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arXiv 2025年

作者： Zaremehrjerdi, Hossein Coffey, Lisa Jubery, Talukder Liu, Huyu Turkus, Jon Linders, Kyle Schnable, James C. Schnable, Patrick S. Ganapathysubramanian, Baskar Department of Electrical and Computer Engineering Iowa State University AmesIA United States Department of Agronomy Iowa State University AmesIA United States Translational AI Research and Education Center Iowa State University AmesIA United States Department of Agronomy University of Nebraska-Lincoln LincolnNE United States Department of Mechanical Engineering Iowa State University AmesIA United States Plant Science Institute Iowa State University AmesIA United States

Quantifying the variation in yield component traits of maize (Zea mays L.), which collectively determine the overall productivity of this globally significant crop, plays a critical role in plant genetics research, plant breeding, and the development of improved agronomic practices. Grain yield per acre is a function of the number of plants per acre × ears per plant × number of kernels per ear × average kernel weight. Kernels per ear is the product of the number of kernel rows per ear (kernel-row-number) and the number of kernels per row (kernels-per-row). Traditional manual approaches to quantifying these two traits, which exhibit differential responses to genetic and environmental factors, are time-consuming limiting the scale of data collect for either trait. Recent automation efforts using image processing and deep learning face challenges of high annotation costs and uncertain generalizability. We address these issues by exploring Large Vision Models for zero-shot, annotation-free maize kernel segmentation. Utilizing an open source large vision model – Segment Anything Model (SAM) – we segment individual kernels in RGB images of maize ears and implement a graph-based algorithm to calculate kernels-per-row. Our approach effectively identifies kernels-per-row across diverse maize ears, demonstrating the potential of zero-shot learning using foundation vision models coupled with image processing routines to enhance automation and reduce subjectivity in agronomic data collection. All our codes are open-sourced to democratize these frugal phenotyping *** Codes 68T07, 68U10 © 2025, CC BY-SA.

关键词： Plant diseases

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Advancements in Ligand-Based Virtual Screening through the Synergistic Integration of Graph Neural Networks and Expert-Crafted Descriptors

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Journal of Chemical Information and Modeling 2025年第10期65卷 2025 May 14页

作者： Liu, Yunchao Moretti, Rocco Wang, Yu Dong, Ha Yan, Bailu Bodenheimer, Bobby Derr, Tyler Meiler, Jens Department of Computer Science Vanderbilt University 2201 West End Ave Nashville 37235 TN United States Department of Chemistry Center for Structural Biology Vanderbilt University 2201 West End Ave Nashville 37235 TN United States School of Computer and Data Sciences University of Oregon 1585 East 13th Avenue Eugene 97403 OR United States Department of Neural Science Amherst College 220 South Pleasant Street Amherst 01002 MA United States Department of Biostatistics Vanderbilt University 2201 West End Ave Nashville 37235 TN United States Department of Computer Science Electrical Engineering and Computer Engineering Vanderbilt University 2201 West End Ave Nashville 37235 TN United States Department of Computer Science Data Science Institute Vanderbilt University 2201 West End Ave Nashville 37235 TN United States Institute of Drug Discovery Leipzig University Medical School Härtelstraße 16-18 Leipzig 04103 Germany Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Humboldtstraße 25 Leipzig 04105 Germany

The fusion of traditional chemical descriptors with graph neural networks (GNNs) offers a compelling strategy for enhancing ligand-based virtual screening methodologies. A comprehensive evaluation revealed that the benefits derived from this integrative strategy vary significantly among different GNNs. Specifically, while GCN and SchNet demonstrate pronounced improvements by incorporating descriptors, SphereNet exhibits only marginal enhancement. Intriguingly, despite SphereNet’s modest gain, all three models-GCN, SchNet, and SphereNet-achieve comparable performance levels when leveraging this combination strategy. This observation underscores a pivotal insight: sophisticated GNN architectures may be substituted with simpler counterparts without sacrificing efficacy, provided that they are augmented with descriptors. Furthermore, our analysis reveals a set of expert-crafted descriptors’ robustness in scaffold-split scenarios, frequently outperforming the combined GNN-descriptor models. Given the critical importance of scaffold splitting in accurately mimicking real-world drug discovery contexts, this finding accentuates an imperative for GNN researchers to innovate models that can adeptly navigate and predict within such frameworks. Our work not only validates the potential of integrating descriptors with GNNs in advancing ligand-based virtual screening but also illuminates pathways for future enhancements in model development and application. Our implementation can be found at https://***/meilerlab/gnn-descriptor. © 2025 The Authors. Published by American Chemical Society.

