Recently, functional network connectivity (FNC) has been extended from static to dynamic analysis to explore the time-varying functional organization of brain networks. Nowadays, a majority of ...dynamic FNC (dFNC) analysis frameworks identified recurring FNC patterns with linear correlations based on the amplitude of fMRI time series. However, the brain is a complex dynamical system and phase synchronization provides more informative measures. This paper proposes a novel framework for the prediction/classification of behaviors and cognitions based on the dFNCs derived from phase locking value. When applying to the analysis of fMRI data from Human Connectome Project (HCP), four dFNC states are identified for the study of sleep quality. State 1 exhibits most intense phase synchronization across the whole brain. States 2 and 3 have low and weak connections, respectively. State 4 exhibits strong phase synchronization in intra and inter-connections of somatomotor, visual and cognitive control networks. Through the two-sample t-test, we reveal that for the group with bad sleep quality, state 4 shows decreased phase synchronization within and between networks such as subcortical, auditory, somatomotor and visual, but increased phase synchronization within cognitive control network, and between this network and somatomotor/visual/default-mode/cerebellar networks. The networks with increased phase synchronization in state 4 behave oppositely in state 2. Group differences are absent in state 3, and weak in state 1. We establish a prediction model by linear regression of FNC against sleep quality, and adopt a support vector machine approach for the classification. We compare the performance between conventional FNC and PLV-based dFNC with cross-validation. Results show that the PLV-based dFNC significantly outperforms the conventional FNC in terms of both predictive power and classification accuracy. We also observe that combining static and dynamic features does not significantly improve the classification over using dFNC features alone. Overall, the proposed approach provides a novel means to assess dFNC, which can be used as brain fingerprints to facilitate prediction and classification.
In response to the global climate change and the need for green and low-carbon development, hydrogen energy has been recognized as a clean energy source that can achieve carbon neutrality unlike ...fossil fuels. As a country with a shortage of energy resources, the development of hydrogen energy is of significant importance for China to adjust its energy structure and accelerate the new era of energy transformation. Based on the development of China's hydrogen energy industry, this paper elaborates on the current status and development trends of key technologies in the entire industrial chain of hydrogen energy in various stages including production, storage, transportation, and application, and identifies the problems and challenges of hydrogen energy development. The paper focuses on the analysis of hydrogen storage and transportation application scenarios and clarifies the selection of hydrogen storage and transportation technologies in different scenarios. To achieve healthy development of China's hydrogen energy industry, it is necessary to strengthen top-level design, make strategic planning, encourage large-scale state-owned energy enterprises to play a leading role, promote the development of the entire industry chain, increase technological research and development efforts, prevent the risk of core technology constraints, and vigorously promote the application of hydrogen energy to realize the construction of a hydrogen energy society.
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•In response to the global climate change and the need for green and low-carbon development, hydrogen energy has been recognized as a clean energy source that can achieve carbon neutrality unlike fossil fuels. As a country with a shortage of energy resources, the development of hydrogen energy is of significant importance for China to adjust its energy structure and accelerate the new era of energy transformation. Based on the development of China's hydrogen energy industry, this paper elaborates on the current status and development trends of key technologies in the entire industrial chain of hydrogen energy in various stages including production, storage, transportation, and application, and identifies the problems and challenges of hydrogen energy development. The paper focuses on the analysis of hydrogen storage and transportation application scenarios and clarifies the selection of hydrogen storage and transportation technologies in different scenarios. To achieve healthy development of China's hydrogen energy industry, it is necessary to strengthen top-level design, make strategic planning, encourage large-scale state-owned energy enterprises to play a leading role, promote the development of the entire industry chain, increase technological research and development efforts, prevent the risk of core technology constraints, and vigorously promote the application of hydrogen energy to realize the construction of a hydrogen energy society.
•Combine the group data with limited samples can improve the quality of directed acyclic graph estimation.•Lower global and local efficiency of the directed network found in schizophrenia ...patients.•Directed network induced features can better differentiate schizophrenia patients from healthy controls.
Functional connectivity (FC) between brain region has been widely studied and linked with cognition and behavior of an individual. FC is usually defined as the correlation or partial correlation of fMRI blood oxygen level-dependent (BOLD) signals between two brain regions. Although FC has been effective to understand brain organization, it cannot reveal the direction of interactions. Many directed acyclic graph (DAG) based methods have been applied to study the directed interactions but their performance was limited by the small sample size while high dimensionality of the available data. By enforcing group regularization and utilizing samples from both case and control groups, we propose a joint DAG model to estimate the directed FC. We first demonstrate that the proposed model is efficient and accurate through a series of simulation studies. We then apply it to the case-control study of schizophrenia (SZ) with data collected from the MIND Clinical Imaging Consortium (MCIC). We have successfully identified decreased functional integration, disrupted hub structures and characteristic edges (CtEs) in SZ patients. Those findings have been confirmed by previous studies with some identified to be potential markers for SZ patients. A comparison of the results between the directed FC and undirected FC showed substantial differences in the selected features. In addition, we used the identified features based on directed FC for the classification of SZ patients and achieved better accuracy than using undirected FC or raw features, demonstrating the advantage of using directed FC for brain network analysis.
