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arXiv 2020年

作者： Susman, Lee Mastrogiuseppe, Francesca Brenner, Naama Barak, Omri Interdisciplinary Program in Applied Mathematics Technion Israel Institute of Technology Haifa32000 Israel Network Biology Research Laboratories Technion Israel Institute of Technology Haifa32000 Israel Gatsby Computational Neuroscience Unit University College London London United Kingdom Dept. of Chemical Engineering Technion Israel Institute of Technology Haifa32000 Israel Rappaport Faculty of Medicine Technion Israel Institute of Technology Haifa32000 Israel

A fundamental feature of complex biological systems is the ability to form feedback interactions with their environment. A prominent model for studying such interactions is reservoir computing, where learning acts on low-dimensional bottlenecks. Despite the simplicity of this learning scheme, the factors contributing to or hindering the success of training in reservoir networks are in general not well understood. In this work, we study non-linear feedback networks trained to generate a sinusoidal signal, and analyze how learning performance is shaped by the interplay between internal network dynamics and target properties. By performing exact mathematical analysis of linearized networks, we predict that learning performance is maximized when the target is characterized by an optimal, intermediate frequency which monotonically decreases with the strength of the internal reservoir connectivity. At the optimal frequency, the reservoir representation of the target signal is high-dimensional, de-synchronized, and thus maximally robust to noise. We show that our predictions successfully capture the qualitative behaviour of performance in non-linear networks. Moreover, we find that the relationship between internal representations and performance can be further exploited in trained non-linear networks to explain behaviours which do not have a linear counterpart. Our results indicate that a major determinant of learning success is the quality of the internal representation of the target, which in turn is shaped by an interplay between parameters controlling the internal network and those defining the task. © 2020, CC BY.

关键词： Quality control

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-dept. literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Centenarian clocks: epigenetic clocks for validating claims of exceptional longevity

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GEROSCIENCE 2023年第3期45卷 1817-1835页

作者： Dec, Eric Clement, James Cheng, Kaiyang Church., George M. Fossel., Michael B. Rehkopf, David. H. Rosero-Bixby, Luis Kobor, Michael S. Lin, David TS. Lu, Ake T. Fei, Zhe Guo, Wei Chew, Yap Ching Yang, Xiaojing Putra, Sulistyo E. Dwi Reiner, Alex P. Correa, Adolfo Vilalta, Adrian Pirazzini, Chiara Passarino, Giuseppe Monti, Daniela Arosio, Beatrice Garagnani, Paolo Franceschi, Claudio Horvath, Steve University of California Department of Pediatrics Division of Genetics and Genomic Medicine Irvine USA (GRID:grid.266093.8) (ISNI:0000 0001 0668 7243) Betterhumans Inc. Gainesville USA (GRID:grid.266093.8) University of Ferrara Department of Chemical Pharmaceutical and Agricultural Sciences Ferrara Italy (GRID:grid.8484.0) (ISNI:0000 0004 1757 2064) University of California Los Angeles Medical Informatics David Geffen School of Medicine Los Angeles USA (GRID:grid.19006.3e) (ISNI:0000 0000 9632 6718) Harvard Medical School Department of Genetics Boston USA (GRID:grid.38142.3c) (ISNI:***) Wyss Institute for Biologically Inspired Engineering Harvard University Cambridge USA (GRID:grid.38142.3c) (ISNI:***) Telocyte LLC Grand Rapids USA (GRID:grid.38142.3c) Stanford University Epidemiology & Population Health and Medicine School of Medicine Stanford USA (GRID:grid.168010.e) (ISNI:***) Universidad de Costa Rica Centro Centroamericano de Población San Pedro Costa Rica (GRID:grid.412889.e) (ISNI:0000 0004 1937 0706) University of British Columbia Edwin S.H. Leong Healthy Aging Program Centre for Molecular Medicine and Therapeutics Vancouver Canada (GRID:grid.17091.3e) (ISNI:0000 0001 2288 9830) University of California Los Angeles Dept. of Human Genetics David Geffen School of Medicine Los Angeles USA (GRID:grid.19006.3e) (ISNI:0000 0000 9632 6718) Altos Labs San Diego USA (GRID:grid.518162.9) (ISNI:0000 0005 0774 3285) University of California Los Angeles Dept. of Biostatistics Fielding School of Public Health Los Angeles USA (GRID:grid.19006.3e) (ISNI:0000 0000 9632 6718) Zymo Research Corp Irvine USA (GRID:grid.19006.3e) University of Surabaya Department of Biology Faculty of Biotechnology Surabaya Indonesia (GRID:grid.444430.3) (ISNI:0000 0000 8739 9595) Public Health Sciences Division Fred Hutchinson Cancer Research Center Seattle USA (GRID:grid.270240.3) (ISNI:0000 0001 2180 1622) University of Mississippi Medical Center Medicine and Population Health Science Ja