关键词： Graph neural networks

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Quantum cryptography as a solution for secure Wireless Sensor Networks: Roadmap, challenges and solutions

Internet of Things (The Netherlands)

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Internet of Things (The Netherlands) 2025年 32卷

作者： Chawla, Diksha Jain, Khushboo Mehra, Pawan Singh Das, Ashok Kumar Bera, Basudeb Department of CSE and IT Jaypee Institute of Information Technology Noida India Data Science Cluster UPES Dehradun India Department of CSE Delhi Technological University Delhi India Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad India Department of Computer Science and Engineering College of Informatics Korea University 145 Anam-ro Seongbuk-gu Seoul South Korea

Wireless Sensor Networks (WSN) can collect information from almost everywhere, so they can be deployed ubiquitously. Nonetheless, data transmission within the WSN communication framework is vulnerable to interception. Therefore, it is necessary to create a secure WSN communication environment. The idea of cryptography has been used for years for secure data communication. The popular schemes are RSA and hash functions. However, with the significant advancement in the field of Quantum Computing, there is a need for unconditional security in data generated and distributed from sensor nodes. Quantum computing is in its early stages, but we can evaluate its impact on conventional cryptographic techniques by analyzing the Quantum Shor's algorithm. Motivated by the aforementioned issues, this paper sheds light on Quantum Cryptography by analyzing various security concerns in WSN. In our work, Quantum Cryptography-based secure key agreement, Quantum Mistrustful communication, Quantum Entanglement and Quantum Teleportation are reviewed. The roles of each technique in association with WSN are analyzed. We proposed a novel Quantum WSN (QWSN) architecture to achieve high data security and effective data transmission. We analyze Quantum simulation tools, security attacks, and the influence of Quantum technology on traditional cryptographic methods. We have also included the demonstrations of quantum circuits on IBM Quantum Composer (IQC). The benefits and results are also discussed. In addition, we also discussed the open issues and challenges in implementing Quantum-based security for WSN. The article identifies and emphasizes several unresolved research challenges and future directions for advancing research and innovation in the domain of Quantum Cryptography. © 2025 Elsevier B.V.

关键词： Entanglement Quantum cryptography Quantum Key Distribution Quantum Wireless Sensor Networks Security

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A Tutorial on Movable Antennas for Wireless Networks

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IEEE Communications Surveys and Tutorials 2025年

作者： Zhu, Lipeng Ma, Wenyan Mei, Weidong Zeng, Yong Wu, Qingqing Ning, Boyu Xiao, Zhenyu Shao, Xiaodan Zhang, Jun Zhang, Rui National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore National Key Laboratory of Wireless Communications Chengdu611731 China Southeast University National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China Shanghai Jiao Tong University Department of Electronic Engineering Shanghai200240 China Beihang University School of Electronic and Information Engineering Beijing100191 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada Beijing Institute of Technology State Key Laboratory of CNS/ATM Miit Key Laboratory of Complex-field Intelligent Sensing Beijing100081 China The Chinese University of HongKong School of Science and Engineering Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China

Movable antenna (MA) has been recognized as a promising technology to enhance the performance of wireless communication and sensing by enabling antenna movement. Such a significant paradigm shift from conventional fixed antennas (FAs) to MAs offers tremendous new opportunities towards realizing more versatile, adaptive and efficient next-generation wireless networks such as 6G. In this paper, we provide a comprehensive tutorial on the fundamentals and advancements in the area of MA-empowered wireless networks. First, we overview the historical development and contemporary applications of MA technologies. Next, to characterize the continuous variation in wireless channels with respect to antenna position and/or orientation, we present new field-response channel models tailored for MAs, which are applicable to narrowband and wideband systems as well as far-field and near-field propagation conditions. Subsequently, we review the state-of-the-art architectures for implementing MAs and discuss their practical constraints. A general optimization framework is then formulated to fully exploit the spatial degrees of freedom (DoFs) in antenna movement for performance enhancement in wireless systems. In particular, we delve into two major design issues for MA systems. First, we address the intricate antenna movement optimization problem for various communication and/or sensing systems to maximize the performance gains achievable by MAs. Second, we deal with the challenging channel acquisition issue in MA systems for reconstructing the channel mapping between arbitrary antenna positions inside the transmitter and receiver regions. Moreover, we show existing prototypes developed for MA-aided communication/sensing and the experimental results based on them. Finally, the extension of MA design to other wireless systems and its synergy with other emerging wireless technologies are discussed. We also highlight promising research directions in this area to inspire future investigatio

