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

Goat Milk Improves Glucose Metabolism in Type 2 Diabetic Mice and Protects Pancreatic β-Cell Functions

SCOPE

Consuming goat milk is known to benefit high-fat diet-fed and streptozocin (STZ)-induced diabetic rats, but the underlying mechanisms are unknown. This study is conducted to investigate the metabolic effects of a goat milk diet (a form of goat milk powder) on glucose homeostasis and pancreatic conditions in a mouse model of Type 2 diabetes mellitus (T2DM) induced by STZ.

METHODS AND RESULTS

T2DM mice are fed with a goat-milk-based diet containing 10.3% w/w goat milk powder for 10 weeks for investigating the in vivo effects; a β-cell line MIN6 cells are used to test the in vitro effects of digested goat milk (DGM). Goat milk diet improves the deleterious effects of STZ on fasting glucose levels and glucose tolerance, accelerates pancreatic structure recovery, and alters blood metabolites in mice. Based on the significant differences observed in metabolites, the key pathways, metabolite regulatory enzymes, metabolite molecular modules, and biochemical reactions are identified as critical integrated pathways. DGM promotes the cell activity, glucose transportation, and AKT activation in cultured STZ-treated MIN6 cells in vitro.

CONCLUSIONS

Goat milk diet improves glucose homeostasis and pancreatic conditions of T2DM mice, in association with improved blood metabolite profiles and activation of pancreatic AKT pathway.

Identifying Amnestic Mild Cognitive Impairment With Convolutional Neural Network Adapted to the Spectral Entropy Heat Map of the Electroencephalogram

Mild cognitive impairment (MCI) is a preclinical stage of Alzheimer's disease (AD), and early diagnosis and intervention may delay its deterioration. However, the electroencephalogram (EEG) differences between patients with amnestic mild cognitive impairment (aMCI) and healthy controls (HC) subjects are not as significant compared to those with AD. This study addresses this situation by proposing a computer-aided diagnostic method that also aims to improve model performance and assess the sensitive areas of the subject's brain. The EEG data of 46 subjects (20HC/26aMCI) were enhanced with windowed moving segmentation and transformed from 1D temporal data to 2D spectral entropy images to measure the efficient information in the time-frequency domain from the point of view of information entropy; A novel convolution module is devised, which considerably reduces the number of model learning parameters and saves computing resources on the premise of ensuring diagnostic performance; One more thing, the cognitive diagnostic contribution of the corresponding channels in each brain region was measured by the weight coefficient of the input and convolution unit. Our results showed that when the segmental window overlap rate was increased from 0 to 75%, the corresponding generalization accuracy increased from 91.673 ± 0.9578% to 94.642 ± 0.4035%; Approximately 35% reduction in model learnable parameters by optimizing the network structure while maintaining accuracy; The top four channels were FP1, F7, T5, and F4, corresponding to the frontal and temporal lobes, in descending order of the mean value of the weight coefficients. This paper proposes a novel method based on spectral entropy image and convolutional neural network (CNN), which provides a new perspective for the identifying of aMCI based on EEG.

Discriminant Subspace Low-Rank Representation Algorithm for Electroencephalography-Based Alzheimer’s Disease Recognition

Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disease that often occurs in the elderly. Electroencephalography (EEG) signals have a strong correlation with neuropsychological test results and brain structural changes. It has become an effective aid in the early diagnosis of AD by exploiting abnormal brain activity. Because the original EEG has the characteristics of weak amplitude, strong background noise and randomness, the research on intelligent AD recognition based on machine learning is still in the exploratory stage. This paper proposes the discriminant subspace low-rank representation (DSLRR) algorithm for EEG-based AD and mild cognitive impairment (MCI) recognition. The subspace learning and low-rank representation are flexibly integrated into a feature representation model. On the one hand, based on the low-rank representation, the graph discriminant embedding is introduced to constrain the representation coefficients, so that the robust representation coefficients can preserve the local manifold structure of the EEG data. On the other hand, the least squares regression, principle component analysis, and global graph embedding are introduced into the subspace learning, to make the model more discriminative. The objective function of DSLRR is solved by the inexact augmented Lagrange multiplier method. The experimental results show that the DSLRR algorithm has good classification performance, which is helpful for in-depth research on AD and MCI recognition.

