Academic literature on the topic 'Pain network'
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Journal articles on the topic "Pain network"
Zheng, Weihao, Choong-Wan Woo, Zhijun Yao, Pavel Goldstein, Lauren Y. Atlas, Mathieu Roy, Liane Schmidt, et al. "Pain-Evoked Reorganization in Functional Brain Networks." Cerebral Cortex 30, no. 5 (December 9, 2019): 2804–22. http://dx.doi.org/10.1093/cercor/bhz276.
Full textMeier, Sarah K., Kimberly L. Ray, Noah C. Waller, Barry C. Gendron, Semra A. Aytur, and Donald A. Robin. "Network Analysis of Induced Neural Plasticity Post-Acceptance and Commitment Therapy for Chronic Pain." Brain Sciences 11, no. 1 (December 23, 2020): 10. http://dx.doi.org/10.3390/brainsci11010010.
Full textCockett, Andrea. "Network on pain management." Paediatric Nursing 14, no. 4 (May 2002): 20. http://dx.doi.org/10.7748/paed.14.4.20.s23.
Full textSingavi, Arun, Guangyu Chen, Nancy Wandersee, Collin Hubler, Amanda M. Brandow, Pippa Simpson, Shi-Jiang Li, and Joshua J. Field. "Daily Pain Is Associated with Alterations in Functional Connectivity of the Brain on fMRI in Adults with Sickle Cell Disease." Blood 128, no. 22 (December 2, 2016): 3656. http://dx.doi.org/10.1182/blood.v128.22.3656.3656.
Full textMogil, Jeffrey S. "Friends in pain: pain tolerance in a social network." Scandinavian Journal of Pain 18, no. 3 (July 26, 2018): 343–44. http://dx.doi.org/10.1515/sjpain-2018-0072.
Full textHosomi, Koichi, Ben Seymour, and Youichi Saitoh. "Modulating the pain network—neurostimulation for central poststroke pain." Nature Reviews Neurology 11, no. 5 (April 21, 2015): 290–99. http://dx.doi.org/10.1038/nrneurol.2015.58.
Full textHuang, Dong, Zhaoqiang Xia, Lei Li, Kunwei Wang, and Xiaoyi Feng. "Pain-awareness multistream convolutional neural network for pain estimation." Journal of Electronic Imaging 28, no. 04 (July 11, 2019): 1. http://dx.doi.org/10.1117/1.jei.28.4.043008.
Full textHe, Hui, Lan Hu, Saiying Tan, Yingjie Tang, Mingjun Duan, Dezhong Yao, Guocheng Zhao, and Cheng Luo. "Functional Changes of White Matter Are Related to Human Pain Sensitivity during Sustained Nociception." Bioengineering 10, no. 8 (August 21, 2023): 988. http://dx.doi.org/10.3390/bioengineering10080988.
Full textSeminowicz, David A., and Karen D. Davis. "Pain Enhances Functional Connectivity of a Brain Network Evoked by Performance of a Cognitive Task." Journal of Neurophysiology 97, no. 5 (May 2007): 3651–59. http://dx.doi.org/10.1152/jn.01210.2006.
Full textPereira, Naiara Lima, Mirelly Tavares Feitosa Pereira, Gisele de Souza Costa, André Luiz Machado das Neves, Izaura Rodrigues Nascimento, and Zilmar Augusto de Souza Filho. "“Body and Soul Pain”." International Journal for Innovation Education and Research 7, no. 12 (December 31, 2019): 644–57. http://dx.doi.org/10.31686/ijier.vol7.iss12.2118.
Full textDissertations / Theses on the topic "Pain network"
Rissacher, Daniel J. "Neural network recognition of pain state in EEG recordings." Thesis, Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/16646.
Full textBoshoff, Susan. "Absenteeism and musculoskeletal pain : an interactive network of variables." Master's thesis, University of Cape Town, 2000. http://hdl.handle.net/11427/3367.
Full textAwang, Mahmud Awang Bulgiba. "Application of statistical and neural network techniques to chest pain diagnosis." Thesis, University of East Anglia, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.430583.
Full textSamineni, Vijaya K. "The role of the periaqueductal gray in modulation of acute and chronic pain: Actions of drugs with analgesic properties on periaqueductal gray neuronal network." OpenSIUC, 2013. https://opensiuc.lib.siu.edu/dissertations/701.
Full textRogoz, Katarzyna. "Signaling Mechanisms in the Neuronal Networks of Pain and Itch." Doctoral thesis, Uppsala universitet, Genetisk utvecklingsbiologi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-183255.