Functional network connectivity (FNC) within the human brain as measured by functional neuroimaging data has attracted increasing attention and has been widely used to study brain development and ...mental disorders. A major focus of the research is analyzing the properties of functional connectivity (FC), including its temporal dynamic property, spatial directionality, and individual variation. The projects contain three sub-projects: assessing the individual variability of the whole brain FC in the group (age, gender, experiment paradigms), estimating dynamic FNC patterns from resting-state functional magnetic resonance imaging (rs-fMRI) time series, and detecting abnormal directed FC in schizophrenia (SZ) patients.To address challenges in the current problem setting, this thesis proposes novel analytical models/pipelines for each of the projects. In the first project, we investigate the individual variance in the group analysis of FC in a unified linear mixed effects modeling(LMM) framework. In the second project, we focus on the windowless dynamic functional connectivity estimation using rs-fMRI data in a high-dimensional setting, in which the number of brain regions is less than the number of observations. In the third project, we focus on the directed acyclic graph(DAG) estimation using task fMRI data when the sample size is limited but the potential group structure is favorable.Through simulation studies and large neuroimaging data sets, the proposed analysis schemes are validated with identical findings from previous studies. By highlighting the prominent brain regions and connections, it can lead to a new understanding of brain function. In addition, we follow a reproducible and open-source strategy and all of our proposed methods and tools are widely available to the scientific community via the release of data and codes.
The Shunbei ultra-deep carbonate strike-slip fault-controlled reservoir in Tarim Basin is rich in reserves. The strike-slip faults and the natural structural fractures are the main storage space and ...flow channels of hydrocarbon resources. Therefore, studying the natural fracture development characteristics in this kind of reservoir is of great significance. The lithology of the Middle and Lower Ordovician strata in the Shunbei area is limestone and dolomite, including packstone, wackestone, grainstone, boundstone, lime mudstone, silicified limestone and silty-fine crystalline dolomite, medium-coarse crystalline dolomite. Based on the statistics of fracture density and brittleness index of core samples, it is found that in limestone, lime mudstone has the highest brittleness index, while boundstone has the lowest; in dolomite, the brittleness index of medium-coarse crystalline dolomite is higher than that of silty-fine crystalline dolomite. The natural structural fracture density has an obvious positive correlation with the rock brittleness index. The brittleness index of dolomite is generally higher than that of limestone. When the brittleness index is the same, dolomite has superior porosity and permeability, whereas limestone has a stronger capacity to fracture. Those kinds of highly brittle layers in carbonate reservoirs are more likely to be geological sweet spots in the Shunbei area.
The combination of multimodal imaging and genomics provides a more comprehensive way for the study of mental illnesses and brain functions. Deep network-based data fusion models have been developed ...to capture their complex associations, resulting in improved diagnosis of diseases. However, deep learning models are often difficult to interpret, bringing about challenges for uncovering biological mechanisms using these models. In this work, we develop an interpretable multimodal fusion model to perform automated diagnosis and result interpretation simultaneously. We name it Grad-CAM guided convolutional collaborative learning (gCAM-CCL), which is achieved by combining intermediate feature maps with gradient-based weights. The gCAM-CCL model can generate interpretable activation maps to quantify pixel-level contributions of the input features. Moreover, the estimated activation maps are class-specific, which can therefore facilitate the identification of biomarkers underlying different groups. We validate the gCAM-CCL model on a brain imaging-genetic study, and demonstrate its applications to both the classification of cognitive function groups and the discovery of underlying biological mechanisms. Specifically, our analysis results suggest that during task-fMRI scans, several object recognition related regions of interests (ROIs) are activated followed by several downstream encoding ROIs. In addition, the high cognitive group may have stronger neurotransmission signaling while the low cognitive group may have problems in brain/neuron development due to genetic variations.
