U-shaped convolutional transformer GAN with multi-resolution consistency loss for restoring brain functional time-series and dementia diagnosis

IntroductionThe blood oxygen level-dependent (BOLD) signal derived from functional neuroimaging is commonly used in brain network analysis and dementia diagnosis. Missing the BOLD signal may lead to bad performance and misinterpretation of findings when analyzing neurological disease. Few studies have focused on the restoration of brain functional time-series data.MethodsIn this paper, a novel U-shaped convolutional transformer GAN (UCT-GAN) model is proposed to restore the missing brain functional time-series data. The proposed model leverages the power of generative adversarial networks (GANs) while incorporating a U-shaped architecture to effectively capture hierarchical features in the restoration process. Besides, the multi-level temporal-correlated attention and the convolutional sampling in the transformer-based generator are devised to capture the global and local temporal features for the missing time series and associate their long-range relationship with the other brain regions. Furthermore, by introducing multi-resolution consistency loss, the proposed model can promote the learning of diverse temporal patterns and maintain consistency across different temporal resolutions, thus effectively restoring complex brain functional dynamics.ResultsWe theoretically tested our model on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and our experiments demonstrate that the proposed model outperforms existing methods in terms of both quantitative metrics and qualitative assessments. The model's ability to preserve the underlying topological structure of the brain functional networks during restoration is a particularly notable achievement.ConclusionOverall, the proposed model offers a promising solution for restoring brain functional time-series and contributes to the advancement of neuroscience research by providing enhanced tools for disease analysis and interpretation

Fusing Structural and Functional Connectivities using Disentangled VAE for Detecting MCI

Brain network analysis is a useful approach to studying human brain disorders because it can distinguish patients from healthy people by detecting abnormal connections. Due to the complementary information from multiple modal neuroimages, multimodal fusion technology has a lot of potential for improving prediction performance. However, effective fusion of multimodal medical images to achieve complementarity is still a challenging problem. In this paper, a novel hierarchical structural-functional connectivity fusing (HSCF) model is proposed to construct brain structural-functional connectivity matrices and predict abnormal brain connections based on functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI). Specifically, the prior knowledge is incorporated into the separators for disentangling each modality of information by the graph convolutional networks (GCN). And a disentangled cosine distance loss is devised to ensure the disentanglement's effectiveness. Moreover, the hierarchical representation fusion module is designed to effectively maximize the combination of relevant and effective features between modalities, which makes the generated structural-functional connectivity more robust and discriminative in the cognitive disease analysis. Results from a wide range of tests performed on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) database show that the proposed model performs better than competing approaches in terms of classification evaluation. In general, the proposed HSCF model is a promising model for generating brain structural-functional connectivities and identifying abnormal brain connections as cognitive disease progresses.Comment: 4 figure

DiffGAN-F2S: Symmetric and Efficient Denoising Diffusion GANs for Structural Connectivity Prediction from Brain fMRI

Mapping from functional connectivity (FC) to structural connectivity (SC) can facilitate multimodal brain network fusion and discover potential biomarkers for clinical implications. However, it is challenging to directly bridge the reliable non-linear mapping relations between SC and functional magnetic resonance imaging (fMRI). In this paper, a novel diffusision generative adversarial network-based fMRI-to-SC (DiffGAN-F2S) model is proposed to predict SC from brain fMRI in an end-to-end manner. To be specific, the proposed DiffGAN-F2S leverages denoising diffusion probabilistic models (DDPMs) and adversarial learning to efficiently generate high-fidelity SC through a few steps from fMRI. By designing the dual-channel multi-head spatial attention (DMSA) and graph convolutional modules, the symmetric graph generator first captures global relations among direct and indirect connected brain regions, then models the local brain region interactions. It can uncover the complex mapping relations between fMRI and structural connectivity. Furthermore, the spatially connected consistency loss is devised to constrain the generator to preserve global-local topological information for accurate intrinsic SC prediction. Testing on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, the proposed model can effectively generate empirical SC-preserved connectivity from four-dimensional imaging data and shows superior performance in SC prediction compared with other related models. Furthermore, the proposed model can identify the vast majority of important brain regions and connections derived from the empirical method, providing an alternative way to fuse multimodal brain networks and analyze clinical disease.Comment: 12 page