Full textEnglish, Amber. "The relationship between pain and arousal: The modulation of noxious sensation by the brain’s alerting network." Thesis, English, Amber (2017) The relationship between pain and arousal: The modulation of noxious sensation by the brain’s alerting network. Honours thesis, Murdoch University, 2017. https://researchrepository.murdoch.edu.au/id/eprint/40612/.
Full textSnyder, Kristian. "Utilizing Convolutional Neural Networks for Specialized Activity Recognition: Classifying Lower Back Pain Risk Prediction During Manual Lifting." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1583999458096255.
Full textAlharbi, Ghaleb. "Evidence-based medicine in neuropathic pain : a systematic review, meta-analysis, sequential analysis and network meta-analysis of randomised controlled trials." Thesis, University of Nottingham, 2018. http://eprints.nottingham.ac.uk/55427/.
Full textCazzanelli, Silvia. "Functional ultrasound (fUS) imaging of brain functional connectivity alterations in a mouse model of neuropathic pain : impact of nociceptive symptoms and associated comorbidities." Electronic Thesis or Diss., Université Paris sciences et lettres, 2024. http://www.theses.fr/2024UPSLS010.
Full textNeuropathic pain is an abnormal pain sensation that persists longer than the temporal course of natural healing. It interferes with the patient’s quality of life and leads to several comorbidities, such as anxiety and depression. It has been suggested that chronic pain may result from abnormal and maladaptive neuronal plasticity in the structures known to be involved in pain perception (Bliss et al. 2016). This means that nerve injury would trigger long-term potentiation of synaptic transmission in pain-related areas (Zhuo et al. 2014). Since these regions are also involved in the emotional aspects of pain, our hypothesis is that the aforementioned maladaptive plasticity in these brain areas could constitute a key mechanism for the development of comorbidities such as anxiety and depression.My PhD aimed at testing this working hypothesis, through the study of brain resting state functional connectivity (FC) using functional ultrasound imaging (fUS) in a mouse model of neuropathic pain. FUS is a relatively recent neuroimaging technique that enabled numerous advances in neuroscience, thanks to its high spatio-temporal resolution, its sensitivity, but also its adaptability, allowing studies in anesthetized or awake animals.In a first study, I developed an experimental protocol allowing the brains of awake mice to be imaged in a reproducible manner and with minimal stress and movement artifacts and was also involved in the development of a new algorithm for the analysis of the signals generated by these acquisitions. As this first approach was carried out with a moving linear probe which does not allow the entire brain to be visualized, in a second study, I participated in the development of a new compiled and motorized probe technology.Building on these technological developments, I then used these new approaches to test my neurobiological hypothesis. I undertook two parallel studies in animals anesthetized for one and awake for the second, in which we studied the temporal link between alterations in cerebral FC and the development of neuropathic pain and/or associated comorbidities. To do this, we measured the resting-state functional connectivity (FC) in anesthetized and in awake head-fixed mice, at three time points: I) 2 weeks after induction of neuropathic pain (cuff around the sciatic nerve), II) at 8 weeks post-induction during the emergence of anxiety (8W) and III) at 12 weeks post-induction during the emergence of depression. This longitudinal follow-up has been conducted concurrently on a control group.Our results show significant changes in FC in major pain-related brain regions in accordance with the development of neuropathic pain symptoms. These findings suggest that the pain network undergoes maladaptive plasticity following nerve injury which could contribute to pain chronification. Moreover, the time course of these connectivity alterations between regions of the pain network could be correlated with the subsequent apparition of associated comorbidities
Morabit, Safaa El. "New Artificial Intelligence techniques for Computer vision based medical diagnosis." Electronic Thesis or Diss., Valenciennes, Université Polytechnique Hauts-de-France, 2023. http://www.theses.fr/2023UPHF0013.
