Mapping Function Research Articles

Neural Correlates of Reactive Inhibition in Gambling Disorder: An FMRI Study with Transcranial Magnetic Stimulation (TMS)

Gambling Disorder (GD) is a condition characterized by a persistent and recurrent pattern of problematic gambling behavior. Despite significant advances in understanding the neurobiological correlates of Gambling Disorder phenomenological manifestation, there are still several unanswered questions regarding the pathophysiological mechanisms underlying GD. There is substantial evidence from fMRI studies on the role of pre-SMA stimulation in psychiatric conditions with impairments in response inhibition such as behavioral addictions on enhancing the activity of the brain circuitry involving the right inferior (IFG) and middle frontal gyrus (MFG) (two cortical areas are differentially associated with two distinct aspects of control inhibition). In this study we utilized task-related functional Magnetic Resonance Imaging (fMRI) to explore the impact of continuous Theta-Burst Stimulation (cTBS) on the pre-Supplementary Motor Area (pre-SMA) in patients with Gambling Disorder (GD). Four GD patients underwent cTBS on bilateral pre-SMA, administered with the MagVenture MagPro R30 stimulator with add-on theta-burst option (MagVenture INC.) using a Cool D-B80 figure-of-eight coil. cTBS consists of bursts of 3 pulses separated by 20 ms (i.e., 50 Hz) delivered repeatedly at theta frequency on the pre-SMA bilaterally. Changes in Functional Connectivity (FC) were assessed before and after Real or Sham treatment using CONN functional connectivity toolbox and Statistical Parametric Mapping (SPM). The study aimed to determine whether cTBS influences the functional connectivity between pre-SMA and right prefrontal areas, specifically the right Inferior Frontal Gyrus (rIFG) and right Middle Frontal Gyrus (rMFG), and whether these changes correlate with treatment outcomes. Results indicated that real cTBS treatment increased functional connectivity between pre-SMA and both rIFG and rMFG, suggesting enhanced control inhibition. This was associated with a reduction in Gambling Disorder symptom severity, assessed with the Pathological Gambling version of the Yale-Brown Obsessive-Compulsive Scale (PG-YBOCS) and the Gambling Urges Questionnaire (GUQ), indicating a treatment response. Conversely, sham cTBS did not elicit the same positive FC changes in the reactive control inhibition network, aligning with behavioral measures. The study highlighted the potential of cTBS on pre-SMA in modulating inhibitory control circuit. Overall, this preliminary investigation provides a foundation for future investigations into the neurobiological mechanisms underlying GD and the potential efficacy of TMS interventions.

Ethnokinesiology: towards a neuromechanical understanding of cultural differences in movement.

Each individual's movements are sculpted by constant interactions between sensorimotor and sociocultural factors. A theoretical framework grounded in motor control mechanisms articulating how sociocultural and biological signals converge to shape movement is currently missing. Here, we propose a framework for the emerging field of ethnokinesiology aiming to provide a conceptual space and vocabulary to help bring together researchers at this intersection. We offer a first-level schema for generating and testing hypotheses about cultural differences in movement to bridge gaps between the rich observations of cross-cultural movement variations and neurophysiological and biomechanical accounts of movement. We explicitly dissociate two interacting feedback loops that determine culturally relevant movement: one governing sensorimotor tasks regulated by neural signals internal to the body, the other governing ecological tasks generated through actions in the environment producing ecological consequences. A key idea is the emergence of individual-specific and culturally influenced motor concepts in the nervous system, low-dimensional functional mappings between sensorimotor and ecological task spaces. Motor accents arise from perceived differences in motor concept topologies across cultural contexts. We apply the framework to three examples: speech, gait and grasp. Finally, we discuss how ethnokinesiological studies may inform personalized motor skill training and rehabilitation, and challenges moving forward.This article is part of the theme issue 'Minds in movement: embodied cognition in the age of artificial intelligence'.