Deep Transfer Learning for Parkinson’s Disease Monitoring by Image-Based Representation of Resting-State EEG Using Directional Connectivity

Parkinson’s disease (PD) is characterized by abnormal brain oscillations that can change rapidly. Tracking neural alternations with high temporal resolution electrophysiological monitoring methods such as EEG can lead to valuable information about alterations observed in PD. Concomitantly, there have been advances in the high-accuracy performance of deep neural networks (DNNs) using few-patient data. In this study, we propose a method to transform resting-state EEG data into a deep latent space to classify PD subjects from healthy cases. We first used a general orthogonalized directed coherence (gOPDC) method to compute directional connectivity (DC) between all pairwise EEG channels in four frequency bands (Theta, Alpha, Beta, and Gamma) and then converted the DC maps into 2D images. We then used the VGG-16 architecture (trained on the ImageNet dataset) as our pre-trained model, enlisted weights of convolutional layers as initial weights, and fine-tuned all layer weights with our data. After training, the classification achieved 99.62% accuracy, 100% precision, 99.17% recall, 0.9958 F1 score, and 0.9958 AUC averaged for 10 random repetitions of training/evaluating on the proposed deep transfer learning (DTL) network. Using the latent features learned by the network and employing LASSO regression, we found that latent features (as opposed to the raw DC values) were significantly correlated with five clinical indices routinely measured: left and right finger tapping, left and right tremor, and body bradykinesia. Our results demonstrate the power of transfer learning and latent space derivation for the development of oscillatory biomarkers in PD.

Classification of ERP signal from amnestic mild cognitive impairment with type 2 diabetes mellitus using single-scale multi-input convolution neural network

BACKGROUND

The application of deep learning models to electroencephalogram (EEG) signal classification has recently become a popular research topic. Several deep learning models have been proposed to classify EEG signals in patients with various neurological diseases. However, no effective deep learning model for event-related potential (ERP) signal classification is yet available for amnestic mild cognitive impairment (aMCI) with type 2 diabetes mellitus (T2DM).

METHOD

This study proposed a single-scale multi-input convolutional neural network (SSMICNN) method to classify ERP signals between aMCI patients with T2DM and the control group. Firstly, the 18-electrode ERP signal on alpha, beta, and theta frequency bands was extracted by using the fast Fourier transform, and then the mean, sum of squares, and absolute value feature of each frequency band were calculated. Finally, these three features are converted into multispectral images respectively and used as the input of the SSMICNN network to realize the classification task.

RESULTS

The results show that the SSMICNN can fuse MSI formed by different features, SSMICNN enriches the feature quantity of the neural network input layer and has excellent robustness, and the errors of SSMICNN can be simultaneously transmitted to the three convolution channels in the back-propagation phase. Comparison with Existing Method(s): SSMICNN could more effectively identify ERP signals from aMCI with T2DM from the control group compared to existing classification methods, including convolution neural network, support vector machine, and logistic regression.

CONCLUSIONS

The combination of SSMICNN and MSI can be used as an effective biological marker to distinguish aMCI patients with T2DM from the control group.

The Current Research of Combining Multi-Modal Brain-Computer Interfaces With Virtual Reality

Combing brain-computer interfaces (BCI) and virtual reality (VR) is a novel technique in the field of medical rehabilitation and game entertainment. However, the limitations of BCI such as a limited number of action commands and low accuracy hinder the widespread use of BCI-VR. Recent studies have used hybrid BCIs that combine multiple BCI paradigms and/or the multi-modal biosensors to alleviate these issues, which may become the mainstream of BCIs in the future. The main purpose of this review is to discuss the current status of multi-modal BCI-VR. This study first reviewed the development of the BCI-VR, and explored the advantages and disadvantages of incorporating eye tracking, motor capture, and myoelectric sensing into the BCI-VR system. Then, this study discussed the development trend of the multi-modal BCI-VR, hoping to provide a pathway for further research in this field.

Icariin Protects Mouse Insulinoma Min6 Cell Function by Activating the PI3K/AKT Pathway

Background

Type 2 diabetes (T2D) is characterized by β-cell dysfunction and insulin resistance. Icariin (ICA), a flavonoid from Epimedium, possesses anti-diabetic and anti-inflammatory properties. However, it is unclear whether ICA acts on pancreatic β-cells. The present study was designed to explore the effects and latent mechanism of ICA on uric acid (UA)-stimulated pancreatic β-cell dysfunction.

Material/Methods

Min6 cells were exposed to various concentrations of ICA for 24 h, and cell viability was assessed by MTT assays. Min6 cells were treated with ICA for 2 h, followed by 5 mg/dl UA for 24 h, and cell viability, apoptosis, apoptosis-associated protein levels and insulin secretion were assessed by MTT, flow cytometry, western blotting and glucose-stimulated insulin secretion assays, respectively. The effects of ICA and UA on the PI3K/Akt pathway were also analyzed by western blotting, as were the effects of the specific PI3K/Akt inhibitor LY294002.

Results

ICA was not cytotoxic toward Min6 cells. UA decreased Min6 cell viability, enhanced cell apoptosis and levels of cleaved caspase-3, and reduced pro-caspase3 levels and insulin secretion, with all of these effects reversed by ICA in a dose-dependent manner. UA inhibited the PI3K/AKT pathway, an effect reversed by ICA treatment. The specific PI3K/Akt inhibitor LY294002, however, reversed these effects of ICA on UA-treated Min6 cells.

Conclusions

ICA protected Min6 cell function, an effect likely mediated by the PI3K pathway. ICA may inhibit the progression of diabetes.