Association Between Z Drugs Use and Risk of Cognitive Impairment in Middle-Aged and Older Patients With Chronic Insomnia.

Background: Benzodiazepines (BZDs) and Non-BZDs (NBZDs) have been widely used for patients with chronic insomnia. Long-term uses of BZDs may cause cognitive impairment and increase the risk for dementia in older patients. NBZD as an agonist of the GABAA receptor complex includes eszopiclone, zopiclone, zolpidem, and zaleplon, also collectively known as Z drugs. However, evaluations for an association between cognitive impairment and Z drug use have been limitedly performed. This study aimed to investigate the association between the risk of cognitive decline and exposure to Z drugs in middle-aged and older patients with chronic insomnia. Methods: Investigations were performed on patients with chronic insomnia who visited the outpatient Department of Neurology, Beijing Friendship Hospital, and were assessed for the global cognitive function (MoCA) and memory (AVLT), executive function (TMT-B), visuospatial ability (CDT), verbal function (BNT-30), and attention (DST). Multiple regression analysis was conducted to determine the independent factors of cognition and evaluated the effect of Z drug use (zolpidem and zopiclone) on cognition. Results: A total of 120 subjects were identified. In our analysis, BZD exposure density (P = 0.025, OR = 1.43, 95% CI, 1.25-1.86) was an independent risk factor of cognitive impairment in middle-aged and older patients with chronic insomnia. Neither Z drug use (P = 0.103) nor Z drug exposure density (P = 0.765) correlated with global cognitive function. Moreover, there was a positive association between Z drug use and attention [(P = 0.002, OR = 0.42, 95% CI, 0.24-0.73)]. Additionally, income level (P = 0.001, OR = 0.23, 95% CI, 0.10-0.53), severity of insomnia (P = 0.019, OR = 1.20, 95% CI, 1.03-1.40) and age (P = 0.044, OR = 1.07, 95% CI, 1.00-1.14) were also independent factors of global cognitive function. Conclusion: BZD exposure density was an independent risk factor of cognitive impairment in middle-aged and older patients with chronic insomnia, but no correlation was found between Z drug use and cognitive impairment. Moreover, the use of Z drugs seemed to be associated with protection for attention. The use for prescription of BZDs, in this case, should be avoided or limited to low doses. Due to the addiction and tolerance, Z drugs should also be prescribed with great caution in middle-aged and elderly patients.

Feature Classification Method of Resting-State EEG Signals From Amnestic Mild Cognitive Impairment With Type 2 Diabetes Mellitus Based on Multi-View Convolutional Neural Network.

The convolutional neural network (CNN) model is an active research topic in the field of EEG signals analysis. However, the classification effect of CNN on EEG signals of amnestic mild cognitive impairment (aMCI) with type 2 diabetes mellitus (T2DM) is not ideal. Even if EEG signals are transformed into multispectral images that are more closely matched with the model, the best classification performance can not be achieved. Therefore, to improve the performance of CNN toward EEG multispectral image classification, a multi-view convolutional neural network (MVCNN) classification model based on inceptionV1 is designed in this study. This model mainly improves and optimizes the convolutional layers and stochastic gradient descent (SGD) in the convolutional architecture model. Firstly, based on the discreteness of EEG multispectral image features, the multi-view convolutional layer structure was proposed. Then the learning rate change function of the SGD was optimized to increase the classification performance. The multi-view convolutional nerve was used in an EEG multispectral classification task involving 19 aMCI with T2DM and 20 normal controls. The results showed that compared with the traditional classification models, MVCNN had a better stability and accuracy. Therefore, MVCNN could be used as an effective feature classification method for aMCI with T2DM.

Resting-state EEG signal classification of amnestic mild cognitive impairment with type 2 diabetes mellitus based on multispectral image and convolutional neural network.

OBJECTIVE: The purpose of this study is to judge whether this combination method of multispectral image and convolutional neural network (CNN) method can be used to distinguish amnestic mild cognitive impairment (aMCI) with Type 2 diabetes mellitus (T2DM) and normal controls (NC) with T2DM effectively. APPROACH: In this study, the authors first combined EEG signals from aMCI patients with T2DM and NC with T2DM on five different frequency bands, including Theta, Alpha1, Alpha2, Beta1, and Beta2. Then, the authors converted these time series into a series of multispectral images. Finally, the images data were classified with the CNN method. MAIN RESULTS: The classification effects of up to 89%, 91%, and 92% are obtained on the three combinations of frequency bands: Theta, Alpha1, and Alpha2; Alpha1, Alpha2, and Beta1; and Alpha2, Beta1, and Beta2. The spatial properties of EEG signals are highlighted, and its classification performance is found to be better than all the previous methods in the field of aMCI and T2DM diagnosis. The combination of multispectral images and CNN can be used as an effective biomarker for distinguishing the EEG signals in patients with aMCI and T2DM and in patients with NC with T2DM. SIGNIFICANCE: The combined approach used in this paper provides a new perspective for the analysis of EEG signals in patients with aMCI and T2DM.

