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Structured dialogue policy with graph neural networks 27

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Structured dialogue policy with graph neural networks

27th International Conference on Computational Linguistics, COLING 2018

作者： Chen, Lu Tan, Bowen Long, Sishan Yu, Kai Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Eng. SpeechLab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781948087506

Recently, deep reinforcement learning (DRL) has been used for dialogue policy optimization. However, many DRL-based policies are not sample-efficient. Most recent advances focus on improving DRL optimization algorithms to address this issue. Here, we take an alternative route of designing neural network structure that is better suited for DRL-based dialogue management. The proposed structured deep reinforcement learning is based on graph neural networks (GNN), which consists of some sub-networks, each one for a node on a directed graph. The graph is defined according to the domain ontology and each node can be considered as a sub-agent. During decision making, these sub-agents have internal message exchange between neighbors on the graph. We also propose an approach to jointly optimize the graph structure as well as the parameters of GNN. Experiments show that structured DRL significantly outperforms previous state-of-the-art approaches in almost all of the 18 tasks of the PyDial benchmark. © 2018 COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings. All rights reserved.

关键词： Reinforcement learning

EEG Data Augmentation for Emotion Recognition Using a Conditional Wasserstein GAN 40

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EEG Data Augmentation for Emotion Recognition Using a Condit...

40th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2018

作者： Luo, Yun Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai200240 China

ISBN: (纸本)9781538636466

Due to the lack of electroencephalography (EEG) data, it is hard to build an emotion recognition model with high accuracy from EEG signals using machine learning approach. Inspired by generative adversarial networks (GANs), we introduce a Conditional Wasserstein GAN (CWGAN) framework for EEG data augmentation to enhance EEG-based emotion recognition. A Wasserstein GAN with gradient penalty is adopted to generate realistic-like EEG data in differential entropy (DE) form. Three indicators are used to judge the qualities of the generated high-dimensional EEG data, and only high quality data are appended to supplement the data manifold, which leads to better classification of different emotions. We evaluate the proposed CWGAN framework on two public EEG datasets for emotion recognition, namely SEED and DEAP. The experimental results demonstrate that using the EEG data generated by CWGAN significantly improves the accuracies of emotion recognition models. © 2018 IEEE.

关键词： Electroencephalography Gallium nitride Training Generators Emotion recognition Generative adversarial networks Brain modeling

Vector-based pedestrian navigation in cities

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arXiv 2021年

作者： Bongiorno, Christian Zhou, Yulun Kryven, Marta Theurel, David Rizzo, Alessandro Santi, Paolo Tenenbaum, Joshua Ratti, Carlo Senseable City Lab Massachusetts Institute of Technology 77 Mass Av CambridgeMA02139 United States Université Paris-Saclay CentraleSupelec Mathematiques et Informatique Pour la Complexite et les Systemes Gif-sur-Yvette91190 France Department of Urban Planning and Design Faculty of Architecture The University of Hong Kong Pokfulam Hong Kong Department of Brain and Cognitive Sciences Massachusetts Institute of Technology 77 Mass Av CambridgeMA02139 United States Department of Physics Massachusetts Institute of Technology 77 Mass Av CambridgeMA02139 United States Dipartimento di Elettronica e Telecomunicazioni Politecnico di Torino Torino TO10128 Italy Istituto di Informatica e Telematica Del CNR Via G. Moruzzi 1 Pisa56124 Italy Office of Innovation New York University Tandon School of Engineering Six MetroTech Center BrooklynNY11201 United States

How do pedestrians choose their paths within city street networks? Researchers have tried to shed light on this matter through strictly controlled experiments, but an ultimate answer based on real-world mobility data is still lacking. Here, we analyze salient features of human path planning through a statistical analysis of a massive dataset of GPS traces, which reveals that (1) people increasingly deviate from the shortest path when the distance between origin and destination increases, and (2) chosen paths are statistically different when origin and destination are swapped. We posit that direction to goal is a main driver of path planning and develop a vector-based navigation model that is a statistically better predictor of human paths than a model based on minimizing distance with stochastic effects. Our findings generalize across two major US cities with different street networks, hinting to the fact that vector-based navigation might be a universal property of human path *** Codes 91D10 Copyright © 2021, The Authors. All rights reserved.

