Modeling Functional Brain Networks for ADHD via Spatial Preservation-based Neural Architecture Search.

Modeling functional brain networks (FBNs) for attention deficit hyperactivity disorder (ADHD) has sparked significant interest since the abnormal functional connectivity is discovered in certain functional magnetic resonance imaging (fMRI)-based brain regions compared to typical developmental control (TC) individuals. However, existing models for modeling FBNs generally use dimensionality reduction techniques to process the high dimensional input data, which results in confusion and an inaccurate representation of voxel interactions between spatially close brain regions, causing misdiagnosis of the disease. To address these issues, we propose a spatial preservation-based neural architecture search (SP-NAS) for FBNs modeling in ADHD. The main work includes three-fold: 1) A spatial preservation module is designed to embed original spatial information into dimensionality reduction data, addressing the challenge of a large number of parameters in the original data and mitigating disease misdiagnosis resulting from voxel confusion between different brain regions caused by dimensionality reduction. 2) A search space using more suitable search operations is constructed to efficiently extract spatial-temporal interaction characteristics of fMRI data in ADHD while narrowing the search space. 3) Cross-regional association differences between ADHD and TC groups are explored for ADHD auxiliary diagnosis since the abnormal activation regions of ADHD relative to TC on the brain regions and the abnormal connectivity between the lesion brain regions are identified. Model validation results on the ADHD-200 dataset show that the FBNs obtained from SP-NAS not only achieve competitive results in ADHD diagnosis but also reveal abnormal connections in the lesion regions of ADHD consistent with clinical diagnosis.

ADHD diagnosis guided by functional brain networks combined with domain knowledge.

Utilizing functional magnetic resonance imaging (fMRI) to model functional brain networks (FBNs) is increasingly prominent in attention-deficit/hyperactivity disorder (ADHD) research, revealing neural impact and mechanisms through the exploration of activated brain regions. However, current FBNs-based methods face two major challenges. The primary challenge stems from the limitations of existing modeling methods in accurately capturing both regional correlations and long-distance dependencies (LDDs) within the dynamic brain, thereby affecting the diagnostic accuracy of FBNs as biomarkers. Additionally, limited sample size and class imbalance also pose a challenge to the learning performance of the model. To address the issues, we propose an automated diagnostic framework, which integrates modeling, multimodal fusion, and classification into a unified process. It aims to extract representative FBNs and efficiently incorporate domain knowledge to guide ADHD classification. Our work mainly includes three-fold: 1) A multi-head attention-based region-enhancement module (MAREM) is designed to simultaneously capture regional correlations and LDDs across the entire sequence of brain activity, which facilitates the construction of representative FBNs. 2) The multimodal supplementary learning module (MSLM) is proposed to integrate domain knowledge from phenotype data with FBNs from neuroimaging data, achieving information complementarity and alleviating the problems of insufficient medical data and unbalanced sample categories. 3) An ADHD automatic diagnosis framework guided by FBNs and domain knowledge (ADF-FAD) is proposed to help doctors make more accurate decisions, which is applied to the ADHD-200 dataset to confirm its effectiveness. The results indicate that the FBNs extracted by MAREM perform well in modeling and classification. After with MSLM, the model achieves accuracy of 92.4%, 74.4%, and 80% at NYU, PU, and KKI, respectively, demonstrating its ability to effectively capture crucial information related to ADHD diagnosis. Codes are available at https://github.com/zhuimengxuebao/ADF-FAD.

Experimental and Numerical Study on Hysteretic Behavior of Frictional Energy Dissipation Steel Truss.

In this study, the hysteretic behavior of a novel frictional energy dissipation steel truss (FED-ST) is examined. The proposed FED-ST incorporates a friction damper with brass as the friction material into the top chord of traditional truss to improve the seismic performance of the staggered truss framing systems. A FED-ST specimen with a scale of 1:2.5 was subjected to a hysteresis test. The hysteretic behavior, ductility, and energy dissipation capability were analyzed considering the test findings. It is demonstrated that the FED-ST specimen has favorable ductility and an energy dissipation capacity that is 7.3 times more than that of a conventional truss specimen. The test findings were then used to compare and validate a finite element (FE) model. The FE analysis results are in strong agreement with the test results, demonstrating the validity of the modeling approach. To further investigate the impact of the cover plate width on the behavior of the FED-ST, preliminary parametric research was also carried out.

A Quantitative Analysis of Country Relations in Foreign Direct Investment.

According to Dunning's eclectic theory, the location advantages play a key role in international investment mode choice, in which the country relations are important determinants. In some previous studies, the country relations and another bilateral factor, the country distance, are often confused, which can result in the inconsistency of conclusions. And excepting political factors, the economic dependence and other relations are insufficiently considered in the literature. This article makes a distinction between relation and distance, and puts forward a simplified analytical framework, the indicator system, and some quantitative methods for country relations. The indicators, including political, economic, and social factors, can better satisfy the horizontal analysis of the outbound investment. The economic and social indicators are determined by the magnitude of interaction as well as the share in the home country, and hence, the evaluation results can reflect the differences between the two countries. Finally, by evaluating the relations of other BRICS countries with China, the rationality is illustrated.