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. In this paper, we propose a multimodal causal adversarial network for DEC learning, named MCAN. The MCAN contains two modules: multimodal causal generator and multimodal causal discriminator. First, MCAN employs a multimodal causal generator with an attention-guided layer to produce a posterior signal and output a set of DEC networks. Then, the proposed method uses a multimodal causal discriminator to unsupervised calculate the joint gradient, which directs the update of the whole network. The experimental results on simulated data sets show that MCAN is superior to other state-of-the-art methods in learning the network structure of DEC and can effectively estimate the brain states. The experimental results on real data sets show that MCAN can better reveal abnormal patterns of brain activity and has good application potential in brain network analysis.

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. An actor-critic framework with an encoder-decoder model was adopted as the actor network. The encoder utilizes a transformer model; the decoder employs a bidirectional long short-term memory network with attention. An FGEC network was constructed for the enrolled participants per fMRI scan, including 65 patients with tinnitus and 28 control participants healthy at the enrollment time. After 6 months of sound therapy for tinnitus and prospective follow-up, fMRI data were acquired again and retrospectively categorized into an effective group (EG) and an ineffective group (IG) according to the treatment effect. Compared with FC and EC, the FGECRL method demonstrated better accuracy in discriminating between different groups, highlighting the advantage of FGECRL in identifying brain network features. For the FGEC network of the EG and IG per state (before and after treatment) and healthy controls, effective therapy is characterized by a similar pattern of FGEC network between patients with tinnitus after treatment and healthy controls. Deactivated information output in the motor network, somatosensory network, and medioventral occipital cortex may biologically indicate effective treatment. The maintenance of decreased EC in the primary auditory cortex may represent a failure of sound therapy, further supporting the Bayesian inference theory for tinnitus perception. The FGEC network can provide direct evidence for the mechanism of sound therapy in patients with tinnitus with distinct outcomes.

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. Additionally, fMRI datasets have limited sample sizes, which significantly constrains the development of these methods. In this paper, we propose a novel brain effective connectivity estimation method based on causal autoencoder with meta-knowledge transfer, called MetaCAE. The proposed approach employs a causal autoencoder (CAE) to extract causal dependencies from non-stationary fMRI time series, and leverages meta-knowledge transfer to improve the estimation accuracy on small-sample data. More specifically, MetaCAE first employs a temporal convolutional encoder to extract non-stationary temporal information from fMRI time series. Then it uses a structural equation model-based decoder to decode causal relationships between brain regions. Finally, it utilizes a model-agnostic meta-learning method to learn the meta-knowledge of the shared brain effective connectivity among different subjects, and transfers the meta-knowledge to the CAE to enhance its estimation ability on small-sample fMRI data. Comprehensive experiments on both simulated and real-world data demonstrate the efficacy of MetaCAE in estimating brain effective connectivity.

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. In this paper, we propose a parallel ant colony optimization algorithm to learn causal biological networks from biological signal data, called PACO. Specifically, PACO first maps the construction of CBNs to ants, then searches for CBNs in parallel by simulating multiple groups of ants foraging, and finally obtains the optimal CBN through pheromone fusion and CBNs fusion between different ant colonies. Extensive experimental results on simulation data sets as well as two real-world data sets, the fMRI signal data set and the Single-cell data set, show that PACO can accurately and efficiently learn CBNs from biological signal data.

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. In detail, AT-EC first employs an amortization transformer to model the dynamics of fMRI time series and infer brain effective connectivity across different subjects, which can train an amortized model that leverages the shared knowledge from different subjects. Then, an assisted learning mechanism based on functional connectivity is designed to assist the estimation of the brain effective connectivity network. Experimental results on both simulated and real-world data demonstrate the efficacy of our method.

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. Sixty-four participants were enrolled in this study: eighteen patients with tinnitus were categorized into the effective group, twenty-two patients into the ineffective group, and twenty-four healthy participants into the healthy control group. We acquired resting-state functional magnetic resonance images prior to sound therapy and constructed an effective connectivity network of the three groups using an artificial bee colony algorithm and transfer entropy. The key feature of patients with tinnitus was the significantly increased signal output of the sensory network, including the auditory, visual, and somatosensory networks, and parts of the motor network. This provided critical insights into the gain theory of tinnitus development. The altered pattern of functional information orchestration, represented by a higher degree of hypervigilance-driven attention and enhanced multisensory integration, may explain poor clinical outcomes. The activated gating function of the thalamus is one of the key factors for a good prognosis in tinnitus treatment. We developed a novel method for analyzing effective connectivity, facilitating an understanding of the tinnitus mechanism and treatment outcome expectation based on the direction of information flow.