关键词： Motion planning

Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 3 — Ex vivo imaging: data processing, comparisons with microscopy, and tractography

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arXiv 2024年

作者： Schilling, Kurt G. Howard, Amy F.D. Grussu, Francesco Ianus, Andrada Hansen, Brian Barrett, Rachel L.C. Aggarwal, Manisha Michielse, Stijn Nasrallah, Fatima Syeda, Warda Wang, Nian Veraart, Jelle Roebroeck, Alard Bagdasarian, Andrew F. Eichner, Cornelius Sepehrband, Farshid Zimmermann, Jan Soustelle, Lucas Bowman, Christien Tendler, Benjamin C. Hertanu, Andreea Jeurissen, Ben Verhoye, Marleen Frydman, Lucio van de Looij, Yohan Hike, David Dunn, Jeff F. Miller, Karla Landman, Bennett A. Shemesh, Noam Anderson, Adam McKinnon, Emilie Farquharson, Shawna Dell' Acqua, Flavio Pierpaoli, Carlo Drobnjak, Ivana Leemans, Alexander Harkins, Kevin D. Descoteaux, Maxime Xu, Duan Huang, Hao Santin, Mathieu D. Grant, Samuel C. Obenaus, Andre Kim, Gene S. Wu, Dan Le Bihan, Denis Blackband, Stephen J. Ciobanu, Luisa Fieremans, Els Bai, Ruiliang Leergaard, Trygve B. Zhang, Jiangyang Dyrby, Tim B. Johnson, G. Allan Cohen-Adad, Julien Budde, Matthew D. Jelescu, Ileana O. Radiology and Radiological Sciences Vanderbilt University Medical Center NashvilleTN United States Vanderbilt University Institute of Imaging Science Vanderbilt University NashvilleTN United States Department of Bioengineering Imperial College London London United Kingdom FMRIB Centre Wellcome Centre for Integrative Neuroimaging Nuffield Department of Clinical Neurosciences University of Oxford Oxford United Kingdom Radiomics Group Vall d’Hebron Institute of Oncology Vall d’Hebron Barcelona Hospital Campus Barcelona Spain Queen Square MS Centre Queen Square Institute of Neurology Faculty of Brain Sciences University College London London United Kingdom Champalimaud Research Champalimaud Foundation Lisbon Portugal Center of Functionally Integrative Neuroscience Aarhus University Aarhus Denmark Department of Neuroimaging Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom NatBrainLab Department of Forensics and Neurodevelopmental Sciences Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins University School of Medicine BaltimoreMD United States Maastricht University Medical Center Maastricht Netherlands The Queensland Brain Institute The University of Queensland Queensland Australia Melbourne Neuropsychiatry Centre The University of Melbourne ParkvilleVIC Australia Department of Radiology and Imaging Sciences Indiana University IN United States Stark Neurosciences Research Institute Indiana University School of Medicine IN United States Center for Biomedical Imaging NYU Grossman School of Medicine New YorkNY United States Faculty of psychology and Neuroscience Maastricht University Maastricht Netherlands Department of Chemical & Biomedical Engineering FAMU-FSU College of Engineering Florida State University TallahasseeFL United States Center for Interdisciplinary Magnetic Res

Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitate high spatial resolution and high signal-to-noise ratio (SNR) images, cutting-edge diffusion contrasts, and direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work concludes a 3-part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre-processing, data processing and model fitting, and tractography. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. We end by providing guidelines on code sharing and data sharing, and point towards open-source software and databases specific to small animal and ex vivo imaging. © 2024, CC BY.

关键词： Data Sharing

Joint inference of kinematic and force trajectories with visuo-tactile sensing

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arXiv 2019年

作者： Lambert, Alexander Mukadam, Mustafa Sundaralingam, Balakumar Ratliff, Nathan Boots, Byron Fox, Dieter Georgia Institute of Technology Robot Learning Lab United States University of Utah Robotics Center School of Computing United States NVIDIA United States University of Washington Paul G. Allen School of Computer Science & Engineering SeattleWA United States

