to these findings, we also discovered that MCT4 depletion may have a pronounced impact on autophagy, which was surmised by observing that the inhibition of autophagy (3MA) pulled the enhanced cytotoxicity of NK cells downwards. Together, these data suggest that the key effect of MCT4 depletion on NK cells probably utilizes inductive autophagy as a compensatory metabolic mechanism to minimize the acidic extracellular microenvironment associated with lactate export in tumors.

[Diagnosis of the DC glow discharge plasma generated inside a metallic tube by optical emission spectroscopy].

Optical emission spectroscopy method was used to diagnose the normal dc glow discharge plasma generated inside a metallic tube. The active species in the plasma were identified. The electron excitation temperature in the plasma was determined by the Boltzmann plot method. The vibrational temperature of N2 molecules in the plasma was determined by analyzing the emission spectrum lines of the N2 second positive system (C3 IIu-->B 3IIg). The dependence of the electron excitation temperature and molecular vibrational temperature on the pressure was investigated. The experiment results show that in the Ar 60% + N2 40% glow discharge plasma at 20 Pa, the active species are the Ar atoms, Ar ions, second positive series of N transitions and theE first negative series of B (2)II2u-->X 2sigma g+; transitons; the electron excitation temperature is (15 270 +/- 250) K, and the vibrational temperature of N2 molecules is (3 290 +/- 100) K. The electron excitation temperature and molecular vibrational temperature decrease with increasing pressure. These results would give some valuable guide to the study on inner surface modification of metallic tubes.