Claims surrounding exceptional longevity are sometimes disputed or dismissed for lack of credible evidence. Here, we present three DNA methylation-based age estimators (epigenetic clocks) for verifying age claims of centenarians. The three centenarian clocks were developed based on n = 7039 blood and saliva samples from individuals older than 40, including n = 184 samples from centenarians, 122 samples from semi-supercentenarians (aged 105 +), and 25 samples from supercentenarians (aged 110 +). The oldest individual was 115 years old. Our most accurate centenarian clock resulted from applying a neural network model to a training set composed of individuals older than 40. An epigenome-wide association study of age in different age groups revealed that age effects in young individuals (age < 40) are correlated (r = 0.55) with age effects in old individuals (age > 90). We present a chromatin state analysis of age effects in centenarians. The centenarian clocks are expected to be useful for validating claims surrounding exceptional old age.

关键词： Centenarian clocks Epigenetic clocks Longevity

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#33: Single-cell RNA sequencing analysis of Zika virus infection in human stem cell-derived neuroprogenitor cells and cerebral organoids

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Journal of the Pediatric Infectious Diseases Society 2021年第SUPPLEMENT_2期10卷 S11-S12页

作者： Ocwieja, K E Hughes, T K Baker, C C M Stanton, A C Antonucci, J M Richards, A L Shalek, A Jaenisch, R Gehrke, L Inst. for Medical Engineering and Science Massachusetts Inst. of Technology (MIT) Dept. of Pediatrics Boston Children’s Hospital Harvard Medical School Virology Graduate Program Harvard Medical School (HMS) Whitehead Inst. for Biomedical Research Dept. of Chemistry and Koch Institute for Integrative Cancer Research MIT Ragon Institute of MGH MIT and Harvard Broad Institute of MIT and Harvard

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Patient-Clinician Brain Response During Clinical Encounter and Pain Treatment

Patient-Clinician Brain Response During Clinical Encounter a...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： A. Anzolin K. Isenburg A. Grahl J. Toppi M. Yücel D.M. Ellingsen J. Gerber A. Ciaramidaro L. Astolfi T.J. Kaptchuk V. Napadow Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital and Harvard Medical School Boston MA USA Dept. of Computer Control and Management Engineering "Sapienza" University of Rome Italy Boston University Neurophotonics Center Department of Education and Human Sciences University of Modena and Reggio Emilia Modena Italy Program in Placebo Studies Beth Israel Deaconess Medical Center Harvard Medical School Boston MA USA Department of Anesthesiology Brigham and Women’s Hospital

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

The patient-clinician relationship is known to significantly affect the pain experience, as empathy, mutual trust and therapeutic alliance can significantly modulate pain perception and influence clinical therapy outcomes. The aim of the present study was to use an EEG hyperscanning setup to identify brain and behavioral mechanisms supporting the patient-clinician relationship while this clinical dyad is engaged in a therapeutic interaction. Our previous study applied fMRI hyperscanning to investigate whether brain concordance is linked with analgesia experienced by a patient while undergoing treatment by the clinician. In this current hyperscanning project we investigated similar outcomes for the patient-clinician dyad exploiting the high temporal resolution of EEG and the possibility to acquire the signals while patients and clinicians were present in the same room and engaged in a face-to-face interaction under an experimentally-controlled therapeutic context. Advanced source localization methods allowed for integration of spatial and spectral information in order to assess brain correlates of therapeutic alliance and pain perception in different clinical interaction contexts. Preliminary results showed that both behavioral and brain responses across the patient-clinician dyad were significantly affected by the interaction *** Relevance- The context of a clinical intervention can significantly impact the treatment of chronic pain. Effective therapeutic alliance, based on empathy, mutual trust, and warmth can improve treatment adherence and clinical outcomes. A deeper scientific understanding of the brain and behavioral mechanisms underlying an optimal patient-clinician interaction may lead to improved quality of clinical care and physician training, as well as better understanding of the social aspects of the biopsychosocial model mediating analgesia in chronic pain patients.