Full textThe ability to feel pain is crucial for life, since it serves as an early warning system forpotential harm to the body. The majority of pain evaluations rely on patient reports. Patients who are unable to express their own pain must instead rely on third-party reportsof their suffering. Due to potential observer bias, pain reports may contain inaccuracies. In addition, it would be impossible for people to keep watch around the clock. Inorder to better manage pain, especially in noncommunicative patients, automatic paindetection technologies might be implemented to aid human caregivers and complementtheir service. Facial expressions are used by all observer-based pain assessment systemsbecause they are a reliable indicator of pain and can be interpreted from a distance.Taking into consideration that pain generally generates spontaneous facial behavior,these facial expressions could be used to detect the presence of pain. In this thesis, weanalyze facial expressions of pain in order to address pain estimation. First, we presenta thorough analysis of the problem by comparing numerous common CNN (Convolutional Neural Network) architectures, such as MobileNet, GoogleNet, ResNeXt-50, ResNet18, and DenseNet-161. We employ these networks in two unique modes: standalone and feature extraction. In standalone mode, models (i.e., networks) are utilized to directly estimate pain. In feature extractor mode, "values" from the middle layer are extracted and fed into classifiers like Support Vector Regression (SVR) and Random Forest Regression (RFR).CNNs have achieved significant results in image classification and have achievedgreat success. The effectiveness of Transformers in computer vision has been demonstrated through recent studies. Transformer-based architectures were proposed in the second section of this thesis. Two distinct Transformer-based frameworks were presented to address two distinct pain issues: pain detection (pain vs no pain) and thedistinction between genuine and posed pain. The innovative architecture for binaryidentification of facial pain is based on data-efficient image transformers (Deit). Twodatasets, UNBC-McMaster shoulder pain and BioVid heat pain, were used to fine-tuneand assess the trained model. The suggested architecture is built on Vision Transformers for the detection of genuine and simulated pain from facial expressions (ViT). Todistinguish between Genuine and Posed Pain, the model must pay particular attentionto the subtle changes in facial expressions over time. The employed approach takes intoaccount the sequential aspect and captures the variations in facial expressions. Experiments on the publicly accessible BioVid Heat Pain Database demonstrate the efficacy of our strategy
Books on the topic "Pain network"
Alexis, Caught, ed. Natural networking: Building your professional network, without pain. North Charleston, SC: CreateSpace, 2014.
Find full textParker, Philip M., and James N. Parker. Chronic pain: A medical dictionary, bibliography, and annotated research guide to Internet references. San Diego, CA: ICON Health Publications, 2003.
Find full textParker, Philip M., and James N. Parker. Chest pain: A medical dictionary, bibliography and annotated research guide to Internet references. San Diego, CA: ICON Health Publications, 2003.
Find full textParker, Philip M., and James N. Parker. Foot pain: A medical dictionary, bibliography, and annotated research guide to Internet references. San Diego, CA: ICON Health Publications, 2003.
Find full textParker, James N., and Philip M. Parker. Oxycontin: A medical dictionary, bibliography, and annotated research guide to Internet references. San Diego, CA: ICON Health Publications, 2004.
Find full textParker, James N., and Philip M. Parker. Acetaminophen: A medical dictionary, bibliography, and annotated research guide to Internet references. San Diego, CA: ICON Health Publications, 2004.
Find full textParker, Philip M., and James N. Parker. Motrin: A medical dictionary, bibliography, and annotated research guide to Internet references. San Diego, CA: ICON Health Publications, 2004.
Find full textParker, Philip M., and James N. Parker. Tylenol: A medical dictionary, bibliography, and annotated research guide to Internet references. San Diego, CA: ICON Health Publications, 2004.
Find full textParker, James N., and Philip M. Parker. Pain medications: A medical dictionary, bibliography, and annotated research guide to Internet references. San Diego, CA: ICON Health, 2004.
Find full textParker, Philip M., and James N. Parker. Neck pain: A medical dictionary, bibliography, and annotated research guide to Internet references. San Diego, CA: ICON Health Publications, 2004.
Find full textBook chapters on the topic "Pain network"
Salekin, Md Sirajus, Ghada Zamzmi, Dmitry Goldgof, Peter R. Mouton, Kanwaljeet J. S. Anand, Terri Ashmeade, Stephanie Prescott, Yangxin Huang, and Yu Sun. "Attentional Generative Multimodal Network for Neonatal Postoperative Pain Estimation." In Lecture Notes in Computer Science, 749–59. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16437-8_72.
Full textLanza, V., and L. Guglielmo. "Local Area Network Videoconferencing for Continuous Anesthesia Quality Improvement." In Anaesthesia, Pain, Intensive Care and Emergency Medicine — A.P.I.C.E., 843–64. Milano: Springer Milan, 2001. http://dx.doi.org/10.1007/978-88-470-2903-3_82.
Full textMiletic, Katarina, Oleksandra Mykhailova, and Jan Treur. "An Adaptive Mental Network Model for Reactions to Social Pain." In Complex Networks & Their Applications X, 619–31. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93409-5_51.
