MCAN: Multimodal Causal Adversarial Networks for Dynamic Effective Connectivity Learning From fMRI and EEG Data.

Dynamic effective connectivity (DEC) is the accumulation of effective connectivity in the time dimension, which can describe the continuous neural activities in the brain. Recently, learning DEC from functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) data has attracted the attention of neuroinformatics researchers. However, the current methods fail to consider the gap between the fMRI and EEG modality, which can not precisely learn the DEC network from multimodal data.

Brain Network Evaluation by Functional-Guided Effective Connectivity Reinforcement Learning Method Indicates Therapeutic Effect for Tinnitus.

Using functional connectivity (FC) or effective connectivity (EC) alone cannot effectively delineate brain networks based on functional magnetic resonance imaging (fMRI) data, limiting the understanding of the mechanism of tinnitus and its treatment. Investigating brain FC is a foundational step in exploring EC. This study proposed a functionally guided EC (FGEC) method based on reinforcement learning (FGECRL) to enhance the precision of identifying EC between distinct brain regions.

MetaCAE: Causal autoencoder with meta-knowledge transfer for brain effective connectivity estimation.

Using machine learning methods to estimate brain effective connectivity networks from functional magnetic resonance imaging (fMRI) data has gradually become one of the hot subjects in the fields of neuroscience. In particular, the encoder-decoder based methods can effectively extract the connections in fMRI time series, which have achieved promising performance. However, these methods generally use Granger causality model, which may identify false directions due to the non-stationary characteristic of fMRI data.

Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm.

A wealth of causal relationships exists in biological systems, both causal brain networks and causal protein signaling networks are very classical causal biological networks (CBNs). Learning CBNs from biological signal data reliably is a critical problem today. However, most of the existing methods are not excellent enough in terms of accuracy and time performance, and tend to fall into local optima because they do not take full advantage of global information.

Amortization Transformer for Brain Effective Connectivity Estimation from fMRI Data.

Using machine learning methods to estimate brain effective connectivity networks from functional magnetic resonance imaging (fMRI) data has garnered significant attention in the fields of neuroinformatics and bioinformatics. However, existing methods usually require retraining the model for each subject, which ignores the knowledge shared across subjects. In this paper, we propose a novel framework for estimating effective connectivity based on an amortization transformer, named AT-EC.

Brain effective connectivity analysis facilitates the treatment outcome expectation of sound therapy in patients with tinnitus.

Tinnitus is associated with abnormal functional connectivity of multiple regions of the brain. However, previous analytic methods have disregarded information on the direction of functional connectivity, leading to only a moderate efficacy of pretreatment planning. We hypothesized that the pattern of directional functional connectivity can provide key information on treatment outcomes.

Exploring Brain Effective Connectivity Networks Through Spatiotemporal Graph Convolutional Models.

Learning brain effective connectivity networks (ECN) from functional magnetic resonance imaging (fMRI) data has gained much attention in recent years. With the successful applications of deep learning in numerous fields, several brain ECN learning methods based on deep learning have been reported in the literature. However, current methods ignore the deep temporal features of fMRI data and fail to fully employ the spatial topological relationship between brain regions.

Case Report: Double-Decker Repair of Partial Pulmonary Venous Return Into the Coronary Sinus.

We present a case of persistent left superior vena cava (LSVC) draining into the right atrium (RA) the coronary sinus (CS), while the left superior pulmonary vein returns abnormally to the CS. The LSVC may have few clinical consequences but complicates surgical repair of partial anomalous pulmonary venous return (PAPVR). Transthoracic echocardiography and computed tomographic angiography (CTA) showed that a persistent LSVC and PAPVR converged behind the left atrium.

Case Report: Reconstruction of the Right Atrium With the Left Atrial Appendage.

We herein present a case of infective endocarditis of the mitral valve and a paravalvular abscess around the tricuspid valve. Preoperative blood culture confirmed the presence of pathogenic diphtheroids. During the operation, an unexpected infection of the free wall of the right atrium (RA) near the tricuspid annulus was found.

A Survey on Brain Effective Connectivity Network Learning.