关键词： Pain Electroencephalography biomedical imaging Hospitals Spatial resolution

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A Method of Swimming Rat Detection in Morris Water Maze by Using Image Processing

A Method of Swimming Rat Detection in Morris Water Maze by U...

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International Electrical engineering Congress (iEECON)

作者： Pattranis Khunarsar Nontapat Benjathum Theekapun Charoenpong Ampom Jariyapongskul Dept. of Biomedical Engineering Srinakharinwirot University Nakhonnayok Thailand Biomedical Sciences Program Srinakharinwirot University Bangkok Thailand Dept. of Physiology Srinakharinwirot University Bangkok Thailand

ISBN: (纸本)9781538623183;9781538623176

To test the effect of drug on the brain in psychology, brain science and medical science, the rat behavior analysis is a necessary experiment. Morris water maze is a popular neurological model to analyze rat behavior for evaluating rat's ability of study and memory. The ability is evaluated by the time and the distance. However, rat detection is principle process for rat behavior analyzation. In this paper, we proposed a method to detect rat's position in Morris water maze by image processing. Gaussian Mixture Models is used for foreground extraction. The Models is used to compute a multivariate distribution of image sequence. The complexity for automatic rat's behavior analyzation is that the rat has same color with the water. All are white color. To test the performance of the proposed method, five image sequence were used. It consists of 11,655 images. Detection accuracy rate was 83.53%. This method perform effectively even though the rat has same color with the water.

关键词： Rats Image color analysis Mathematical model Image sequences Sports Gaussian mixture model

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AHR is a Zika virus host factor and a candidate target for antiviral therapy (vol 52, pg 941, 2020)

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NATURE NEUROSCIENCE 2020年第10期23卷 1307-1307页

作者： Giovannoni, Federico Bosch, Irene Polonio, Carolina Manganeli Torti, Maria F. Wheeler, Michael A. Li, Zhaorong Romorini, Leonardo Rodriguez Varela, Maria S. Rothhammer, Veit Barroso, Andreia Tjon, Emily C. Sanmarco, Liliana M. Takenaka, Maisa C. Modaresi, Seyed Mohamad Sadegh Gutierrez-Vazquez, Cristina Zanluqui, Nagela Ghabdan dos Santos, Nilton Barreto Munhoz, Carolina Demarchi Wang, Zhongyan Damonte, Elsa B. Sherr, David Gehrke, Lee Peron, Jean Pierre Schatzmann Garcia, Cybele C. Quintana, Francisco J. Ann Romney Center for Neurologic Diseases Brigham and Women’s Hospital Harvard Medical School Boston MA USA Laboratorio de Estrategias Antivirales Departamento de Química Biológica Facultad de Ciencias Exactas y Naturales Universidad de Buenos Aires. CONICET—Instituto de Química Biológica Buenos Aires Argentina Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA USA Department of Medicine Mount Sinai School of Medicine New York NY USA Neuroimmune Interactions Laboratory Immunology Department—ICB IV University of São Paulo São Paulo Brazil Scientific Platform Pasteur—USP University of São Paulo São Paulo Brazil Laboratorio de Investigación aplicada a Neurociencias Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia Buenos Aires Argentina Immunopathology and Allergy Post Graduate Program School of Medicine University of São Paulo São Paulo Brazil Department of Pharmacology Institute of Biomedical Science University of São Paulo São Paulo Brazil Dept. of Environmental Health Boston University School of Public Health Boston MA USA Program in Virology Division of Medical Sciences Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA

Zika virus (ZIKV) is a flavivirus linked to multiple birth defects including microcephaly, known as congenital ZIKV syndrome. The identification of host factors involved in ZIKV replication may guide efficacious therapeutic interventions. In genome-wide transcriptional studies, we found that ZIKV infection triggers aryl hydrocarbon receptor (AHR) activation. Specifically, ZIKV infection induces kynurenine (Kyn) production, which activates AHR, limiting the production of type I interferons (IFN-I) involved in antiviral immunity. Moreover, ZIKV-triggered AHR activation suppresses intrinsic immunity driven by the promyelocytic leukemia (PML) protein, which limits ZIKV replication. AHR inhibition suppressed the replication of multiple ZIKV strains in vitro and also suppressed replication of the related flavivirus dengue. Finally, AHR inhibition with a nanoparticle-delivered AHR antagonist or an inhibitor developed for human use limited ZIKV replication and ameliorated newborn microcephaly in a murine model. In summary, we identified AHR as a host factor for ZIKV replication and PML protein as a driver of anti-ZIKV intrinsic immunity.