Estimating dynamic functional network connectivity (dFNC) of the brain from functional magnetic resonance imaging (fMRI) data can reveal both spatial and temporal organization and can be applied to ...track the developmental trajectory of brain maturity as well as to study mental illness. Resting state fMRI (rs-fMRI) is regarded as a promising task since it reflects the spontaneous brain activity without an external stimulus. The sliding window method has been successfully used to extract dFNC but typically assumes a fixed window size. The hidden Markov model (HMM) based method is an alternative approach for estimating time-varying connectivity. In this paper, we propose a sparse HMM based on Gaussian HMM and Gaussian graphical model (GGM). In this model, the time-varying neural processes are represented as discrete brain states which are described with functional connectivity networks. By enforcing the sparsity on the precision matrix, we can get interpretable connectivity between different functional regions. The optimization of our model can be realized with the expectation maximization (EM) and graphical least absolute shrinkage and selection operator (glasso) algorithms. The proposed model is validated on both simulated blood oxygenation-level dependent (BOLD) time series and rs-fMRI data. Results indicate that the proposed model can capture both stationary and abrupt brain activity fluctuations. We also compare dFNC patterns between children and young adults from the Philadelphia Neurodevelopmental Cohort (PNC) study. Both spatial and temporal behavior of the dFNC are analyzed and compared. The results provide insight into the developmental trajectory across childhood and motivate further research on brain connectivity.
Graph-based semi-supervised learning (GSSL) attracts considerable attention in recent years. The performance of a general GSSL method relies on the quality of Laplacian weighted graph (LWR) composed ...of the similarity imposed on input examples. A key for constructing an effective LWR is on the proper selection of the neighborhood size K or ε on the construction of KNN graph or ε-neighbor graph on training samples, which constitutes the fundamental elements in LWR. Specifically, too large K or ε will result in “shortcut” phenomenon while too small ones cannot guarantee to represent a complete manifold structure underlying data. To this issue, this study attempts to propose a method, called adaptive Laplacian graph trimming (ALGT), to make an automatic tuning to cut improper inter-cluster shortcut edges while enhance the connection between intra-cluster samples, so as to adaptively fit a proper LWR from data. The superiority of the proposed method is substantiated by experimental results implemented on synthetic and UCI data sets.
•A method which can adaptively fit a proper Laplacian weighted graph from data.•A penalty helping cut inter-cluster shortcuts and enhance intra-cluster connections.•A graph-based SSL model is less sensitive to neighborhood size by integrating ALGT.•Superiority of ALGT is verified by experimental results on synthetic and UCI data.
Functional connectivity (FC) within the human brain evaluated through functional magnetic resonance imaging (fMRI) data has attracted increasing attention and has been employed to study the ...development of the brain or health conditions of the brain. Many different approaches have been proposed to estimate FC from fMRI data, whereas many of them rely on an implicit assumption that functional connectivity should be static throughout the fMRI scan session. Recently, the fMRI community has realized the limitation of assuming static connectivity and dynamic approaches are more prominent in the resting state fMRI (rs-fMRI) analysis. The sliding window technique has been widely used in many studies to capture network dynamics, but has a number of limitations. In this study, we apply a time-varying graphical lasso (TVGL) model, an extension from the traditional graphical lasso, to address the challenge, which can greatly improve the estimation of FC. The performance of estimating dynamic FC is evaluated with the TVGL through both simulated experiments and real rs-fMRI data from the Philadelphia Neurodevelopmental Cohort project. Improved performance is achieved over the sliding window technique. In particular, group differences and transition behaviors between young adults and children are investigated using the estimated dynamic connectivity networks, which help us to better unveil the mechanisms underlying the evolution of the brain over time.
Graph convolutional deep learning has emerged as a promising method to explore the functional organization of the human brain in neuroscience research. This paper presents a novel framework that ...utilizes the gated graph transformer (GGT) model to predict individuals' cognitive ability based on functional connectivity (FC) derived from fMRI. Our framework incorporates prior spatial knowledge and uses a random-walk diffusion strategy that captures the intricate structural and functional relationships between different brain regions. Specifically, our approach employs learnable structural and positional encodings (LSPE) in conjunction with a gating mechanism to efficiently disentangle the learning of positional encoding (PE) and graph embeddings. Additionally, we utilize the attention mechanism to derive multi-view node feature embeddings and dynamically distribute propagation weights between each node and its neighbors, which facilitates the identification of significant biomarkers from functional brain networks and thus enhances the interpretability of the findings. To evaluate our proposed model in cognitive ability prediction, we conduct experiments on two large-scale brain imaging datasets: the Philadelphia Neurodevelopmental Cohort (PNC) and the Human Connectome Project (HCP). The results show that our approach not only outperforms existing methods in prediction accuracy but also provides superior explainability, which can be used to identify important FCs underlying cognitive behaviors.
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