Abstract： Diseases prognosis is often influenced by multiple factors, and some intricate non-linear relationships exist among those factors. Artificial neural network （ANN）, an artificial intelligence model, simulates the work mode of biological neurons and has a b capability to analyze multi-factor non-linear relationships. In recent years, ANN is increasingly applied in clinical medical fields, especially for the prediction of disease prognosis. This article focuses on the basic principles of ANN and its application in disease prognosis research.
The electroencephalogram (EEG) signal is a general reflection of the neurophysiological activity of the brain, which has the advantages of being safe, efficient, real-time and dynamic. With the development and advancement of machine learning research, automatic diagnosis of Alzheimer’s diseases based on deep learning is becoming a research hotspot. Started from feedforward neural networks, this paper compared and analysed the structural properties of neural network models such as recurrent neural networks, convolutional neural networks and deep belief networks and their performance in the diagnosis of Alzheimer’s disease. It also discussed the possible challenges and research trends of this research in the future, expecting to provide a valuable reference for the clinical application of neural networks in the EEG diagnosis of Alzheimer’s disease.
To enhance the accuracy of computer-aided diagnosis of adolescent depression based on electroencephalogram signals, this study collected signals of 32 female adolescents (16 depressed and 16 healthy, age: 16.3 ± 1.3) with eyes colsed for 4 min in a resting state. First, based on the phase synchronization between the signals, the phase-locked value (PLV) method was used to calculate brain functional connectivity in the θ and α frequency bands, respectively. Then based on the graph theory method, the network parameters, such as strength of the weighted network, average characteristic path length, and average clustering coefficient, were calculated separately (P < 0.05). Next, using the relationship between multiple thresholds and network parameters, the area under the curve (AUC) of each network parameter was extracted as new features (P < 0.05). Finally, support vector machine (SVM) was used to classify the two groups with the network parameters and their AUC as features. The study results show that with strength, average characteristic path length, and average clustering coefficient as features, the classification accuracy in the θ band is increased from 69% to 71%, 66% to 77%, and 50% to 68%, respectively. In the α band, the accuracy is increased from 72% to 79%, 69% to 82%, and 65% to 75%, respectively. And from overall view, when AUC of network parameters was used as a feature in the α band, the classification accuracy is improved compared to the network parameter feature. In the θ band, only the AUC of average clustering coefficient was applied to classification, and the accuracy is improved by 17.6%. The study proved that based on graph theory, the method of feature optimization of brain function network could provide some theoretical support for the computer-aided diagnosis of adolescent depression.
Developmental and epileptic encephalopathy (DEE) is a group of diseases that severely affects the neurological development of children, characterized by frequent seizures and significant neurodevelopmental impairments. These diseases not only impact the quality of life of affected children but also impose a heavy burden on families and society. In recent years, the development of brain network theory has provided a new perspective on understanding the pathological mechanisms of DEE, especially the role of structural and functional brain networks in the process of epilepsy. This review systematically summarized the research progress of structural and functional brain networks in DEE, highlighted their importance in seizure activity, disease progression, and prognosis evaluation.
Currently, about one-third of patients with anti-epilepsy drug or resective surgery continue to have sezure, the mechanism remin unknown. Up to date, the main target for presurgical evaluation is to determene the EZ and SOZ. Since the early nineties of the last century network theory was introduct into neurology, provide new insights into understanding the onset, propagation and termination. Focal seizure can impact the function of whole brain, but the abnormal pattern is differet to generalized seizure. Brain network is a conception of mathematics. According to the epilepsy, network node and hub are related to the treatment. Graphy theory and connectivity are main algorithms. Understanding the mechanism of epilepsy deeply, since study the theory of epilepsy network, can improve the planning of surgery, resection epileptogenesis zone, seizure onset zone and abnormal node of hub simultaneously, increase the effect of resectiv surgery and predict the surgery outcome. Eventually, develop new drugs for correct the abnormal network and increase the effect. Nowadays, there are many algorithms for the brain network. Cooperative study by the clinicans and biophysicists instituted standard and extensively applied algorithms is the precondition of widely used clinically.