关键词： Immunology Microbiology

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Roadmap on Deep Learning for Microscopy

arXiv

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arXiv 2023年

作者： Volpe, Giovanni Wählby, Carolina Tian, Lei Hecht, Michael Yakimovich, Artur Monakhova, Kristina Waller, Laura Sbalzarini, Ivo F. Metzler, Christopher A. Xie, Mingyang Zhang, Kevin Lenton, Isaac C.D. Rubinsztein-Dunlop, Halina Brunner, Daniel Bai, Bijie Ozcan, Aydogan Midtvedt, Daniel Wang, Hao Sladoje, Nataša Lindblad, Joakim Smith, Jason T. Ochoa, Marien Barroso, Margarida Intes, Xavier Qiu, Tong Yu, Li-Yu You, Sixian Liu, Yongtao Ziatdinov, Maxim A. Kalinin, Sergei V. Sheridan, Arlo Manor, Uri Nehme, Elias Goldenberg, Ofri Shechtman, Yoav Moberg, Henrik K. Langhammer, Christoph Špačková, Barbora Helgadottir, Saga Midtvedt, Benjamin Argun, Aykut Thalheim, Tobias Cichos, Frank Bo, Stefano Hubatsch, Lars Pineda, Jesus Manzo, Carlo Bachimanchi, Harshith Selander, Erik Homs-Corbera, Antoni Fränzl, Martin de Haan, Kevin Rivenson, Yair Korczak, Zofia Adiels, Caroline Beck Mijalkov, Mite Veréb, Dániel Chang, Yu-Wei Pereira, Joana B. Matuszewski, Damian Kylberg, Gustaf Sintorn, Ida-Maria Caicedo, Juan C. Cimini, Beth A. Bell, Muyinatu Lediju A. Saraiva, Bruno M. Jacquemet, Guillaume Henriques, Ricardo Ouyang, Wei Le, Trang Gómez-De-Mariscal, Estibaliz Sage, Daniel Muñoz-Barrutia, Arrate Lindqvist, Ebba Josefson Bergman, Johanna Department of Physics University of Gothenburg Gothenburg41296 Sweden Dept. IT and SciLifeLab Uppsala University Sweden Department of Electrical and Computer Engineering Boston University BostonMA02215 United States Department of Biomedical Engineering Boston University BostonMA02215 United States Görlitz Germany Department of Renal Medicine Division of Medicine University College London Royal Free Hospital Campus London United Kingdom Artificial Intelligence for Life Sciences CIC Dorset United Kingdom Roche Pharma International Informatics Roche Diagnostics GmbH Mannheim Germany Department of Electrical Engineering and Computer Sciences UC Berkeley United States Technische Universität Dresden Faculty of Computer Science Dresden Germany Max Planck Institute of Molecular Cell Biology and Genetics Center for Systems Biology Dresden Dresden Germany University of Maryland College Park United States Klosterneuburg3400 Austria School of Mathematics and Physics The University of Queensland Brisbane4072 Australia Australian Research Council Centre of Excellence for Engineered Quantum Systems The University of Queensland Brisbane4072 Australia Département d’Optique P. M. Duffieux Institut FEMTO-ST Université Franche-Comté CNRS UMR 6174 Besançon France Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA United States Department of Information Technology Uppsala University Sweden Department of Biomedical Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Elephas 1 Erdman Pl. Madison53705 United States University of Wisconsin–Madison Department of Medical Physics MadisonWI53705 United States Department of Molecular and Cellular Physiology Albany Medical College AlbanyNY12208 United States Research Laboratory of Electronics Electrical Engineering and Computer Science Massachusetts Institute of Technology BostonMA United States Center for Nanophase Materials Sciences Oak R