A Certificate-Less Distributed Key Management Scheme for Space Networks

The specificity and complexity of space networks render the traditional key management mechanism no longer applicable. The certificate-less-based distributed spatial network key management scheme proposed in this paper combines the characteristics of space networks, solving the problems regarding the difficulty of implementing centralized key management in space networks and the excessive overhead required for maintaining public key certificates by constructing a distributed key generation center and establishing strategies such as private key updates, master key component updates, and session key negotiation. This method also avoids the key escrow problem inherent in existing identity-based key management schemes. This scheme provides the DPKG construction method for space networks; designs the update strategy for the DPKG node’s master key sharing, providing a specific update algorithm; introduces the batch private key update mechanism; and uses the mapping function to evenly distribute the node’s update requests throughout the update time period, avoiding the problem of overly concentrated update requests. After analysis and simulation verification, it was proven that the scheme can meet the necessary security requirements, offering good stability and scalability.

Prediction of stable structure and unique charge transfer in Li–Pt intermetallic compounds under pressure

The exploration of new intermetallic compounds is of great significance for basic research and practical application. There is a huge electronegativity difference between Li and Pt, and the metals exhibit interesting and diverse properties; however, the structural behavior of Li–Pt intermetallics with different ratios under high pressure has not been systematically studied. In this study, an intelligent structure search method based on a particle swarm optimization algorithm combined with first-principles calculations was used to extensively explore the stable structures and unique conditions governing charge transfer in Li–Pt intermetallic compounds under different pressures. In addition to reproducing the known LiPt (P6‾m2) and Li2Pt (P6/mmm), the simulation was consistent with the experimental results. New phases were also found by calculation: LiPt3(Cmmm), Li2Pt (P3‾m1), and Li4Pt (I4/m) at ambient pressure; Li3Pt (Fm3‾m), Li4Pt (R3‾m), and Li5Pt (P6/mmm) at 10 GPa; and Li5Pt (P3‾m1) at 20 GPa. Bader charge analysis and electron localization function (ELF) mapping showed that the transition metal (Pt) atoms exhibit unusual oxidation states both under ambient and high pressure, and more electrons were localized on Pt as the Li content increased. The highest negative valence state was approximately −4. Intermetallic compounds LiPt3 (Cmmm) and Li2Pt (P6/mmm) were prepared successfully by arc melting furnace. The reliability of structure prediction method and pseudo potential selection was verified. This work demonstrates that tuning the pressure and stoichiometry is an effective means of forming novel, stable intermetallic compounds.

Semi-supervised action recognition with dynamic temporal information fusion

The most advanced semi-supervised models available are based on images for innovation, and the use of semi-supervised learning models augmented with temporal data for video-level action recognition still suffers from severe model mismatches, and the models are not sufficiently capable of capturing both local and global information about the action. Secondly the use of constant-threshold pseudo-labeling leads to low utilization of unlabeled data for difficult actions in the early stages of training, poor pseudo-labeling quality and affects recognition accuracy. To make the semi-supervised framework FixMatch more suitable for action recognition, we propose Time-Mixer and Dynamic Threshold, respectively. Time-Mixer explores complementary information between time sequences through the fusion of two-channel temporal context information. Dynamic Threshold utilizes a new core mapping function (Normal Distribution Function) to enhance pseudo-labeling quality. Extensive experiments were conducted on three action recognition datasets (Kinetics-400, UCF-101, and HMDB-51). Comprehensive experiments show that the performance of the semi-supervised model in action recognition improves considerably after using dynamic thresholding and temporal context information fusion, with a 14.4% improvement over the baseline and a 1.8% improvement over the TG (with a labeling rate of 10%) in UCF101, whereas an overall good performance is obtained for DTIF.