To perform complex tasks, robots must be able to interact with and manipulate their surroundings. One of the key challenges in accomplishing this is robust state estimation during physical interactions, where the state involves not only the robot and the object being manipulated, but also the state of the contact itself. In this work, within the context of planar pushing, we extend previous inference-based approaches to state estimation in several ways. We estimate the robot, object, and the contact state on multiple manipulation platforms configured with a vision-based articulated model tracker, and either a biomimetic tactile sensor or a force-torque sensor. We show how to fuse raw measurements from the tracker and tactile sensors to jointly estimate the trajectory of the kinematic states and the forces in the system via probabilistic inference on factor graphs, in both batch and incremental settings. We perform several benchmarks with our framework and show how performance is affected by incorporating various geometric and physics based constraints, occluding vision sensors, or injecting noise in tactile sensors. We also compare with prior work on multiple datasets and demonstrate that our approach can effectively optimize over multi-modal sensor data and reduce uncertainty to find better state estimates. Copyright © 2019, The Authors. All rights reserved.

关键词： Kinematics

Ultrabright ratiometric Raman-guided epilepsy surgery by intraoperatively visualizing proinflammatory microglia

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Cell Reports Medicine 2025年第6期6卷 102155页

作者： Wang, Cong Li, Zhi Zhu, Xiao Sun, Wanbing Ding, Yue Duan, Wenjia Wang, Difei Jiang, Yiqing Chen, Ming Chen, Yuncan Hu, Jiayi Cai, Zheping Zhao, Jing Wang, Junfeng Fan, Zhen Zheng, Faming Zhou, Xingyu Xie, Fang Zhang, Jianping Guan, Yihui Yan, Kui Lei, Zuhai Wang, Qinyue Wang, Luting Xiao, Xiao Zheng, Hairong Chen, Liang Li, Cong Mao, Ying MOE Key Laboratory of Smart Drug Delivery MOE Innovative Center for New Drug Development of Immune Inflammatory Diseases School of Pharmacy Zhongshan Hospital Fudan University Shanghai China Greater Bay Area Institute of Precision Medicine (Guangzhou) Fudan University Guangzhou China The Key Laboratory of Biomedical Imaging Science and System Chinese Academy of Sciences State Key Laboratory of Biomedical Imaging Science and System Shenzhen 518055 China Department of Neurosurgery Huashan Hospital Fudan University Shanghai China Department of Neurosurgery/Neuro-oncology State Key Laboratory of Oncology in South China Guangdong Provincial Clinical Research Center for Cancer Sun Yat-sen University Cancer Center Guangzhou China Department of Clinical Pharmacy and Pharmacy Administration School of Pharmacy Fudan University Shanghai China Department of Neurology and Research Center of Neurology in Second Affiliated Hospital and Key Laboratory of Medical Neurobiology of Zhejiang Province Zhejiang University School of Medicine Hangzhou China Shanghai Mental Health Center Shanghai Jiao Tong University School of Medicine Shanghai China Academy of Forensic Science Shanghai Key Laboratory of Forensic Medicine (21DZ2270800) Shanghai Key Laboratory of Forensic Medicine Key Laboratory of Forensic Science Ministry of Justice Shanghai 200063 China Department of Neurosurgery Kings College Hospital NHS Foundation Trust Denmark Hill London United Kingdom Department of Neurology Huashan Hospital Fudan University Shanghai China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Ministry of Education Behavioral and Cognitive Neuroscience Center Institute of Science and Technology for Brain-Inspired Intelligence Shanghai China State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Shanghai China State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Scie

Resective surgery is an effective approach for long-term seizure control in drug-resistant focal epilepsy when the epileptic focus (EF) can be accurately delineated and removed. However, intraoperative mapping of EF with electrocorticography is laborious, time-consuming, and highly vulnerable to the effects of anesthesia. Here, we demonstrated that activated microglia can be reliable biomarkers for EF localization. Leveraging a newly developed ratiometric Raman nanosensor, ultraHOCls, we successfully visualize proinflammatory microglia in live epileptic mice, allowing for precise EF delineation without the interference of anesthesia. Compared to electrocorticography-guided surgery, ultraHOCl-guided surgery results in a substantial 61% reduction in total seizure burden in epileptic mouse models. Notably, ultraHOCls sprayed on freshly excised human brain tissues can effectively discriminate epileptic regions from non-epileptic tissues with high sensitivity (94.89%) and specificity (93.3%). This work provides an alternative strategy for delineating the EF intraoperatively, potentially revolutionizing surgery outcomes in epilepsy patients. © 2025 The Authors