关键词： Antenna arrays

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Ultra-Sensitive Optical Fiber Spr Biosensor for Precise Prostate Cancer Diagnosis by Label-Free Detection of Mirna-21

SSRN

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SSRN 2025年

作者： Wang, Jiale Deng, Minghua Niu, Kunpeng Wang, Chuanquan Wang, Ning Li, Jinhua Xia, Binyun Ding, Liyun Yang, Huan Chen, Xiaohan Zhu, Jiayi National Engineering Research Center of Fiber Optic Sensing Technology and Networks Wuhan University of Technology Wuhan430070 China School of Computer and Information Engineering Hubei Normal University Huangshi China Hubei Optical Fundamental Research Center Wuhan China Collaborative Innovation Center for Advanced Organic Chemical Materials Co-constructed by the Province and Ministry Key Laboratory for the Green Preparation and Application of Functional Materials Ministry of Education Hubei Key Laboratory of Polymer Materials School of Materials Science and Engineering Hubei University Wuhan430062 China School of Physical Science and Technology Lanzhou University Lanzhou730000 China Department of Urology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan430030 China

Efficient and rapid identification of miRNA-21 may be conducive to the early diagnosis of prostate cancer. In this work, a label-free and highly sensitive optical fiber Surface Plasmon Resonance (SPR) biosensor was developed for detecting the miRNA-21, which was fabricated simply by coating a layer of Au film and AuNPs onto a multi-mode-single-mode-multi-mode (MSM) optical fiber, and then biofunctionalized with ssDNA enabling the quantification of miRNA-21. The working principle involves initiating the change of the effective refractive index (RI) around the optical fiber's surface, which was caused by nucleic acid hybridization between the ssDNA and the miRNA-21. As a result, the interaction would result in a noticeable shift of the SPR wavelengths in the transmission spectra. In the experiment, a substantial enhancement was exhibited in the RI sensitivity of the optical fiber sensor due to the surface plasmon resonance (SPR) coupling effect of AuNPs and the Au film, measuring approximately 3021.48 nm/RIU. Excitingly, the biosensors, which have a simple composition, were experimentally verified to have an ultra-low limit of detection (LOD) of 10-20 M, significantly lower than current detection methods. And trace miRNA-21 was enabled for detection without RNA amplification. The SPR sensors were also experimentally proved with excellent linearity, specificity and stability. When employed to detect the serum samples, the results indicated that the proposed sensors could brilliantly distinguish clinical patients with and without prostate cancer. Compared to the commonly used PSA quantification in clinical practice, the method developed in this work has higher detection accuracy. The proposed sensing platform processes the merits of simple configuration as well as low cost and may be applied for the precise diagnosis of prostate cancer. © 2025, The Authors. All rights reserved.

关键词： Surface plasmon resonance

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Randomized Latent Factor Model for High-dimensional and Sparse Matrices from Industrial applications

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 131-141页

作者： Mingsheng Shang Xin Luo Zhigang Liu Jia Chen Ye Yuan MengChu Zhou the Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of Sciences the School of Computer Science and Engineering Beihang University the Department of Electrical and Computer Engineering New Jersey Institute of Technology

Latent factor(LF) models are highly effective in extracting useful knowledge from High-Dimensional and Sparse(HiDS) matrices which are commonly seen in various industrial applications. An LF model usually adopts iterative optimizers,which may consume many iterations to achieve a local optima,resulting in considerable time cost. Hence, determining how to accelerate the training process for LF models has become a significant issue. To address this, this work proposes a randomized latent factor(RLF) model. It incorporates the principle of randomized learning techniques from neural networks into the LF analysis of HiDS matrices, thereby greatly alleviating computational burden. It also extends a standard learning process for randomized neural networks in context of LF analysis to make the resulting model represent an HiDS matrix *** results on three HiDS matrices from industrial applications demonstrate that compared with state-of-the-art LF models, RLF is able to achieve significantly higher computational efficiency and comparable prediction accuracy for missing data.I provides an important alternative approach to LF analysis of HiDS matrices, which is especially desired for industrial applications demanding highly efficient models.