Unified framework for stochastic dynamic responses and system reliability analysis of long-span cable-stayed bridges under near-fault ground motions

Long-span cable-stayed bridges are complex systems with numerous components, and prone to failures of multiple components under near-fault ground motions with long-period velocity pulses. The evaluation of dynamic system reliability of cable-stayed bridges is an important challenging issue. In this work, a unified framework for simultaneously determining both the stochastic dynamic responses and system reliabilities of long-span cable-stayed bridges under near-fault stochastic ground motions is proposed based on direct probability integral method (DPIM) with adaptive strategy. Firstly, the 3-dimensional finite element model of typical cable-stayed bridge and the stochastic synthesis model of near-fault ground motions are established. Based on probability density integral equation, new formulas in input probability space of time-variant moments of response are derived. Then, DPIM with adaptive strategy is suggested to calculate the mean value and standard deviation of response via new formulas. Dynamic system reliability of cable-stayed bridge under near-fault stochastic ground motions is evaluated by establishing failure criteria of extreme value mappings of multiple performance functions. Finally, numerical results indicate that the long-period velocity pulse of near-fault motions imposes an important effect, resulting in large mean responses and failure of cables. Failure probability of cables is significantly increased with the occurrence of velocity pulses, which causes the decrease of system reliability of cable-stayed bridge. Thus, the randomness and velocity pulse effect of near-fault ground motions should be seriously considered for reliability-based design and safety assessment of cable-stayed bridges.

Calculation of key parameters of tropospheric mapping function based on random forest method

The tropospheric mapping functions (MFs) play a crucial role in estimating the delay of electromagnetic waves as they traverse the troposphere, particularly in space geodetic techniques like GNSS and VLBI, where these waves are the primary measurement tool. Currently, empirical models that rely on non-meteorological parameters are commonly used to calculate MFs, offering convenience but often yielding less accurate results in regions with volatile climates. Such inaccuracies can lead to errors in slant total delays reaching the meter-level, subsequently affecting other data products. Although authoritative bodies periodically release essential meteorological parameters for MF calculation or provide ready-made MF products, these often come with a delay of several days or require specific authorization. To address this, we introduced a method leveraging the random forest (RF) model to rapidly derive key MFs from surface observations. Experimental results indicate that the RF model significantly enhances the accuracy of MFs, with improvements exceeding 60% for the hydrostatic component and over 20% for the wet component, effectively eliminating seasonal systematic deviations; although it is somewhat inferior to the MFs from VMF3-FC, RF does not require an internet connection and can independently train and construct models, making it very suitable for independently operated systems. We further trained a series of RF models using varied sample spaces and analyzed their performance under different combinations of feature dimensions and time spans. This analysis suggests that an optimal balance exists when considering the challenges in acquiring sample data, training time, model size, computational efficiency, and accuracy.

Layout optimization of pore microstructure in fluid-saturated porous media using Galerkin decoupling technology and independent continuous mapping method (GDT-ICM)

A layout optimal design method combining Galerkin decoupling technology and independent continuous mapping method (GDT-ICM) is proposed to rearrange the layout of pore microstructure. Firstly, the Galerkin decoupling technology (GDT) is employed to solve the Brinkman equation for fluid flow in fluid-saturated porous media, which integrates the Stokes equation and Darcy's law. Within this framework a self-programmable element balance equation is developed. Secondly, a permeability interpolation function is defined for each element, incorporating the permeability of the fluid and pore microstructure. This function distinguishes strictly between the fluid domain and the pore domain. Pore microstructures with arbitrary shape including permeability information are established in an Euler mesh by introducing the Carman-Kozeny equation, shape functions, the Joukowski mapping and filter functions (the modified arctan mapping function and the ‘inpolygon’ function) based on the ICM method. Thirdly, a layout optimization model aiming at minimizing energy dissipation is established to accomplish the optimal distribution of pore microstructures. A sensitivity analysis is calculated with respect to design variables and the optimal model is solved by a genetic algorithm. Numerical results demonstrate that the proposed method is effective and feasible in 2D-space. The maximum velocity within the fluid field is reduced by 45%, and the issue of localized high pressure occurring around the pore is solved. This paper provides guidance for solving the Brinkman coupled equation and the layout optimization of the microstructure in porous media.