关键词： electrocorticography epilepsy HOCl microglia myeloperoxidase Raman imaging ratiometric surgery

LimeSoDa: A Dataset Collection for Benchmarking of Machine Learning Regressors in Digital Soil Mapping

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arXiv 2025年

作者： Schmidinger, Jonas Vogel, Sebastian Barkov, Viacheslav Pham, Anh-Duy Gebbers, Robin Tavakoli, Hamed Correa, Jose Tavares, Tiago R. Filippi, Patrick Jones, Edward J. Lukas, Vojtech Boenecke, Eric Ruehlmann, Joerg Schroeter, Ingmar Kramer, Eckart Paetzold, Stefan Kodaira, Masakazu Wadoux, Alexandre M.J.-C. Bragazza, Luca Metzger, Konrad Huang, Jingyi Valente, Domingos S.M. Safanelli, Jose L. Bottega, Eduardo L. Dalmolin, Ricardo S.D. Farkas, Csilla Steiger, Alexander Horst, Taciara Z. Ramirez-Lopez, Leonardo Scholten, Thomas Stumpf, Felix Rosso, Pablo Costa, Marcelo M. Zandonadi, Rodrigo S. Wetterlind, Johanna Atzmueller, Martin Osnabrück University Joint Lab Artificial Intelligence and Data Science Osnabrück Germany Department of Agromechatronics Potsdam Germany Piracicaba Brazil The University of Sydney Sydney Institute of Agriculture Sydney Australia Mendel University in Brno Department of Agrosystems and Bioclimatology Brno Czech Republic Leibniz Institute of Vegetable and Ornamental Crops Next Generation Horticultural Systems Grossbeeren Germany Eberswalde University for Sustainable Development Landscape Management and Nature Conservation Eberswalde Germany Soil Science and Soil Ecology Bonn Germany Tokyo University of Agriculture and Technology Institute of Agriculture Tokyo Japan LISAH Univ. Montpellier AgroParisTech INRAE IRD L'Institut Agro Montpellier France Agroscope Field-Crop Systems and Plant Nutrition Nyon Switzerland University of Wisconsin-Madison Department of Soil Science Madison United States Federal University of Viçosa Department of Agricultural Engineering Viçosa Brazil Woodwell Climate Research Center Falmouth United States Academic Coordination Santa Maria Brazil Soil Department Santa Maria Brazil Division of Environment and Natural Resources Aas Norway University of Rostock Chair of Geodesy and Geoinformatics Rostock Germany Federal Technological University of Paraná Dois Vizinhos Brazil BÜCHI Labortechnik AG Data Science Department Flawil Switzerland Imperial College London Imperial College Business School London United Kingdom University of Tübingen Department of Geosciences Tübingen Germany University of Tübingen DFG Cluster of Excellence Machine Learning for Science’ Germany Bern University of Applied Sciences Competence Center for Soils Zollikofen Switzerland Simulation and Data Science Müncheberg Germany Federal University of Jataí Institute of Agricultural Sciences Jatai Brazil Federal University of Mato Grosso Instute of Agricultural and Environmental Scinces Sinop Brazil Department of Soil and Environment Skara

Digital soil mapping (DSM) relies on a broad pool of statistical methods, yet determining the optimal method for a given context remains challenging and contentious. Benchmarking studies on multiple datasets are needed to reveal strengths and limitations of commonly used methods. Existing DSM studies usually rely on a single dataset with restricted access, leading to incomplete and potentially misleading conclusions. To address these issues, we introduce an open-access dataset collection called Precision Liming Soil Datasets (LimeSoDa). LimeSoDa consists of 31 field- and farm-scale datasets from various countries. Each dataset has three target soil properties: (1) soil organic matter or soil organic carbon, (2) clay content and (3) pH, alongside a set of features. Features are dataset-specific and were obtained by optical spectroscopy, proximal- and remote soil sensing. All datasets were aligned to a tabular format and are ready-to-use for modeling. We demonstrated the use of LimeSoDa for benchmarking by comparing the predictive performance of four learning algorithms across all datasets. This comparison included multiple linear regression (MLR), support vector regression (SVR), categorical boosting (CatBoost) and random forest (RF). The results showed that although no single algorithm was universally superior, certain algorithms performed better in specific contexts. MLR and SVR performed better on high-dimensional spectral datasets, likely due to better compatibility with principal components. In contrast, CatBoost and RF exhibited considerably better performances when applied to datasets with a moderate number ( © 2025, CC BY-SA.