Full textXu, Xuebin, Meng Lei, Dehua Liu, and Muyu Wang. "Pain Expression Recognition Based on Dual-Channel Convolutional Neural Network." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery, 35–42. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-20738-9_5.
Full textBeser-Robles, María, Vicente Sanchis-Alfonso, and Luis Martí-Bonmatí. "Brain Network Functional Connectivity Clinical Relevance and Predictive Diagnostic Models in Anterior Knee Pain Patients." In Anterior Knee Pain and Patellar Instability, 731–43. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09767-6_57.
Full textSubramaniam, Saranya Devi, and Brindha Dass. "An Efficient Convolutional Neural Network for Acute Pain Recognition Using HRV Features." In Advances in Intelligent Systems and Computing, 119–32. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2123-9_9.
Full textXu, Haochen, and Manhua Liu. "A Deep Attention Transformer Network for Pain Estimation with Facial Expression Video." In Biometric Recognition, 112–19. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86608-2_13.
Full textPikulkaew, Kornprom, Waraporn Boonchieng, and Ekkarat Boonchieng. "Real-Time Pain Detection Using Deep Convolutional Neural Network for Facial Expression and Motion." In Proceedings of Seventh International Congress on Information and Communication Technology, 341–49. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1610-6_29.
Full textBhalla, Parinishtha, Anukriti Verma, Bhawna Rathi, Shivani Sharda, and Pallavi Somvanshi. "Exploring Molecular Signatures in Spondyloarthritis: A Step Towards Early Diagnosis." In Proceedings of the Conference BioSangam 2022: Emerging Trends in Biotechnology (BIOSANGAM 2022), 142–55. Dordrecht: Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-020-6_15.
Full textMamontov, Danila, Iana Polonskaia, Alina Skorokhod, Eugene Semenkin, Viktor Kessler, and Friedhelm Schwenker. "Evolutionary Algorithms for the Design of Neural Network Classifiers for the Classification of Pain Intensity." In Lecture Notes in Computer Science, 84–100. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20984-1_8.
Full textConference papers on the topic "Pain network"
Choudhury, Abhinav, Shruti Kaushik, and Varun Dutt. "Social-Network Analysis for Pain Medications." In ASONAM '17: Advances in Social Networks Analysis and Mining 2017. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3110025.3110113.
Full textMonwar, Md, and Siamak Rezaei. "Pain Recognition Using Artificial Neural Network." In 2006 IEEE International Symposium on Signal Processing and Information Technology. IEEE, 2006. http://dx.doi.org/10.1109/isspit.2006.270764.
Full textNaufal Mansor, Muhammad, Syahrull Hi-Fi Syam, Muhammad Nazri Rejab, and Addzrull Hi-Fi Syam B. "Pain assessment using neural network classifier." In 2012 International Symposium on Instrumentation & Measurement, Sensor Network and Automation (IMSNA). IEEE, 2012. http://dx.doi.org/10.1109/msna.2012.6324599.
Full textMonwar, Md M., and S. Rezaei. "Appearance-based Pain Recognition from Video Sequences." In The 2006 IEEE International Joint Conference on Neural Network Proceedings. IEEE, 2006. http://dx.doi.org/10.1109/ijcnn.2006.247069.
Full textSemwal, Ashish, and Narendra D. Londhe. "Automated Pain Severity Detection Using Convolutional Neural Network." In 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS). IEEE, 2018. http://dx.doi.org/10.1109/ctems.2018.8769123.
Full textvon Leupoldt, A., T. Sommer, D. Schoen, HJ Baumann, H. Klose, M. Rosenkranz, B. Dahme, and C. Buechel. "Dyspnea and Pain Share Affect-Related Brain Network." In American Thoracic Society 2009 International Conference, May 15-20, 2009 • San Diego, California. American Thoracic Society, 2009. http://dx.doi.org/10.1164/ajrccm-conference.2009.179.1_meetingabstracts.a3692.
Full textCarlini, Lucas, Leonardo Ferreira, Gabriel Coutrin, Victor Varoto, Tatiany Marcondes, Rita Balda, Marina Barros, Ruth Guinsburg, and Carlos Thomaz. "Mobile Convolutional Neural Network for Neonatal Pain Assessment." In LatinX in AI at Computer Vision and Pattern Recognition Conference 2021. Journal of LatinX in AI Research, 2021. http://dx.doi.org/10.52591/lxai202106258.