Exploring the genetic landscape of the brain-heart axis: A comprehensive analysis of pleiotropic effects between heart disease and psychiatric disorders

BackgroundThe genetic links between heart disease and psychiatric disorders are complex and not well understood. This study uses genome-wide association studies (GWAS) and advanced multilevel analyses to explore these connections. MethodsWe analyzed GWAS data from seven psychiatric disorders and five types of heart disease. Genetic correlations and overlaps were examined using linkage disequilibrium score regression (LDSC), high-definition likelihood (HDL), and Genetic analysis incorporating Pleiotropy and Annotation (GPA). Pleiotropic single-nucleotide variations (SNVs) were identified with pleiotropic analysis under the composite null hypothesis (PLACO) and annotated via Functional mapping and annotation of genetic associations (FUMA). Potential pleiotropic genes were identified using Multi-marker Analysis of GenoMic Annotation (MAGMA) and Summary data-based Mendelian Randomization (SMR). Mendelian randomization was employed to assess causal relationships. ResultsAmong 35 trait pairs, 32 showed significant genetic correlations or overlaps. PLACO identified 15,077 SNVs, with 287 recognized as pleiotropic loci and 20 colocalization sites. MAGMA and SMR revealed 75 potential pleiotropic genes involved in diverse pathways, including cancer, neurodevelopment, and cellular organization. Mouse Genome Informatics (MGI) queries provided evidence linking multiple genes to heart or psychiatric disorders. ConclusionsThis analysis reveals loci and genes with pleiotropic effects between heart disease and psychiatric disorders, highlighting shared biological pathways. These findings illuminate the genetic mechanisms underlying the brain-heart axis and suggest shared biological foundations for these conditions, offering potential targets for future prevention and treatment strategies.

P4.07G.05 Prediction of Quality-of-Life Results after Lung Stereotactic Body Radiotherapy Using Functional Mapping on Gallium-68 Perfusion PET/CT

P4.07G.05 Prediction of Quality-of-Life Results after Lung Stereotactic Body Radiotherapy Using Functional Mapping on Gallium-68 Perfusion PET/CT

Adaptive Finite-Time Consensus Tracking Control for Nonlinear Multi-Agent Systems: An Improved Tan-Type Nonlinear Mapping Function Method

Adaptive Finite-Time Consensus Tracking Control for Nonlinear Multi-Agent Systems: An Improved Tan-Type Nonlinear Mapping Function Method

A rule-driven method for disassembly sequence real-time planning of intelligent mixed-flow disassembly line

Intelligent mixed-flow disassembly serves an important role in enhancing the flexible operation capability of the disassembly line for multi-variety and small-batch used electromechanical products, and in forming the scale benefit of recycling. However, in mixed-flow disassembly lines, pre-planning the disassembly sequence for products is typically unfeasible and can lead to “over-disassembly” or “under-disassembly”, resulting in economic losses. This is attributed to significant variations in disassembly features, such as internal structural damage and subassembly recycling income, even within the same type of used electromechanical product. These differences contribute to considerable uncertainties in disassembly sequence planning. To this end, a method is proposed to leverage dynamic disassembly feature information for mining disassembly rules, efficiently driving disassembly sequences real-time planning, and realizing intelligent optimization of product disassembly profit on mixed-flow disassembly line. First, a multi-matrix collaboration based dynamic representing method of product disassembly feature information is proposed. It aims to eliminates the influence of uncertain information on the disassembly sequence planning by recording real-time disassembly feature information, such as disassembly operation time and subassembly recycling income. On this basis, a rule-driven disassembly sequences real-time planning model is built. This model realizes disassembly sequence real-time planning based on offline mined rules using the updated disassembly feature information in matrices. Finally, an improved gene expression programming-based disassembly rule offline mining algorithm is proposed, which incorporates a mutation probability mapping function to improve the efficiency and effectiveness of disassembly rule mining. A case study containing two products with differences in structure and function are used to confirm the effectiveness of the proposed method.