关键词： Adversarial machine learning

Author Correction: A consensus protocol for functional connectivity analysis in the rat brain

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Nature neuroscience 2023年第6期26卷 2023 Apr 18页

作者： Joanes Grandjean Gabriel Desrosiers-Gregoire Cynthia Anckaerts Diego Angeles-Valdez Fadi Ayad David A Barrière Ines Blockx Aleksandra Bortel Margaret Broadwater Beatriz M Cardoso Marina Célestine Jorge E Chavez-Negrete Sangcheon Choi Emma Christiaen Perrin Clavijo Luis Colon-Perez Samuel Cramer Tolomeo Daniele Elaine Dempsey Yujian Diao Arno Doelemeyer David Dopfel Lenka Dvořáková Claudia Falfán-Melgoza Francisca F Fernandes Caitlin F Fowler Antonio Fuentes-Ibañez Clément M Garin Eveline Gelderman Carla E M Golden Chao C G Guo Marloes J A G Henckens Lauren A Hennessy Peter Herman Nita Hofwijks Corey Horien Tudor M Ionescu Jolyon Jones Johannes Kaesser Eugene Kim Henriette Lambers Alberto Lazari Sung-Ho Lee Amanda Lillywhite Yikang Liu Yanyan Y Liu Alejandra López-Castro Xavier López-Gil Zilu Ma Eilidh MacNicol Dan Madularu Francesca Mandino Sabina Marciano Matthew J McAuslan Patrick McCunn Alison McIntosh Xianzong Meng Lisa Meyer-Baese Stephan Missault Federico Moro Daphne M P Naessens Laura J Nava-Gomez Hiroi Nonaka Juan J Ortiz Jaakko Paasonen Lore M Peeters Mickaël Pereira Pablo D Perez Marjory Pompilus Malcolm Prior Rustam Rakhmatullin Henning M Reimann Jonathan Reinwald Rodrigo Triana Del Rio Alejandro Rivera-Olvera Daniel Ruiz-Pérez Gabriele Russo Tobias J Rutten Rie Ryoke Markus Sack Piergiorgio Salvan Basavaraju G Sanganahalli Aileen Schroeter Bhedita J Seewoo Erwan Selingue Aline Seuwen Bowen Shi Nikoloz Sirmpilatze Joanna A B Smith Corrie Smith Filip Sobczak Petteri J Stenroos Milou Straathof Sandra Strobelt Akira Sumiyoshi Kengo Takahashi Maria E Torres-García Raul Tudela Monica van den Berg Kajo van der Marel Aran T B van Hout Roberta Vertullo Benjamin Vidal Roël M Vrooman Victora X Wang Isabel Wank David J G Watson Ting Yin Yongzhi Zhang Stefan Zurbruegg Sophie Achard Sarael Alcauter Dorothee P Auer Emmanuel L Barbier Jürgen Baudewig Christian F Beckmann Nicolau Beckmann Guillaume J P C Becq Erwin L A Blezer Radu Bolbos Susann Boretius Sandrine Bouvard Eike Budinger Joseph D Buxbaum Diana Cash Victor Donders Institute for Brain Behaviour and Cognition Radboud University Nijmegen The Netherlands. joanes.grandjean@radboudumc.nl. Department for Medical Imaging Radboud University Medical Center Nijmegen The Netherlands. joanes.grandjean@radboudumc.nl. Cerebral Imaging Centre Douglas Mental Health University Institute Verdun QC Canada. Integrated Program in Neuroscience McGill University Montreal QC Canada. Bio-imaging Lab University of Antwerp Antwerp Belgium. µNEURO Research Centre of Excellence University of Antwerp Antwerp Belgium. Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro Mexico. Biological and Biomedical Engineering McGill University Montreal QC Canada. McConnell Brain Imaging Centre McGill University Montreal QC Canada. Montreal Neurological Institute McGill University Montreal QC Canada. UMR INRAE/CNRS 7247 Physiologie des Comportements et de la Reproduction Physiologie de la reproduction et des comportements Centre de recherche INRAE de Nouzilly Tours France. Department of Neurology and Neurosurgery McGill University Montreal QC Canada. Center for Animal MRI University of North Carolina at Chapel Hill Chapel Hill NC USA. Department of Neurology University of North Carolina at Chapel Hill Chapel Hill NC USA. Biomedical Research Imaging Center University of North Carolina at Chapel Hill Chapel Hill NC USA. Preclinical MRI Champalimaud Research Champalimaud Centre for the Unknown Lisbon Portugal. Laboratoire des Maladies Neurodégénératives Molecular Imaging Research Center (MIRCen) Université Paris-Saclay Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA) CNRS Fontenay-aux-Roses France. Departamento de Neurobiología Conductual y Cognitiva Instituto de Neurobiología Universidad Nacional Autónoma de México Campus Juriquilla Querétaro México. Translational Neuroimaging and Neural Control Group High-Field Magnetic Resonance Max Planck Institute for Biological Cyberneti