Rent-seeking decisions of the main participants in construction projects based on evolutionary-game and system dynamics

The performance of a construction project can be severely harmed by its participants’ rent-seeking. In order to prevent such attempt, this research integrates the evolutionary game theory with system dynamics method to simulate the impact of the change of some factors that may cause/reduce rent-seeking. Based on the analysis of the behavioral characteristics and interactive relationships of the main participants (the owner, supervisor, and contractor), an evolutionary game model is constructed and simulated with the method of system dynamics based on the replication dynamic equation of the mixed strategy solution of the three-party static game model. By assigning the parameters of project scale, supervision likelihood, supervision success rate, supervision cost, and penalty intensity, the interaction mechanism of the participants on each factor is revealed through a case-based simulation. The results show that the impacts of these factors on participants’ rent-seeking decisions are significantly different. Furthermore, some management suggestions are provided to prevent rent-seeking for project owner according to the research conclusions. This research can help the project owners take proper measures to prevent rent-seeking of the supervisors and the contractors to improve the performances of the projects

The Environmental Influencing Factors of the Realization of Engineering Construction Harmony from the Perspective of Ren–Shi–Wu: Evidence from China

Engineering construction involves many internal factors and external environmental factors, resulting in conflict or uncoordinated problems in engineering management. The harmonious management of engineering construction is the process of coordinating and solving the contradiction between construction elements and the problems between them and the external environment. The connotations of three subsystems of engineering harmony, namely, Ren harmony (RH), Wu harmony (WH), and Shi harmony (SH), are defined, and the system architecture of engineering harmony is constructed. Then, a hypothetical model is proposed to deeply explore the impacts of subsystems such as Ren harmony, Wu harmony, and Shi harmony on engineering harmony, as well as the moderating effects of the natural ecology, social humanities, and political economy on engineering harmony. The results show that (1) natural ecology has a significant promotion effect on RH, SH, and engineering harmony; (2) social humanities have a significant enhancement effect on SH and engineering harmony; and (3) political economy does not play a significant role in any process. “Engineering harmony” is used to measure the effectiveness of engineering management, and a scientific scale is used to reflect this index. It provides a new idea for theoretical exploration and practical guidance in engineering construction management

Engineering harmony under multi-constraint objectives: the perspective of meta-analysis

Harmony is the process of coordinated development between the elements, subsystems and the environment in each Engineering stage of the engineering implementation. Quality, duration, cost and risk are the key factors to achieve engineering harmony. Clarifying the influencing factors of engineering harmony and its mechanism can improve the possibility of success. The meta-analysis method is used to carry out a restudy of existing researches of engineering harmony. First, quality, duration, cost and risk are selected as the variables of achieving engineering harmony. Second, the paper collects 29 existing researches including many countries and regions around the world on the relationship between the variables and engineering harmony. Third, each value is calculated and corrected according to literature coding. Forth, publication deviation and total effect test are checked. Finally, the research conclusions and engineering management implications are given based on the results of meta-analysis. The results show that quality objective, duration objective, cost objective and risk management objective all have positive impact on achieving engineering harmony. The engineering type has no regulatory effect on positive impact of the duration objective and cost objective, but has regulatory effect on positive impact of the quality objective and risk management objective on the engineering harmony. First published online 23 January 202

Alzheimer’s Disease Prediction via Brain Structural-Functional Deep Fusing Network

Fusing structural-functional images of the brain has shown great potential to analyze the deterioration of Alzheimer’s disease (AD). However, it is a big challenge to effectively fuse the correlated and complementary information from multimodal neuroimages. In this work, a novel model termed cross-modal transformer generative adversarial network (CT-GAN) is proposed to effectively fuse the functional and structural information contained in functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI). The CT-GAN can learn topological features and generate multimodal connectivity from multimodal imaging data in an efficient end-to-end manner. Moreover, the swapping bi-attention mechanism is designed to gradually align common features and effectively enhance the complementary features between modalities. By analyzing the generated connectivity features, the proposed model can identify AD-related brain connections. Evaluations on the public ADNI dataset show that the proposed CT-GAN can dramatically improve prediction performance and detect AD-related brain regions effectively. The proposed model also provides new insights into detecting AD-related abnormal neural circuits