Improving PPP positioning and troposphere estimates using an azimuth-dependent weighting scheme

Asymmetric troposphere modeling is crucial in Precise Point Positioning (PPP). The functional model of the asymmetric troposphere has been thoroughly studied, while the stochastic model lacks discussion. Currently, there is no suitable stochastic model for asymmetric tropospheric conditions, potentially degrading the positioning accuracy and the reliability of Zenith Total/Wet Delay (ZTD/ZWD) estimates. This paper introduces an Azimuth-Dependent Weighting (ADW) scheme that utilizes information from asymmetric mapping functions to adaptively weight Global Navigation Satellite System (GNSS) observations affected by azimuth-dependent errors. The concept of ADW has been validated using Numerical Weather Prediction data and International GNSS Service data. The results indicate that ADW effectively improves the coordinate repeatability of the PPP solution by approximately 10%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$10\\%$$\\end{document} in the horizontal and 20%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$20\\%$$\\end{document} in the vertical direction. Additionally, ADW appears to be capable to improve the ZWD estimates during the PPP convergence period and yields smoother ZWD estimates. Consequently, it is recommended to adopt this new weighting scheme in PPP applications when an asymmetric mapping functions is employed.

Bridging Hydrological Ensemble Simulation and Learning Using Deep Neural Operators

AbstractEnsemble‐based simulation and learning (ESnL) has long been used in hydrology for parameter inference, but computational demands of process‐based ESnL can be quite high. To address this issue, we propose a deep neural operator learning approach. Neural operators are generic machine learning algorithms that can learn functional mappings between infinite‐dimensional spaces, providing a highly flexible tool for scientific machine learning. Our approach is built upon DeepONet, a specific deep neural operator, and is designed to address several common problems in hydrology, namely, model parameter estimation, prediction at ungaged locations, and uncertainty quantification. Here we demonstrate the effectiveness of our DeepONet‐based workflow using an existing large model ensemble created for an eastern U.S. watershed that is instrumented with 10 streamflow gages. Results suggest DeepONet achieves high efficiency in learning an ML surrogate model from the model ensemble, with the modified Kling‐Gupta Efficiency exceeding 0.9 on holdout test sets. Parameter inference, carried out using the trained DeepONet surrogate model and genetic algorithm, also yields robust results. Additionally, we formulate and train a separate DeepONet model for physics‐informed, seq‐to‐seq streamflow forecasting, which further reduces biases in the pre‐trained DeepONet surrogate model. While this study focuses primarily on a single watershed, our approach is general and may be extended to enable learning from model ensembles across multiple basins or models. Thus, this research represents a significant contribution to the application of hybrid machine learning in hydrology.

ID: 331462 Stereoelectroencephalography Based Functional Mapping in Patients with Epilepsy

ID: 331462 Stereoelectroencephalography Based Functional Mapping in Patients with Epilepsy

ID: 317152 Intraoperative Functional Mapping with Deep Brain Stimulation Local Evoked Potentials in Awake and Anesthetized States

ID: 317152 Intraoperative Functional Mapping with Deep Brain Stimulation Local Evoked Potentials in Awake and Anesthetized States

The morphospace of the brain-cognition organisation.

Over the past three decades, functional neuroimaging has amassed abundant evidence of the intricate interplay between brain structure and function. However, the potential anatomical and experimental overlap, independence, granularity, and gaps between functions remain poorly understood. Here, we show the latent structure of the current brain-cognition knowledge and its organisation. Our approach utilises the most comprehensive meta-analytic fMRI database (Neurosynth) to compute a three-dimensional embedding space-morphospacecapturing the relationship between brain functions as we currently understand them. The space structure enables us to statistically test the relationship between functions expressed as the degree to which the characteristics of each functional map can be anticipated based on its similarities with others-the predictability index. The morphospace can also predict the activation pattern of new, unseen functions and decode thoughts and inner states during movie watching. The framework defined by the morphospace will spur the investigation of novel functions and guide the exploration of the fabric of human cognition.