Beyond FITT: addressing density in understanding the dose-response relationships of physical activity with health-an example based on brain health

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European journal of applied physiology 2025年 1-29页

作者： Fabian Herold Liye Zou Paula Theobald Patrick Manser Ryan S Falck Qian Yu Teresa Liu-Ambrose Arthur F Kramer Kirk I Erickson Boris Cheval Yanxia Chen Matthew Heath Zhihao Zhang Toru Ishihara Keita Kamijo Soichi Ando Joseph T Costello Mats Hallgren David Moreau Vahid Farrahi David A Raichlen Emmanuel Stamatakis Michael J Wheeler Neville Owen Sebastian Ludyga Henning Budde Thomas Gronwald Department of Physiology Faculty of Medicine HMU Health and Medical University Erfurt Erfurt Germany. Institute of Interdisciplinary Exercise Science and Sports Medicine MSH Medical School Hamburg Am Kaiserkai 1 20457 Hamburg Germany. Research Group Degenerative and Chronic Diseases Movement Faculty of Health Sciences Brandenburg University of Potsdam Potsdam Germany. Body-Brain-Mind Laboratory Shenzhen University Shenzhen China. Motor Control and Learning Group Department of Health Sciences and Technology Institute of Human Movement Sciences and Sport ETH Zurich Zurich Switzerland. Division of Physiotherapy Department of Neurobiology Care Sciences and Society Karolinska Institute Stockholm Sweden. School of Biomedical Engineering The University of British Columbia Vancouver BC Canada. Aging Mobility and Cognitive Health Laboratory Department of Physical Therapy The University of British Columbia Vancouver BC Canada. Djavad Mowafaghian Centre for Brain Health Vancouver Coastal Health Research Institute Vancouver BC Canada. Centre for Aging Solutions for Mobility Activity Rehabilitation and Technology (SMART) at Vancouver Coastal Health Vancouver Coastal Health Research Institute Vancouver BC Canada. Center for Cognitive and Brain Health Northeastern University Boston MA USA. Department of Psychology Northeastern University Boston MA USA. Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign Urbana IL USA. Department of Neuroscience AdventHealth Research Institute AdventHealth Orlando FL USA. Department of Psychology University of Pittsburgh Pittsburgh PA USA. Center for the Neural Basis of Cognition University of Pittsburgh Pittsburgh PA USA. Department of Sport Sciences and Physical Education Ecole Normale Supérieure Rennes Bruz France. Laboratory VIPS2 University of Rennes Rennes France. School of Kinesiology Faculty of Health Sciences University of Western Ontario London ON N6A 3K7 Ca

Research on physical activity (PA) and health has a fundamental concern with dose-response relationships. The variables of (1) Frequency, (2) Intensity, (3) Time, and (4) Type (i.e., the FITT principle) have traditionally been used to operationalize the dosage of PA. We consider some limitations of FITT and propose that it can be complemented by the additional variable density (from the German exercise and training variable Belastungsdichte), which can be defined as the timing of successive work bouts within a single PA bout as well as the timing between successive PA bouts within a specific time period; it does so by quantifying the temporal intervals between successive work or PA bouts (i.e., time spent at a lower PA intensity or resting such as in napping/sleeping or sedentary behaviors). Using the field of PA and brain health as an example, we discuss the opportunities and challenges for further research employing the variable density and consider its potential to improve the understanding of dose-response relationships between PA and health outcomes.

关键词： Brain Cognition Dose Physical exercise Sedentary behavior