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. Thus, how to picture and grasp deeper features of brain ECNs from functional magnetic resonance imaging (fMRI) data is currently an important and active research area of the human brain connectome. In this survey, we first show some typical applications and analyze existing challenging problems in learning brain ECNs from fMRI data. Second, we give a taxonomy of ECN learning methods from the perspective of computational science and describe some representative methods in each category. Third, we summarize commonly used evaluation metrics and conduct a performance comparison of several typical algorithms both on simulated and real datasets. Finally, we present the prospects and references for researchers engaged in learning ECNs.

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. In this article, we propose a novel method for learning brain ECN based on spatiotemporal graph convolutional models (STGCM), named STGCMEC, in which we first adopt the temporal convolutional network to extract the deep temporal features of fMRI data and utilize the graph convolutional network to update the spatial features of each brain region by aggregating information from neighborhoods, which makes the features of brain regions more discriminative. Then, based on such features of brain regions, we design a joint loss function to guide STGCMEC to learn the brain ECN, which includes a task prediction loss and a graph regularization loss. The experimental results on a simulated dataset and a real Alzheimer's disease neuroimaging initiative (ADNI) dataset show that the proposed STGCMEC is able to better learn brain ECN compared with some state-of-the-art methods.

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) via 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. During the operation, the left atrium was adjacent to the confluence part. We resected a portion of the adjacent left atrium to create an inlet of the pulmonary veins and used two autologous pericardial patches to reconstruct a tunnel directing flow from the left pulmonary veins to the surgically created inlet in the adjacent left atrium, and another upper tunnel directing flow from the LSVC to the dilated CS. Pulmonary CTA confirmed that both PAPVR flow to LA and LSVC flow to RA were unobstructed. At a 12-month follow-up, the patient was asymptomatic. No supraventricular arrhythmia was detected. We would like to present this additional technique to our armamentarium to treat PAPVR in combination with LSVC.

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. In this article, we propose a novel score function for inferring effective connectivity from fMRI data based on the conditional entropy and transfer entropy (TE) between brain regions. The new score employs the TE to capture the temporal information and can effectively infer connection directions between brain regions. Experimental results on both simulated and real-world data demonstrate the efficacy of our proposed score function.

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. We harvested the left atrial appendage (LAA) en bloc. After resection of the infected and abnormal tissues, the resected LAA was used to reconstruct the RA. The infected mitral valve was replaced with a mechanical valve without any accident. Postoperative echocardiography showed that the RA had a supple shape, with no kinking.

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. The proposed framework employs the generator that consists of a set of effective connectivity generators based on recurrent neural networks to generate the fMRI time series of each brain region, and uses the discriminator to distinguish between the joint distributions of the real and generated fMRI time series. When the model is well-trained and generated fMRI data is similar to real fMRI data, EC-RGAN outputs the effective connectivity by means of the causal parameters of the effective connectivity generators. Experimental results on both simulated and real-world fMRI time series data demonstrate the efficacy of our proposed framework.

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. Autologous pericardium was used to repair the valve. The pathologic analysis confirmed the mass as a lipoma. In the follow-up, no recurrent mass or symptom was found.

Gene-modified Exosomes Protect the Brain Against Prolonged Deep Hypothermic Circulatory Arrest.

BACKGROUND: Neurologic deficit remains a major complication after cardiovascular surgeries with deep hypothermic circulatory arrest (DHCA). We hypothesized that exosomes derived from bone marrow mesenchymal stem cells (MSCs) may conduct cerebral protection against prolonged DHCA in rats, and overexpressing microRNA-214 (miR-214) may further enhance the neuroprotection. METHODS: Cultured MSCs were transfected with lentivirus vectors containing pre-miR-214 or control vectors. Exosomes were isolated by centrifugation. The DHCA was conducted for 60 minutes when the pericranial temperature was cooled to 18°C. Exosomes from MSCs, MSCs transfected with control vectors, or pre-miR-214 were administered by intracerebroventricular injection 1 day before DHCA. RESULTS: Transfection of pre-miR-214 significantly enhanced the miR-214 expression in exosomes from MSCs. All exosome-pretreating groups exhibited lower levels of interleukin-1ß and tumor necrosis factor-α, higher capillary density, more significant neurogenesis and angiogenesis, and more normal neurons in the hippocampus than those of the control group. Exosome pretreatment markedly improved the spatial learning and memory function and vestibulomotor function. Compared with exosomes from MSCs or MSCs transfected with control vectors, miR-214-enriched exosomes remarkably enhanced the miR-214 level and expressions of phosphor-protein kinase B and Bcl-2, inhibited expressions of phosphate and tension homology, Bcl-2 interacting mediator of cell death, Bcl-2-associated X protein, and cleaved Caspase-3, and increased the number of survival neurons. Significantly better neurologic functions were also detected in rats pretreated with miR-214-enriched exosomes. CONCLUSIONS: Exosomes from MSCs conduct powerful neuroprotection against cerebral injury induced by DHCA, which can be further enhanced by genetic modification of the exosomes to overexpress miR-214.