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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Considerations and Recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 1 — In vivo small-animal imaging

arXiv

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arXiv 2022年

作者： Jelescu, Ileana O. 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 de Looij, Yohan van Hike, David Dunn, Jeff F. Miller, Karla Landman, Bennett A. Shemesh, Noam Anderson, Adam McKinnon, Emilie Farquharson, Shawna Acqua, Flavio Dell 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. Schilling, Kurt G. Department of Radiology Lausanne University Hospital University of Lausanne Lausanne Switzerland CIBM Center for Biomedical Imaging Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland 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 Resonance National HIgh Magnetic Field Laboratory TallahasseeFL United States Department of Neuropsychology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany USC Stevens Neuroimaging and Informatics Instit

Small-animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected recommendations and guidelines from the diffusion community, on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We then give guidelines for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including pre-processing, model-fitting, and tractography. Finally, we provide an online resource which lists publicly available preclinical dMRI datasets and software packages, to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. While we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal herein is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge. © 2022, CC BY-NC-ND.

关键词： Invertebrates

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AB0561 AUTOMATIC QUANTIFICATION OF INTERSTITIAL LUNG DISEASE FROM CHEST COMPUTED TOMOGRAPHY IN SYSTEMIC SCLERODERMA

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Annals of the Rheumatic Diseases 2020年 79卷 1577-1578页

作者： A. Carvalho A. Guimarães F.R. Sztajnbok R. Rodrigues B. Silva A. Lopes I. Almeida M. França Medical School Hosp. de Santo António (HSA) Centro Hospitalar do Porto (CHP) Instituto de Ciências Biomédicas Abel Salazar (ICBAS) Univ. do Porto Department of Radiology Porto Portugal Laboratory of Pulmonary Engineering Biomedical Engineering Program Alberto Luiz Coimbra Institute of Post-Graduation and Research in Engineering Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil Department of Reumathology State University of Rio de Janeiro Rio de Janeiro Brazil Department of Radiology Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil Postgraduate Program in Medical Sciences School of Medical Sciences State University of Rio de Janeiro Rio de Janeiro Brazil Clinical Immunology Unit Dept. of Medicine Hosp. de Santo António (HSA) Centro Hospitalar do Porto (CHP) Instituto de Ciências Biomédicas Abel Salazar (ICBAS) Univ. do Porto Porto Portugal Radiology Unit Dept. of Radiology Hosp. de Santo António (HSA) Centro Hospitalar do Porto (CHP) Instituto de Ciências Biomédicas Abel Salazar (ICBAS) Univ. do Porto Porto Portugal

Background: Scleroderma-associated interstitial lung disease (SSc-ILD) is often observed in patients with systemic scleroderma (SSc) and its diagnosis contributes to early treatment decisions 1,2 . Objectives: The present study aims to automatically quantify SSc-ILD from high-resolution chest-computed tomography (HRCT) and to evaluate the association between interstitial lung disease (ILD) extension and lung function impairment. Methods: Ninety-four patients with SSc and 27 lung-healthy subjects matched for gender, weight, height, and age underwent HRCT, spirometry and carbon monoxide diffusion capacity (DL CO ). SSc-ILD was determined as the tissue mass present between -500 and +100 Hounsfield Units normalized by the total lung tissue mass (TLM). Cut off was the highest value obtained in the control group (25% of TLM). All data are presented as mean and standard deviations (Table I). An ANOVA test followed by Bonferroni post-hoc correction was used for comparisons among groups. Results: From 94 patients with SSc, 64 were classified as having pulmonary involvement (SSc-ILD) and 30 as not having pulmonary involvement (SSc No-ILD). In SSc-ILD subjects, there was a significant reduction in forced vital capacity (FVC), carbon monoxide diffusion capacity (DL CO ) and carbon monoxide diffusion capacity normalized by alveolar ventilation (DL CO /A) when compared with SSc No-ILD and control group. Conclusion: The proposed method allows the automatic quantification of SSc-ILD from HRCT and ILD extent is associated with pulmonary function impairment. References: [1]Doyle TJ, Dellaripa PF. Lung Manifestations in the Rheumatic Diseases. CHEST. 2017 Dec;152(6):1283–95. [2]Saketkoo LA, Magnus JH, Doyle MK. The primary care physician in the early diagnosis of systemic sclerosis: the cornerstone of recognition and hope. The American Journal of the Medical Sciences. 2014 Jan;347(1):54–63. Disclosure of Interests: None declared Table 1 . Demographic variables, pulmonary function test

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