关键词： Big data high-dimensional and sparse matrix latent factor analysis latent factor model randomized learning

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Low-cost video-based air quality estimation system using structured deep learning with selective state space modeling

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Environment International 2025年 199卷 109496页

作者： Ahmed, Maqsood Zhang, Xiang Shen, Yonglin Ahmed, Tanveer Ali, Shahid Ali, Ayaz Gulakhmadov, Aminjon Nam, Won-Ho Chen, Nengcheng National Engineering Research Center of Geographic Information System School of Geography and Information Engineering China University of Geosciences Wuhan430074 China Department of Computer Science School of Engineering and Digital Sciences Nazarbayev University Astana Kazakhstan Software College Northeastern University Shenyang110169 China Department of Cybernetics Nanotechnology and Data Processing Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland Research Center of Ecology and Environment in Central Asia Xinjiang Institute of Ecology and Geography Chinese Academy of Sciences Urumqi830011 China Institute of Water Problems Hydropower and Ecology of the National Academy of Sciences of Tajikistan Dushanbe734042 Tajikistan School of Social Safety and Systems Engineering Institute of Agricultural Environmental Science National Agricultural Water Research Center Hankyong National University Anseong Korea Republic of

air quality is crucial for both public health and environmental sustainability. An efficient and cost-effective model is essential for accurate air quality predictions and proactive pollution control. However, existing research primarily focuses on single static image analysis, which does not account for the dynamic and temporal nature of air pollution. Meanwhile, research on video-based air quality estimation remains limited, particularly in achieving accurate multi-pollutant outputs. This study proposes air Quality Prediction-Mamba (AQP-Mamba), a video-based deep learning model that integrates a structured Selective State Space Model (SSM) with a selective scan mechanism and a hybrid predictor (HP) to estimate air quality. The spatiotemporal forward and backward SSM dynamically adjusts parameters based on input, ensures linear complexity, and effectively captures long-range dependencies by bidirectional processing of spatiotemporal features through four scanning techniques (row-wise, column-wise, and their vertical reversals), which allows the model to accurately track pollutant concentrations and air quality variations over time. Thus, the model efficiently extracts spatiotemporal features from video and simultaneously performs regression (PM2.5, PM10, and AQI), and classification (AQI) tasks, respectively. A high-quality outdoor hourly air quality dataset (LMSAQV) with 13,176 videos collected from six monitoring stations in Lahore, Pakistan, was utilized as the case study. The experimental results demonstrate that the AQP-Mamba significantly outperforms several state-of-the-art models, including VideoSwin-T, VideoMAE, I3D, VTHCL, and TimeSformer. The proposed model achieves strong regression performance (PM2.5: R2 = 0.91, PM10: R2 = 0.90, AQI: R2 = 0.92) and excellent classification metrics: accuracy (94.57 %), precision (93.86 %), recall (94.20 %), and F1-score (93.44 %), respectively. The proposed model delivers consistent, real-time performance with a latency

关键词： air quality

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Self-assembled Gold@silver-ZIF structure-induced dual-enhancement luminescence synergized with interpretable machine learning empower precise monitoring of inflammatory homeostasis

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Nano Today 2025年 64卷

作者： Xu, Gaoxiang Wang, Mengke Li, Qing Fan, Lianghui Shen, Runpu Xiao, Zhikang Xu, Jianzhong Wang, Kun Chen, Junyang School of Life Sciences Zhengzhou University Zhengzhou450001 China Department of Orthopedics The First Affiliated Hospital of Zhengzhou University Zhengzhou450052 China Department of Orthopedics the Fourth Medical Center Chinese PLA General Hospital Beijing100853 China Nanozyme Laboratory in Zhongyuan Henan Academy of Innovations in Medical Science Zhengzhou451163 China Application Center for Precision Medicine the Second Affiliated Hospital of Zhengzhou University Henan 450052 China Zhejiang Engineering Research Center of Fat-soluble Vitamin College of Chemistry and Chemical Engineering Shaoxing University Zhejiang Shaoxing312000 China School of Computer Xi'an University of Posts and Telecommunications Shanxi Xi'an710121 China