The feature extraction of resting-state EEG signal from amnestic mild cognitive impairment with type 2 diabetes mellitus based on feature-fusion multispectral image method.

Recently, combining feature extraction and classification method of electroencephalogram (EEG) signals has been widely used in identifying mild cognitive impairment. However, it remains unclear which feature of EEG signals is best effective in assessing amnestic mild cognitive impairment (aMCI) with type 2 diabetes mellitus (T2DM) when combining one classifier. This study proposed a novel feature extraction method of EEG signals named feature-fusion multispectral image method (FMIM) for diagnosis of aMCI with T2DM. The FMIM was integrated with convolutional neural network (CNN) to classify the processed multispectral image data. The results showed that FMIM could effectively identify aMCI with T2DM from the control group compared to existing multispectral image method (MIM), with improvements including the type and quantity of feature extraction. Meanwhile, part of the invalid calculation could be avoided during the classification process. In addition, the classification evaluation indexes were best under the combination of Alpha2-Beta1-Beta2 frequency bands in data set based on FMIM-1, and were also best under the combination of the Theta-Alpha1-Alpha2-Beta1-Beta2 frequency bands in data set based on FMIM-2. Therefore, FMIM can be used as an effective feature extraction method of aMCI with T2DM, and as a valuable biomarker in clinical applications.

MicroRNA-22 Suppresses Breast Cancer Cell Growth and Increases Paclitaxel Sensitivity by Targeting NRAS.

In recent study, microRNAs have various important functions in diverse biological processes and progression of cancer. In human breast cancer, microRNA-22 has been reported to be downregulated. However, molecular mechanism of microRNA-22 in breast cancer progression and chemosensitivity has not been well studied. In our study, these results demonstrated that microRNA-22 expression levels were significantly reduced in 40 pairs of human breast cancer tissues when compared to normal tissues. Enforced expression of microRNA-22 inhibited activity of cell proliferation and cell migration in breast cancer cells. Furthermore, microRNA-22 targeted NRAS proto-oncogene, GTPase (NRAS) in breast cancer cells. The expression levels of NRAS in human clinical specimens were higher in breast cancer tissues when compared to normal tissues. Moreover, microRNA-22 sensitized breast cancer cells to paclitaxel by regulation of NRAS. Our results then demonstrated that microRNA-22 functioned as a tumor suppressor microRNA and indicated potential application for the diagnosis and treatment of cancer in the future.

A new EEG synchronization strength analysis method: S-estimator based normalized weighted-permutation mutual information.

Synchronization is an important mechanism for understanding information processing in normal or abnormal brains. In this paper, we propose a new method called normalized weighted-permutation mutual information (NWPMI) for double variable signal synchronization analysis and combine NWPMI with S-estimator measure to generate a new method named S-estimator based normalized weighted-permutation mutual information (SNWPMI) for analyzing multi-channel electroencephalographic (EEG) synchronization strength. The performances including the effects of time delay, embedding dimension, coupling coefficients, signal to noise ratios (SNRs) and data length of the NWPMI are evaluated by using Coupled Henon mapping model. The results show that the NWPMI is superior in describing the synchronization compared with the normalized permutation mutual information (NPMI). Furthermore, the proposed SNWPMI method is applied to analyze scalp EEG data from 26 amnestic mild cognitive impairment (aMCI) subjects and 20 age-matched controls with normal cognitive function, who both suffer from type 2 diabetes mellitus (T2DM). The proposed methods NWPMI and SNWPMI are suggested to be an effective index to estimate the synchronization strength.

Resting-state EEG coupling analysis of amnestic mild cognitive impairment with type 2 diabetes mellitus by using permutation conditional mutual information.

OBJECTIVE: This study was meant to explore whether the coupling strength and direction of resting-state electroencephalogram (rsEEG) could be used as an indicator to distinguish the patients of type 2 diabetes mellitus (T2DM) with or without amnestic mild cognitive impairment (aMCI). METHODS: Permutation conditional mutual information (PCMI) was used to calculate the coupling strength and direction of rsEEG signals between different brain areas of 19 aMCI and 20 normal control (NC) with T2DM on 7 frequency bands: Delta, Theta, Alpha1, Alpha2, Beta1, Beta2 and Gamma. The difference in coupling strength or direction of rsEEG between two groups was calculated. The correlation between coupling strength or direction of rsEEG and score of different neuropsychology scales were also calculated. RESULTS: We have demonstrated that PCMI can calculate effectively the coupling strength and directionality of EEG signals between different brain regions. The significant difference in coupling strength and directionality of EEG signals was found between the patients of aMCI and NC with T2DM on different brain regions. There also existed significant correlation between sex or age and coupling strength or coupling directionality of EEG signals between a few different brain regions from all subjects. CONCLUSIONS: The coupling strength or directionality of EEG signals calculated by PCMI are significantly different between aMCI and NC with T2DM. SIGNIFICANCE: These results showed that the coupling strength or directionality of EEG signals calculated by PCMI might be used as a biomarker in distinguishing the aMCI from NC with T2DM.