Full textSousa, Miguel Angelo de Abreu de, and Thiago Felipe de Jesus Torres. "Modeling of Pain on a FPGA-based Neural Network." In Artificial Intelligence and Applications. Calgary,AB,Canada: ACTAPRESS, 2013. http://dx.doi.org/10.2316/p.2013.793-034.
Full textGuo, Wenqiang, Ziwei Xu, Zhigao Guo, Lingling Mao, Yongyan Hou, and Zixuan Huang. "Pain Assessment Using Facial Action Units and Bayesian Network." In 2021 40th Chinese Control Conference (CCC). IEEE, 2021. http://dx.doi.org/10.23919/ccc52363.2021.9550304.
Full textMansor, Muhammad Naufal, Shahryull Hi-Fi Syam Mohd Jamil, Mohd Nazri Rejab, and Addzrull Hi-Fi Syam Mohd Jamil. "K-nn algorithm for fast infant pain detection." In 2012 International Symposium on Instrumentation & Measurement, Sensor Network and Automation (IMSNA). IEEE, 2012. http://dx.doi.org/10.1109/msna.2012.6324593.
Full textReports on the topic "Pain network"
Wu, Boyu, Lei Yang, Chengwei Fu, Gonghui Jian, Yue Zhuo, and Hui Xiong. Acupuncture for Acute Low Back Pain: A Network Meta-Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, December 2020. http://dx.doi.org/10.37766/inplasy2020.12.0025.
Full textLai, Chih Chin, and Yu Kang Tu. Effectiveness of Exercise Training for Pain Reduction in Adults With and Without Pain: A Systematic Review and Network Meta-Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2023. http://dx.doi.org/10.37766/inplasy2023.11.0019.
Full textGuo, Ji xing, Chang chun Ji, Chao ju Xie, Xiang Rao, Zhang yin Sun, Yu Xing, Rong ni Zhang, Qiang qiang Qu, You peng Dong, and Jin sheng Yang. Network Meta-analysis of Various Acupuncture Therapies for Managing Nonspecific Low Back Pain. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, September 2024. http://dx.doi.org/10.37766/inplasy2024.9.0033.
Full textJo, Hyo-Rim, Eun-Ji Noh, Se-Hee Oh, Seong-Kyeong Choi, Won-Suk Sung, Su-ji Choi, Dong-il Kim, Seung-Ug Hong, and Eun-Jung Kim. Effectiveness of different acupuncture therapies for neck pain: A systematic review and network meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, February 2021. http://dx.doi.org/10.37766/inplasy2021.2.0041.
Full textCanellas, Joao Vitor, Fabio Ritto, and Paul Tiwana. Comparative efficacy and safety of pharmacological interventions to reduce inflammatory complications after mandibular third molar surgery: a systematic review and network meta-analysis protocol. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, July 2021. http://dx.doi.org/10.37766/inplasy2021.7.0069.
Full textAlvitos, Rodrigo, Bruno Teixeira Gonçalves Rodrigues, François Isnaldo Dias Caldeira, João Vitor Canellas, Paulo Jose Medeiros, Emmanuel Silva, and Gustavo De Deus. Comparative efficacy of different topical anesthetics to reduce the perception of pain during intraoral anesthesia: A systematic review and network meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, December 2022. http://dx.doi.org/10.37766/inplasy2022.12.0034.
Full textKwak, Sang Gyu, Yoo Jin Choo, Soyoung Kwak, and Min Cheol Chang. Efficacy of Transforaminal, Interlaminar, and Caudal Epidural Injections in Lumbosacral Disc Herniation: A Systematic Review and Network Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, August 2022. http://dx.doi.org/10.37766/inplasy2022.8.0091.
Full textCanellas, Joao Vitor, Fabio Ritto, and Paul Tiwana. Comparative efficacy and safety of different corticosteroids to reduce inflammatory complications after mandibular third molar surgery: a systematic review and network meta-analysis protocol. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, September 2021. http://dx.doi.org/10.37766/inplasy2021.9.0023.
Full textTsai, I.-Chen, and Ke-Vin Chang. Comparative Effectiveness of Different Exercises for Reducing Pain Intensity in Primary Dysmenorrhea: A Network Meta-Analysis of Randomized Controlled Trials. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, March 2023. http://dx.doi.org/10.37766/inplasy2023.3.0050.
Full textLin, Wenqian. Which analgesia is better for preventing chronic post-thoracotomy pain syndrome(CPTPS): a Bayesia network meta-analysis. INPLASY - International Platform of Registered Systematic Review Protocols, April 2020. http://dx.doi.org/10.37766/inplasy2020.4.0065.
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