Human brain effective connectivity characterizes the causal effects of neural activities among different brain regions. Studies of brain effective connectivity networks (ECNs) for different populations contribute significantly to the understanding of the pathological mechanism associated with neuropsychiatric diseases and facilitate finding new brain network imaging markers for the early diagnosis and evaluation for the treatment of cerebral diseases. A deeper understanding of brain ECNs also greatly promotes brain-inspired artificial intelligence (AI) research in the context of brain-like neural networks and machine learning.

Estimating Effective Connectivity by Recurrent Generative Adversarial Networks.

Estimating effective connectivity from functional magnetic resonance imaging (fMRI) time series data has become a very hot topic in neuroinformatics and brain informatics. However, it is hard for the current methods to accurately estimate the effective connectivity due to the high noise and small sample size of fMRI data. In this paper, we propose a novel framework for estimating effective connectivity based on recurrent generative adversarial networks, called EC-RGAN.

Inferring Effective Connectivity Networks From fMRI Time Series With a Temporal Entropy-Score.

Inferring brain-effective connectivity networks from neuroimaging data has become a very hot topic in neuroinformatics and bioinformatics. In recent years, the search methods based on Bayesian network score have been greatly developed and become an emerging method for inferring effective connectivity. However, the previous score functions ignore the temporal information from functional magnetic resonance imaging (fMRI) series data and may not be able to determine all orientations in some cases.

Successful Resection of a Posterior Tricuspid Leaflet Lipoma and Reconstruction of the Tricuspid Valve.

We present a case with posterior tricuspid leaflet mass involving a tricuspid annulus and the right ventricle. Echocardiography demonstrated a lobulated mass arising from posterior tricuspid valve and partially obstructing the orifice of the tricuspid valve. At the operation, a yellowish, walnut-sized mass arising from the posterior tricuspid leaflet tightly adherent to the posterior tricuspid annular was excised en bloc with posterior tricuspid leaflet and adherent ventricular myocardium and annulus.

Aortic and mitral valve surgery for infective endocarditis with reconstruction of the intervalvular fibrous body: an analysis of clinical outcomes.

Background: Reconstruction of the aorto-mitral curtain (AMC) for invasive double-valve infective endocarditis (IE) is a rare and challenging procedure. This study presents the short- and mid-term results of reconstruction of AMC in a single center.

Methods: From 2016 to 2019, 14 patients with invasive double-valve underwent surgical reconstruction of the AMC, along with either double valve replacement or aortic valve replacement with mitral valve repair.

ACOEC-FD: Ant Colony Optimization for Learning Brain Effective Connectivity Networks From Functional MRI and Diffusion Tensor Imaging.

Identifying brain effective connectivity (EC) networks from neuroimaging data has become an effective tool that can evaluate normal brain functions and the injuries associated with neurodegenerative diseases. So far, there are many methods used to identify EC networks. However, most of the research currently focus on learning EC networks from single modal imaging data such as functional magnetic resonance imaging (fMRI) data.

Learning Brain Effective Connectivity Network Structure Using Ant Colony Optimization Combining With Voxel Activation Information.

Learning brain effective connectivity (EC) networks from functional magnetic resonance imaging (fMRI) data has become a new hot topic in the neuroinformatics field. However, how to accurately and efficiently learn brain EC networks is still a challenging problem. In this paper, we propose a new algorithm to learn the brain EC network structure using ant colony optimization (ACO) algorithm combining with voxel activation information, named as VACOEC.

Inhibition of long noncoding RNA growth arrest-specific 5 attenuates cerebral injury induced by deep hypothermic circulatory arrest in rats.

Objective: We sought to investigate cerebroprotection by targeting long noncoding RNA growth arrest-specific 5 in a rat model of prolonged deep hypothermic circulatory arrest.

Methods: Deep hypothermic circulatory arrest was conducted for 60 minutes when the pericranial temperature was cooled to 18°C in rats. Dual luciferase assay was used to detect the binding relationship between growth arrest-specific 5 and putative target microRNAs.

Learning Effective Connectivity Network Structure from fMRI Data Based on Artificial Immune Algorithm.

Many approaches have been designed to extract brain effective connectivity from functional magnetic resonance imaging (fMRI) data. However, few of them can effectively identify the connectivity network structure due to different defects. In this paper, a new algorithm is developed to infer the effective connectivity between different brain regions by combining artificial immune algorithm (AIA) with the Bayes net method, named as AIAEC.