The maintenance of an optimal inflammatory homeostasis within the body is crucial for its sustained viability within a natural environment replete with pathogenic factors. The real-time, precise detection of two antagonistic cytokines (pro- and anti-inflammatory) in an inflammatory state is fundamental to the guidance of precise clinical treatments and thus the achievement of inflammatory homeostasis. Herein, we synthesized AuAg-ZIF, which resulted in a dual enhancement of the fluorescence properties of AuNCs (35-fold increase in fluorescence intensity and 18.5-fold increase in quantum yield) by the anti-galvanic reaction between the surface Ag(I) and core Au (0), as well as by the confinement effect of ZIF-8. Furthermore, a generalized fluorescence immunoassay utilizing liposome-mediated Cu2 +-induced fluorescence quenching of AuAg-ZIF has been developed, resulting in amplification of the antigenic signal. Additionally, an interpretable machine learning prediction algorithm was constructed, comprising feature extraction, feature dimensionality reduction, model construction and validation, and model interpretation. This algorithm achieves immediate and accurate detection of factors related to anti-inflammatory, pro-inflammatory, and inflammatory levels in the human inflammatory homeostasis (R2 > 0.95), which is in line with the accuracy of the current commercial assay kits. This integration of dual-enhanced fluorescent nanoscale materials, amplification strategies, and interpretable machine learning enables the real-time, accurate observation of inflammatory homeostasis, thereby facilitating the delivery of precision clinical treatments. © 2025

关键词： Fluorescence quenching

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Using artificial intelligence to predict the next deceptive movement based on video sequence analysis: A case study on a professional cricket player's movements

Journal of Engineering Research (Kuwait)

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Journal of engineering research (Kuwait) 2025年

作者： Mutawa, A.M. Rajesh Kumar, Korupalli V. K, Hemachandran Murugappan, M. Computer Engineering Department College of Engineering and Petroleum Kuwait University Safat 5969 13060 Kuwait CoE Sports Analytics and Management and AI Research Centre School of Business Woxsen University Hyderabad India Intelligent Signal Processing (ISP) Research Lab Department of Electronics and Communication Engineering Kuwait College of Science and Technology Block 4 Doha 13133 Kuwait Department of Electronics and Communication Engineering School of Engineering Vels Institute of Sciences Technology and Advanced Studies Tamilnadu Chennai India Center of Excellence for Unmanned Aerial Systems (CoEUAS) Universiti Malaysia Perlis Perlis 02600 Malaysia

This research develops an artificial intelligence-based model to predict the next deceptive movement of athletes by analyzing video sequences of previous movements. Utilizing advanced deep neural network models, we analyze deceptive movements to forecast the next move, with a practical application on the deceptive movements of a professional cricket player. The model employs machine learning techniques such as Random Forest (RF), Decision Trees (DT), and K-Nearest Neighbor (KNN) to enhance prediction accuracy. Achieving up to 70 % accuracy, this model rivals human capability, as even highly skilled players can easily fall for deceptive actions. The ability to predict deceptive movements sets humans apart from many intelligent creatures, allowing athletes to avoid predictable actions and gain an edge in various sports. This study applies this concept to cricket, leveraging video data to improve training methods. The results highlight the potential of artificial intelligence in revolutionizing training and performance optimization in sports. © 2025 The Authors

关键词： Artificial intelligence Cricket Deceptive movements Machine learning Performance optimization

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ENIGMA-Meditation: Worldwide Consortium for Neuroscientific Investigations of Meditation Practices

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Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2025年第4期10卷 425-436页