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. Two patients were diagnosed as Behcet's disease. Bicuspid aortic valve was detected in six patients. Mean follow up was 18.9±12.2 months. RESULTS: Positive blood culture was found in 10 (71.4%) patients: 3 of Abiotrophia defective (21.4%). The mean cardiopulmonary bypass (CPB) time was 154.5±25.9 minutes and the mean cross-clamp time was 116.8±18.2 minutes. One patient died of multiple organ failure (7.1%) 60 days after surgery. There was 1 (7.1%) case of stroke, 1 (7.1%) of atrioventricular block with pacemaker implantation, 1 (7.1%) of reoperation for bleeding. There was no late death during follow-up. The survival at 3 years was 92.9%. Freedom from reoperation at 1, 2, and 3 years was 100%, 100%, and 100% during follow-up, respectively. CONCLUSIONS: The double-valve replacement and AMC reconstruction (the Commando procedure) is an effective technique in complex heart valve disease. The short- and mid-term results with this technique are optimal, with a very low in-hospital mortality and nearly 100% of long-term survival during follow-up.

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. Adeno-associated viral vectors containing growth arrest-specific 5 small interfering RNA or negative control small interfering RNA were administered by intracerebroventricular injection 14 days before deep hypothermic circulatory arrest. Expressions of growth arrest-specific 5, microRNA-23a, phosphate and tension homology, Bcl-2-associated X protein, Bcl-2, phospho-protein kinase B, protein kinase B, and cleaved caspase-3 in the hippocampus were measured by quantitative reverse transcription polymerase chain reaction and Western blot. Spatial learning and memory functions were evaluated by the Morris water maze test. The hippocampus was harvested for histologic examinations and terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick-end labeling staining. RESULTS: Luciferase assay showed that growth arrest-specific 5 targeted and inhibited microRNA-23a expression. After deep hypothermic circulatory arrest, hippocampal growth arrest-specific 5 expression was significantly enhanced with a robust decrease of hippocampal microRNA-23a expression. Small interfering RNA growth arrest-specific 5 significantly inhibited growth arrest-specific 5 expression and enhanced microRNA-23a expression in the hippocampus, accompanied with decreases of phosphate and tension homology and Bcl-2-associated X protein expression, and increases of Bcl-2 expression and phospho-protein kinase B/protein kinase B ratio. Growth arrest-specific 5 knockdown inhibited neuronal apoptosis, attenuated histologic damages, and increased the number of surviving neurons in the hippocampus. Spatial learning and memory functions after deep hypothermic circulatory arrest were also markedly improved by growth arrest-specific 5 inhibition. CONCLUSIONS: Inhibition of large noncoding RNA growth arrest-specific 5 can provide a powerful cerebroprotection against deep hypothermic circulatory arrest, which may be mediated through microRNA-23a/phosphate and tension homology pathway.

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. First, VACOEC uses the voxel activation information to measure the independence between each pair of brain regions and effectively restricts the space of candidate solutions, which makes many unnecessary searches of ants be avoided. Then, by combining the global score increase of a solution with the voxel activation information, a new heuristic function is designed to guide the process of ACO to search for the optimal solution. The experimental results on simulated datasets show that the proposed method can accurately and efficiently identify the directions of the brain EC networks. Moreover, the experimental results on real-world data show that patients with Alzheimers disease (AD) exhibit decreased effective connectivity not only in the intra-network within the default mode network (DMN) and salience network (SN), but also in the inter-network between DMN and SN, compared with normal control (NC) subjects. The experimental results demonstrate that VACOEC is promising for practical applications in the neuroimaging studies of geriatric subjects and neurological patients.