Metabolic preferences of astrocytes: Functional metabolic mapping reveals butyrate outcompetes acetate.

Disruptions to the gut-brain-axis have been linked to neurodegenerative disorders. Of these disruptions, reductions in the levels of short-chain fatty acids (SCFAs), like butyrate, have been observed in mouse models of Alzheimer's disease (AD). Butyrate supplementation in mice has shown promise in reducing neuroinflammation, amyloid-β accumulation, and enhancing memory. However, the underlying mechanisms remain unclear. To address this, we investigated the impact of butyrate on energy metabolism in mouse brain slices, primary cultures of astrocytes and neurons and in-vivo by dynamic isotope labelling with [U-13C]butyrate and [1,2-13C]acetate to map metabolism via mass spectrometry. Metabolic competition assays in cerebral cortical slices revealed no competition between butyrate and the ketone body, β-hydroxybutyrate, but competition with acetate. Astrocytes favoured butyrate metabolism compared to neurons, suggesting that the astrocytic compartment is the primary site of butyrate metabolism. In-vivo metabolism investigated in the 5xFAD mouse, an AD pathology model, showed no difference in 13C-labelling of TCA cycle metabolites between wild-type and 5xFAD brains, but butyrate metabolism remained elevated compared to acetate in both groups, indicating sustained uptake and metabolism in 5xFAD mice. Overall, these findings highlight the role of astrocytes in butyrate metabolism and the potential use of butyrate as an alternative brain fuel source.

Graph-Based Semi-Supervised Learning with Bipartite Graph for Large-Scale Data and Prediction of Unseen Data

Recently, considerable attention has been directed toward graph-based semi-supervised learning (GSSL) as an effective approach for data labeling. Despite the progress achieved by current methodologies, several limitations persist. Firstly, many studies treat all samples equally in terms of weight and influence, disregarding the potential increased importance of samples near decision boundaries. Secondly, the detection of outlier-labeled data is crucial, as it can significantly impact model performance. Thirdly, existing models often struggle with predicting labels for unseen test data, restricting their utility in practical applications. Lastly, most graph-based algorithms rely on affinity matrices that capture pairwise similarities across all data points, thus limiting their scalability to large-scale databases. In this paper, we propose a novel GSSL algorithm tailored for large-scale databases, leveraging anchor points to mitigate the challenges posed by large affinity matrices. Additionally, our method enhances the influence of nodes near decision boundaries by assigning different weights based on their importance and using a mapping function from feature space to label space. Leveraging this mapping function enables direct label prediction for test samples without requiring iterative learning processes. Experimental evaluations on two extensive datasets (Norb and Covtype) demonstrate that our approach is scalable and outperforms existing GSSL methods in terms of performance metrics.

Overview of High-Dynamic-Range Image Quality Assessment.

In recent years, the High-Dynamic-Range (HDR) image has gained widespread popularity across various domains, such as the security, multimedia, and biomedical fields, owing to its ability to deliver an authentic visual experience. However, the extensive dynamic range and rich detail in HDR images present challenges in assessing their quality. Therefore, current efforts involve constructing subjective databases and proposing objective quality assessment metrics to achieve an efficient HDR Image Quality Assessment (IQA). Recognizing the absence of a systematic overview of these approaches, this paper provides a comprehensive survey of both subjective and objective HDR IQA methods. Specifically, we review 7 subjective HDR IQA databases and 12 objective HDR IQA metrics. In addition, we conduct a statistical analysis of 9 IQA algorithms, incorporating 3 perceptual mapping functions. Our findings highlight two main areas for improvement. Firstly, the size and diversity of HDR IQA subjective databases should be significantly increased, encompassing a broader range of distortion types. Secondly, objective quality assessment algorithms need to identify more generalizable perceptual mapping approaches and feature extraction methods to enhance their robustness and applicability. Furthermore, this paper aims to serve as a valuable resource for researchers by discussing the limitations of current methodologies and potential research directions in the future.