Cortical activities of heat-sensitization responses in suspended moxibustion: an EEG source analysis with sLORETA.

Moxibustion is under active research as a complementary and alternative treatment for various diseases such as pain. "Heat-sensitization" responses have been reported during suspended moxibustion, whose occurrence is associated with significantly better therapeutic effects. The present study aimed to investigate the cortical activities of this interesting phenomenon by a standardized low-resolution brain electromagnetic tomography. We performed electroencephalography recording in a group of patients with chronic low back pain before, during, and after moxibustion treatment at Yaoyangguan (DU3) areas. 11 out of 21 subjects experienced strong heat-sensitization during moxibustion, which were accompanied with significant decreases of current densities in the beta frequency bands in prefrontal, primary and second somatosensory, and cingulate cortices, as well as increased current densities in the alpha2 band in the left insula. No changes were detected in patients without sensitization responses, or in the post-moxibustion phase of either group. These data indicated widespread activity changes across different frequency bands during heat-sensitization. Cortical oscillatory activities could be used to evaluate the "heat-sensitization" responses during suspended moxibustion.

Complex network analysis of resting state EEG in amnestic mild cognitive impairment patients with type 2 diabetes.

PURPOSE: Diabetes is a great risk factor for dementia and mild cognitive impairment (MCI). This study investigates whether complex network-derived features in resting state EEG (rsEEG) could be applied as a biomarker to distinguish amnestic mild cognitive impairment (aMCI) from normal cognitive function in subjects with type 2 diabetes (T2D). METHOD: In this study, EEG was recorded in 28 patients with T2D (16 aMCI patients and 12 controls) during a no-task eyes-closed resting state. Pair-wise synchronization of rsEEG signals were assessed in six frequency bands (delta, theta, lower alpha, upper alpha, beta, and gamma) using phase lag index (PLI) and grouped into long distance (intra- and inter-hemispheric) and short distance interactions. PLI-weighted connectivity networks were also constructed, and characterized by mean clustering coefficient and path length. The correlation of these features and Montreal Cognitive Assessment (MoCA) scores was assessed. RESULTS: Main findings of this study were as follows: (1) In comparison with controls, patients with aMCI had a significant decrease of global mean PLI in lower alpha, upper alpha, and beta bands. Lower functional connection at short and long intra-hemispheric distance mainly appeared on the left hemisphere. (2) In the lower alpha band, clustering coefficient was significantly lower in aMCI group, and the path length significantly increased. (3) Cognitive status measured by MoCA had a significant positive correlation with cluster coefficient and negative correlation with path length in lower alpha band. CONCLUSIONS: The brain network of aMCI patients displayed a disconnection syndrome and a loss of small-world architecture. The correlation between cognitive states and network characteristics suggested that the more in deterioration of the diabetes patients' cognitive state, the less optimal the network organization become. Hence, the complex network-derived biomarkers based on EEG could be employed to track cognitive function of diabetic patients and provide a new diagnosis tool for aMCI.

[Multi-channel Synchronization Analysis of Mild Cognitive Impairment in Type 2 Diabetes Patients].

The cognitive impairment of type 2 diabetes patients caused by long-term metabolic disorders has been the current focus of attention. In order to find the related electroencephalogram (EEG) characteristics to the mild cognitive impairment (MCI) of diabetes patients, this study analyses the EEG synchronization with the method of multichannel synchronization analysis--S estimator based on phase synchronization. The results showed that the S estimator values in each frequency band of diabetes patients with MCI were almost lower than that of control group. Especially, the S estimator values decreased significantly in the delta and alpha band, which indicated the EEG synchronization decrease. The MoCA scores and S value had a significant positive correlation in alpha band.

Cortical source multivariate EEG synchronization analysis on amnestic mild cognitive impairment in type 2 diabetes.

Is synchronization altered in amnestic mild cognitive impairment (aMCI) and normal cognitive functions subjects in type 2 diabetes mellitus (T2DM)? Resting eye-closed EEG data were recorded in 8 aMCI subjects and 11 age-matched controls in T2DM. Three multivariate synchronization algorithms (S-estimator (S), synchronization index (SI), and global synchronization index (GSI)) were used to measure the synchronization in five ROIs of sLORETA sources for seven bands. Results showed that aMCI group had lower synchronization values than control groups in parietal delta and beta2 bands, temporal delta and beta2 bands, and occipital theta and beta2 bands significantly. Temporal (r = 0.629; P = 0.004) and occipital (r = 0.648; P = 0.003) theta S values were significantly positive correlated with Boston Name Testing. In sum, each of methods reflected that the cortical source synchronization was significantly different between aMCI and control group, and these difference correlated with cognitive functions.

