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

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 GABA receptor complex includes eszopiclone, zopiclone, zolpidem, and zaleplon, also collectively known as Z drugs.

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.

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.

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.

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.

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.

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.

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.

[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.

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.

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.

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.

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.

[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.

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.

[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.