作者： Ganesan, Saampras Barrios, Fernando A. Batta, Ishaan Bauer, Clemens C.C. Braver, Todd S. Brewer, Judson A. Brown, Kirk Warren Cahn, Rael Cain, Joshua A. Calhoun, Vince D. Cao, Lei Chetelat, Gaël Ching, Christopher R.K. Creswell, J. David Dagnino, Paulina Clara Davanger, Svend Davidson, Richard J. Deco, Gustavo Dutcher, Janine M. Escrichs, Anira Eyler, Lisa T. Fani, Negar Farb, Norman A.S. Fialoke, Suruchi Fresco, David M. Garg, Rahul Garland, Eric L. Goldin, Philippe Hafeman, Danella M. Jahanshad, Neda Kang, Yoona Khalsa, Sahib S. Kirlic, Namik Lazar, Sara W. Lutz, Antoine McDermott, Timothy J. Pagnoni, Giuseppe Piguet, Camille Prakash, Ruchika S. Rahrig, Hadley Reggente, Nicco Saccaro, Luigi F. Sacchet, Matthew D. Siegle, Greg J. Tang, Yi-Yuan Thomopoulos, Sophia I. Thompson, Paul M. Torske, Alyssa Treves, Isaac N. Tripathi, Vaibhav Tsuchiyagaito, Aki Turner, Matthew D. Vago, David R. Valk, Sofie Zeidan, Fadel Zalesky, Andrew Turner, Jessica A. King, Anthony P. Department of Biomedical Engineering The University of Melbourne Carlton VIC Australia Contemplative Studies Centre Melbourne School of Psychological Sciences The University of Melbourne Melbourne VIC Australia Systems Lab of Neuroscience Neuropsychiatry and Neuroengineering The University of Melbourne Parkville VIC Australia Universidad Nacional Autónoma de México Instituto de Neurobiolgía México Querétaro Mexico Center for Translational Research in Neuroimaging and Data Science: Georgia State University Georgia Institute of Technology and Emory University Atlanta GA United States Department of Psychology Northeastern University Boston Massachusetts Brain and Cognitive Science McGovern Institute for Brain Research Massachusetts Institute of Technology Cambridge Massachusetts Department of Psychological and Brain Sciences Washington University St. Louis Missouri Department of Behavioral and Social Sciences Brown University School of Public Health Providence Rhode Island Department of Psychology Carnegie Mellon University Pittsburgh PA United States University of Southern California Department of Psychiatry & Behavioral Sciences Los Angeles California University of Southern California Center for Mindfulness Science Los Angeles California Institute for Advanced Consciousness Studies Santa Monica California Department of Psychiatry and Behavioral Health The Ohio State University College of Medicine Columbus OH United States Normandie University Université de Caen Normandie INSERM U1237 Neuropresage Team Cyceron Caen France Imaging Genetics Center Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of the University of Southern California Los Angeles California Desert-Pacific Mental Illness Research Education and Clinical Center VA San Diego Healthcare System San Diego California Computational Neuroscience Group Center for Brain and Cognition Department of Information and Communication Technologies Universitat Pompeu Fa

Meditation is a family of ancient and contemporary contemplative mind-body practices that can modulate psychological processes, awareness, and mental states. Over the last 40 years, clinical science has manualized meditation practices and designed various meditation interventions that have shown therapeutic efficacy for disorders including depression, pain, addiction, and anxiety. Over the past decade, neuroimaging has been used to examine the neuroscientific basis of meditation practices, effects, states, and outcomes for clinical and nonclinical populations. However, the generalizability and replicability of current neuroscientific models of meditation have not yet been established, because they are largely based on small datasets entrenched with heterogeneity along several domains of meditation (e.g., practice types, meditation experience, clinical disorder targeted), experimental design, and neuroimaging methods (e.g., preprocessing, analysis, task-based, resting-state, structural magnetic resonance imaging). These limitations have precluded a nuanced and rigorous neuroscientific phenotyping of meditation practices and their potential benefits. Here, we present ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)–Meditation, the first worldwide collaborative consortium for neuroscientific investigations of meditation practices. ENIGMA-Meditation will enable systematic meta- and mega-analyses of globally distributed neuroimaging datasets of meditation using shared, standardized neuroimaging methods and tools to improve statistical power and generalizability. Through this powerful collaborative framework, existing neuroscientific accounts of meditation practices can be extended to generate novel and rigorous neuroscientific insights that account for multidomain heterogeneity. ENIGMA-Meditation will inform neuroscientific mechanisms that underlie therapeutic action of meditation practices on psychological and cognitive attributes, thereby advancing the f

关键词： Consortium ENIGMA Magnetic resonance imaging (MRI) Meditation Mindfulness Neuroimaging

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