Characterizing heat-sensitization responses in suspended moxibustion with high-density EEG.

OBJECTIVE: We have reported "heat-sensitization" responses during suspended moxibustion, whose occurrence is associated with significantly better therapeutic effects. The present study aimed to characterize the electrophysiological features of this interesting phenomenon with high-density electroencephalography (EEG). METHODS: We performed EEG recording in a group of patients with chronic low back pain before, during, and after moxibustion treatment at DU3. RESULTS: 12 out of 25 subjects experienced strong heat-sensitization during moxibustion, which was accompanied by increased power spectral densities (PSDs) at the theta, alpha, and beta frequency bands. The scalp topographies of averaged power indicated that the theta and beta PSD changes were most obvious in fronto-central regions, whereas those of the alpha band were more global. In addition, nonsensitized and sensitized groups showed distinct activity patterns, with heat-sensitization inducing increased phase coherence at the theta and beta ranges. CONCLUSIONS: These data were the first objective evidence of heat-sensitization responses during suspended moxibustion, which were characterized by widespread oscillatory changes in scalp EEG.

Relative power and coherence of EEG series are related to amnestic mild cognitive impairment in diabetes.

OBJECTIVE: Diabetes is a risk factor for dementia and mild cognitive impairment. The aim of this study was to investigate whether some features of resting-state EEG (rsEEG) could be applied as a biomarker to distinguish the subjects with amnestic mild cognitive impairment (aMCI) from normal cognitive function in type 2 diabetes. MATERIALS AND METHODS: In this study, 28 patients with type 2 diabetes (16 aMCI patients and 12 controls) were investigated. Recording of the rsEEG series and neuropsychological assessments were performed. The rsEEG signal was first decomposed into delta, theta, alpha, beta, gamma frequency bands. The relative power of each given band/sum of power and the coherence of waves from different brain areas were calculated. The extracted features from rsEEG and neuropsychological assessments were analyzed as well. RESULTS: The main findings of this study were that: (1) compared with the control group, the ratios of power in theta band [P(theta)] vs. power in alpha band [P(alpha)] [P(theta)/P(alpha)] in the frontal region and left temporal region were significantly higher for aMCI, and (2) for aMCI, the alpha coherences in posterior, fronto-right temporal, fronto-posterior, right temporo-posterior were decreased; the theta coherences in left central-right central (LC-RC) and left posterior-right posterior (LP-RP) regions were also decreased; but the delta coherences in left temporal-right temporal (LT-RT) region were increased. CONCLUSION: The proposed indexes from rsEEG recordings could be employed to track cognitive function of diabetic patients and also to help in the diagnosis of those who develop aMCI.

[Study on diabetes-induced cognitive impairment].

The diabetes-induced cognitive impairment complications have serious effects on the patients' lives, and there is an enormous and financial burden on patients, their families and society as a whole. This review investigates the current research status of diabetes-induced cognitive impairment from different view points including molecular, models, clinics and electrophysiology. The relationship between diabetes and cognitive function and developments of research are hereby summarized. And finally, future issues of diabetes-induced cognitive impairments are pointed out, and the effective rehabilitation methods should be considered.

Effect of Pilates training on alpha rhythm.

In this study, the effect of Pilates training on the brain function was investigated through five case studies. Alpha rhythm changes during the Pilates training over the different regions and the whole brain were mainly analyzed, including power spectral density and global synchronization index (GSI). It was found that the neural network of the brain was more active, and the synchronization strength reduced in the frontal and temporal regions due to the Pilates training. These results supported that the Pilates training is very beneficial for improving brain function or intelligence. These findings maybe give us some line evidence to suggest that the Pilates training is very helpful for the intervention of brain degenerative diseases and cogitative dysfunction rehabilitation.

[Studies on the ERP underlying premeditated and temporary deception].

The event-related potential (ERP) P300 was recorded to analyze the temporal sequences character and the P300 compositions in premeditated and temporary deception. When 15 healthy undergraduates watched the stolen, familiar but not stolen and strange object pictures, the reaction time was recorded and EEG data were collected to analyze the amplitudes of N1, N2 and P3 sub-component of P300 in honesty and deception group respectively through analog theft paradigm. The results showed that the amplitudes of N1, N2 and P3 in premeditated deception group were markedly larger than those in temporary deception, and the reaction time of deception group was longer than that of honesty group. Compared with temporary deception, more attention resources were invested and intensely response conflict was induced by premeditated deception.