In order to more accurately and effectively understand the intermuscular coupling of different temporal and spatial levels from the perspective of complex networks, a new multi-scale intermuscular coupling network analysis method was proposed in this paper. The multivariate variational modal decomposition (MVMD) and Copula mutual information (Copula MI) were combined to construct an intermuscular coupling network model based on MVMD-Copula MI, and the characteristics of intermuscular coupling of multiple muscles of upper limbs in different time-frequency scales during reaching exercise in healthy subjects were analyzed by using the network parameters such as node strength and clustering coefficient. The experimental results showed that there are obvious differences in the characteristics of intermuscular coupling in the six time-frequency scales. Specifically, the triceps brachii (TB) had relatively high coupling strength with the middle deltoid (MD) and posterior deltoid (PD), and the intermuscular function was closely connected. However, the biceps brachii (BB) was independent of other muscles. The intermuscular coupling network had scale differences. MVMD-Copula MI can quantitatively describe the relationship of multi-scale intermuscular coupling strength, which has good application prospects.
With the rapidly growing literature across the surgical disciplines, there is a corresponding need to critically appraise and summarize the currently available evidence so they can be applied appropriately to patient care. The interpretation of systematic reviews is particularly challenging in cases where few robust clinical trials have been performed to address a particular question. However, risk of bias can be minimized and potentially useful conclusions can be drawn if strict review methodology is adhered to, including an exhaustive literature search, quality appraisal of primary studies, appropriate statistical methodology, assessment of confidence in estimates and risk of bias. Therefore, the following article aims to: (Ⅰ) summarize to the important features of a thorough and rigorous systematic review or meta-analysis for the surgical literature; (Ⅱ) highlight several underused statistical approaches which may yield further interesting insights compared to conventional pair-wise data synthesis techniques; and (Ⅲ) propose a guide for thorough analysis and presentation of results.
Electrical impedance tomography (EIT) is a non-radiation, non-invasive visual diagnostic technique. In order to improve the imaging resolution and the removing artifacts capability of the reconstruction algorithms for electrical impedance imaging in human-chest models, the HMANN algorithm was proposed using the Hadamard product to optimize multilayer artificial neural networks (MANN). The reconstructed images of the HMANN algorithm were compared with those of the generalized vector sampled pattern matching (GVSPM) algorithm, truncated singular value decomposition (TSVD) algorithm, backpropagation (BP) neural network algorithm, and traditional MANN algorithm. The simulation results showed that the correlation coefficient of the reconstructed images obtained by the HMANN algorithm was increased by 17.30% in the circular cross-section models compared with the MANN algorithm. It was increased by 13.98% in the lung cross-section models. In the lung cross-section models, some of the correlation coefficients obtained by the HMANN algorithm would decrease. Nevertheless, the HMANN algorithm retained the image information of the MANN algorithm in all models, and the HMANN algorithm had fewer artifacts in the reconstructed images. The distinguishability between the objects and the background was better compared with the traditional MANN algorithm. The algorithm could improve the correlation coefficient of the reconstructed images, and effectively remove the artifacts, which provides a new direction to effectively improve the quality of the reconstructed images for EIT.
Alzheimer’s disease (AD) is a chronic central neurodegenerative disease. The pathological features of AD are the extracellular deposition of senile plaques formed by amyloid-β oligomers (AβOs) and the intracellular accumulation of neurofibrillary tangles formed by hyperphosphorylated tau protein. In this paper, an in vitro pathological model of AD based on neuronal network chip and its real-time dynamic analysis were presented. The hippocampal neuronal network was cultured on the microelectrode array (MEA) chip and induced by AβOs as an AD model in vitro to simultaneously record two firing patterns from the interneurons and pyramidal neurons. The spatial firing patterns mapping and cross-correlation between channels were performed to validate the degeneration of neuronal network connectivity. This biosensor enabled the detection of the AβOs toxicity responses, and the identification of connectivity and interactions between neuronal networks, which can be a novel technique in the research of AD pathological model in vitro.